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http://dx.doi.org/10.5050/KSNVN.2009.19.7.726

Fault Detection and Diagnosis for Induction Motors Using Variance, Cross-correlation and Wavelets  

Tuan, Do Van (울산대학교 컴퓨터정보통신공학부)
Cho, Sang-Jin (울산대학교 전기전자정보시스템공학부)
Chong, Ui-Pil (울산대학교 컴퓨터정보통신공학부)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.19, no.7, 2009 , pp. 726-735 More about this Journal
Abstract
In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.
Keywords
Wavelet; Variance; Cross-correlation; Feature Extraction; Fault Detection and Diagnosis; Induction Motor;
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Times Cited By KSCI : 3  (Citation Analysis)
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