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Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals
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 Title & Authors
Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals
Hwang, Don-Ha; Youn, Young-Woo; Sun, Jong-Ho; Choi, Kyeong-Ho; Lee, Jong-Ho; Kim, Yong-Hwa;
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 Abstract
In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in three-phase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.
 Keywords
Bearing fault;Induction motor;Fault diagnosis;Vibration signal;
 Language
English
 Cited by
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An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique, Circuits and Systems, 2016, 07, 08, 1569  crossref(new windwow)
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Diagnosis of stator faults of the single-phase induction motor using acoustic signals, Applied Acoustics, 2016  crossref(new windwow)
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