Real-Time Fault Detection and Diagnosis of Industrial Electronics Equipment
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 375
Special Issue Editor
Interests: motor drives; servo control; fault-tolerant control and fault diagnosis of AC machines; electrified transportation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Electric equipment plays a vital role in many areas including industrial production, transportation and energy supply. During long-term operation, electric equipment suffers from many issues including overvoltage, overcurrent, insulation aging and component wear. These issues can cause faults in electric equipment and degrade its performance and efficiency. In some cases, it may even lead to major safety accidents and huge economic losses. Hence, novel advancements in real-time fault detection and diagnosis of electric equipment requires deeper exploration.
In order to achieve precise and reliable fault detection, signal sampling, signal processing analysis and machine learning technologies are applied to algorithm design. In addition, condition monitoring can also be applied for extremely minor faults.
This Special Issue of Electronics, entitled ‘Real-Time Fault Detection and Diagnosis of Industrial Electronics Equipment’, aims to present the latest research on real-time fault detection and diagnosis for electric equipment, providing a platform for researchers from both academic and industrial fields to showcase their theoretical and technological advancements.
Original research articles and reviews are welcome on topics including (but not limited to) the following research areas:
(1) Techniques for real-time fault diagnosis in electric equipment;
(2) Development of real-time condition monitoring techniques;
(3) Integrated research on advanced fault diagnosis algorithms;
(4) Application of machine learning in fault diagnosis;
(5) Fault feature extraction and processing of sensor signals.
We look forward to receiving your contributions.
Prof. Dr. Wei Wang
Guest Editor
Manuscript Submission Information
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Keywords
- fault detection and diagnosis
- condition monitoring
- diagnostic algorithms integration
- machine learning
- signal processing
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