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Neural Computing and Applications
Published in: Neural Computing and Applications 2/2014

01-08-2014 | Original Article

A new gear fault feature extraction method based on hybrid time–frequency analysis

Authors: Wenyi Liu, Jiguang Han, Xiangning Lu

Published in: Neural Computing and Applications | Issue 2/2014

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Abstract

Gear is one of the popular and important components in the rotary machinery transmission. Vibration monitoring is the common way to take gear feature extraction and fault diagnosis. The gear vibration signal collected in the running time often reflects the characteristics such as non-Gaussian and nonlinear, which is difficult in time domain or frequency domain analysis. This paper proposed a novel gear fault feature extraction method based on hybrid time–frequency analysis. This method combined the Mexican hat wavelet filter de-noise method and the auto term window method at the first time. This method can not only de-noise noise jamming in raw vibration signal, but also extract gear fault features effectively. The final experimental analysis proved the feasibility and the availability of this new method.
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Metadata
Title
A new gear fault feature extraction method based on hybrid time–frequency analysis
Authors
Wenyi Liu
Jiguang Han
Xiangning Lu
Publication date
01-08-2014
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 2/2014
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1502-z

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