Detect, model, and diagnose faults in permanent magnet machines
Permanent magnet motors operate under conditions that produce inadequate lubrication, mechanical shocks, thermal stress, and excessive loading, all of which elevate the likelihood of catastrophic failure. Fault Diagnosis of Permanent Magnet Machines, written by a team of experienced electrical engineers and researchers, provides the theory, modeling approaches, simulation techniques, experimental validation, and practical implementation strategies needed to identify and address these fault mechanisms.
The book details multiple modeling methods, such as lumped-parameter models and finite element techniques, and supports them with machine learning-based modeling techniques. With a structured comparison between permanent magnet synchronous machines and induction machines, the book also includes:
- Coverage of fault origins across automotive, industrial automation, and renewable energy applications where permanent magnet machines are deployed
- Guidance on repair strategies and practical implementation considerations for maintaining operational resilience in PM motor systems
- Detailed examination of how abnormal operating conditions and adverse environmental factors contribute to elevated internal stress levels
- Machine learning techniques applied to fault modeling and detection, expanding beyond traditional analytical and numerical diagnostic methods
- Experimental validation procedures that connect theoretical fault models to real-world diagnostic outcomes in PM machine operation
Designed for engineers responsible for safe operation of PM machines in industrial product lines and researchers working in condition monitoring, this book also serves as supplementary reading for advanced graduate courses on fault diagnosis of PM machines, providing the analytical tools needed to enhance reliability across applications.
