Application of the Teager-Kaiser energy operator in bearing fault diagnosis
Application of the Teager-Kaiser energy operator in bearing fault diagnosis
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DOI：
10.1016/j.isatra.2012.12.006
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发表时间：
2013-03-01
期刊：
影响因子：
7.3
通讯作者：
Travieso, Carlos M.
中科院分区：
文献类型：
--
作者：
Henriquez Rodriguez, Patricia;Alonso, Jesus B.;Travieso, Carlos M.
旋转机械的状态监测对于防止故障的发生具有重要意义。由于大多数机器故障都与轴承故障有关，因此开发了几种轴承诊断技术。有的利用统计方法对轴承振动信号进行特征提取，有的从振动信号的AM分量中提取轴承故障特征频率。在本文中，我们建议将振动信号变换到Teager-Kaiser域，并使用统计和基于能量的度量对其进行特征提取。使用了包含正常轴承和故障轴承的轴承数据库。诊断使用两个分类器：神经网络分类器和最小二乘支持向量机。实验表明，Teager域特征优于基于时间或AM信号的特征。(C)2012年《国际行政程序法》。爱思唯尔有限公司出版。保留所有权利。
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.