Authors: YeanRen Hwang KuoKuang Jen YuTa Shen
Publish Date: 2009/10/14
Volume: 23, Issue: 10, Pages: 2730-
Abstract
This paper proposes an integrated system for motor bearing diagnosis that combines the cepstrum coefficient method for feature extraction from motor vibration signals and artificial neural network ANN models We divide the motor vibration signal obtain the corresponding cepstrum coefficients and classify the motor systems through ANN models Utilizing the proposed method one can identify the characteristics hiding inside a vibration signal and classify the signal as well as diagnose the abnormalities To evaluate this method several tests for the normal and abnormal conditions were performed in the laboratory The results show the effectiveness of cepstrum and ANN in detecting the bearing condition The proposed method successfully extracted the corresponding feature vectors distinguished the difference and classified bearing faults correctlyYeanRen Hwang received his PhD degree from the University of California at Berkeley He is currently a Professor at the Department of Mechanical Engineering at National Central University in Taiwan ROC His research areas include control manufacturing and machine visionKuoKuang Jen received his BS degree from National Defense University MS degree from ChungHua University and PhD degree from National Central University He is currently an Assistant Research Fellow in ChungShan Institute of Science and Technology in Taiwan focusing on related topics in the control fault diagnosis and measurement areasYuTa Shen received his BS degree from YuanZe University He is currently a PhD candidate at the Department of Mechanical Engineering National Central University in Taiwan ROC His research topics are focused on the controlrelated areas for vehicles and manufacturing systems
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