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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 3430

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 (3 papers)

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Research

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 255
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 2 | Viewed by 958
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 4 | Viewed by 1558
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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