Vibration signal analysis for electrical fault detection of induction machine using neural networks

Abstract

Fault detection is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Many of these faulty situations in three-phase induction motors originate from an electrical source. Vibration signal analysis is found to be sensitive to electrical faults. However, conventional methods require detailed information on motor design characteristics and cannot be applied effectively to vibration diagnosis because of their nonadaptability and the random nature of the vibration signals. 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 is used to process the quasi-steady vibration signals for continuous spectra so that the neural network model can be trained. The electrical faults are detected from changes in the expectation of modeling errors. Experimental observations show that a robust and automatic electrical fault detection system is produced whose effectiveness is demonstrated while minimizing the triggering of false alarms due to power supply imbalance.