Structural Fault Diagnosis of Rotating Machinery Based on Distinctive Frequency Components and Support Vector Machines

被引：0

作者：

Xue, Hongtao ^{[1
]}

Wang, Huaqing ^{[2
]}

Song, Liuyang ^{[2
]}

Chen, Peng ^{[1
]}

机构：

[1] Mie Univ, Grad Sch Bioresources, Tsu, Mie 5148507, Japan

[2] Beijing Univ Chem Technol, Beijing, Peoples R China

来源：

ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2012年 / 6839卷

关键词：

Support vector machine; distinctive frequency component; structural fault; sequential diagnosis;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the field of rotating machinery diagnosis using traditional intelligent diagnosis method, the state judgment and fault detection are usually carried out by symptom parameters (SPs). However, it is difficult to rind the general and highly sensitive SPs for rotating machinery diagnosis. Intelligent methods, such as neural networks, genetic algorithms, etc., often cannot converge when being trained. In order to solve these problems, this paper proposes a new intelligent diagnosis based on distinctive frequency components (DFCs) and support vector machines (SVMs) which can be used to detect faults and recognize fault types of rotating machinery. The method has been applied to detect the. structural faults of rotating machinery, and the efficiency of the method is verified by practical examples.

引用

页码：341 / +

页数：2

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