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

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-fr...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2008-01, Vol.2008 (1), Article 647135
Main Authors: Cheng, Junsheng, Yu, Dejie, Yang, Yu
Format: Article
Language:English
Subjects:
Engineering
Quantum Information Technology
Research Article
Signal,Image and Speech Processing
Spintronics
The Empirical Mode Decomposition and the Hilbert-Huang Transform
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Summary: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.
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1155/2008/647135