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Electronics
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Advances in Fault Detection and Diagnosis
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Advances in Fault Detection and Diagnosis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 1044

Special Issue Editors

Dr. Bingchang Hou

E-Mail Website
Guest Editor
State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China
Interests: machinery condition monitoring, fault diagnosis, and prognostics; intelligent autonomous systems; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Quan Qian

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: transfer learning; intelligent fault diagnosis; RUL prediction; weak electromagnetic signal detection
Special Issues, Collections and Topics in MDPI journals
Dr. Jiahui Cao

E-Mail Website
Guest Editor
National Key Laboratory of Aerospace Power System and Plasma Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: rotating blade monitoring; mechanical fault diagnosis; blade tip timing; compressed sensing; signal sampling and recovery

Special Issue Information

Dear Colleagues,

This Special Issue invites the submission of original research articles exploring cutting-edge developments in fault detection and diagnosis (FDD) across complex engineering systems and industrial processes. With the increasing demand for safety, reliability, efficiency, and reduced downtime, advanced FDD techniques are more critical than ever. This issue seeks to highlight novel theoretical frameworks, advanced computational methods (including AI, machine learning, deep learning, and data analytics), and practical applications pushing the boundaries of how we detect, isolate, and understand faults. Researchers and practitioners are encouraged to contribute their latest findings to foster innovation and address current challenges in ensuring the reliability and safety of critical engineering systems worldwide.

Topics of interest include, but are not limited to, the following:

  • Novel FDD Methodologies: AI/ML/DL approaches, model-based techniques, signal processing, and hybrid methods.
  • Novel Sensing Techniques for FDD: non-contact measurement, non-invasive measurement, blade tip timing, microwave sensing, and ultrasonic methods.
  • New Signal Processing Theories and Methods for FDD: signal decomposition, compressed sensing, cyclostationary signal analysis, graph signal processing, hybrid physics-informed signal processing, undersampling signal processing, and unlimited sampling.
  • Data-Driven Advances: handling big data, feature extraction, sensor fusion, and transfer learning.
  • Emerging Applications: FDD for Industry 4.0, cyber–physical systems, renewable energy, autonomous systems, and complex networks.
  • Real-World Implementation: case studies, computational efficiency, scalability, and industrial validation.
  • Prognostics and Health Management: Integrating FDD with predictive maintenance strategies.

Dr. Bingchang Hou
Dr. Quan Qian
Dr. Jiahui Cao
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 250 words) can be sent to the Editorial Office for assessment.

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 detection, diagnosis, and prognostics
  • signal processing
  • machine learning
  • deep learning
  • health indicator construction
  • fault feature extraction

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

17 pages, 3801 KB  
Article
An Online Remaining Useful Life Prediction Method for Tantalum Capacitors Based on Temperature Measurements
by Zhongsheng Huang, Guoming Li, Quan Zhou and Yanchi Chen
Electronics 2025, 14(22), 4393; https://doi.org/10.3390/electronics14224393 - 11 Nov 2025
Viewed by 654
Abstract
Accurate remaining useful life (RUL) prediction of tantalum capacitors is essential for enhancing the reliability and maintainability of power electronic systems. However, online RUL prediction remains a challenging task due to the difficulty of accessing internal degradation states and the non-stationarity of operating [...] Read more.
Accurate remaining useful life (RUL) prediction of tantalum capacitors is essential for enhancing the reliability and maintainability of power electronic systems. However, online RUL prediction remains a challenging task due to the difficulty of accessing internal degradation states and the non-stationarity of operating conditions. This paper presents a novel CNN-LSTM-Attention-based deep learning framework for accurate online RUL prediction of tantalum capacitors, leveraging infrared surface temperature measurements and ambient thermal compensation. The proposed framework initiates with the collection of degradation temperature data under controlled accelerated aging experiments, where true degradation indicators are extracted by eliminating ambient temperature interference through dual-sensor compensation. The resulting preprocessed data are used to train a hybrid deep neural network model that integrates convolutional layers for local feature extraction, long short-term memory (LSTM) units for sequential dependency modeling, and a soft attention mechanism to selectively focus on the critical degradation patterns. A channel attention module is further embedded to adaptively optimize the importance of different feature channels. Experimental validation using three groups of aging data demonstrates the effectiveness and superiority of the proposed method over conventional LSTM and CNN-LSTM baselines. The CNN-LSTM-Attention model achieves a substantial improvement in prediction accuracy, with mean absolute percentage error (MAPE) reductions of up to 60.97%, root mean squared error (RMSE) reductions of up to 65.63%, and coefficient of determination (R2) increases of up to 68.67%. The results confirm the ability to deliver precise and robust online RUL predictions for tantalum capacitors under complex operational conditions. Full article
(This article belongs to the Special Issue Advances in Fault Detection and Diagnosis)
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