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 Magnets 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.

Jawad Faiz, PhD, is a Distinguished Professor and Director of the Center of Excellence on Applied Electromagnetic Systems at the University of Tehran, Iran. A Life Fellow of IEEE and Fellow of Iran Academy of Sciences, he has authored more than 350 journal papers, 3 books published by Springer, IET and IEEE/Wiley. Dr. Faiz received many international and National awards, including the silver Einstein medal from UNESCO.

Ehsan Mazaheri-Tehrani is currently pursuing a Ph.D. in electrical engineering at the University of Tehran, Iran. His research interests include modeling, control, and fault diagnosis of electric machines, as well as the application of machine learning techniques in power engineering.

Bashir-Mahdi Ebrahimi, PhD, is a Principal Engineer in Aura Systems Inc., USA, leading the design of high-performance electric motors and actuators for eVTOL and defense applications. He has authored over 100 publications and received more than 11 national and international honors across two decades of work.