Bearing performance degradation assessment based on the rough support vector data description

被引：98

作者：

Zhu, Xiaoran ^{[1
]}

Zhang, Youyun ^{[1
]}

Zhu, Yongsheng ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Key Lab, Educ Minist Modern Design & Rotor Bearing Syst, Xian 710049, Peoples R China

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2013年 / 34卷 / 1-2期

基金：

中国国家自然科学基金;

关键词：

Performance degradation assessment method; Rough support vector data description; Incremental learning; MACHINE;

D O I：

10.1016/j.ymssp.2012.08.008

中图分类号：

TH [机械、仪表工业];

学科分类号：

120111 [工业工程];

摘要：

The performance degradation assessment based on the support vector data description (SVDD) has been receiving more attention recently. However, there are three main drawbacks to this approach. First, the SVDD is sensitive to outliers and may result in an over-fitting problem. Second, the initial status model, which is not changed as time goes on, does not effectively reflect the latest status of the bearing. Third, the previous assessment indicator only contains distance information without spatial position information. To address these critical issues, a novel one-class classifier called the rough support vector data description (RSVDD) is proposed based on the rough set notion. Then, the incremental rough support vector data description (IRSVDD) is designed based on the RSVDD. Finally, the new assessment indicator and assessment process are proposed. The effectiveness of the proposed methods is validated through experiments. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：203 / 217

页数：15

共 34 条

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