A neural network method for induction machine fault detection with vibration signal

Authors:
Hua Su;Kil To Chong;A. G. Parlos
Affiliations:
Department of Electrical and Computer Engineering, Chonbuk National University, Jeonju, Korea;Department of Electrical and Computer Engineering, Chonbuk National University, Jeonju, Korea;Department of Mechanical Engineering, Texas A&M University, College Station, TX
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
Year:
2005

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Abstract

Early detection and diagnosis of induction machine incipient faults are desirable for online condition monitoring, product quality assurance, and improved operational efficiency. However, conventional methods have to work with explicit motor models and cannot be used for vibration signal case because of their non-adaptation and the random nature of vibration signal. In this paper, a neural network method is developed for induction machine fault detection, using FFT. The neural network model is trained with vibration spectra and faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness and accuracy of the proposed approach in detecting a wide range of mechanical faults is demonstrated through staged motor faults, and it is shown that a robust and reliable induction machine fault detection system has been produced.