SVM Diagnosis of Gear Fault based on Improved Feature Selection Method

Jingjing Tan

doi:10.16578/j.issn.1004.2539.2021.04.015

您当前的位置：

首页 >

文章列表页 >

SVM Diagnosis of Gear Fault based on Improved Feature Selection Method

Test·Analysis | 更新时间：2022-11-10

- SVM Diagnosis of Gear Fault based on Improved Feature Selection Method
- Journal of Mechanical Transmission Vol. 45, Issue 4, Pages: 88-93(2021)
- 作者机构：
  
  1.郑州旅游职业学院信息工程学院，河南郑州 450000
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2021.04.015
  CLC：
扫描看全文
谭晶晶.基于改进特征选择方法的齿轮故障SVM诊断[J].机械传动,2021,45(04):88-93.

Tan Jingjing.SVM Diagnosis of Gear Fault based on Improved Feature Selection Method[J].Journal of Mechanical Transmission,2021,45(04):88-93.
谭晶晶.基于改进特征选择方法的齿轮故障SVM诊断[J].机械传动,2021,45(04):88-93. DOI： 10.16578/j.issn.1004.2539.2021.04.015.

Tan Jingjing.SVM Diagnosis of Gear Fault based on Improved Feature Selection Method[J].Journal of Mechanical Transmission,2021,45(04):88-93. DOI： 10.16578/j.issn.1004.2539.2021.04.015.

摘要

为提高齿轮故障诊断的精度，对常用的共享特征选择方法（Share feature selection，SFS）进行改进，提出了改进的特征选择方法（Improved feature selection，IFS）。改进的特征选择方法结合齿轮两两故障类型之间的特点，在齿轮两两故障之间建立独立的故障特征集，用以取代所有故障类型的共享特征集；而后，通过建立多个二分类的支持向量机，对独立的故障特征集进行识别，得到诊断结果。齿轮故障诊断实例表明，改进的特征选择方法排除了无用特征的干扰，提高了诊断精度，具有一定的优势。

Abstract

In order to improve fault diagnosis accuracy of gear， an improved feature selection （IFS） method is proposed based on share feature selection（SFS） which is usually used. In the IFS， an independent fault feature set is established for binary fault type based on the characteristics of the two fault types of gears which used to instead unified fault feature set for all fault types of gears. And then， the independent fault feature sets are identified by establishing multiple support vector machines with multi binary classification support vector machine and the diagnosis results are obtained. The example of gear fault diagnosis shows that the improved feature selection method eliminates the interference of useless feature， the diagnosis accuracy is improved and has certain advantages.

关键词

特征选择改进故障诊断齿轮

Keywords

Feature selectionImprovedFault diagnosisGear

references

李梅红，连威.基于变分模态分解和符号熵的齿轮故障诊断方法［J］.机械传动，2019，43（3）：161-165.

LI Meihong，LIAN Wei.Gear fault diagnosis method based on variational modal decomposition and symbol entropy［J］.Journal of Mechanical Transmission，2019，43（3）：161-165.

王宜静，谭海燕.基于多维度互近似熵的滚动轴承故障诊断方法研究［J］.机械设计与研究，2019，35（1）：110-112.

WANG Yijing，TAN Haiyan.Research on fault diagnosis method of rolling bearing based on multi-dimension cross approximate entropy［J］.Machine Design and Research，2019，35（1）：110-112.

张超，朱腾飞，王大勇.基于VMD能量熵与支持向量机的齿轮故障诊断［J］.机械设计与研究，2018，34（2）：81-84.

ZHANG Chao，ZHU Tengfei，WANG Dayong.Fault diagnosis method of gear based on VMD energy entropy and LS-SVM［J］.Machine Design and Research，2018，34（2）：81-84.

DING Z Y，ZHOU B，ZHAI L D，et al.A word position related LDA model［J］.International Journal of Pattern Recognition and Artificial Intelligence，2011，25（6）：909-925.

MIKA S，RATSCH G，WESTON J，et al.Fisher discriminant analysis with kernels［J］.Neural Networks for Signal Processing，1999，9：41-48.

WANG L.Feature selection with kernel class separability［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2008，30（9）：1534-1546.

王广斌，刘义伦，黄良沛.基于核schur正交局部Fisher判别的转子故障诊断［J］.仪器仪表学报，2010，31（5）：1005-1009.

WANG Guangbin，LIU Yilun，HUANG Liangpei.Rotor fault diagnosis based on kernel schurorthogonal local Fisher discriminant［J］.Chinese Journal of Scientific Instrument，2010，31（5）：1005-1009.

LÜ Y，YANG H Z.A multimodel approach for soft sensor development based on feature extraction using weighted kernel fisher criterion［J］.Chinese Journal of Chemical Engineering，2014，22（2）：146-152.

于磊，陈森，张瑞，等.深度支持向量机在齿轮故障诊断中的应用［J］.机械传动，2019，43（8）：150-156.

YU Lei，CHEN Sen，ZHANG Rui，et al.Application of deep support vector machine in gear fault diagnosis［J］.Journal of Mechanical Transmission，2019，43（8）：150-156.

韩佳佳，贾继德，梅检民，等.基于优选小波包和PSO-SVM的失火故障诊断［J］.机械设计与研究，2019，35（2）：137-141.

HAN Jiajia，JIA Jide，MEI Jianmin，et al.Misfire fault diagnosis based on wavelet packet basis function optimization and PSO-SVM［J］.Machine Design and Research，2019，35（2）：137-141.

于洋，白瑞，杨平.基于遗传算法优化的支持向量机在齿轮故障声发射检测中的应用［J］.机械传动，2018，42（1）：163-166.

YU Yang，BAI Rui，YANG Ping.Application of the support vector machine based on genetic algorithm optimization on the acoustic emission detection of gear fault［J］.Journal of Mechanical Transmission，2018，42（1）：163-166.

季云峰，冯立元，匡亮.基于改进的有监督正交邻域保持嵌入的故障辨识［J］.机械传动，2017，41（1）：16-19.

JI Yunfeng，FENG Liyuan，KUANG Liang.Fault identification based on improved supervised orthogonal neighborhood preserving embedding［J］.Journal of Mechanical Transmission，2017，41（1）：16-19.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on Gear Fault Diagnosis Method based on Local Characteristic Scale Decomposition and Mathematical Morphological Filtering

Gear Fault Diagnosis based on EEMD and Choi-Williams Distribution

Gear Fault Diagnosis Method based on Variational Modal Decomposition and Symbol Entropy

Gearbox Fault Diagnosis Method Based on Improved Multi-Scale Mean Permutation Entropy and Parameter Optimization SVM

Gear Fault Diagnosis based on VMD and Generalized Warblet Transform

Related Author

No data

Related Institution

Coastal Defense College， Naval Aviation University

School of Mechanical and Power Engineering， Henan Polytechnic University

Department of Mechanical Engineering， Tianjin Polytechnic College

Chongqing University， College of Mechanical Engineering

Faculty of Mechanical and Electrical Engineering，Kunming University of Science and Technology

⁰