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Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 23568

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Prof. Dr. Qi Zhou

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Guest Editor

School of Aerospace Engineering, Huazhong University of Science & Technology, Wuhan 430074, Hubei, China
Interests: machine learning; structural optimization; additive manufacturing; metamodeling; systems design; product development; materials processing; engineering design; simulation modeling; robust optimization

Prof. Dr. Zhen Hu

E-Mail Website
Guest Editor

Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA
Interests: integrated design and manufacturing; machine learning; optimization; intelligent systems; design under uncertainty; bayesian statistics; uncertainty quantification; reliability; sampling; stochastic; control systems

Dr. Longchao Cao

E-Mail Website
Guest Editor

School of Aerospace Engineering, Huazhong University of Science & Technology, Wuhan 430074, China
Interests: machine learning; laser welding; additive manufacturing; processing parameter optimization; mechanical properties; microstructure; materials processing; mechanical engineering; numerical simulation; patient simulation; manufacturing engineering
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Special Issue Information

Dear Colleagues,

Over the past few decades, machine learning and artificial intelligence (ML/AI) techniques, such as the emerging deep learning methods, have attracted much attention in computer-based advanced manufacturing and prognostic and health management. A comprehensive information physical system based on advanced sensing and machine learning, however, is still missing in advanced manufacturing and fault diagnosis. Developing such a comprehensive diagnostics system requires novel developments related to intelligent information physical systems, advanced sensing techniques, deep analysis and sensor fusion, adaptability of artificial intelligence technology to complex environments, and specific working conditions.

This Special Issue is dedicated to novel articles covering advanced sensing and machine learning in process monitoring and fault diagnosis. Topics of interest include but are not strictly limited to the following:

Generalized approaches for data fusion from multiple sensors;
Novel approaches for processing of multiple sensors’ signals at multiple scales;
Novel machine learning approaches for fault diagnosis;
Data-driven-based process monitoring;
Data-driven-based fault diagnosis;
Hybrid-model-based and data-driven process monitoring;
Hybrid-model-based and data-driven fault diagnosis;
Deep learning fault diagnosis method under unbalanced data;
Fault diagnosis method with limited fault data;
Smart sensor systems for defect detection and quality evolution.

Prof. Dr. Qi Zhou
Prof. Dr. Zhen Hu
Dr. Longchao Cao
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (11 papers)

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Research

20 pages, 8519 KiB

Open AccessArticle

Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis

by Lukas Kaupp, Bernhard Humm, Kawa Nazemi and Stephan Simons

Sensors 2022, 22(21), 8259; https://doi.org/10.3390/s22218259 - 28 Oct 2022

Cited by 3 | Viewed by 1209

Abstract

Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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23 pages, 5469 KiB

Open AccessArticle

A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis

by Zhifeng Li, Yaqin Song, Runchen Li, Sen Gu and Xuze Fan

Sensors 2022, 22(21), 8187; https://doi.org/10.3390/s22218187 - 26 Oct 2022

Cited by 3 | Viewed by 1247

Abstract

Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from the experiments, which is insufficient to meet the requirements for training an effective classification and recognition model. In this paper, we start with an existing data augmentation method called DBA (for dynamic time warping barycenter averaging) and propose a new data enhancement method called AWDBA (adaptive weighting DBA). We first validated the proposed method by synthesizing new data from insulator sample datasets. The results show that the AWDBA proposed in this study has significant advantages relative to DBA in terms of data enhancement. Then, we used AWDBA and two other data augmentation methods to synthetically generate new data on the original dataset of insulators. Moreover, we compared the performance of different machine learning algorithms for insulator health diagnosis on the dataset with and without data augmentation. In the SVM algorithm especially, we propose a new parameter optimization method based on GA (genetic algorithm). The final results show that the use of the data augmentation method can significantly improve the accuracy of insulator defect identification. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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22 pages, 43317 KiB

Open AccessArticle

Domain-Adaptive Prototype-Recalibrated Network with Transductive Learning Paradigm for Intelligent Fault Diagnosis under Various Limited Data Conditions

by Jiachen Kuang, Tangfei Tao, Qingqiang Wu, Chengcheng Han, Fan Wei, Shengchao Chen, Wenjie Zhou, Cong Yan and Guanghua Xu

Sensors 2022, 22(17), 6535; https://doi.org/10.3390/s22176535 - 30 Aug 2022

Cited by 1 | Viewed by 1464

Abstract

In real industrial scenarios, intelligent fault diagnosis based on data-driven methods has been widely researched in the past decade. However, data scarcity is widespread in fault diagnosis tasks owning to the difficulties in collecting adequate data. As a result, there is an increasing demand for both researchers and engineers for fault identification with scarce data. To address this issue, an innovative domain-adaptive prototype-recalibrated network (DAPRN) based on a transductive learning paradigm and prototype recalibration strategy (PRS) is proposed, which has the potential to promote the generalization ability from the source domain to target domain in a few-shot fault diagnosis. Within this scheme, the DAPRN is composed of a feature extractor, a domain discriminator, and a label predictor. Concretely, the feature extractor is jointly optimized by the minimization of few-shot classification loss and the maximization of domain-discriminative loss. The cosine similarity-based label predictor, which is promoted by the PRS, is exploited to avoid the bias of naïve prototypes in the metric space and recognize the health conditions of machinery in the meta-testing process. The efficacy and advantage of DAPRN are validated by extensive experiments on bearing and gearbox datasets compared with seven popular and well-established few-shot fault diagnosis methods. In practical application, the proposed DAPRN is expected to solve more challenging few-shot fault diagnosis scenarios and facilitate practical fault identification problems in modern manufacturing. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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23 pages, 6448 KiB

Open AccessArticle

Single-Phase Grounding Fault Types Identification Based on Multi-Feature Transformation and Fusion

by Min Fan, Jialu Xia, Xinyu Meng and Ke Zhang

Sensors 2022, 22(9), 3521; https://doi.org/10.3390/s22093521 - 05 May 2022

Cited by 8 | Viewed by 2487

Abstract

The frequent occurrence of single-phase grounding faults affects the reliable operation of power systems. When a single-phase grounding fault occurs, it is difficult to accurately identify the fault type because of the weak characterization and subtle distinction between different fault types. Therefore, this paper proposes a single-phase grounding fault type identification method based on the multi-feature transformation and fusion. Firstly, the Hilbert–Huang transform (HHT) was used to preprocess the fault recorded wave data to highlight the characteristics between different fault types. Secondly, the deep learning model ResNet18 and the long short-term memory (LSTM) are designed to extract the complex abstract features and time-series correlation features from the preprocessed data set separately. Finally, it designs a fusion model to combine the advantages of heterogeneous models to identify the type of single-phase grounding fault. Experiments validate that the method is good at fully mining the characteristics of the fault types contained in the fault recorded wave data, so it can identify multiple types of faults with strong robustness and provide a reliable basis for the subsequent formulation of targeted fault-handling measures. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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12 pages, 673 KiB

Open AccessArticle

Geometric Analysis of Signals for Inference of Multiple Faults in Induction Motors

by Jose L. Contreras-Hernandez, Dora L. Almanza-Ojeda, Sergio Ledesma, Arturo Garcia-Perez, Rogelio Castro-Sanchez, Miguel A. Gomez-Martinez and Mario A. Ibarra-Manzano

Sensors 2022, 22(7), 2622; https://doi.org/10.3390/s22072622 - 29 Mar 2022

Cited by 3 | Viewed by 1525

Abstract

Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failures. This paper presents a novel methodology for detecting multiple motor faults based on quaternion signal analysis (QSA). This method couples the measured signals from the motor current and the triaxial accelerometer mounted on the induction motor chassis to the quaternion coefficients. The QSA calculates the quaternion rotation and applies statistics such as mean, variance, kurtosis, skewness, standard deviation, root mean square, and shape factor to obtain their features. After that, four classification algorithms are applied to predict motor states. The results of the QSA method are validated for ten classes: four single classes (healthy condition, unbalanced pulley, bearing fault, and half-broken bar) and six combined classes. The proposed method achieves high accuracy and performance compared to similar works in the state of the art. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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14 pages, 3614 KiB

Open AccessArticle

A Gaussian Process State Space Model Fusion Physical Model and Residual Analysis for Fatigue Evaluation

by Aijun Yin, Junlin Zhou and Tianyou Liang

Sensors 2022, 22(7), 2540; https://doi.org/10.3390/s22072540 - 25 Mar 2022

Cited by 1 | Viewed by 1883

Abstract

Residual stress is closely related to the evolution process of the component fatigue state, but it can be affected by various sources. Conventional fatigue evaluation either focuses on the physical process, which is limited by the complexity of the physical process and the environment, or on monitored data to form a data-driven model, which lacks a relation to the degenerate process and is more sensitive to the quality of the data. This paper proposes a fusion-driven fatigue evaluation model based on the Gaussian process state–space model, which considers the importance of physical processes and the residuals. Through state–space theory, the probabilistic space evaluation results of the Gaussian process and linear physical model are used as the hidden state evaluation results and hidden state change observation function, respectively, to construct a complete Gaussian process state–space framework. Then, through the solution of a particle filter, the importance of the residual is inferred and the fatigue evaluation model is established. Fatigue tests on titanium alloy components were conducted to verify the effectiveness of the fatigue evaluation model. The results indicated that the proposed models could correct evaluation results that were far away from the input data and improve the stability of the prediction. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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19 pages, 7281 KiB

Open AccessEditor’s ChoiceArticle

Research on the Applicability of Vibration Signals for Real-Time Train and Track Condition Monitoring

by Ireneusz Celiński, Rafał Burdzik, Jakub Młyńczak and Maciej Kłaczyński

Sensors 2022, 22(6), 2368; https://doi.org/10.3390/s22062368 - 18 Mar 2022

Cited by 10 | Viewed by 2148

Abstract

The purpose of this research was to analyze the possibilities for the application of vibration signals in real-time train and track control. Proper experiments must be performed for the validation of the methods. Research on vibration in the context of transport must entail many of the different nonlinear dynamic forces that may occur while driving. Therefore, the paper addresses two research cases. The developed application contains the identification of movement and dynamics and the evaluation of the technical state of the rail track. The statistics and resultant vector methods are presented. The paper presents other useful metrics to describe the dynamical properties of the driving train. The angle of the resultant horizontal and vertical accelerations is defined for the evaluation of the current position of cabin. It is calculated as an inverse tangent function of current longitudinal and transverse, longitudinal and vertical, transverse, and vertical accelerations. Additionally, the resultant vectors of accelerations are calculated. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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27 pages, 8625 KiB

Open AccessArticle

A Neural Algorithm for the Detection and Correction of Anomalies: Application to the Landing of an Airplane

by Angel Mur, Louise Travé-Massuyès, Elodie Chanthery, Renaud Pons and Pauline Ribot

Sensors 2022, 22(6), 2334; https://doi.org/10.3390/s22062334 - 17 Mar 2022

Cited by 4 | Viewed by 2564

Abstract

The location of the plane is key during the landing operation. A set of sensors provides data to get the best estimation of plane localization. However, data can contain anomalies. To guarantee correct behavior of the sensors, anomalies must be detected. Then, either the faulty sensor is isolated or the detected anomaly is filtered. This article presents a new neural algorithm for the detection and correction of anomalies named NADCA. This algorithm uses a compact deep learning prediction model and has been evaluated using real and simulated anomalies in real landing signals. NADCA detects and corrects both fast-changing and slow-moving anomalies; it is robust regardless of the degree of oscillation of the signals and sensors with abnormal behavior do not need to be isolated. NADCA can detect and correct anomalies in real time regardless of sensor accuracy. Likewise, NADCA can deal with simultaneous anomalies in different sensors and avoid possible problems of coupling between signals. From a technical point of view, NADCA uses a new prediction method and a new approach to obtain a smoothed signal in real time. NADCA has been developed to detect and correct anomalies during the landing of an airplane, hence improving the information presented to the pilot. Nevertheless, NADCA is a general-purpose algorithm that could be useful in other contexts. NADCA evaluation has given an average F-score value of 0.97 for anomaly detection and an average root mean square error (RMSE) value of 2.10 for anomaly correction. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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15 pages, 2714 KiB

Open AccessArticle

Effects of Wind Conditions on Wind Turbine Temperature Monitoring and Solution Based on Wind Condition Clustering and IGA-ELM

by Zhengnan Hou and Shengxian Zhuang

Sensors 2022, 22(4), 1516; https://doi.org/10.3390/s22041516 - 15 Feb 2022

Cited by 3 | Viewed by 2076

Abstract

To reduce maintenance costs of wind turbines (WTs), WT health monitoring has attracted wide attention, and different methods have been proposed. However, most existing WT temperature monitoring methods ignore the fact that various wind conditions can directly affect internal temperature of WT, such as main bearing temperature. This paper analyzes the effects of wind conditions on WT temperature monitoring. To reduce these effects, this paper also proposes a novel WT temperature monitoring solution. Compared with existing solutions, the proposed solution has two advantages: (1) wind condition clustering (WCC) is applied and then a normal turbine behavior model is built for each wind condition; (2) extreme learning machine (ELM) is optimized by an improved genetic algorithm (IGA) to avoid local minimum due to the irregularity of wind condition change and the randomness of initial coefficients. Cases of real SCADA data validate the effectiveness and advantages of the proposed solution. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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19 pages, 2079 KiB

Open AccessArticle

Single Fault Diagnosis Method of Sensors in Cascade System Based on Data-Driven

by Wenbo Na, Siyu Guo, Yanfeng Gao, Jianxing Yang and Junjie Huang

Sensors 2021, 21(21), 7340; https://doi.org/10.3390/s21217340 - 04 Nov 2021

Cited by 2 | Viewed by 2005

Abstract

The reliability and safety of the cascade system, which is widely applied, have attached attention increasingly. Fault detection and diagnosis can play a significant role in enhancing its reliability and safety. On account of the complexity of the double closed-loop system in operation, the problem of fault diagnosis is relatively complex. For the single fault of the second-order valued system sensors, a real-time fault diagnosis method based on data-driven is proposed in this study. Off-line data is employed to establish static fault detection, location, estimation, and separation models. The static models are calibrated with on-line data to obtain the real-time fault diagnosis models. The real-time calibration, working flow and anti-interference measures of the real-time diagnosis system are given. Experiments results demonstrate the validity and accuracy of the fault diagnosis method, which is suitable for the general cascade system. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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20 pages, 5324 KiB

Open AccessArticle

Deep Learning Based Monitoring of Spatter Behavior by the Acoustic Signal in Selective Laser Melting

by Shuyang Luo, Xiuquan Ma, Jie Xu, Menglei Li and Longchao Cao

Sensors 2021, 21(21), 7179; https://doi.org/10.3390/s21217179 - 28 Oct 2021

Cited by 14 | Viewed by 2673

Abstract

As one of the most promising metal additive manufacturing (AM) technologies, the selective laser melting (SLM) process has high expectations ofr its use in aerospace, medical, and other fields. However, various defects such as spatter, crack, and porosity seriously hinder the applications of the SLM process. In situ monitoring is a vital technique to detect the defects in advance, which is expected to reduce the defects. This work proposed a method that combined acoustic signals with a deep learning algorithm to monitor the spatter behaviors. The acoustic signals were recorded by a microphone and the spatter information was collected by a coaxial high-speed camera simultaneously. The signals were divided into two types according to the number and intensity of spatter during the SLM process with different combinations of processing parameters. Deep learning models, one-dimensional Convolutional Neural Network (1D-CNN), two-dimensional Convolutional Neural Network (2D-CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were trained to establish the relationships between the acoustic signals and characteristics of spatter. After K-fold verification, the highest classification confidence of models is 85.08%. This work demonstrates that it is feasible to use acoustic signals in monitoring the spatter defect during the SLM process. It is possible to use cheap and simple microphones instead of expensive and complicated high-speed cameras for monitoring spatter behaviors. Full article

(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)

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