Condition monitoring for electrical failures in induction machine using neural network modelling of vibration signal

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Many of these faulty situations in three phase induction motors have an electrical reason. Vibration signal analysis is found to be sensitive to electrical faults. However, the conventional methods require detailed information on motor design characteristics, and cannot be applied effectively for vibration diagnosis because of their non-adaptation and the random nature of vibration signal. This paper presents the development of an online electrical fault detection system that uses neural network modeling of induction motor in vibration spectra. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals to continuous spectra so that the neural network model can be trained. The electrical faults are detected from changes in the expectation of modeling error. Based on the experimental observations, the effectiveness of the system is demonstrated, while minimizing the impact of false alarms resulting from power supply imbalance, and it is shown that a robust and automatic electrical fault detection system has been produced.