Neural network based expert system for induction motor faults detection |
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Authors: | Hua Su Kil To Chong |
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Affiliation: | (1) Department of Computation for Design and Optimization, MIT, 02139 Cambridge, MA, USA;(2) Faculty of Electronics & Information Engineering, Chonbuk National University, 664-14 Duckjin-Dong, Duckjin-Gu, 561-756 Jeonju, Korea |
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Abstract: | Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential
damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because
it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing
problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady
vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed
neural network expert system is evaluated. The results show that a neural network expert system can be developed based on
vibration measurements acquired on-line from the machine. |
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Keywords: | Vibration Signal Neural Network Fault Detection Expert System |
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