A fault diagnosis approach for roller bearings based on EMD method and AR model
A fault diagnosis approach for roller bearings based on EMD method and AR model
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DOI：
10.1016/j.ymssp.2004.11.002
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发表时间：
2006-02-01
影响因子：
8.4
通讯作者：
Yang, Y
中科院分区：
文献类型：
--
作者：
Cheng, JS;Yu, DJ;Yang, Y
本文的主要目的是提出一种基于经验模态分解（EMD）方法和自回归（AR）模型的滚动轴承故障特征提取新方法。AR模型是一种提取振动信号故障特征的有效方法，在不建立数学模型和研究系统故障机理的情况下，通过提取的故障特征可以直接识别故障模式。然而，AR模型只能应用于平稳信号，而滚动轴承的故障振动信号是非平稳的。针对这一问题，本文采用EMD方法作为预处理，将滚子轴承的非平稳振动信号分解为多个平稳的本征模态函数（IMF）分量，然后建立每个IMF分量的AR模型。将IMF各分量AR模型的AR参数和残差方差作为特征向量。利用马氏距离准则函数识别滚子轴承的状态和故障模式。实验分析结果表明，该方法可以有效地提取滚动轴承故障特征。(c) 2004 Elsevier Ltd.版权所有。
The main purpose of this paper is to propose a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings. AR model is an effective approach to extract the fault feature of the vibration signals and the fault pattern can be identified directly by the extracted fault features without establishing the mathematical model and studying the fault mechanism of the system. However, AR model can only be applied to stationary signals, while the fault vibration signals of a roller bearing are non-stationary. Aiming at this problem, in this paper, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary, then the AR model of each IMF component can be established. The AR parameters and the remnant's variance of the AR models of each IMF components are regarded as the feature vectors. The Mahalanobis distance criterion function is used to identify the condition and fault pattern of a roller bearing. Experimental analysis results show that the roller bearing fault features can be extracted by the proposed approach effectively. (c) 2004 Elsevier Ltd. All rights reserved.