Fault Diagnosis in Electric Machines to Ease Predictive Maintenance
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 404
Special Issue Editor
Interests: electrical engineering; fault diagnosis; predictive maintenance; electrical machines; machine design; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Fault detection and diagnosis in electric machines and power drives is an active and multidisciplinary research field involving several skills, including machine knowledge, power electronics, signal processing or artificial intelligence, among others. Fault diagnosis allows applying predictive maintenance strategies, thus limiting the extent and consequences of the faults because the failed components can be replaced before failure. In this way, unscheduled faults and downtime are minimized, while component lifetime is maximized.
This Special Issue invites contributions on the topic of fault diagnosis in electric machines based on both experimental and simulation studies. Of special interest are submissions from the fields of fault diagnosis strategies in electric machines and power drives focused to ease predictive maintenance tasks, minimizing fault occurrence and limiting the consequences of the faults. Contributions from the fields of signal processing, artificial intelligence applied to fault diagnosis in electric machines and drives, and RUL models are also welcome, including areas such as industry, automotive, and aerospace applications.
Dr. Jordi-Roger Riba Ruiz
Guest Editor
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Keywords
- Electric machines
- Power drives
- Fault diagnosis
- Prognosis
- Remaining useful life (RUL) models
- Steady state conditions
- Transient conditions
- Observers
- Fault indexes
- Signal processing techniques
- Artificial intelligence
- Industry applications
- Automotive applications
- Aerospace applications
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