Gear Fault Diagnosis based on Feature Fusion and Sparse Representation

Wang Jiangping; Duan Tengfei

doi:10.16578/j.issn.1004.2539.2017.01.012

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Gear Fault Diagnosis based on Feature Fusion and Sparse Representation

更新时间：2022-10-20

- Gear Fault Diagnosis based on Feature Fusion and Sparse Representation
- Vol. 41, Issue 1, Pages: 54-58(2017)
- 作者机构：
  
  1. 西安石油大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2017.01.012
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[1]王江萍,段腾飞.基于特征融合和稀疏表示的齿轮故障诊断[J].机械传动,2017,41(01):54-58.

Wang Jiangping, Duan Tengfei. Gear Fault Diagnosis based on Feature Fusion and Sparse Representation. [J]. 41(1):54-58(2017)
[1]王江萍,段腾飞.基于特征融合和稀疏表示的齿轮故障诊断[J].机械传动,2017,41(01):54-58. DOI： 10.16578/j.issn.1004.2539.2017.01.012.

Wang Jiangping, Duan Tengfei. Gear Fault Diagnosis based on Feature Fusion and Sparse Representation. [J]. 41(1):54-58(2017) DOI： 10.16578/j.issn.1004.2539.2017.01.012.

摘要

提出了基于特征融合和稀疏表示的机械故障诊断方法。用小波包分解齿轮振动信号,提取各频带的能量,组成初始特征向量。为了去除初始特征向量中的冗余信息,采用核主元分析进行特征融合,对初始特征向量进行降维,将降维之后的特征向量进行归一化,作为稀疏表示分类的输入。齿轮故障诊断实验表明,所提方法能较好地诊断出齿轮不同类型的故障。

Abstract

A kind of the mechanical fault diagnosis method based on feature fusion and sparse representation is proposed. The energy of every subband of gear vibration signal is extracted with wavelet packet decomposition,and the energy constitutes original feature vector. The feature fusion is implemented with KPCA,and the dimension reduction of original feature vector is used as the input of classification based on sparse representation. The experiment demonstrates that the different fault type of gear is detected by the presented method.

关键词

故障诊断特征融合稀疏表示齿轮

Keywords

Fault diagnosisFeature fusionSparse representationGear

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