Monitoring and Fault Diagnostics of Electrical Machines
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".
Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 317
Special Issue Editors
Interests: design, analysis, and control of electrical machines; fault-tolerant control; sensorless motor drives; model predictive control; hybrid and electric vehicles
Special Issues, Collections and Topics in MDPI journals
Interests: new PM motor drive and control; condition monitoring and fault diagnosis; new energy power generation
Special Issue Information
Dear Colleagues,
With the popularization of electrification and automation technology, the application of electric machines has expanded from conventional industrial driving to wind power generation, ship power, electrified transportation, aerospace, defense and military, and other fields, where high reliability are key requirements for the safety of electrical machines. Condition monitoring and fault diagnostics, which can realize scheduling preventive maintenance and fault-tolerant operations, are essential to improve the reliability of electrical-machine-based electric drives. Extensive methods have been proposed from model, signal, and data perspectives to monitor and diagnose drive systems. However, current techniques still suffer from time-varying factors and complex operation conditions. Following this Special Issue, innovations in monitoring and fault diagnostics are crucial to further enhance the reliability of machine drives.
This Special Issue aims to provide a platform for researchers from both academic and industrial fields to report their recent results and overlook emerging research directions in the monitoring and fault diagnostics of electrical machines.
Topics of interest include, but not limited to the following:
- High-reliability machine design concepts and methods;
- Emerging monitoring and diagnosis technology;
- Fault mechanism and characterization;
- Reliable fault identification methods;
- Fault prediction and health management technology;
- New detection techniques based on physical phenomena;
- Reliability evaluation and analysis;
- Fault severity evaluation;
- Artificial intelligence in monitoring and diagnosis;
- Multi-source information technology.
Prof. Dr. Wei Hua
Dr. Jun Hang
Dr. Wentao Huang
Guest Editors
Manuscript Submission Information
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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
- electrical machines
- condition monitoring
- fault diagnosis
- reliability
- artificial intelligence
