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Electronics
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Fault Detection Technology Based on Deep Learning
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Fault Detection Technology Based on Deep Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 5796

Special Issue Editors

Dr. Séjir Khojet El Khil

E-Mail Website
Guest Editor
Laboratoire des Systèmes Electriques (LR11ES15), Université de Tunis El Manar, Ecole Nationale d’Ingénieurs de Tunis, Tunis 1002, Tunisia
Interests: fault diagnosis; closed loop systems; electric current control; invertors; maximum power point trackers; permanent magnet generators; photovoltaic power systems; power grids; predictive control; synchronous generators; DC-DC power convertors; control engineering
Dr. Chiara Boccaletti

E-Mail Website
Guest Editor
Department of Astronautics, Electrical and Energetic Engineering, Sapienza University of Rome, Rome, Italy
Interests: cover power plants based on renewable sources; cogeneration and trigeneration
Special Issues, Collections and Topics in MDPI journals
Dr. Monia Ben Khader Bouzid

E-Mail Website
Guest Editor
Laboratoire des Systèmes Electriques (LR11ES15), Université de Tunis El Manar, Ecole Nationale d’Ingénieurs de Tunis, Tunis 1002, Tunisia
Interests: modeling and fault diagnosis of electrical machines, renewable energy systems, and power quality

Special Issue Information

Dear Colleagues,

In recent years, there has been increasing interest in and investment on electrical-based systems in various applications, such as Industry 4.0, electric vehicles, renewables, micro- and smart grids, and so on. Such systems should have high performance, reliability, and availability. Indeed, they are exposed to several types of failures due to external and internal sources. Failures may affect energy sources, actuators, sensors, or controllers. Consequently, predictive maintenance based on accurate fault diagnosis approaches and fault-tolerant control strategies is of upmost importance.

State-of-the-art reviews have shown that fault diagnosis methods are mainly classified in model-based approaches and signal-based approaches. However, with the increase in data acquisition and processing algorithms, artificial intelligence (AI) tools have become more attractive for fault diagnosis and fault classification issues. Indeed, AI approaches are only based on recorded data obtained from measured quantities instead of specific complex mathematical models.

The main purpose of this Special Issue is to share high-quality original research articles and reviews in the area of fault diagnosis based on deep learning and its applications.

The topics of interest of this Special Issue include but are not limited to:

  • Fault detection and fault diagnosis based on deep learning;
  • Fault-tolerant control strategies based on deep learning algorithms;
  • Predictive maintenance with deep learning;
  • Implementation of deep-learning-based algorithms and architectures for diagnosis.

Dr. Séjir Khojet El Khil
Dr. Chiara Boccaletti 
Dr. Monia Ben Khader Bouzid
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. Electronics is an international peer-reviewed open access semimonthly 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

  • fault diagnosis
  • fault detection
  • condition monitoring
  • predictive maintenance
  • deep learning
  • machine learning

Published Papers (6 papers)

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Research

24 pages, 7120 KiB  
Article
Remaining Useful Life Prediction of Aero-Engine Based on KSFA-GMM-BID-Improved Autoformer
by Jiashun Wei, Zhiqiang Li, Yang Li and Ying Zhang
Electronics 2024, 13(14), 2741; https://doi.org/10.3390/electronics13142741 - 12 Jul 2024
Viewed by 275
Abstract
Addressing the limitation of traditional deep learning models in capturing the spatio-temporal characteristics of flight data and the constrained prediction accuracy due to sequence length in aero-engine life prediction, this study proposes an aero-engine remaining life prediction approach integrating a kernel slow feature [...] Read more.
Addressing the limitation of traditional deep learning models in capturing the spatio-temporal characteristics of flight data and the constrained prediction accuracy due to sequence length in aero-engine life prediction, this study proposes an aero-engine remaining life prediction approach integrating a kernel slow feature analysis, a Gaussian mixture model, and an improved Autoformer model. Initially, the slow degradation features of gas path performance parameters over time are extracted through kernel slow feature analysis, followed by the establishment of a Gaussian mixture model to create a health state representation using Bayesian inferred distances for quantifying the aero-engine’s health status. Moreover, a spatial attention mechanism is introduced alongside the autocorrelation mechanism of the Autoformer model to augment the global feature extraction capacity. Additionally, a multilayer perceptron is employed to further elucidate the degradation trends, which enhances the model’s learning and predictive capabilities for extended sequences. Subsequently, experiments are conducted using authentic aero-engine operational data, comparing the proposed method with the standard Autoformer and Transformer models. The results demonstrate that the proposed method outperforms both models in swiftly and accurately predicting the remaining life of aero-engines with robustness and high prediction accuracy. Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
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20 pages, 6916 KiB  
Article
An Improved YOLOv5 Algorithm for Tyre Defect Detection
by Mujun Xie, Heyu Bian, Changhong Jiang, Zhong Zheng and Wei Wang
Electronics 2024, 13(11), 2207; https://doi.org/10.3390/electronics13112207 - 5 Jun 2024
Viewed by 436
Abstract
In this study, a tyre defect detection model is improved and optimized under the YOLOv5 framework, aiming at radial tyre defects with characteristics such as an elongated shape and various target sizes and defect types. The DySneakConv module is introduced to replace the [...] Read more.
In this study, a tyre defect detection model is improved and optimized under the YOLOv5 framework, aiming at radial tyre defects with characteristics such as an elongated shape and various target sizes and defect types. The DySneakConv module is introduced to replace the first BotteneckCSP in the Backbone network. The deformation offset of the DySneakConv module is used to make the convolutional energy freely adapt to the structure to improve the recognition rate of tyre defects with elongated features; the AIFI module is introduced to replace the fourth BotteneckCSP, and the self-attention mechanism and the processing of large-scale features are used to improve the recognition rate of tyre defects with elongated features using the AIFI module. This latter module has a self-attention mechanism and the ability to handle large-scale features to solve the problems of diverse tyre defects and different sizes. Secondly, the CARAFE up-sampling operator is introduced to replace the up-sampling operator in the Neck network. The up-sampling kernel prediction module in the CARAFE operator is used to increase the receptive field and allow the feature reorganization module to capture more semantic information to overcome the information loss problem of the up-sampling operator. Finally, based on the improved YOLOv5 detection algorithm, the Channel-wise Knowledge Distillation algorithm lightens the model, reducing its computational requirements and size while ensuring detection accuracy. Experimental studies were conducted on a dataset containing four types of tyre defects. Experimental results for the training set show that the improved algorithm improves the mAP0.5 by 4.6 pp, reduces the model size by 25.6 MB, reduces the computational complexity of the model by 31.3 GFLOPs, and reduces the number of parameters by 12.7 × 106 compared to the original YOLOv5m algorithm. Experimental results for the test set show that the improved algorithm improves the mAP0.5 by 2.6 pp compared to the original YOLOv5m algorithm. This suggests that the improved algorithm is more suitable for tyre defect detection than the original YOLOv5. Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
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22 pages, 11780 KiB  
Article
Rolling Bearing Residual Useful Life Prediction Model Based on the Particle Swarm Optimization-Optimized Fusion of Convolutional Neural Network and Bidirectional Long–Short-Term Memory–Multihead Self-Attention
by Jianzhong Yang, Xinggang Zhang, Song Liu, Ximing Yang and Shangfang Li
Electronics 2024, 13(11), 2120; https://doi.org/10.3390/electronics13112120 - 29 May 2024
Viewed by 354
Abstract
In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper proposes a technique that utilizes [...] Read more.
In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper proposes a technique that utilizes particle swarm optimization (PSO) in combination with the fusing of a one-dimensional convolutional neural network (CNN) and a multihead self-attention (MHSA) bidirectional long short-term memory (BiLSTM) network called PSO-CNN-BiLSTM-MHSA. Initially, the original signals undergo correlation signal processing to calculate the features, such as standard deviation, variance, and kurtosis, to help identify the beginning location of the rolling bearing degradation stage. A new dataset is constructed with similar degradation trend features. Subsequently, the particle swarm optimization (PSO) algorithm is employed to find the optimal values of important hyperparameters in the model. Then, a convolutional neural network (CNN) is utilized to extract the deterioration features of rolling bearings in order to predict their remaining lifespan. The degradation features are inputted into the BiLSTM-MHSA network to facilitate the learning process and estimate the remaining lifespan of rolling bearings. Finally, the degradation features are converted to the remaining usable life (RUL) via the fully connected layer. The XJTU-SY rolling bearing accelerated life experimental dataset was used to verify the effectiveness of the proposed method by k-fold cross-validation. After comparing our model to the CNN-LSTM network model and other models, we found that our model can achieve reductions in mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 9.27%, 6.76%, and 2.35%, respectively. Therefore, the experimental results demonstrate the model’s accuracy in forecasting remaining lifetime and support its ability to forecast breakdowns. Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
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18 pages, 6030 KiB  
Article
Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning
by Eyal Weiss, Shir Caplan, Kobi Horn and Moshe Sharabi
Electronics 2024, 13(8), 1551; https://doi.org/10.3390/electronics13081551 - 19 Apr 2024
Viewed by 821
Abstract
This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This [...] Read more.
This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This proactive approach ensures that defective components are rejected before mounting, effectively preventing issues from ever occurring, thus significantly enhancing efficiency and reliability. Leveraging rapid network protocols such as gRPC and orchestration via Kubernetes, in conjunction with C++ programming and TensorFlow, this approach achieves an impressive average turnaround time of less than 5 milli-seconds. Rigorously tested on 20 operational production machines, it not only ensures adherence to IPC-A-610 and IPC-STD-J-001 standards but also optimizes production efficiency and reliability. Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
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16 pages, 5994 KiB  
Article
Rolling Bearing Fault Diagnosis Based on SVD-GST Combined with Vision Transformer
by Fengyun Xie, Gan Wang, Haiyan Zhu, Enguang Sun, Qiuyang Fan and Yang Wang
Electronics 2023, 12(16), 3515; https://doi.org/10.3390/electronics12163515 - 19 Aug 2023
Cited by 5 | Viewed by 1174
Abstract
Aiming at rolling bearing fault diagnosis, the collected vibration signal contains complex noise interference, and one-dimensional information cannot be used to fully mine the data features of the problem. This paper proposes a rolling bearing fault diagnosis method based on SVD-GST combined with [...] Read more.
Aiming at rolling bearing fault diagnosis, the collected vibration signal contains complex noise interference, and one-dimensional information cannot be used to fully mine the data features of the problem. This paper proposes a rolling bearing fault diagnosis method based on SVD-GST combined with the Vision Transformer. Firstly, the one-dimensional vibration signal is preprocessed to reduce noise using singular value decomposition (SVD) to obtain a more accurate and useful signal. Then, the generalized S-transform (GST) is used to convert the processed one-dimensional vibration signal into a two-dimensional time–frequency image and make full use of the advantages of deep learning in image classification with higher recognition accuracy. In order to avoid the problem of limited sensory fields in CNN and the need for an RNN to compute step by step over time when processing sequence data, the use of a Vision Transformer model for pattern recognition classification is proposed. Finally, an experimental platform for the fault diagnosis of rolling bearings is built. The model is experimentally validated, achieving an average accuracy of 98.52% over multiple tests. Additionally, compared with the SVD-GST-2DCNN, STFT-CNN-LSTM, SVD-GST-LSTM, and GST-ViT fault diagnosis models, the proposed method has higher diagnostic accuracy and stability, providing a new method for rolling bearing fault diagnosis. Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
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17 pages, 6677 KiB  
Article
Gearbox Fault Diagnosis Based on Gramian Angular Field and CSKD-ResNeXt
by Yanlin Liu, Shuihai Dou, Yanping Du and Zhaohua Wang
Electronics 2023, 12(11), 2475; https://doi.org/10.3390/electronics12112475 - 31 May 2023
Cited by 5 | Viewed by 1876
Abstract
For most rotating mechanical transmission systems, condition monitoring and fault diagnosis of the gearbox are of great significance to avoid accidents and maintain stability in operation. To strengthen the comprehensiveness of feature extraction and improve the utilization rate of fault signals to accurately [...] Read more.
For most rotating mechanical transmission systems, condition monitoring and fault diagnosis of the gearbox are of great significance to avoid accidents and maintain stability in operation. To strengthen the comprehensiveness of feature extraction and improve the utilization rate of fault signals to accurately identify the different operating states of a gearbox, a gearbox fault diagnosis model combining Gramian angular field (GAF) and CSKD-ResNeXt (channel shuffle and kernel decomposed ResNeXt) was proposed. The original one-dimensional vibration signal of the gearbox was converted into a two-dimensional image by GAF transformation, and the image was used as the input of the subsequent diagnosis network. To solve the problem of channel independence and incomplete information caused by group convolution, the idea of channel shuffle is introduced to enable the branches of the group convolution part to establish information exchange. In addition, to improve the semantic expression ability of the model, the convolutional kernel of the network backbone is split and replaced. The model is verified under the different working conditions of the gearbox and compared with other methods. The experimental results show that the diagnostic accuracy of the model is up to 99.75%, and the precise identification of gearbox faults is realized. Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
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