Fault Diagnosis and Condition Monitoring of Power Electronics Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".
Deadline for manuscript submissions: 30 October 2026 | Viewed by 200
Special Issue Editors
Interests: digital electronics; embedded systems and thermographic analysis; AI
Special Issues, Collections and Topics in MDPI journals
Interests: silicon and wide bandgap semiconductor sensors; diode, BJT and MOSFET integrated sensors; photodetectors; RFID and microchip wireless sensors; integrated photonics and optical resonant cavity sensors
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the latest advances in fault diagnosis, condition monitoring and prognostics for power electronics systems and their integration with energy storage and application platforms. Power electronics are critical in renewable energy, electric vehicles, smart grids and industrial drives, but are subject to increasing stresses from electrical/thermal cycling, mechanical vibration and environmental variations. Early fault diagnosis and detection are essential to improve reliability, prevent unplanned downtime, reduce safety risks and extend service life. In this Special Issue, contributions are sought that develop innovative methodologies, ranging from physics-based degradation modelling and accelerated ageing studies to real-time monitoring, artificial intelligence- and machine learning-based diagnostics and residual life estimation. This Issue welcomes original research and comprehensive reviews that promote cutting-edge techniques for the detection, classification and prediction of failures in semiconductor devices, converters, sensors and system-level power electronics applications.
Dr. Filippo Laganà
Prof. Dr. Francesco Giuseppe Della Corte
Guest Editors
Manuscript Submission Information
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Keywords
- power electronics
- fault diagnosis
- condition monitoring
- prognostics/RUL estimation
- energy storage systems
- machine learning/AI diagnostics
- real-time monitoring
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