Vibrational analysis using neural network classifier for motor fault detection

Hua Su; Yeong Cheol Kim; Yidong Lee; Kil To Chong

doi:10.1117/12.664666

2 May 2006 Vibrational analysis using neural network classifier for motor fault detection

Hua Su, Yeong Cheol Kim, Yidong Lee, Kil To Chong

Proceedings Volume 6042, ICMIT 2005: Control Systems and Robotics; 60422K (2006) https://doi.org/10.1117/12.664666
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

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.

Citation Download Citation

Hua Su, Yeong Cheol Kim, Yidong Lee, and Kil To Chong "Vibrational analysis using neural network classifier for motor fault detection", Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 60422K (2 May 2006); https://doi.org/10.1117/12.664666

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