Proceedings of the Korean Society for Noise and Vibration Engineering Conference (한국소음진동공학회:학술대회논문집)
- 2007.11a
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- Pages.891-894
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- 2007
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- 1598-2548(pISSN)
Fault Diagnosis of Low Speed Bearing Using Support Vector Machine
- Widodo, Achmad (Pukyong National University) ;
- Son, Jong-Duk (Pukyong National University) ;
- Yang, Bo-Suk (Pukyong National University) ;
- Gu, Dong-Sik (Gyongsang National University) ;
- Choi, Byeong-Keun (Gyongsang National University) ;
- Kim, Yong-Han (Queensland University of Technology) ;
- Tan, Andy C.C (Queensland University of Technology) ;
- Mathew, Joseph (Queensland University of Technology)
- Published : 2007.11.15
Abstract
This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer. The aim of this study is to compare the performance of fault diagnosis based on AE signal and vibration signal with same load and speed. A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. In this study, component analysis was also performed to extract the feature and reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that extracted feature from AE sensor gave better performance in faults classification.