This article is part of the series The Empirical Mode Decomposition and the Hilbert-Huang Transform.

Open Access Research Article

A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

Junsheng Cheng*, Dejie Yu and Yu Yang

Author Affiliations

The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China

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EURASIP Journal on Advances in Signal Processing 2008, 2008:647135  doi:10.1155/2008/647135


The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2008/1/647135


Received: 24 July 2007
Revisions received: 28 February 2008
Accepted: 15 April 2008
Published: 8 May 2008

© 2008 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An accurate autoregressive (AR) model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD), which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs), is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM) is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

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