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 August 2026 | Viewed by 8
Special Issue Editors
Interests: machinery condition monitoring, fault diagnosis, and prognostics; intelligent autonomous systems; robotics
Interests: transfer learning; intelligent fault diagnosis; RUL prediction; weak electromagnetic signal detection
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 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 detection, diagnosis, and prognostics
- signal processing
- machine learning
- deep learning
- health indicator construction
- fault feature extraction
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