Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	15.8 Days	CHF 2300
Machines machines	2.6	2.1	2013	15 Days	CHF 2400
Processes processes	3.5	4.7	2013	13.9 Days	CHF 2400
Sensors sensors	3.9	6.8	2001	16.4 Days	CHF 2600
Journal of Manufacturing and Materials Processing jmmp	3.2	5.5	2017	15.6 Days	CHF 1600
Chips chips	-	-	2022	15.0 days *	CHF 1000

Open AccessArticle

A Methodology to Automatically Segment 3D Ultrasonic Data Using X-ray Computed Tomography and a Convolutional Neural Network

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Juan-Ignacio Caballero

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Guillermo Cosarinsky

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Jorge Camacho

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Ernestina Menasalvas

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Consuelo Gonzalo-Martin

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Federico Sket

Appl. Sci. 2023, 13(10), 5933; https://doi.org/10.3390/app13105933 - 11 May 2023

Viewed by 961

Abstract

Ultrasonic non-destructive testing (UT) is a proficient method for detecting damage in composite materials; however, conventional manual testing procedures are time-consuming and labor-intensive. We propose a semi-automated defect segmentation methodology employing a convolutional neural network (CNN) on 3D ultrasonic data, facilitated by the [...] Read more.

Ultrasonic non-destructive testing (UT) is a proficient method for detecting damage in composite materials; however, conventional manual testing procedures are time-consuming and labor-intensive. We propose a semi-automated defect segmentation methodology employing a convolutional neural network (CNN) on 3D ultrasonic data, facilitated by the fusion of X-ray computed tomography (XCT) and Phased-Array Ultrasonic Testing (PAUT) data. This approach offers the ability to develop supervised datasets for cases where UT techniques inadequately assess defects and enables the creation of models with genuine defects rather than artificially introduced ones. During the training process, we recommend processing the 3D volumes as a sequence of 2D slices derived from each technique. Our methodology was applied to segment porosity, a common defect in composite materials, for which characteristics such as void size and shape remain immeasurable via UT. Precision, recall, F1 score, and Intersection over Union (IoU) metrics were used in the evaluation. The results of the evaluation show that the following challenges have to be faced for improvement: (i) achieving accurate 3D registration, (ii) discovering suitable similar keypoints for XCT and UT data registration, (iii) differentiating ultrasonic echoes originating from porosity versus those related to noise or microstructural features (interfaces, resin pockets, fibers, etc.), and, (iv) single out defect echoes located near the edges of the component. In fact, an average F1 score of 0.66 and IoU of 0.5 were obtained. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

A Quantitative Review of Automated Neural Search and On-Device Learning for Tiny Devices

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Danilo Pietro Pau

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Prem Kumar Ambrose

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Fabrizio Maria Aymone

Chips 2023, 2(2), 130-141; https://doi.org/10.3390/chips2020008 - 09 May 2023

Viewed by 1092

Abstract

This paper presents a state-of-the-art review of different approaches for Neural Architecture Search targeting resource-constrained devices such as microcontrollers, as well as the implementations of on-device learning techniques for them. Approaches such as MCUNet have been able to drive the design of tiny [...] Read more.

This paper presents a state-of-the-art review of different approaches for Neural Architecture Search targeting resource-constrained devices such as microcontrollers, as well as the implementations of on-device learning techniques for them. Approaches such as MCUNet have been able to drive the design of tiny neural architectures with low memory and computational requirements which can be deployed effectively on microcontrollers. Regarding on-device learning, there are various solutions that have addressed concept drift and have coped with the accuracy drop in real-time data depending on the task targeted, and these rely on a variety of learning methods. For computer vision, MCUNetV3 uses backpropagation and represents a state-of-the-art solution. The Restricted Coulomb Energy Neural Network is a promising method for learning with an extremely low memory footprint and computational complexity, which should be considered for future investigations. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization

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Nadikatla Chandrasekhar

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Samineni Peddakrishna

Processes 2023, 11(4), 1210; https://doi.org/10.3390/pr11041210 - 14 Apr 2023

Cited by 4 | Viewed by 2471

Abstract

In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets [...] Read more.

In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets from the Cleveland and IEEE Dataport. Optimizing model accuracy, GridsearchCV, and five-fold cross-validation are employed. In the Cleveland dataset, logistic regression surpassed others with 90.16% accuracy, while AdaBoost excelled in the IEEE Dataport dataset, achieving 90% accuracy. A soft voting ensemble classifier combining all six algorithms further enhanced accuracy, resulting in a 93.44% accuracy for the Cleveland dataset and 95% for the IEEE Dataport dataset. This surpassed the performance of the logistic regression and AdaBoost classifiers on both datasets. This study’s novelty lies in the use of GridSearchCV with five-fold cross-validation for hyperparameter optimization, determining the best parameters for the model, and assessing performance using accuracy and negative log loss metrics. This study also examined accuracy loss for each fold to evaluate the model’s performance on both benchmark datasets. The soft voting ensemble classifier approach improved accuracies on both datasets and, when compared to existing heart disease prediction studies, this method notably exceeded their results. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Applying Industrial Internet of Things Analytics to Manufacturing

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Machines 2023, 11(4), 448; https://doi.org/10.3390/machines11040448 - 02 Apr 2023

Viewed by 981

Abstract

The proliferation of Industry 4.0 (I4.0) technologies has created a new manufacturing landscape for manufacturing, requiring that companies follow I4.0 trends to stay competitive. However, in this novel digital automated environment, these companies must also ensure that lean manufacturing principles are upheld. This [...] Read more.

The proliferation of Industry 4.0 (I4.0) technologies has created a new manufacturing landscape for manufacturing, requiring that companies follow I4.0 trends to stay competitive. However, in this novel digital automated environment, these companies must also ensure that lean manufacturing principles are upheld. This study proposes a data-driven framework for analysing raw data across machines in manufacturing systems that can provide a comprehensive understanding of idle time and facilitate adjustments to reduce defect rates. This framework offers an alternative approach to improving manufacturing processes that involves utilising the power of I4.0 technologies in conjunction with lean manufacturing principles. This study’s examination of unprocessed data also provides guidance on improving legislation. The findings of this study provide direction for future research in the field of manufacturing and offer useful advice to businesses wishing to integrate I4.0 technologies into their operations. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Buckle Pose Estimation Using a Generative Adversarial Network

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Appl. Sci. 2023, 13(7), 4220; https://doi.org/10.3390/app13074220 - 27 Mar 2023

Viewed by 575

Abstract

The buckle before the lens coating is still typically disassembled manually. The difference between the buckle and the background is small, while that between the buckles is large. This mechanical disassembly can also damage the lens. Therefore, it is important to estimate pose [...] Read more.

The buckle before the lens coating is still typically disassembled manually. The difference between the buckle and the background is small, while that between the buckles is large. This mechanical disassembly can also damage the lens. Therefore, it is important to estimate pose with high accuracy. This paper proposes a buckle pose estimation method based on a generative adversarial network. An edge extraction model is designed based on a segmentation network as the generator. Spatial attention is added to the discriminator to help it better distinguish between generated and real graphs. The generator thus generates delicate external contours and center edge lines with help from the discriminator. The external rectangle and the least square methods are used to determine the center position and deflection angle of the buckle, respectively. The center point and angle accuracies of the test datasets are 99.5% and 99.3%, respectively. The pixel error of the center point distance and the absolute error of the angle to the horizontal line are within 7.36 pixels and 1.98°, respectively. This method achieves the highest center point and angle accuracies compared to Hed, RCF, DexiNed, and PidiNet. It can meet practical requirements and boost the production efficiency of lens coatings. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Cluster-Based Regression Transfer Learning for Dynamic Multi-Objective Optimization

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Xi Zhang

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Feng Qian

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Liping Zhang

Processes 2023, 11(2), 613; https://doi.org/10.3390/pr11020613 - 17 Feb 2023

Viewed by 813

Abstract

Many multi-objective optimization problems in the real world have conflicting objectives, and these objectives change over time, known as dynamic multi-objective optimization problems (DMOPs). In recent years, transfer learning has attracted growing attention to solve DMOPs, since it is capable of leveraging historical [...] Read more.

Many multi-objective optimization problems in the real world have conflicting objectives, and these objectives change over time, known as dynamic multi-objective optimization problems (DMOPs). In recent years, transfer learning has attracted growing attention to solve DMOPs, since it is capable of leveraging historical information to guide the evolutionary search. However, there is still much room for improvement in the transfer effect and the computational efficiency. In this paper, we propose a cluster-based regression transfer learning-based dynamic multi-objective evolutionary algorithm named CRTL-DMOEA. It consists of two components, which are the cluster-based selection and cluster-based regression transfer. In particular, once a change occurs, we employ a cluster-based selection mechanism to partition the previous Pareto optimal solutions and find the clustering centroids, which are then fed into autoregression prediction model. Afterwards, to improve the prediction accuracy, we build a strong regression transfer model based on TrAdaboost.R2 by taking advantage of the clustering centroids. Finally, a high-quality initial population for the new environment is predicted with the regression transfer model. Through a comparison with some chosen state-of-the-art algorithms, the experimental results demonstrate that the proposed CRTL-DMOEA is capable of improving the performance of dynamic optimization on different test problems. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction

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Machines 2023, 11(2), 297; https://doi.org/10.3390/machines11020297 - 16 Feb 2023

Cited by 2 | Viewed by 1065

Abstract

In order to improve the accuracy of tool wear prediction, an attention-based composite neural network, referred to as the ConvLSTM-Att model (1DCNN-LSTM-Attention), is proposed. Firstly, local multidimensional feature vectors are extracted with the help of a one-dimensional convolutional neural network (1D-CNN), which avoids [...] Read more.

In order to improve the accuracy of tool wear prediction, an attention-based composite neural network, referred to as the ConvLSTM-Att model (1DCNN-LSTM-Attention), is proposed. Firstly, local multidimensional feature vectors are extracted with the help of a one-dimensional convolutional neural network (1D-CNN), which avoids the loss of wear features caused by manual feature extraction. Then the temporal relationship learning between multidimensional feature vectors is performed by introducing a long short-term memory (LSTM) network to make up for the lack of long-short distance dependence of the captured sequence of the CNN network. Finally, an attention mechanism is applied to strengthen the ability to extract key information from tool-wearing temporal features. The proposed ConvLSTM-Att model is trained with the measured tool wear data and then performs as a tool wear predictor. The model is compared with several state-of-the-art models on the PHM tool wear data sets. It significantly outperforms the other models in terms of prediction accuracy, but with similar computational complexity. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Pixel-Level Concrete Crack Segmentation Using Pyramidal Residual Network with Omni-Dimensional Dynamic Convolution

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Hao Tan

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Shaojiang Dong

Processes 2023, 11(2), 546; https://doi.org/10.3390/pr11020546 - 10 Feb 2023

Cited by 2 | Viewed by 1156

Abstract

Automated crack detection technologies based on deep learning have been extensively used as one of the indicators of performance degradation of concrete structures. However, there are numerous drawbacks of existing methods in crack segmentation due to the fine and microscopic properties of cracks. [...] Read more.

Automated crack detection technologies based on deep learning have been extensively used as one of the indicators of performance degradation of concrete structures. However, there are numerous drawbacks of existing methods in crack segmentation due to the fine and microscopic properties of cracks. Aiming to address this issue, a crack segmentation method is proposed. First, a pyramidal residual network based on encoder–decoder using Omni-Dimensional Dynamic Convolution is suggested to explore the network suitable for the task of crack segmentation. Additionally, the proposed method uses the mean intersection over union as the network evaluation index to lessen the impact of background features on the network performance in the evaluation and adopts a multi-loss calculation of positive and negative sample imbalance to weigh the negative impact of sample imbalance. As a final step in performance evaluation, a dataset for concrete cracks is developed. By using our dataset, the proposed method is validated to have an accuracy of 99.05% and an mIoU of 87.00%. The experimental results demonstrate that the concrete crack segmentation method is superior to the well-known networks, such as SegNet, DeeplabV3+, and Swin-unet. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Efficient Convolutional Neural Networks for Semiconductor Wafer Bin Map Classification

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Eunmi Shin

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Chang D. Yoo

Sensors 2023, 23(4), 1926; https://doi.org/10.3390/s23041926 - 08 Feb 2023

Viewed by 1206

Abstract

The results obtained in the wafer test process are expressed as a wafer map and contain important information indicating whether each chip on the wafer is functioning normally. The defect patterns shown on the wafer map provide information about the process and equipment [...] Read more.

The results obtained in the wafer test process are expressed as a wafer map and contain important information indicating whether each chip on the wafer is functioning normally. The defect patterns shown on the wafer map provide information about the process and equipment in which the defect occurred, but automating pattern classification is difficult to apply to actual manufacturing sites unless processing speed and resource efficiency are supported. The purpose of this study was to classify these defect patterns with a small amount of resources and time. To this end, we explored an efficient convolutional neural network model that can incorporate three properties: (1) state-of-the-art performances, (2) less resource usage, and (3) faster processing time. In this study, we dealt with classifying nine types of frequently found defect patterns: center, donut, edge-location, edge-ring, location, random, scratch, near-full type, and None type using open dataset WM-811K. We compared classification performance, resource usage, and processing time using EfficientNetV2, ShuffleNetV2, MobileNetV2 and MobileNetV3, which are the smallest and latest light-weight convolutional neural network models. As a result, the MobileNetV3-based wafer map pattern classifier uses 7.5 times fewer parameters than ResNet, and the training speed is 7.2 times and the inference speed is 4.9 times faster, while the accuracy is 98% and the F1 score is 89.5%, achieving the same level. Therefore, it can be proved that it can be used as a wafer map classification model without high-performance hardware in an actual manufacturing system. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Detection of Missing Insulator Caps Based on Machine Learning and Morphological Detection

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Zhaoyun Zhang

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Hefan Chen

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Shihong Huang

Sensors 2023, 23(3), 1557; https://doi.org/10.3390/s23031557 - 31 Jan 2023

Viewed by 970

Abstract

Missing insulator caps are the key focus of transmission line inspection work. Insulators with a missing cap will experience decreased insulation and mechanical strength and cause transmission line safety accidents. As missing insulator caps often occur in glass and porcelain insulators, this paper [...] Read more.

Missing insulator caps are the key focus of transmission line inspection work. Insulators with a missing cap will experience decreased insulation and mechanical strength and cause transmission line safety accidents. As missing insulator caps often occur in glass and porcelain insulators, this paper proposes a detection method for missing insulator caps in these materials. First, according to the grayscale and color characteristics of these insulators, similar characteristic regions of the insulators are extracted from inspection images, and candidate boxes are generated based on these characteristic regions. Second, the images captured by these boxes are input into the classifier composed of SVM (Support Vector Machine) to identify and locate the insulators. The accuracy, recall and average accuracy of the classifier are all higher than 90%. Finally, this paper proposes a processing method based on the insulator morphology to determine whether an insulator cap is missing. The proposed method can also detect the number of remaining insulators, which can help power supply enterprises to evaluate the degree of insulator damage. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessReview

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

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Sensors 2023, 23(3), 1305; https://doi.org/10.3390/s23031305 - 23 Jan 2023

Cited by 5 | Viewed by 4239

Abstract

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent [...] Read more.

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Early Fault Diagnosis of Rolling Bearing Based on Threshold Acquisition U-Net

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Machines 2023, 11(1), 119; https://doi.org/10.3390/machines11010119 - 15 Jan 2023

Cited by 1 | Viewed by 1119

Abstract

Considering the problem that the early fault signal of rolling bearing is easily interfered with by background information, such as noise, and it is difficult to extract fault features, a method of rolling bearing early fault diagnosis based on the threshold acquisition U-Net [...] Read more.

Considering the problem that the early fault signal of rolling bearing is easily interfered with by background information, such as noise, and it is difficult to extract fault features, a method of rolling bearing early fault diagnosis based on the threshold acquisition U-Net (TA-UNet) is proposed. First, to improve the feature extraction ability of U-Net, the channel spatial threshold acquisition network (CS-TAN) and the dilated convolution module (DCM) based on different dilated rate combinations are introduced into the U-Net to construct the TA-UNet. Among them, the CS-TAN can adaptively learn the threshold, reduce the interference of noise in the signal, and the DCM can improve the multi-scale feature extraction ability of the network. Then, the TA-UNet is used for early fault diagnosis, and the method is divided into two steps: The model training phase and the vibration signal fault feature extraction phase. In the first step, additive gaussian white noise is added to the vibration signal to obtain the noise-added vibration signal, and the TA-UNet is trained to learn how to denoise the noise-added vibration signal. In the second step, the trained TA-UNet is used to extract the fault features of vibration signals and diagnose the early fault types of rolling bearing. The two-step method solves the problem that U-Net, as a supervised neural network, needs corresponding labeled data to be trained, as it realizes the fault diagnosis of unlabeled data. The feature extraction capability of the TA-UNet is evaluated by denoising the simulated signal of rolling bearing. The effectiveness of the proposed diagnostic method is demonstrated by the early fault diagnosis of open-source datasets. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

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Waseem Alwan

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Nor Hasrul Akhmal Ngadiman

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Adnan Hassan

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Syahril Ramadhan Saufi

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Salwa Mahmood

Machines 2023, 11(1), 115; https://doi.org/10.3390/machines11010115 - 14 Jan 2023

Viewed by 851

Abstract

Manufacturing processes have become highly accurate and precise in recent years, particularly in the chemical, aerospace, and electronics industries. This has attracted researchers to investigate improved procedures for monitoring and detection of small process variations to remain in line with such advances. Among [...] Read more.

Manufacturing processes have become highly accurate and precise in recent years, particularly in the chemical, aerospace, and electronics industries. This has attracted researchers to investigate improved procedures for monitoring and detection of small process variations to remain in line with such advances. Among these techniques, statistical process controls (SPC), in particular the control chart pattern (CCP), have become a popular choice for monitoring process variance, being utilized in numerous industrial and manufacturing applications. This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. Before advancing to the classification step, Nelson’s Rus Rules were utilized as a monitoring rule to distinguish between stable and unstable processes. The study’s findings indicate that the proposed method improves classification performance for patterns with mean changes of less than 1.5 sigma, and confirm that the performance of the ensemble classifier is superior to that of the individual classifier. The ensemble classifier can distinguish unstable pattern types with a classification accuracy of 99.55% and an ARL1 of 11.94. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

An Objective Metallographic Analysis Approach Based on Advanced Image Processing Techniques

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J. Manuf. Mater. Process. 2023, 7(1), 17; https://doi.org/10.3390/jmmp7010017 - 04 Jan 2023

Cited by 1 | Viewed by 2819

Abstract

Metallographic analyses of nodular iron casting methods are based on visual comparisons according to measuring standards. Specifically, the microstructure is analyzed in a subjective manner by comparing the extracted image from the microscope to pre-defined image templates. The achieved classifications can be confused, [...] Read more.

Metallographic analyses of nodular iron casting methods are based on visual comparisons according to measuring standards. Specifically, the microstructure is analyzed in a subjective manner by comparing the extracted image from the microscope to pre-defined image templates. The achieved classifications can be confused, due to the fact that the features extracted by a human being could be interpreted differently depending on many variables, such as the conditions of the observer. In particular, this kind of problem represents an uncertainty when classifying metallic properties, which can influence the integrity of castings that play critical roles in safety devices or structures. Although there are existing solutions working with extracted images and applying some computer vision techniques to manage the measurements of the microstructure, those results are not too accurate. In fact, they are not able to characterize all specific features of the image and, they cannot be adapted to several characterization methods depending on the specific regulation or customer. Hence, in order to solve this problem, we propose a framework to improve and automatize the evaluations by combining classical machine vision techniques for feature extraction and deep learning technologies, to objectively make classifications. To adapt to the real analysis environments, all included inputs in our models were gathered directly from the historical repository of metallurgy from the Azterlan Research Centre (labeled using expert knowledge from engineers). The proposed approach concludes that these techniques (a classification under a pipeline of deep neural networks and the quality classification using an ANN classifier) are viable to carry out the extraction and classification of metallographic features with great accuracy and time, and it is possible to deploy software with the models to work on real-time situations. Moreover, this method provides a direct way to classify the metallurgical quality of the molten metal, allowing us to determine the possible behaviors of the final produced parts. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Convolution Neural Network with Laser-Induced Breakdown Spectroscopy as a Monitoring Tool for Laser Cleaning Process

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Soojin Choi

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Changkyoo Park

Sensors 2023, 23(1), 83; https://doi.org/10.3390/s23010083 - 22 Dec 2022

Viewed by 1031

Abstract

In this study, eight different painted stainless steel 304L specimens were laser-cleaned using different process parameters, such as laser power, scan speed, and the number of repetitions. Laser-induced breakdown spectroscopy (LIBS) was adopted as the monitoring tool for laser cleaning. Identification of LIBS [...] Read more.

In this study, eight different painted stainless steel 304L specimens were laser-cleaned using different process parameters, such as laser power, scan speed, and the number of repetitions. Laser-induced breakdown spectroscopy (LIBS) was adopted as the monitoring tool for laser cleaning. Identification of LIBS spectra with similar chemical compositions is challenging. A convolutional neural network (CNN)-based deep learning method was developed for accurate and rapid analysis of LIBS spectra. By applying the LIBS-coupled CNN method, the classification CNN model accuracy of laser-cleaned specimens was 94.55%. Moreover, the LIBS spectrum analysis time was 0.09 s. The results verified the possibility of using the LIBS-coupled CNN method as an in-line tool for the laser cleaning process. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Bearing Fault Diagnosis of Split Attention Network Based on Deep Subdomain Adaptation

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Haitao Wang

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Lindong Pu

Appl. Sci. 2022, 12(24), 12762; https://doi.org/10.3390/app122412762 - 12 Dec 2022

Cited by 3 | Viewed by 969

Abstract

The insufficient learning ability of traditional convolutional neural network for key fault features, as well as the characteristic distribution of vibration data of rolling bearing collected under variable working conditions is inconsistent, and decreases the bearing fault diagnosis accuracy. To address the problem, [...] Read more.

The insufficient learning ability of traditional convolutional neural network for key fault features, as well as the characteristic distribution of vibration data of rolling bearing collected under variable working conditions is inconsistent, and decreases the bearing fault diagnosis accuracy. To address the problem, a deep subdomain adaptation split attention network (SPDSAN) is proposed for intelligent fault diagnosis of bearings. Firstly, the time-frequency diagram of a vibration signal is obtained by the continuous wavelet transform to show the time-frequency characteristics. Secondly, a residual split-attention network (ResNeSt) that integrates multi-path and channel attention mechanisms is constructed to extract the key features of rolling bearings to prevent feature loss. Then, a subdomain adaptation layer is added to ResNeSt to align the distribution of related subdomain data by minimizing the local maximum mean difference. Finally, the SPDSAN model is validated using the Case Western Reserve University datasets. The results show that the average diagnostic accuracy of the proposed method is 99.9% when the test set samples are not labeled, which is higher compared to the accuracy of other mainstream intelligent fault diagnosis models. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Transfer-Learning-Based Opinion Mining for New-Product Portfolio Configuration over the Case-Based Reasoning Cycle

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Shui Ming Li

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Carman Ka Man Lee

Appl. Sci. 2022, 12(23), 12477; https://doi.org/10.3390/app122312477 - 06 Dec 2022

Viewed by 803

Abstract

Due to the ever-changing business environment, enterprises are facing unprecedented challenges in their new-product development (NPD) processes, while the success and survival of NPD projects have become increasingly challenging in recent years. Thus, most enterprises are eager to revamp existing NPD processes so [...] Read more.

Due to the ever-changing business environment, enterprises are facing unprecedented challenges in their new-product development (NPD) processes, while the success and survival of NPD projects have become increasingly challenging in recent years. Thus, most enterprises are eager to revamp existing NPD processes so as to enhance the likelihood of new products succeeding in the market. In addition to the determination of sustainable new-product ideas and designs, new-product portfolio management (NPPM) is an active research area for allocating adequate resources to boost project development, while projects that perform poorly can be terminated. Since the existing new-product portfolio configuration is manually decided, this study explores the possibility of standardising NPPM, particularly the configuration mechanism, in a systematic manner. Subsequently, case-based reasoning can be applied to structure the entire NPPM process, in which past knowledge and successful cases can be used to configure new projects. Furthermore, customer feedback was analyzed using the transfer-learning-based text classification model in the case-retrieval process to balance the values of enterprises and customers. A new-product portfolio was therefore configured to facilitate NPPM under an agile–stage-gate model. To verify the effectiveness of the proposed system, a case study in a printer manufacturing company was conducted, where positive feedback and performances were found. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Working Condition Recognition of a Mineral Flotation Process Using the DSFF-DenseNet-DT

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Appl. Sci. 2022, 12(23), 12223; https://doi.org/10.3390/app122312223 - 29 Nov 2022

Cited by 1 | Viewed by 728

Abstract

The commonly used working condition recognition method in the mineral flotation process is based on shallow features of flotation froth images. However, the shallow features of flotation froth images frequently have an excessive amount of redundant and noisy information, which has an impact [...] Read more.

The commonly used working condition recognition method in the mineral flotation process is based on shallow features of flotation froth images. However, the shallow features of flotation froth images frequently have an excessive amount of redundant and noisy information, which has an impact on the recognition effect and prevents the flotation process from being effectively optimized. Therefore, a working condition recognition method for the mineral flotation process based on a deep and shallow feature fusion densely connected network decision tree (DSFF-DenseNet-DT) is proposed in this paper. Firstly, the color texture distribution (CTD) and size distribution (SD) of a flotation froth image obtained in advance are approximated by the nonparametric kernel density estimation method, and a set of kernel function weights is obtained to represent the color texture and size features, while the deep features of the flotation froth image are extracted through the densely connected network (DenseNet). Secondly, a two-stage feature fusion method based on a stacked autoencoder after Concat (Cat-SAE) is proposed to fuse and reduce the dimensionality of the extracted shallow features and deep features so as to maximize the comprehensive description of the features and eliminate redundant and noisy information. Finally, the feature vectors after fusion dimensionality reduction are fed into the densely connected network decision tree (DenseNet-DT) for working condition recognition. Multiple experiments employing self-built industrial datasets reveal that the suggested method’s average recognition accuracy, precision, recall and F1 score reach 92.67%, 93.9%, 94.2% and 0.94, respectively. These results demonstrate the proposed method’s usefulness. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessEditor’s ChoiceArticle

Fault Detection for CNC Machine Tools Using Auto-Associative Kernel Regression Based on Empirical Mode Decomposition

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Processes 2022, 10(12), 2529; https://doi.org/10.3390/pr10122529 - 28 Nov 2022

Cited by 1 | Viewed by 1099

Abstract

In manufacturing processes using computerized numerical control (CNC) machines, machine tools are operated repeatedly for a long period for machining hard and difficult-to-machine materials, such as stainless steel. These operating conditions frequently result in tool breakage. The failure of machine tools significantly degrades [...] Read more.

In manufacturing processes using computerized numerical control (CNC) machines, machine tools are operated repeatedly for a long period for machining hard and difficult-to-machine materials, such as stainless steel. These operating conditions frequently result in tool breakage. The failure of machine tools significantly degrades the product quality and efficiency of the target process. To solve these problems, various studies have been conducted for detecting faults in machine tools. However, the most related studies used only the univariate signal obtained from CNC machines. The fault-detection methods using univariate signals have a limitation in that multivariate models cannot be applied. This can restrict in performance improvement of the fault detection. To address this problem, we employed empirical mode decomposition to construct a multivariate dataset from the univariate signal. Subsequently, auto-associative kernel regression was used to detect faults in the machine tool. To verify the proposed method, we obtained a univariate current signal measured from the machining center in an actual industrial plant. The experimental results demonstrate that the proposed method successfully detects faults in the actual machine tools. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Two-Stage Ultrasound Signal Recognition Method Based on Envelope and Local Similarity Features

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Machines 2022, 10(12), 1111; https://doi.org/10.3390/machines10121111 - 23 Nov 2022

Viewed by 925

Abstract

Accurate identification of ultrasonic signals can effectively improve the accuracy of a defect detection and inversion. Current methods, based on machine learning and deep learning have been able to classify signals with significant differences. However, the ultrasonic internal detection signal is interspersed with [...] Read more.

Accurate identification of ultrasonic signals can effectively improve the accuracy of a defect detection and inversion. Current methods, based on machine learning and deep learning have been able to classify signals with significant differences. However, the ultrasonic internal detection signal is interspersed with a large number of anomalous signals of an unknown origin and is affected by the time shift of echo features and noise interference, which leads to the low recognition accuracy of the ultrasonic internal detection signal, at this stage. To address the above problems, this paper proposes a two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES). In the first stage, a normal signal classification method, based on the envelope feature extraction and fusion is proposed to solve the problem of the low ultrasonic signal classification accuracy under the conditions of the echo feature time shift and noise interference. In the second stage, an abnormal signal detection method, based on the local similarity feature extraction and enhancement is proposed to solve the problem of detecting abnormal signals in ultrasound internal detection data. The experimental results show that the accuracy of the two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES) in this paper is 97.43%, and the abnormal signal detection accuracy and recall rate are as high as 99.7% and 97.81%. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

AIE-YOLO: Auxiliary Information Enhanced YOLO for Small Object Detection

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Sensors 2022, 22(21), 8221; https://doi.org/10.3390/s22218221 - 27 Oct 2022

Cited by 8 | Viewed by 2971

Abstract

Small object detection is one of the key challenges in the current computer vision field due to the low amount of information carried and the information loss caused by feature extraction. You Only Look Once v5 (YOLOv5) adopts the Path Aggregation Network to [...] Read more.

Small object detection is one of the key challenges in the current computer vision field due to the low amount of information carried and the information loss caused by feature extraction. You Only Look Once v5 (YOLOv5) adopts the Path Aggregation Network to alleviate the problem of information loss, but it cannot restore the information that has been lost. To this end, an auxiliary information-enhanced YOLO is proposed to improve the sensitivity and detection performance of YOLOv5 to small objects. Firstly, a context enhancement module containing a receptive field size of 21×21 is proposed, which captures the global and local information of the image by fusing multi-scale receptive fields, and introduces an attention branch to enhance the expressive ability of key features and suppress background noise. To further enhance the feature expression ability of small objects, we introduce the high- and low-frequency information decomposed by wavelet transform into PANet to participate in multi-scale feature fusion, so as to solve the problem that the features of small objects gradually disappear after multiple downsampling and pooling operations. Experiments on the challenging dataset Tsinghua–Tencent 100 K show that the mean average precision of the proposed model is 9.5% higher than that of the original YOLOv5 while maintaining the real-time speed, which is better than the mainstream object detection models. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Super-Resolving Methodology for Noisy Unpaired Datasets

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Sung-Jun Min

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Young-Su Jo

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Suk-Ju Kang

Sensors 2022, 22(20), 8003; https://doi.org/10.3390/s22208003 - 20 Oct 2022

Viewed by 1007

Abstract

Although it is possible to acquire high-resolution and low-resolution paired datasets, their use in directly supervised learning is impractical in real-world applications. In the present work, we focus on a practical methodology for image acquisition in real-world conditions. The main method of noise [...] Read more.

Although it is possible to acquire high-resolution and low-resolution paired datasets, their use in directly supervised learning is impractical in real-world applications. In the present work, we focus on a practical methodology for image acquisition in real-world conditions. The main method of noise reduction involves averaging multiple noisy input images into a single image with reduced noise; we also consider unpaired datasets that contain misalignments between the high-resolution and low-resolution images. The results show that when more images are used for average denoising, better performance is achieved in the super-resolution task. Quantitatively, for a fixed noise level with a variance of 60, the proposed method of using 16 images for average denoising shows better performance than using 4 images for average denoising; it shows 0.68 and 0.0279 higher performance for the peak signal-to-noise ratio and structural similarity index map metrics, as well as 0.0071 and 1.5553 better performance for the learned perceptual image patch similarity and natural image quality evaluator metrics, respectively. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Distribution Network Fault-Line Selection Method Based on MICEEMDAN–Recurrence Plot–Yolov5

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Sizu Hou

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Yan Xu

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Wei Guo

Processes 2022, 10(10), 2127; https://doi.org/10.3390/pr10102127 - 19 Oct 2022

Cited by 2 | Viewed by 919

Abstract

Distribution system fault signals contain severe noise components. In order to solve the problem of distribution network fault-line selection, a fault-line selection method based on modifying the Improved Complete Ensemble Empirical Mode Decomposition Adaptive Noise (MICEEMDAN) algorithm, Recurrence Plot, and Yolov5 network is [...] Read more.

Distribution system fault signals contain severe noise components. In order to solve the problem of distribution network fault-line selection, a fault-line selection method based on modifying the Improved Complete Ensemble Empirical Mode Decomposition Adaptive Noise (MICEEMDAN) algorithm, Recurrence Plot, and Yolov5 network is proposed. First, ICEEMDAN is optimized using multi-scale weighted permutation entropy (MWPE). MICEEMDAN can decompose an electrical signal into a series of intrinsic mode functions (IMFs). Recurrence Plot transformation of all IMFs, obtained from decomposition and stitching from top to bottom, realizes the conversion of 1D time series to 2D images. Then, the recurrence maps obtained from all lines in the distribution network are stitched to obtain the distribution network recurrence map, realizing the mining of the fault-signal features of the whole distribution network. Finally, the Yolov5 network is used to mine the fault features of the recurrence map of the distribution network autonomously to realize the fault-line selection. The experiments show that the method has a good noise immunity and 99.98% fault-selection accuracy, which can effectively complete the distribution network fault selection. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Single-Stage Underwater Target Detection Based on Feature Anchor Frame Double Optimization Network

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Sensors 2022, 22(20), 7875; https://doi.org/10.3390/s22207875 - 17 Oct 2022

Cited by 1 | Viewed by 917

Abstract

Objective: The shallow underwater environment is complex, with problems of color shift, uneven illumination, blurring, and distortion in the imaging process. These scenes are very unfavorable for the reasoning of the detection network. Additionally, typical object identification algorithms struggle to maintain high resilience [...] Read more.

Objective: The shallow underwater environment is complex, with problems of color shift, uneven illumination, blurring, and distortion in the imaging process. These scenes are very unfavorable for the reasoning of the detection network. Additionally, typical object identification algorithms struggle to maintain high resilience in underwater environments due to picture domain offset, making underwater object detection problematic. Methods: This paper proposes a single-stage detection method with the double enhancement of anchor boxes and features. The feature context relevance is improved by proposing a composite-connected backbone network. The receptive field enhancement module is introduced to enhance the multi-scale detection capability. Finally, a prediction refinement strategy is proposed, which refines the anchor frame and features through two regressions, solves the problem of feature anchor frame misalignment, and improves the detection performance of the single-stage underwater algorithm. Results: We achieved an effect of 80.2 mAP on the Labeled Fish in the Wild dataset, which saves some computational resources and time while still improving accuracy. On the original basis, UWNet can achieve 2.1 AP accuracy improvement due to the powerful feature extraction function and the critical role of multi-scale functional modules. At an input resolution of 300 × 300, UWNet can provide an accuracy of 32.4 AP. When choosing the number of prediction layers, the accuracy of the four and six prediction layer structures is compared. The experiments show that on the Labeled Fish in the Wild dataset, the six prediction layers are better than the four. Conclusion: The single-stage underwater detection model UWNet proposed in this research has a double anchor frame and feature optimization. By adding three functional modules, the underwater detection of the single-stage detector is enhanced to address the issue that it is simple to miss detection while detecting small underwater targets. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Intelligent Fault Diagnosis for Inertial Measurement Unit through Deep Residual Convolutional Neural Network and Short-Time Fourier Transform

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Machines 2022, 10(10), 851; https://doi.org/10.3390/machines10100851 - 23 Sep 2022

Viewed by 1237

Abstract

An Inertial Measurement Unit (IMU) is a significant component of a spacecraft, and its fault diagnosis results directly affect the spacecraft’s stability and reliability. In recent years, deep learning-based fault diagnosis methods have made great achievements; however, some problems such as how to [...] Read more.

An Inertial Measurement Unit (IMU) is a significant component of a spacecraft, and its fault diagnosis results directly affect the spacecraft’s stability and reliability. In recent years, deep learning-based fault diagnosis methods have made great achievements; however, some problems such as how to extract effective fault features and how to promote the training process of deep networks are still to be solved. Therefore, in this study, a novel intelligent fault diagnosis approach combining a deep residual convolutional neural network (CNN) and a data preprocessing algorithm is proposed. Firstly, the short-time Fourier transform (STFT) is adopted to transform the raw time domain data into time–frequency images so the useful information and features can be extracted. Then, the Z-score normalization and data augmentation strategies are both explored and exploited to facilitate the training of the subsequent deep model. Furthermore, a modified CNN-based deep diagnosis model, which utilizes the Parameter Rectified Linear Unit (PReLU) as activation functions and residual blocks, automatically learns fault features and classifies fault types. Finally, the experiment’s results indicate that the proposed method has good fault features’ extraction ability and performs better than other baseline models in terms of classification accuracy. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures

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Machines 2022, 10(9), 809; https://doi.org/10.3390/machines10090809 - 15 Sep 2022

Cited by 5 | Viewed by 1238

Abstract

Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a [...] Read more.

Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Optimization of Shearer Drum Based on Multi-Objective Bat Algorithm with Grid (MOBA/G)

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Machines 2022, 10(9), 733; https://doi.org/10.3390/machines10090733 - 26 Aug 2022

Cited by 6 | Viewed by 950

Abstract

The shearer drum undertakes the main function of coal falling and loading, and picks distributed on it have a great impact on the performance of the drum. However, few studies have optimized the pick and drum at the same time. In this paper, [...] Read more.

The shearer drum undertakes the main function of coal falling and loading, and picks distributed on it have a great impact on the performance of the drum. However, few studies have optimized the pick and drum at the same time. In this paper, parameters of pick and drum are considered as design variables, and the response functions of design variables are established based on the central composite experiment method. The optimal structural and working parameters of the pick and the drum of MG500/1130-WD shearer are obtained by using the multi-objective bat algorithm and multi-objective bat algorithm with grid, respectively. Comparing results of the two algorithms, the multi-objective bat algorithm with grid is more effective in improving the comprehensive performance of the drum. According to the optimized design variables, a coal mining test is carried out to verify the optimization effect of the algorithm. The result provides some theoretical references for the design and production of the drum and has some engineering application value. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models

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Teth Azrael Cortes-Aguilar

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Jose Antonio Cantoral-Ceballos

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Adriana Tovar-Arriaga

Sensors 2022, 22(17), 6383; https://doi.org/10.3390/s22176383 - 24 Aug 2022

Viewed by 1076

Abstract

Data reliability is of paramount importance for decision-making processes in the industry, and for this, having quality links for wireless sensor networks plays a vital role. Process and machine monitoring can be carried out through ANDON towers with wireless transmission and machine learning [...] Read more.

Data reliability is of paramount importance for decision-making processes in the industry, and for this, having quality links for wireless sensor networks plays a vital role. Process and machine monitoring can be carried out through ANDON towers with wireless transmission and machine learning algorithms that predict link quality (LQE) to save time, hence reducing expenses by early failure detection and problem prevention. Indeed, alarm signals used in conjunction with LQE classification models represent a novel paradigm for ANDON towers, allowing low-cost remote sensing within industrial environments. In this research, we propose a deep learning model, suitable for implementation in small workshops with limited computational resources. As part of our work, we collected a novel dataset from a realistic experimental scenario with actual industrial machinery, similar to that commonly found in industrial applications. Then, we carried out extensive data analyses using a variety of machine learning models, each with a methodical search process to adjust hyper-parameters, achieving results from common features such as payload, distance, power, and bit error rate not previously reported in the state of the art. We achieved an accuracy of 99.3% on the test dataset with very little use of computational resources. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Data-Based Stakeholder Identification in Technical Change Management

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Appl. Sci. 2022, 12(16), 8205; https://doi.org/10.3390/app12168205 - 17 Aug 2022

Cited by 1 | Viewed by 1116

Abstract

The efficient and effective handling of technical changes in product and production is seen as an important factor for the long-term success of manufacturing companies. Within the associated processes, the engineering and manufacturing change management, the identification and involvement of all relevant stakeholders, [...] Read more.

The efficient and effective handling of technical changes in product and production is seen as an important factor for the long-term success of manufacturing companies. Within the associated processes, the engineering and manufacturing change management, the identification and involvement of all relevant stakeholders, i.e., departments and employees, plays an essential role. Overlooking relevant stakeholders can lead to unforeseen impacts, such as production stops or further necessary changes, and can cause unforseen increased costs. In particular, in large companies, this task is complex and error-prone due to the high number of changes and departments involved, as well as the abundant variety of changes that can take place. Therefore, this contribution introduces an approach for stakeholder identification in technical change management, which allows the automated identification of relevant stakeholders at the beginning of the reactive phases of the change management process. The approach describes all necessary steps from data preparation to the evaluation of the obtained classification models. It is based on a text-classification approach and focuses in particular on the additional integration of expert knowledge to increase model quality. The approach has been successfully applied in cooperation with a German automotive company, and the obtained model quality has been compared to an expert-based classification. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Sintering Quality Prediction Model Based on Semi-Supervised Dynamic Time Feature Extraction Framework

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Yuxuan Li

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Chunjie Yang

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Youxian Sun

Sensors 2022, 22(15), 5861; https://doi.org/10.3390/s22155861 - 05 Aug 2022

Cited by 2 | Viewed by 989

Abstract

In the sintering process, it is difficult to obtain the key quality variables in real time, so there is lack of real-time information to guide the production process. Furthermore, these labeled data are too few, resulting in poor performance of conventional soft sensor [...] Read more.

In the sintering process, it is difficult to obtain the key quality variables in real time, so there is lack of real-time information to guide the production process. Furthermore, these labeled data are too few, resulting in poor performance of conventional soft sensor models. Therefore, a novel semi-supervised dynamic feature extraction framework (SS-DTFEE) based on sequence pre-training and fine-tuning is proposed in this paper. Firstly, based on the DTFEE model, the time features of the sequences are extended and extracted. Secondly, a novel weighted bidirectional LSTM unit (BiLSTM) is designed to extract the latent variables of original sequence data. Based on improved BiLSTM, an encoder-decoder model is designed as a pre-training model with unsupervised learning to obtain the hidden information in the process. Next, through model migration and fine-tuning strategy, the prediction performance of labeled datasets is improved. The proposed method is applied in the actual sintering process to estimate the FeO content, which shows a significant improvement of the prediction accuracy, compared to traditional methods. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Unsupervised Tool Wear Monitoring in the Corner Milling of a Titanium Alloy Based on a Cutting Condition-Independent Method

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Machines 2022, 10(8), 616; https://doi.org/10.3390/machines10080616 - 27 Jul 2022

Cited by 1 | Viewed by 916

Abstract

Real-time tool condition monitoring (TCM) for corner milling often poses significant challenges. On one hand, corner milling requires configuring complex milling paths, leading to the failure of conventional feature extraction methods to characterize tool conditions. On the other hand, it is costly to [...] Read more.

Real-time tool condition monitoring (TCM) for corner milling often poses significant challenges. On one hand, corner milling requires configuring complex milling paths, leading to the failure of conventional feature extraction methods to characterize tool conditions. On the other hand, it is costly to obtain sufficient test data on corner milling for most of the current pattern recognition methods, which are based on the supervised method. In this work, we propose a time-frequency intrinsic feature extraction strategy of acoustic emission signal (AEs) to construct a cutting condition-independent method for tool wear monitoring. The proposed new feature-extraction strategy is used to obtain the tool wear conditions through the intrinsic information of the time-frequency image of AEs. In addition, an unsupervised tool condition recognition framework, including the unsupervised feature selection, the clustering based on adjacent grids searching (CAGS) and the density factor based on CAGS, is proposed to determine the relationship between tool wear values and AE features. To test the effectiveness of the monitoring system, the experiment is conducted through the corner milling of a titanium alloy workpiece. Five metrics, PUR, CSM, NMI, CluCE and ClaCE, are used to evaluate the effectiveness of the recognition results. Compared with the state-of-the-art supervised methods, our method provides commensurate monitoring effectiveness but requires much fewer test data to build the model, which greatly reduces the operating cost of the TCM system. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Evaluation of Internal Cracks in Turbine Blade Thermal Barrier Coating Using Enhanced Multi-Scale Faster R-CNN Model

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Appl. Sci. 2022, 12(13), 6446; https://doi.org/10.3390/app12136446 - 24 Jun 2022

Cited by 1 | Viewed by 1129

Abstract

Thermal Barrier Coatings (TBCs) have good performance in heat insulation during service on turbine blades. However, the accumulated residual stress will form cracks, which can easily lead to coating failure. To ensure safe operation, it is necessary to find a method that can [...] Read more.

Thermal Barrier Coatings (TBCs) have good performance in heat insulation during service on turbine blades. However, the accumulated residual stress will form cracks, which can easily lead to coating failure. To ensure safe operation, it is necessary to find a method that can evaluate the health of the coating. In this paper, a non-destructive evaluation technique based on Multi-Scale Enhanced-Faster R-CNN (MSE-Faster R-CNN) is proposed. Firstly, the Visual Geometry Group Network19 layer (VGG-19) was adopted as the baseline network to find the candidate crack Region of Interest (ROI). Considering the influence of the crack on the surroundings, the ROI was expanded to obtain the context information. Secondly, a multi-scale Faster R-CNN detector was used to refine the candidate regions, and provided a comprehensive feature for better crack detection. Finally, a fusion lifetime prediction model was proposed to estimate the remaining lifetime of the TBC. Extensive experiments were conducted to evaluate the performance of the proposed method. The results demonstrated that the proposed method can accurately locate (0.898) and detect (0.806) the cracks in different scales, and the lifetime estimation result reached the best level (Root Mean Square Error (RMSE) = 2.7); there wasas also an acceptable time cost (1.63 s), and all detection conditions of the error rates were below 15%, achieving the best results among the state-of-art methods. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Online Detection of Fabric Defects Based on Improved CenterNet with Deformable Convolution

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Jun Xiang

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Ruru Pan

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Weidong Gao

Sensors 2022, 22(13), 4718; https://doi.org/10.3390/s22134718 - 22 Jun 2022

Cited by 3 | Viewed by 1855

Abstract

The traditional manual defect detection method has low efficiency and is time-consuming and laborious. To address this issue, this paper proposed an automatic detection framework for fabric defect detection, which consists of a hardware system and detection algorithm. For the efficient and high-quality [...] Read more.

The traditional manual defect detection method has low efficiency and is time-consuming and laborious. To address this issue, this paper proposed an automatic detection framework for fabric defect detection, which consists of a hardware system and detection algorithm. For the efficient and high-quality acquisition of fabric images, an image acquisition assembly equipped with three sets of lights sources, eight cameras, and a mirror was developed. The image acquisition speed of the developed device is up to 65 m per minute of fabric. This study treats the problem of fabric defect detection as an object detection task in machine vision. Considering the real-time and precision requirements of detection, we improved some components of CenterNet to achieve efficient fabric defect detection, including the introduction of deformable convolution to adapt to different defect shapes and the introduction of i-FPN to adapt to defects of different sizes. Ablation studies demonstrate the effectiveness of our proposed improvements. The comparative experimental results show that our method achieves a satisfactory balance of accuracy and speed, which demonstrate the superiority of the proposed method. The maximum detection speed of the developed system can reach 37.3 m per minute, which can meet the real-time requirements. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Picking Path Planning Method of Dual Rollers Type Safflower Picking Robot Based on Improved Ant Colony Algorithm

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Processes 2022, 10(6), 1213; https://doi.org/10.3390/pr10061213 - 17 Jun 2022

Cited by 5 | Viewed by 1659

Abstract

Aiming at the problem of automatic path planning for the whole safflower bulbs during the operation of safflower picking robots, an improved ant colony algorithm (ACA) was proposed to plan the three-dimensional path of the safflower picking points. The shortest time and distance [...] Read more.

Aiming at the problem of automatic path planning for the whole safflower bulbs during the operation of safflower picking robots, an improved ant colony algorithm (ACA) was proposed to plan the three-dimensional path of the safflower picking points. The shortest time and distance were taken as the overall goal of path planning to comprehensively improve the working efficiency of safflower picking robots. First, in order to shorten time, the angle induction factor was introduced to reduce the angle rotation of the end-effector. Second, in order to shorten the length of the picking path, the picking track was optimized. Finally, the design of the secondary path optimization reduced the number of picking points, which not only shortened the length of the picking path, but also shortened the picking time. The simulation results show that the path planned by the improved ACA was reduced by three picking points, shortening the total length by 74.32%, and reducing the picking time by 0.957 s. The simulation results verify the feasibility of the improved ACA for safflower picking path planning, which provides theoretical reference and technical support for the picking path planning of dual roller safflower picking robots. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Rolling Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and SOA-SVM

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Machines 2022, 10(6), 485; https://doi.org/10.3390/machines10060485 - 16 Jun 2022

Cited by 7 | Viewed by 1341

Abstract

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of the fault identification of rolling bearings, a fault-detection method based on multiscale [...] Read more.

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of the fault identification of rolling bearings, a fault-detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

The Development of an Automatic Rib Sequence Labeling System on Axial Computed Tomography Images with 3-Dimensional Region Growing

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Sensors 2022, 22(12), 4530; https://doi.org/10.3390/s22124530 - 15 Jun 2022

Cited by 1 | Viewed by 1339

Abstract

This paper proposes a development of automatic rib sequence labeling systems on chest computed tomography (CT) images with two suggested methods and three-dimensional (3D) region growing. In clinical practice, radiologists usually define anatomical terms of location depending on the rib’s number. Thus, with [...] Read more.

This paper proposes a development of automatic rib sequence labeling systems on chest computed tomography (CT) images with two suggested methods and three-dimensional (3D) region growing. In clinical practice, radiologists usually define anatomical terms of location depending on the rib’s number. Thus, with the manual process of labeling 12 pairs of ribs and counting their sequence, it is necessary to refer to the annotations every time the radiologists read chest CT. However, the process is tedious, repetitive, and time-consuming as the demand for chest CT-based medical readings has increased. To handle the task efficiently, we proposed an automatic rib sequence labeling system and implemented comparison analysis on two methods. With 50 collected chest CT images, we implemented intensity-based image processing (IIP) and a convolutional neural network (CNN) for rib segmentation on this system. Additionally, three-dimensional (3D) region growing was used to classify each rib’s label and put in a sequence label. The IIP-based method reported a 92.0% and the CNN-based method reported a 98.0% success rate, which is the rate of labeling appropriate rib sequences over whole pairs (1st to 12th) for all slices. We hope for the applicability thereof in clinical diagnostic environments by this method-efficient automatic rib sequence labeling system. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

The Amalgamation of the Object Detection and Semantic Segmentation for Steel Surface Defect Detection

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Mansi Sharma

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Jongtae Lim

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Hansung Lee

Appl. Sci. 2022, 12(12), 6004; https://doi.org/10.3390/app12126004 - 13 Jun 2022

Cited by 7 | Viewed by 2263

Abstract

Steel surface defect detection is challenging because it contains various atypical defects. Many studies have attempted to detect metal surface defects using deep learning and had success in applying deep learning. Despite many previous studies to solve the steel surface defect detection, it [...] Read more.

Steel surface defect detection is challenging because it contains various atypical defects. Many studies have attempted to detect metal surface defects using deep learning and had success in applying deep learning. Despite many previous studies to solve the steel surface defect detection, it remains a difficult problem. To resolve the atypical defects problem, we introduce a hierarchical approach for the classification and detection of defects on the steel surface. The proposed approach has a hierarchical structure of the binary classifier at the first stage and the object detection and semantic segmentation algorithms at the second stage. It shows 98.6% accuracy in scratch and other types of defect classification and 77.12% mean average precision (mAP) in defect detection using the Northeastern University (NEU) surface defect detection dataset. A comparative analysis with the previous studies shows that the proposed approach achieves excellent results on the NEU dataset. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Fault Diagnosis for Power Transformers through Semi-Supervised Transfer Learning

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Sensors 2022, 22(12), 4470; https://doi.org/10.3390/s22124470 - 13 Jun 2022

Cited by 3 | Viewed by 1391

Abstract

The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnosis method [...] Read more.

The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnosis method based on deep neural networks and a semi-supervised transfer learning framework called adaptive reinforcement (AR) to solve the above limitations. The innovation of this framework consists of its enhancement of the consistency regularization algorithm. The experiments were conducted on real-world 110 kV power transformers’ three-phase fault grounding currents of the iron cores from various devices with four types of faults: Phases A, B, C and ABC to ground. We trained the model on the source domain and then transferred the model to the target domain, which included the unbalanced and undefined fault datasets. The results show that our proposed model reaches over

95 %

accuracy in classifying the type of fault and outperforms other popular networks. Our AR framework fits target devices’ fault data with fewer dozen epochs than other novel semi-supervised techniques. Combining the deep neural network and the AR framework helps diagnose the power transformers, which lack diagnosis knowledge, with much less training time and reliable accuracy. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Comparative Study of Structural Anomaly Diagnosis Based on ANN Model Using Random Displacement and Acceleration Responses with Incomplete Measurements

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Zhi-Gang Ruan

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Zu-Guang Ying

Sensors 2022, 22(11), 4128; https://doi.org/10.3390/s22114128 - 29 May 2022

Cited by 1 | Viewed by 982

Abstract

Structural anomaly diagnosis, such as damage identification, is a continuously interesting issue. Artificial neural networks have an excellent ability to model complex structure dynamics. In this paper, an artificial neural network model is used to describe the relationship between structural responses and anomalies [...] Read more.

Structural anomaly diagnosis, such as damage identification, is a continuously interesting issue. Artificial neural networks have an excellent ability to model complex structure dynamics. In this paper, an artificial neural network model is used to describe the relationship between structural responses and anomalies such as stiffness reduction due to damages. Random acceleration and displacement responses as generally measured data are used as the input to the artificial neural network, and the output of the artificial neural network is the anomaly severity. The artificial neural network model is set up by training and then validated using random vibration responses with different structural anomalies. The structural anomaly diagnosis method based on the artificial neural network model using random acceleration and displacement responses is applied to a five-story building structure under random base excitations (seismic loading). Anomalies in the structure are denoted by stiffness reduction. Structural anomaly diagnosis using random acceleration responses is compared with that using random displacement responses. The numerical results show the effects of different random vibration responses used on the accuracy of predicting stiffness reduction. The actual incomplete measurements include intensive noise, finite sampling time length, and limited measurement points. The effects of the incomplete measurements on the accuracy of predicting results are also discussed. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessReview

Overview of Equipment Health State Estimation and Remaining Life Prediction Methods

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Machines 2022, 10(6), 422; https://doi.org/10.3390/machines10060422 - 26 May 2022

Cited by 2 | Viewed by 1901

Abstract

Health state estimation can quantitatively evaluate the current degradation state of equipment, and remaining life prediction can quantitatively predict the remaining service time of equipment. These two technologies can provide a basis for condition-based maintenance and predictive maintenance of equipment, respectively. In recent [...] Read more.

Health state estimation can quantitatively evaluate the current degradation state of equipment, and remaining life prediction can quantitatively predict the remaining service time of equipment. These two technologies can provide a basis for condition-based maintenance and predictive maintenance of equipment, respectively. In recent years, a large amount of research has been implemented in these two technologies. However, there is not any systematic review that covers these two technologies, and their engineering applications, comprehensively. To fill the gap, this paper makes a comparative analysis of existing health state estimation and remaining life prediction methods, and details the characteristics and limitations of various methods. The engineering applications of these two methods are summarized, and their applicable objects are briefly given. Finally, these two methods are summarized, and their feasibility for engineering application is discussed. This work provides guidance for the selection of industrial equipment health assessment and remaining life prediction methods. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Research on Repetition Counting Method Based on Complex Action Label String

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Machines 2022, 10(6), 419; https://doi.org/10.3390/machines10060419 - 26 May 2022

Cited by 1 | Viewed by 1360

Abstract

Smart factories have real-time demands for the statistics of productivity to meet the needs of quick reaction capabilities. To solve this problem, a counting method based on our decomposition strategy of actions was proposed for complex actions. Our method needs to decompose complex [...] Read more.

Smart factories have real-time demands for the statistics of productivity to meet the needs of quick reaction capabilities. To solve this problem, a counting method based on our decomposition strategy of actions was proposed for complex actions. Our method needs to decompose complex actions into several essential actions and define a label string for each complex action according to the sequence of the essential actions. While counting, we firstly employ an online action recognition algorithm to transform video frames into label numbers, which will be stored in a result queue. Then, the label strings are searched for their results in queue. If the search succeeds, a complex action will be considered to have occurred. Meanwhile, the corresponding counter should be updated to accomplish counting. The comparison test results in a video dataset of workers’ repetitive movements in package printing production lines and illustrate that our method has a lower counting errors, MAE (mean absolute error) less than 5% as well as an OBOA (off-by-one accuracy) more than 90%. Moreover, to enhance the adaptability of the action recognition model to deal with the change of action duration, we propose an adaptive parameter module based on the Kalman filter, which improves counting performances to a certain extent. The conclusions are that our method can achieve high counting performance, and the adaptive parameter module can further improve performances. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

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Sensors 2022, 22(11), 3997; https://doi.org/10.3390/s22113997 - 25 May 2022

Cited by 4 | Viewed by 1280

Abstract

With the complexity and refinement of industrial systems, fast fault diagnosis is crucial to ensuring the stable operation of industrial equipment. The main limitation of the current fault diagnosis methods is the lack of real-time performance in resource-constrained industrial embedded systems. Rapid online [...] Read more.

With the complexity and refinement of industrial systems, fast fault diagnosis is crucial to ensuring the stable operation of industrial equipment. The main limitation of the current fault diagnosis methods is the lack of real-time performance in resource-constrained industrial embedded systems. Rapid online detection can help deal with equipment failures in time to prevent equipment damage. Inspired by the ideas of compressed sensing (CS) and deep extreme learning machines (DELM), a data-driven general method is proposed for fast fault diagnosis. The method contains two modules: data sampling and fast fault diagnosis. The data sampling module non-linearly projects the intensive raw monitoring data into low-dimensional sampling space, which effectively reduces the pressure of transmission, storage and calculation. The fast fault diagnosis module introduces the kernel function into DELM to accommodate sparse signals and then digs into the inner connection between the compressed sampled signal and the fault types to achieve fast fault diagnosis. This work takes full advantage of the sparsity of the signal to enable fast fault diagnosis online. It is a general method in industrial embedded systems under data-driven conditions. The results on the CWRU dataset and real platforms show that our method not only has a significant speed advantage but also maintains a high accuracy, which verifies the practical application value in industrial embedded systems. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Prediction of the Comprehensive Error Field in the Machining Space of the Five-Axis Machine Tool Based on the “S”-Shaped Specimen Family

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Machines 2022, 10(5), 408; https://doi.org/10.3390/machines10050408 - 23 May 2022

Cited by 2 | Viewed by 1462

Abstract

In order to quickly and accurately predict the spatial geometric error field of the five-axis machine tool processing, a method for predicting the comprehensive error field of the five-axis machine tool processing space based on the “S”-shaped specimen family is studied. Firstly, for [...] Read more.

In order to quickly and accurately predict the spatial geometric error field of the five-axis machine tool processing, a method for predicting the comprehensive error field of the five-axis machine tool processing space based on the “S”-shaped specimen family is studied. Firstly, for the five-axis CNC machine tool in the form of A-C dual turntable, the geometric error model of the rotating axis is established based on the multi-body dynamics theory; the error mapping relationship between the processing technology system and the workpiece is analyzed based on the “S”-shaped specimen family, and the identification of 12 geometric errors of the two rotating shafts. Then, the error value of the sampling point is measured based on the “S”-shaped test piece in machine contact, and the double-circle center coordinate value is determined according to the curvature of the measured wire of the test piece, in order to identify the geometric errors of the two rotation axes of the five-axis machine tool. Finally, based on the prediction method, the comprehensive error field of the five-axis CNC machine tool processing space is analyzed. Compared with other geometric error identification methods, the measurement accuracy of this method meets the processing requirements and can further evaluate the comprehensive performance of the machine tool. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Bearing Faulty Prediction Method Based on Federated Transfer Learning and Knowledge Distillation

by

Yiqing Zhou

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Jian Wang

and

Zeru Wang

Machines 2022, 10(5), 376; https://doi.org/10.3390/machines10050376 - 16 May 2022

Cited by 5 | Viewed by 1636

Abstract

In this paper, a novel bearing faulty prediction method based on federated transfer learning and knowledge distillation is proposed with three stages: (1) a “signal to image” conversion method based on the continuous wavelet transform is used as the data pre-processing method to [...] Read more.

In this paper, a novel bearing faulty prediction method based on federated transfer learning and knowledge distillation is proposed with three stages: (1) a “signal to image” conversion method based on the continuous wavelet transform is used as the data pre-processing method to satisfy the input characteristic of the proposed faulty prediction model; (2) a novel multi-source based federated transfer learning method is introduced to acquire knowledge from multiple different but related areas, enhancing the generalization ability of the proposed model; and (3) a novel multi-teacher-based knowledge distillation is introduced as the knowledge transference way to transfer multi-source knowledge with dynamic importance weighting, releasing the target data requirement and the target model parameter size, which makes it possible for the edge-computing based deployment. The effectiveness of the proposed bearing faulty prediction approach is evaluated on two case studies of two public datasets offered by the Case Western Reserve University and the Paderborn University, respectively. The evaluation result shows that the proposed approach outperforms other state-of-the-art faulty prediction approaches in terms of higher accuracy and lower parameter size with limited labeled target data. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Fault Prediction of Rolling Element Bearings Using the Optimized MCKD–LSTM Model

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Machines 2022, 10(5), 342; https://doi.org/10.3390/machines10050342 - 06 May 2022

Cited by 3 | Viewed by 1351

Abstract

The reliability and safety of rotating equipment depend on the performance of bearings. For complex systems with high reliability and safety needs, effectively predicting the fault data in the use stage has important guiding significance for reasonably formulating reliability plans and carrying out [...] Read more.

The reliability and safety of rotating equipment depend on the performance of bearings. For complex systems with high reliability and safety needs, effectively predicting the fault data in the use stage has important guiding significance for reasonably formulating reliability plans and carrying out reliability maintenance activities. Many methods have been used to solve the problem of reliability prediction. Due to its convenience and efficiency, the data-driven method is increasingly widely used in practical reliability prediction. In order to ensure the reliability of bearing operation, the main objective of the present study is to establish a novel model based on the optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to realize early bearing fault warnings by predicting bearing fault time series. The proposed model is based on the lifecycle vibration signal of the bearing. In the first step, the cuckoo search (CS) is utilized to optimize the parameter filter length and deconvolution period of MCKD, considering the influence of periodic bearing time series, and to improve the fault impact component of the optimized MCKD deconvolution time series. Then the LSTM learning rate is selected according to the deconvolution time series. Finally, the dataset obtained through various preprocessing approaches is used to train and predict the LSTM model. The analyses performed using the XJTU-SY bearing dataset demonstrate that the prediction results are in good consistency with real fault data, and the average prediction accuracy of the optimized MCKD–LSTM model is 26% higher than that of the original time series. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Causal Network Structure Learning Based on Partial Least Squares and Causal Inference of Nonoptimal Performance in the Wastewater Treatment Process

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Processes 2022, 10(5), 909; https://doi.org/10.3390/pr10050909 - 05 May 2022

Viewed by 1276

Abstract

Due to environmental fluctuations, the operating performance of complex industrial processes may deteriorate and affect economic benefits. In order to obtain maximal economic benefits, operating performance assessment is a novel focus. Therefore, this paper proposes a whole framework from operating performance assessment to [...] Read more.

Due to environmental fluctuations, the operating performance of complex industrial processes may deteriorate and affect economic benefits. In order to obtain maximal economic benefits, operating performance assessment is a novel focus. Therefore, this paper proposes a whole framework from operating performance assessment to nonoptimal cause identification based on partial-least-squares-based Granger causality analysis (PLS-GC) and Bayesian networks (BNs). The proposed method has three main contributions. First, a multiblock operating performance assessment model is established to correspondingly extract economic-related information and dynamic information. Then, a Bayesian network structure is established by PLS-GC that excludes the strong coupling of variables and simplifies the network structure. Lastly, nonoptimal root cause and and nonoptimal transmission path are identified by Bayesian inference. The effectiveness of the proposed method was verified on Benchmark Simulation Model 1. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface

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Sensors 2022, 22(9), 3467; https://doi.org/10.3390/s22093467 - 02 May 2022

Cited by 52 | Viewed by 6749

Abstract

With the development of artificial intelligence technology and the popularity of intelligent production projects, intelligent inspection systems have gradually become a hot topic in the industrial field. As a fundamental problem in the field of computer vision, how to achieve object detection in [...] Read more.

With the development of artificial intelligence technology and the popularity of intelligent production projects, intelligent inspection systems have gradually become a hot topic in the industrial field. As a fundamental problem in the field of computer vision, how to achieve object detection in the industry while taking into account the accuracy and real-time detection is an important challenge in the development of intelligent detection systems. The detection of defects on steel surfaces is an important application of object detection in the industry. Correct and fast detection of surface defects can greatly improve productivity and product quality. To this end, this paper introduces the MSFT-YOLO model, which is improved based on the one-stage detector. The MSFT-YOLO model is proposed for the industrial scenario in which the image background interference is great, the defect category is easily confused, the defect scale changes a great deal, and the detection results of small defects are poor. By adding the TRANS module, which is designed based on Transformer, to the backbone and detection headers, the features can be combined with global information. The fusion of features at different scales by combining multi-scale feature fusion structures enhances the dynamic adjustment of the detector to objects at different scales. To further improve the performance of MSFT-YOLO, we also introduce plenty of effective strategies, such as data augmentation and multi-step training methods. The test results on the NEU-DET dataset show that MSPF-YOLO can achieve real-time detection, and the average detection accuracy of MSFT-YOLO is 75.2, improving about 7% compared to the baseline model (YOLOv5) and 18% compared to Faster R-CNN, which is advantageous and inspiring. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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Open AccessArticle

Feature-Based and Process-Based Manufacturing Cost Estimation

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Machines 2022, 10(5), 319; https://doi.org/10.3390/machines10050319 - 28 Apr 2022

Cited by 1 | Viewed by 1803

Abstract

The demand for mass custom parts is increasing, estimating the cost of parts to a high degree of efficiency is a matter of great concern to most manufacturing companies. Under the premise of machining operations, cost estimation based on features and processes yields [...] Read more.

The demand for mass custom parts is increasing, estimating the cost of parts to a high degree of efficiency is a matter of great concern to most manufacturing companies. Under the premise of machining operations, cost estimation based on features and processes yields high estimation accuracy, but it necessitates accurately identifying a part’s machining features and establishing the relationship between the feature and the cost. Accordingly, a feature recognition method based on syntactic pattern recognition is proposed herein. The proposed method provides a more precise feature definition and easily describes complex features using constraints. To establish the relationships between geometric features, processing modes, and cost, this study proposes a method of describing the features and the processing mode using feature quantities and adopts deep learning technology to establish the relationship between feature quantities and cost. By comparing a back propagation (BP) network and a convolutional neural network (CNN) it can be concluded that a CNN using the “RMSProp” optimizer exhibits higher accuracy. Full article

(This article belongs to the Topic Artificial Intelligence in Smart Industrial Diagnostics and Manufacturing)

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