Induction machine fault detection using SOM-based RBF neural networks
Wu, S.
Chow, T.W.S.
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, China;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb. 2004
Volume: 51,
Issue: 1
On page(s): 183- 194
ISSN: 0278-0046
INSPEC Accession Number: 7980564
Digital Object Identifier: 10.1109/TIE.2003.821897
Current Version Published: 2004-02-19
Abstract
A radial-basis-function (RBF) neural-network-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an RBF-type neural network for fault identification and classification. The optimal network architecture of the RBF network is determined automatically by our proposed cell-splitting grid algorithm. This facilitates the conventional laborious trial-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults, but the system is also able to estimate the extent of faults.
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