Condition Monitoring and Failure Prevention of Electric Machines

doi:10.3390/books978-3-0365-9396-8

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Condition Monitoring and Failure Prevention of Electric Machines

Edited by

November 2023

204 pages

ISBN978-3-0365-9397-5 (Hardback)
ISBN978-3-0365-9396-8 (PDF)

https://doi.org/10.3390/books978-3-0365-9396-8

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This book is a reprint of the Special Issue Condition Monitoring and Failure Prevention of Electric Machines that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Physical Sciences

Summary

This is a reprint of a Special Issue of Energies, titled "Condition Monitoring and Failure Prevention of Electric Machines". This Special Issue primarily focused on the issues related to the advanced monitoring, diagnosis, and prevention of typical and complex faults in all kinds of electric machines. Four guest editors, namely Prof. Yu-Ling He (Department of Mechanical Engineering, North China Electric Power University, China), Prof. David Gerada (PEMC group, University of Nottingham, UK), Prof. Conggan Ma (School of Automotive Engineering, Harbin Institute of Technology, China), and Prof. Haisen Zhao (School of Electrical and Electronics Engineering, North China Electric Power University, China), worked together on this Special Issue.

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Hardback

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Keywords

synchronous generator; dynamic rotor interturn short circuit (DRISC); electromagnetic torque (EMT); magneto-motive force (MMF); harmonic component; line start permanent magnet assisted synchronous reluctance motor; power factor curve valley; efficiency; whole load region; in situ efficiency; induction motors; quantum particle swarm optimization; trust region algorithm; QPSO-TRA; rotor slot harmonics frequencies; linear motor feeding system; lack of abnormal samples; deep neural network; anomaly detection; semi-supervised anomaly detection generative adversarial network (GANomaly); long short-term memory (LSTM) network; doubly fed induction generator (DFIG); radial static air-gap eccentricity (RSAGE); radial dynamic air-gap eccentricity (RDAGE); radial hybrid air-gap eccentricity (RHAGE); circulating current inside parallel branches (CCPB); hydro-turbine modeling; dynamic modeling; transient processes; hydropower; hydropower plants; mathematical modeling; water flow inertia; artificial neural networks; contrast estimation; fault diagnosis; short-circuited turns; transformer fault; vibroacoustic signals; condition monitoring; demagnetization; direct torque control; eccentricity; fault diagnosis; field oriented control; high resistance connection; turn-to-turn short circuit; permanent magnet synchronous machine; supervised classification; power supply systems; unbalanced load flows; unbalanced load; parameter identification; hybrid surrogate model; performance optimization; Kriging; RBF; genetic algorithm; pulse-jet cleaning; demagnetization; direct torque control; eccentricity; field-oriented control; high resistance connection; symmetrical components; turn-to-turn short circuit; permanent magnet synchronous machine