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Machines
Special Issues
Machine Learning and Artificial Intelligence in Machinery Condition Monitoring
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Machine Learning and Artificial Intelligence in Machinery Condition Monitoring

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 8424

Special Issue Editors

Prof. Dr. Asoke K. Nandi

E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, Brunel University London, London, UK
Interests: signal processing; wireless communication; machine condition monitoring; biomedical signal processing; data analytics; machine learning; higher order statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. M. L. Dennis Wong

E-Mail Website
Guest Editor
Deputy Provost, Heriot-Watt University Malaysia, Putrajaya, Malaysia
Interests: condition monitoring; VLSI signal processing; pattern classification; statistical process control
Special Issues, Collections and Topics in MDPI journals
Dr. Manjeevan Seera

E-Mail Website
Guest Editor
School of Business, Monash University Malaysia, Selangor, Malaysia
Interests: machine learning; data analytics; financial fraud detection

Special Issue Information

Dear Colleagues,

Over the past few decades, there has been significant interest among researchers in developing the ability to detect and diagnose faults in machinery, as well as to predict when a fault is emerging to inform the maintenance schedule. Since the advent of machine learning and artificial intelligence (ML/AI), researchers have been working on computational-intelligence-based solutions for machinery diagnostics and prognostics. In recent years, the technology for machinery diagnostics and prognostics has become even more robust and mature with the introduction of deep-learning-based approaches. This Special Issue aims solicits the latest developments in ML/AI-based solutions for this important area of work for the industry toward developing an environmentally friendly world.

Suitable topics for this Special Issue include but are not limited to:

  • Feature design and engineering for ML/AI-based machinery-related fault diagnosis and prognosis;
  • Data-driven approaches for fault detection, diagnosis, and prognosis, including those based on anomaly detection;
  • Deep learning models for fault detection, diagnosis, and prognosis;
  • Rule-based methods for machinery health monitoring;
  • Learning machines, e.g., SVM-based approach;
  • Fuzzy-logic-based approach for machine condition monitoring;
  • Evolutionary algorithms for fault detection and identification;
  • Health management system design and engineering;
  • Real-life applications involving large or small machines;
  • Industry-ready laboratory prototypes.

Prof. Dr. Asoke K. Nandi
Prof. Dr. M. L. Wong
Dr. Manjeevan Seera
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

Keywords

  • machinery health diagnostics and prognostics
  • condition monitoring
  • deep learning
  • predictive maintenance

Published Papers (5 papers)

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Research

24 pages, 1331 KiB  
Article
Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers
by Nada El Bouharrouti, Daniel Morinigo-Sotelo and Anouar Belahcen
Machines 2024, 12(1), 17; https://doi.org/10.3390/machines12010017 - 27 Dec 2023
Viewed by 1053
Abstract
Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the [...] Read more.
Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies. Three classes of algorithms are tested: linear models, tree-based models, and neural networks. These algorithms are trained and evaluated on vibration data collected experimentally and then downsampled to various intermediate levels of sampling, from 48 kHz to 1 kHz, using a fractional downsampling method. The study highlights the trade-off between fault detection accuracy and sampling frequency. It shows that, depending on the machine learning algorithm used, better training accuracies are not systematically achieved when training with vibration signals sampled at a relatively high frequency. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Machinery Condition Monitoring)
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22 pages, 8647 KiB  
Article
Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms
by Muhammad Amir Khan, Bilal Asad, Toomas Vaimann, Ants Kallaste, Raimondas Pomarnacki and Van Khang Hyunh
Machines 2023, 11(10), 963; https://doi.org/10.3390/machines11100963 - 16 Oct 2023
Cited by 4 | Viewed by 1194
Abstract
The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of [...] Read more.
The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Machinery Condition Monitoring)
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14 pages, 10065 KiB  
Article
Slice-Aided Defect Detection in Ultra High-Resolution Wind Turbine Blade Images
by Imad Gohar, Abderrahim Halimi, John See, Weng Kean Yew and Cong Yang
Machines 2023, 11(10), 953; https://doi.org/10.3390/machines11100953 - 12 Oct 2023
Viewed by 1415
Abstract
The processing of aerial images taken by drones is a challenging task due to their high resolution and the presence of small objects. The scale of the objects varies diversely depending on the position of the drone, which can result in loss of [...] Read more.
The processing of aerial images taken by drones is a challenging task due to their high resolution and the presence of small objects. The scale of the objects varies diversely depending on the position of the drone, which can result in loss of information or increased difficulty in detecting small objects. To address this issue, images are either randomly cropped or divided into small patches before training and inference. This paper proposes a defect detection framework that harnesses the advantages of slice-aided inference for small and medium-size damage on the surface of wind turbine blades. This framework enables the comparison of different slicing strategies, including a conventional patch division strategy and a more recent slice-aided hyper-inference, on several state-of-the-art deep neural network baselines for the detection of surface defects in wind turbine blade images. Our experiments provide extensive empirical results, highlighting the benefits of using the slice-aided strategy and the significant improvements made by these networks on an ultra high-resolution drone image dataset. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Machinery Condition Monitoring)
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23 pages, 4723 KiB  
Article
Convolutional-Transformer Model with Long-Range Temporal Dependencies for Bearing Fault Diagnosis Using Vibration Signals
by Hosameldin O. A. Ahmed and Asoke K. Nandi
Machines 2023, 11(7), 746; https://doi.org/10.3390/machines11070746 - 17 Jul 2023
Cited by 1 | Viewed by 1322
Abstract
Fault diagnosis of bearings in rotating machinery is a critical task. Vibration signals are a valuable source of information, but they can be complex and noisy. A transformer model can capture distant relationships, which makes it a promising solution for fault diagnosis. However, [...] Read more.
Fault diagnosis of bearings in rotating machinery is a critical task. Vibration signals are a valuable source of information, but they can be complex and noisy. A transformer model can capture distant relationships, which makes it a promising solution for fault diagnosis. However, its application in this field has been limited. This study aims to contribute to this growing area of research by proposing a novel deep-learning architecture that combines the strengths of CNNs and transformer models for effective fault diagnosis in rotating machinery. Thus, it captures both local and long-range temporal dependencies in the vibration signals. The architecture starts with CNN-based feature extraction, followed by temporal relationship modelling using the transformer. The transformed features are used for classification. Experimental evaluations are conducted on two datasets with six and ten health conditions. In both case studies, the proposed model achieves high accuracy, precision, recall, F1-score, and specificity all above 99% using different training dataset sizes. The results demonstrate the effectiveness of the proposed method in diagnosing bearing faults. The convolutional-transformer model proves to be a promising approach for bearing fault diagnosis. The method shows great potential for improving the accuracy and efficiency of fault diagnosis in rotating machinery. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Machinery Condition Monitoring)
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23 pages, 2556 KiB  
Article
Logistic Model Tree Forest for Steel Plates Faults Prediction
by Bita Ghasemkhani, Reyat Yilmaz, Derya Birant and Recep Alp Kut
Machines 2023, 11(7), 679; https://doi.org/10.3390/machines11070679 - 24 Jun 2023
Viewed by 1521
Abstract
Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the [...] Read more.
Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the progress of abnormal events. The goal of this study is to precisely classify the surface defects in stainless steel plates during industrial production. In this paper, a new machine learning approach, entitled logistic model tree (LMT) forest, is proposed since the ensemble of classifiers generally perform better than a single classifier. The proposed method uses the edited nearest neighbor (ENN) technique since the target class distribution in fault prediction problems reveals an imbalanced dataset and the dataset may contain noise. In the experiment that was conducted on a real-world dataset, the LMT forest method demonstrated its superiority over the random forest method in terms of accuracy. Additionally, the presented method achieved higher accuracy (86.655%) than the state-of-the-art methods on the same dataset. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Machinery Condition Monitoring)
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