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Proceedings Paper

Vibrational analysis using neural network classifier for motor fault detection
Author(s): Hua Su; Yeong Cheol Kim; Yidong Lee; Kil To Chong
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Paper Abstract

Early detection and diagnosis of induction machine incipient faults are desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. However, fault detection using analytical method is not always possible because it requires perfect knowledge of a process model. A neural network based expert system was proposed for diagnostic problems of the induction motors using vibration analysis. The short-time Fourier transform (STFT) was used to process the quasi-steady vibration signals, and the neural network was trained and tested using the vibration spectra. The efficiency of the developed neural network expert system was evaluated. The obtained results lead to a conclusion that neural network expert system can be developed based on vibration measurements acquired online from the machine.

Paper Details

Date Published: 2 May 2006
PDF: 6 pages
Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 60422K (2 May 2006); doi: 10.1117/12.664666
Show Author Affiliations
Hua Su, Chonbuk National Univ. (South Korea)
Yeong Cheol Kim, Kunsan National Univ. (South Korea)
Yidong Lee, Chonbuk National Univ. (South Korea)
Kil To Chong, Chonbuk National Univ. (South Korea)

Published in SPIE Proceedings Vol. 6042:
ICMIT 2005: Control Systems and Robotics
Yunlong Wei; Kil To Chong; Takayuki Takahashi; Shengping Liu; Zushu Li; Zhongwei Jiang; Jin Young Choi, Editor(s)

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