Application of the Teager-Kaiser energy operator in bearing fault diagnosis

被引：89

作者：

Henriquez Rodriguez, Patricia ^{[1
]}

Alonso, Jesus B. ^{[1
]}

Ferrer, Miguel A. ^{[1
]}

Travieso, Carlos M. ^{[1
]}

机构：

[1] Univ Las Palmas Gran Canaria, Dept Senales & Comunicac, Inst Desarrollo Tecnol & Innovac Comunicac IDeTIC, Las Palmas Gran Canaria 35017, Spain

来源：

ISA TRANSACTIONS | 2013年 / 52卷 / 02期

关键词：

Vibration fault diagnosis; Teager-Kaiser energy operator; Feature selection; Neural networks; LS-SVM; ALGORITHM;

D O I：

10.1016/j.isatra.2012.12.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager-Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal. (C) 2012 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：278 / 284

页数：7

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