Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals
- Authors
- Hwang, Don-Ha; Youn, Young-Woo; Sun, Jong-Ho; Choi, Kyeong-Ho; Lee, Jong-Ho; Kim, Yong-Hwa
- Issue Date
- Jul-2015
- Publisher
- KOREAN INST ELECTR ENG
- Keywords
- Bearing fault; Induction motor; Fault diagnosis; Vibration signal
- Citation
- JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, v.10, no.4, pp.1558 - 1565
- Journal Title
- JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
- Volume
- 10
- Number
- 4
- Start Page
- 1558
- End Page
- 1565
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/10366
- DOI
- 10.5370/JEET.2015.10.4.1558
- ISSN
- 1975-0102
- 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.
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