Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine

Authors:
Achmad Widodo;Eric Y. Kim;Jong-Duk Son;Bo-Suk Yang;Andy C. C. Tan;Dong-Sik Gu;Byeong-Keun Choi;Joseph Mathew
Affiliations:
Mechanical Engineering Department, Diponegoro University, Jl. Prof. Sudarto Tembalang, Semarang 50275, Indonesia;CRC for Integrated Engineering Asset Management, Queensland University of Technology, 2 George St, Brisbane, QLD 4001, Australia;School of Mechanical Engineering, Pukyong National University, San 100 Yongdang-dong, Nam-gu, Busan 608-739, South Korea;School of Mechanical Engineering, Pukyong National University, San 100 Yongdang-dong, Nam-gu, Busan 608-739, South Korea;CRC for Integrated Engineering Asset Management, Queensland University of Technology, 2 George St, Brisbane, QLD 4001, Australia;School of Mechanical and Aerospace Engineering, Institute of Marine Industry, Gyeongsang National University, 445 InPyeong-dong, Tongyeong, GyeongSang Nam Do 650-160, South Korea;School of Mechanical and Aerospace Engineering, Institute of Marine Industry, Gyeongsang National University, 445 InPyeong-dong, Tongyeong, GyeongSang Nam Do 650-160, South Korea;CRC for Integrated Engineering Asset Management, Queensland University of Technology, 2 George St, Brisbane, QLD 4001, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This study concerns with fault diagnosis of low speed bearing using multi-class relevance vector machine (RVM) and support vector machine (SVM). A low speed test rig was developed to simulate various types of bearing defects associated with shaft speeds as low as 10rpm under several loading conditions. The data was acquired from the low speed bearing test rig using acoustic emission (AE) and accelerometer sensors under a constant load with different speeds. The aim of this study is to address the problem of detecting an incipient bearing fault and to find reliable methods for low speed machine fault diagnosis. In this paper, two methods of multi-class classification techniques for fault diagnosis through RVM and SVM are presented and the effectiveness of using AE and vibration signals due to low impact rate for low speed diagnosis. In the present study, component analysis was performed initially to extract the features and to reduce the dimensionality of original data features. The classification for fault diagnosis was also conducted using original data feature and without feature extraction. The result shows that multi-class RVM produces promising results and has the potential for use in fault diagnosis of low speed machine.