A hybrid model for bearing performance degradation assessment based on support vector data description and fuzzy c-means

被引：28
|
作者：
Pan, Y. N. [1 ]
Chen, J. [1 ]
Dong, G. M. [1 ]
机构：
[1] Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China
来源：
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE | 2009年 / 223卷 / 11期
基金：
国家高技术研究发展计划(863计划);
关键词：
performance degradation assessment; Support vector data description; fuzzy c-means; accelerated bearing life test; ROLLING ELEMENT BEARINGS; FAULT-DETECTION; MACHINE; VIBRATION; DIAGNOSIS; ALGORITHM; SCHEME;
D O I：
10.1243/09544062JMES1447
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Bearing performance degradation assessment is more effective than fault diagnosis to realize condition-based maintenance. In this article, a hybrid model is proposed for it based on a support vector data description (SVDD) and fuzzy c-means (F-CM). SVDD, which holds excellent robustness to Outliers. is used to obtain the clustering centre of normal state. The subjection of tested data to normal state is defined as a degradation indicator, which is computed by a FCM algorithm with final failure data. The results of applying this hybrid model to an accelerated bearing life test show that it can effectively assess bearing performance degradation. Furthermore, it is robust to the outliers in the training set and IS not influenced by the Gaussian kernel parameter.
引用
收藏
页码：2687 / 2695
页数：9
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