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http://dx.doi.org/10.5050/KSNVN.2004.14.11.1091

Fault Diagnosis of Induction Motors Using Data Fusion of Vibration and Current Signals  

김광진 (부경대학교 대학원 기계공학부)
한천 (부경대학교 대학원 기계공학부)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.14, no.11, 2004 , pp. 1091-1100 More about this Journal
Abstract
This paper presents an approach for the monitoring and detection of faults in induction machine by using data fusion technique and Dempster-Shafer theory Features are extracted from motor stator current and vibration signals. Neural network is trained and Hosted by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electric and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real time application.
Keywords
Dempster-Shafer Theory; Data fusion; Feature Extraction; Neural Network; Fault Diagnosis; Electric Motors; Vibration; Stator Current;
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