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http://dx.doi.org/10.3745/KIPSTB.2011.18B.3.147

Highly Reliable Fault Detection and Classification Algorithm for Induction Motors  

Hwang, Chul-Hee (울산대학교 전기공학부)
Kang, Myeong-Su (울산대학교 컴퓨터정보통신공학과)
Jung, Yong-Bum (울산대학교 전기공학부)
Kim, Jong-Myon (울산대학교 전기공학부)
Publication Information
The KIPS Transactions:PartB / v.18B, no.3, 2011 , pp. 147-156 More about this Journal
Abstract
This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.
Keywords
Induction Motor; Fault Detection and Classification Algorithm; Discrete Cosine Transform; Back Propagation Neural Network;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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