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.
Fault Modeling, Diagnosis, and Implementation in One Volume
Based on years of research and development at the Electrical Machines & Power Electronics (EMPE) Laboratory at Texas A&M University, this book describes practical analysis and implementation strategies that readers can use in their work. It brings together, in one volume, the fundamentals of motor fault conditions, advanced fault modeling theory, fault diagnosis techniques, and low-cost DSP-based fault diagnosis implementation strategies.
About the Author: Prof. Toliyat is currently a Raytheon Company endowed professor of electrical and computer engineering at Texas A&M University. He has received several awards, including the prestigious Cyrill Veinott Award in Electromechanical Energy Conversion from the IEEE Power Engineering Society (2004), the Patent and Innovation Award from Texas A&M University System Office of Technology Commercialization (2007), the TEES Faculty Fellow Award (2006), the Texas A&M Select Young Investigator Award (1999), and the Space Act Award from NASA (1999). He has also received four prize paper awards from the IEEE. Prof. Toliyat has published more than 370 technical papers (including more than 110 in IEEE Transactions) and has 12 issued and pending U.S. patents.
