A neural network method for induction machine fault detection with vibration signal
作者： Hua Su , Kil To Chong , A. G. Parlos
DOI: 10.1007/11424826_137
关键词: Artificial neural network 、 Condition monitoring 、 Fault detection and isolation 、 Signal 、 Control theory 、 Embedded system 、 Vibration 、 Detection theory 、 Random vibration 、 Induction motor 、 Computer science
摘要: Early detection and diagnosis of induction machine incipient faults are desirable for online condition monitoring, product quality assurance, improved operational efficiency. However, conventional methods have to work with explicit motor models cannot be used vibration signal case because their non-adaptation the random nature signal. In this paper, a neural network method is developed fault detection, using FFT. The model trained spectra detected from changes in expectation modeling error. effectiveness accuracy proposed approach detecting wide range mechanical demonstrated through staged faults, it shown that robust reliable system has been produced.
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