Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis

Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis


: 9781138073975

: Book



    • Reviews the different types of faults in electric machines and the techniques used to detect them
    • Describes the physics behind the production of fault signatures
    • Focuses on the latest noninvasive methods, including the motor current signature analysis (MCSA) method
    • Presents low-cost DSP-based fault diagnosis implementation strategies
    • Provides experimental results and examples in every chapter
    • Includes extensive references for further research
    • Contains more than 160 illustration



With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condition monitoring scheme—thus improving safety and reliability in electric motor operation. It also supplies a solid foundation in the fundamentals of fault cause and effect.

Combines Theoretical Analysis and Practical Application

Written by experts in electrical engineering, the book approaches the fault diagnosis of electrical motors through the process of theoretical analysis and practical application. It begins by explaining how to analyze the fundamentals of machine failure using the winding functions method, the magnetic equivalent circuit method, and finite element analysis. It then examines how to implement fault diagnosis using techniques such as the motor current signature analysis (MCSA) method, frequency domain method, model-based techniques, and a pattern recognition scheme. Emphasizing the MCSA implementation method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid vehicles.



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