Induction Machine Condition Monitoring Using Neural Network Modeling
Hua Su
Kil To Chong
Dept. of Comput. for Design & Optimization, MIT, Cambridge, MA;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb. 2007
Volume: 54,
Issue: 1
On page(s): 241-249
ISSN: 0278-0046
INSPEC Accession Number: 9299047
Digital Object Identifier: 10.1109/TIE.2006.888786
Current Version Published: 2007-02-05
Abstract
Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Model-based methods are efficient monitoring systems for providing warning and predicting certain faults at early stages. However, the conventional methods must work with explicit motor models, and cannot be applied effectively for vibration signal diagnosis due to their nonadaptation and the random nature of vibration signal. In this paper, an analytical redundancy method using neural network modeling of the induction motor in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform is used to process the quasi-steady vibration signals to continuous spectra for the neural network model training. The faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results, and it is shown that a robust and automatic induction machine condition monitoring system has been produced
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