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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Veröffentlicht in:2015 Prognostics and System Health Management Conference (PHM) S. 1 - 4
Hauptverfasser: Haodong Ma, Yi Xiong, Hongzheng Fang, Lichao Gu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2015
Schlagworte:
Acceleration
Data mining
Fault Diagnosis
Fault feature
FCM
FSVM
Interference
Production
Rolling Bearing
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Zusammenfassung: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.
DOI:10.1109/PHM.2015.7380059