Research of the Method of Gear Fault Classification based on Contourlet Transform and Local Binary Pattern

Zhang Fangdong

doi:10.16578/j.issn.1004.2539.2018.12.031

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Research of the Method of Gear Fault Classification based on Contourlet Transform and Local Binary Pattern

更新时间：2022-10-20

- Research of the Method of Gear Fault Classification based on Contourlet Transform and Local Binary Pattern
- Vol. 42, Issue 12, Pages: 166-169(2018)
- 作者机构：
  
  1. 山西机电职业技术学院机械系
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.12.031
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[1]张方东.基于轮廓波变换和局部二元模式的齿轮箱故障分类方法研究[J].机械传动,2018,42(12):166-169+183.

Zhang Fangdong. Research of the Method of Gear Fault Classification based on Contourlet Transform and Local Binary Pattern. [J]. 42(12):166-169(2018)
[1]张方东.基于轮廓波变换和局部二元模式的齿轮箱故障分类方法研究[J].机械传动,2018,42(12):166-169+183. DOI： 10.16578/j.issn.1004.2539.2018.12.031.

Zhang Fangdong. Research of the Method of Gear Fault Classification based on Contourlet Transform and Local Binary Pattern. [J]. 42(12):166-169(2018) DOI： 10.16578/j.issn.1004.2539.2018.12.031.

摘要

针对实际工况中难于提取齿轮箱故障特征的问题,根据轮廓波变换的全局纹理和局部二元模式的局部纹理特性,提出了一种基于振动信号时频图像的故障特征提取方法。首先,利用小波变换将振动信号变换到时频域并得到其时频灰度图像;然后,对该灰度图像进行轮廓波变换,得到低频和高频子带部分,提取低频子带的均值和标准差以及高频子带各层的能量均值作为一部分特征向量;同时,对该时频灰度图像进行局部二元模式的特征值提取并得到另一部分特征向量,将两部分特征向量进行组合连接得到最终的特征向量;最后,利用支持向量机对齿轮箱不同程度故障进行分类测试,实验结果表明了该方法的有效性,为机械设备的模式识别提供了一种方法。

Abstract

For the problem that the gear fault feature is difficult to extract in actual working condition,a fault feature extraction method based on vibration signal time-frequency image is proposed,which is based on the global texture of the contourlet transform and the local texture of the local binary pattern. Firstly,the vibration signal is transformed into the time-frequency domain by wavelet transform and then the time-frequency gray image is obtained. Then,contourlet transform is performed on the gray image,the low frequency and high frequency subbands are gotten,the low-frequency mean,standard deviation and the high frequency subbands of each layer of the average energy are extracted as part of a feature vector. At the same time,the feature values of the local binary pattern of the time-frequency gray image are extracted and another feature vector is obtained,and the final feature vectors are obtained by the combination of the two feature vectors. Finally,the data of different conditions from a gearbox and rolling bearing are classified and tested by using SVM,the experimental results show the effectiveness of the method,an effective method for the pattern recognition of mechanical equipment is provided.

关键词

轮廓波局部二元模式时频图像支持向量机模式识别

Keywords

ContourletLocal binary patternTime-frequency imageSVMPattern recognition

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School of Mechanical Engineering, Changzhou University

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