Fault diagnosis of bearing based on fuzzy support vector machine
In rotating machinery equipment, bearing is one of the most common parts. Because of the complex working conditions, the bearing system is subject to get failure. The running state of bearing system, which is normal or not, will directly affect the safety of the production line, or even cause some a...
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| Vydáno v: | 2015 Prognostics and System Health Management Conference (PHM) s. 1 - 4 |
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| Jazyk: | angličtina |
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IEEE
01.10.2015
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| Abstract | In rotating machinery equipment, bearing is one of the most common parts. Because of the complex working conditions, the bearing system is subject to get failure. The running state of bearing system, which is normal or not, will directly affect the safety of the production line, or even cause some accidents. Therefore, the technology of fault diagnosis of rolling bearing has important theoretical value and practical significance in production safety. In the light of the vibration data of rolling bearing, including the normal operation of rolling bearing, the single point fault of the inner ring, the single point fault of the outer ring and the single point fault of the ball, those four cases, time, envelope and frequency analysis were performed to extract fault features. Considering the interference of noise and outliers, support vector machine (SVM) theory combined with the fuzzy c-means (FCM) clustering algorithm was used to establish the fuzzy support vector machine (FSVM) model. Train the samples, using the founded model of FSVM, and then the test and identification of bearing fault would be obtained. |
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| AbstractList | In rotating machinery equipment, bearing is one of the most common parts. Because of the complex working conditions, the bearing system is subject to get failure. The running state of bearing system, which is normal or not, will directly affect the safety of the production line, or even cause some accidents. Therefore, the technology of fault diagnosis of rolling bearing has important theoretical value and practical significance in production safety. In the light of the vibration data of rolling bearing, including the normal operation of rolling bearing, the single point fault of the inner ring, the single point fault of the outer ring and the single point fault of the ball, those four cases, time, envelope and frequency analysis were performed to extract fault features. Considering the interference of noise and outliers, support vector machine (SVM) theory combined with the fuzzy c-means (FCM) clustering algorithm was used to establish the fuzzy support vector machine (FSVM) model. Train the samples, using the founded model of FSVM, and then the test and identification of bearing fault would be obtained. |
| Author | Lichao Gu Haodong Ma Yi Xiong Hongzheng Fang |
| Author_xml | – sequence: 1 surname: Haodong Ma fullname: Haodong Ma email: mahaodong@126.com organization: Beijing Aerosp. Meas. & Control Corp. Ltd., Beijing, China – sequence: 2 surname: Yi Xiong fullname: Yi Xiong email: xiaoy_86@hotmail.com organization: Beijing Aerosp. Meas. & Control Corp. Ltd., Beijing, China – sequence: 3 surname: Hongzheng Fang fullname: Hongzheng Fang email: hongzhengf@163.com organization: Beijing Aerosp. Meas. & Control Corp. Ltd., Beijing, China – sequence: 4 surname: Lichao Gu fullname: Lichao Gu email: 1365127710@qq.com organization: Beijing Univ. of Technol., Beijing, China |
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| Snippet | In rotating machinery equipment, bearing is one of the most common parts. Because of the complex working conditions, the bearing system is subject to get... |
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| SubjectTerms | Acceleration Data mining Fault Diagnosis Fault feature FCM FSVM Interference Production Rolling Bearing |
| Title | Fault diagnosis of bearing based on fuzzy support vector machine |
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