RELEVANCE VECTOR MACHINE BASED BEARING FAULT DIAGNOSIS

摘要：

This paper introduces a new bearing fault detection and diagnosis scheme based on Relevance Vector Machine (RVM)of vibration signals, i.e. two relevance vector machines are viewed as observer and classifier respectively. The observer is applied to identify and estimate various faults of bearing to gain fault state residual sequence while the classifier is used to classify multiple fault modes of bearings. Also, the algorithms constructing observer and classifier are discussed and reasoned. From the experimental results, we can see that estimation and classification based on RVM perform well in bearing fault diagnosis compared with neural networks approach, which indicates that this fault diagnosis method is valid and has promising application.

关键词： Fault diagnosis Bearing Relevance Vector Machine (RVM)

作者: LIANG-YU LEI QING ZHANG

作者单位: Jiangsu Teachers University of Technology, Changzhou 213001, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3492-3496

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）