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Early Detection of Faults in Induction Motors
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In modern industries, induction motors are the backbone of numerous applications, powering everything from manufacturing facilities to transportation systems. While they are known for their reliability, unexpected failures can still occur, leading to increased operational costs, facility damage, or service interruptions. "Early Detection and Fault Diagnosis of Induction Motors" is a comprehensive volume that compiles ten innovative journal articles focused on maintaining these machines. The papers explore a variety of techniques that introduce new ideas to the field.
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Keywords
- AC machines
- apFFT
- artificial intelligence
- back EMF
- bearing diagnosis
- broken rotor bar
- condition monitoring
- current signal analysis
- data-driven
- deep learning
- dynamic information fusion
- early damage detection
- early detection
- electric machine
- Fast Fourier Transform
- fault classification
- fault detection
- fault diagnosis
- fault severity
- fault-tolerant control
- feedforward compensation
- frequency analysis
- History of engineering & technology
- incipient fault
- induction machines
- induction motor
- induction motors
- ITSC fault
- Machine learning
- measurement techniques
- multiple coupled circuit model
- negative sequence currents
- parameter identification
- phasor compensation
- physical variables
- power connection failures
- Prony method
- rotating machines
- shorted turn faults
- signal analysis
- Signal processing
- spectral analysis
- supervised learning
- SVM
- Technology, engineering, agriculture
- Technology: general issues
- time-frequency decompositions
- traction motor
- unlabeled learning
Links
DOI: 10.3390/books978-3-0365-9334-0Editions