A novel fault diagnosis method for rotating machinery based on S transform and morphological pattern spectrum

被引：4

作者：

Gao, Jingwei ^{[1
]}

Wang, Ruichen ^{[2
]}

Zhang, Rui ^{[1
]}

Li, Yuan ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Basic Educ, Changsha 410073, Hunan, Peoples R China

[2] Univ Huddersfield, Ctr Efficiency & Performance Engn, Huddersfield HD1 3DH, W Yorkshire, England

来源：

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING | 2016年 / 38卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Rotating machinery; Bearing; Fault diagnosis; S transform; Morphological pattern spectrum; TIME-FREQUENCY ANALYSIS; CLASSIFICATION; MODEL;

D O I：

10.1007/s40430-015-0474-6

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

With the continuing expansion of the applications of rotating machinery, an earlier and more accurate fault diagnosis method is required. In this paper, a novel characterization method based on S transform and morphological pattern spectrum (ST-MPS) was put forward. In order to verify the application of the method, ST-MPS was applied to a set of experimental signals obtained in a bearing test bench, and the results verified that the proposed feature extraction method is an effective approach to accurately classify the types of bearing fault.

引用

页码：1575 / 1584

页数：10

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