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

Guo Panpan; Zhang Wenbin; Cui Ben; Xu Han

doi:10.16578/j.issn.1004.2539.2024.04.021

您当前的位置：

首页 >

文章列表页 >

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

Development·Application | 更新时间：2024-04-19

- Gearbox Fault Diagnosis Method Based on Improved Multi-scale Mean Permutation Entropy and Parameter Optimization SVM
- Journal of Mechanical Transmission Vol. 48, Issue 4, Pages: 154-161(2024)
- 作者机构：
  
  1.昆明理工大学机电工程学院，云南昆明 650500
  2.昆明学院机电工程学院，云南昆明 650214
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2024.04.021
  CLC：
- Published：15 April 2024，
  
  Received：28 December 2022，
  
  Revised：10 January 2023，
扫描看全文
郭盼盼,张文斌,崔奔等.基于改进多尺度均值排列熵和参数优化SVM的齿轮箱故障诊断方法[J].机械传动,2024,48(04):154-161.

Guo Panpan,Zhang Wenbin,Cui Ben,et al.Gearbox Fault Diagnosis Method Based on Improved Multi-scale Mean Permutation Entropy and Parameter Optimization SVM[J].Journal of Mechanical Transmission,2024,48(04):154-161.
郭盼盼,张文斌,崔奔等.基于改进多尺度均值排列熵和参数优化SVM的齿轮箱故障诊断方法[J].机械传动,2024,48(04):154-161. DOI： 10.16578/j.issn.1004.2539.2024.04.021.

Guo Panpan,Zhang Wenbin,Cui Ben,et al.Gearbox Fault Diagnosis Method Based on Improved Multi-scale Mean Permutation Entropy and Parameter Optimization SVM[J].Journal of Mechanical Transmission,2024,48(04):154-161. DOI： 10.16578/j.issn.1004.2539.2024.04.021.

摘要

当齿轮箱传动系统发生故障时，不同振动信号的多尺度均值排列熵（Multi-scale Mean Permutation Entropy，MMPE）与其故障状态有一定的对应关系，但MMPE提取故障特征的效果取决于参数的选取。因此，提出了一种基于改进MMPE和参数优化支持向量机（Support Vector Machine，SVM）的齿轮箱故障识别方法。首先，引用粒子群优化（Particle Swarm Optimization，PSO）算法对MMPE的参数进行优化；其次，对采集到的齿轮振动信号计算其MMPE；最后，采用PSO-SVM对齿轮的故障状态进行了识别。试验结果验证了所提方法的有效性且具有较高的准确率。

Abstract

When a gearbox transmission system fails

the multi-scale mean permutation entropy （MMPE） of different vibration signals corresponds to the fault state to a certain extent. However

the effect of multi-scale mean permutation entropy extraction fault features depends on the selection of parameters. Therefore

this study proposes a gearbox fault identification method based on the improved multi-scale mean permutation entropy and the parameter optimization support vector machine（SVM）. Firstly

the particle swarm optimization （PSO） is referenced to optimize parameters of multi-scale mean permutation entropy. Secondly

the multi-scale mean permutation entropy of the collected gear vibration signals is calculated.Finally

the particle swarm optimization is used to optimize the support vector machine to identify the fault state of the gear. Experimental analysis results are conducted to validate the effectiveness of this proposed method.

关键词

多尺度均值排列熵粒子群优化算法支持向量机故障诊断齿轮

Keywords

Multi-scale mean permutation entropyParticle swarm optimization algorithmSupport vector machineFault diagnosisGear

references

时献江，王桂荣，司俊山.机械故障诊断及典型案例解析［M］.北京：化学工业出版社，2013：117-138.

SHI Xianjiang，WANG Guirong，SI Junshan.Mechanical fault diagnosis and analysis of typical cases［M］.Beijing：Chemical Industry Press，2013：117-138.

SHANNON C E.A mathematical theory of communication［J］.Bell System Technical Journal，1948，27（4）：623-656.

YAN R，GAO R X.Approximate entropy as a diagnostic tool for machine health monitoring［J］.Mechanical Systems & Signal Processing，2007，21（2）：824-839.

LAKE D E，RICHMAN J S，GRIFFIN M P，et al.Sample entropy analysis of neonatal heart rate variability［J］.American Journal of Physiology-Regulatory，Integrative and Comparative Physiology，2002，283（3）：789-797.

CAO Y，TUNG W W，GAO J B，et al.Detecting dynamical changes in time series using the permutation entropy［J］.Physical Review E，2004，70（4）：046217.

杨望灿，张培林，王怀光，等.基于EEMD的多尺度模糊熵的齿轮故障诊断［J］.振动与冲击，2015，34（14）：163-167.

YANG Wangcan，ZHANG Peilin，WANG Huaiguang，et al.Gear fault diagnosis based on multi-scale fuzzy entropy of EEMD［J］.Journal of Vibration and Shock，2015，34（14）：163-167.

AZIZ W，ARIF M.Multiscale permutation entropy of physiological time series［C］//2005 Pakistan Section Multitopic Conference，December，24-25，2005，Karachi，Pakistan，IEEE，2005：1-6.

郑近德，程军圣，杨宇.多尺度排列熵及其在滚动轴承故障诊断中的应用［J］.中国机械工程，2013，24（19）：2641-2646.

ZHENG Jinde，CHENG Junsheng，YANG Yu.Multi-scale permutation entropy and its applications to rolling bearing fault diagnosis［J］.China Mechanical Engineering，2013，24（19）：2641-2646.

王贡献，张淼，胡志辉，等.基于多尺度均值排列熵和参数优化支持向量机的轴承故障诊断［J］.振动与冲击，2022，41（1）：221-228.

WANG Gongxian，ZHANG Miao，HU Zhihui，et al.Bearing fault diagnosis of support vector machine based on multi-scale mean arrangement entropy and parameter optimization［J］.Journal of Vibration and Shock，2022，41（1）：221-228.

饶国强，冯辅周，司爱威，等.排列熵算法参数的优化确定方法研究［J］.振动与冲击，2014，33（1）：188-193.

RAO Guoqiang，FENG Fuzhou，SI Aiwei，et al.Research on optimization method for determining parameters of permutation entropy algorithm［J］.Journal of Vibration and Shock，2014，33（1）：188-193.

陈东宁，张运东，姚成玉，等.基于FVMD多尺度排列熵和GK模糊聚类的故障诊断［J］.机械工程学报，2018，54（14）：16-27.

CHEN Dongning，ZHANG Yundong，YAO Chengyu，et al.Fault diagnosis based on FVMD multi-scale permutation entropy and GK fuzzy clustering［J］.Journal of Mechanical Engineering，2018，54（14）：16-27.

陈景年.一种适于多分类问题的支持向量机加速方法［J］.计算机科学，2022，49（S1）：297-300.

CHEN Jingnian.A support vector machine acceleration method for multi-classification problems［J］.Computer Science，2022，49（S1）：297-300.

POLI R，KENNEDY J，BLACKWELL T.Particle swarm optimization［J］.Swarm Intelligence，1995，1（1）：33-57.

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.

吉根林.遗传算法研究综述［J］.计算机应用与软件，2004，21（2）：69-73.

JI Genlin.Survey on genetic algorithm［J］.Computer Applications and Software，2004，21（2）：69-73.

RAFIEE J，ARVANI F，HARIFI A，et al.Intelligent condition monitoring of a gearbox using artificial neural network［J］.Mechanical Systems and Signal Processing，2007，21（4）：1746-1754.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

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

Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information Coefficient

Fault Diagnosis of Vehicle Gearbox based on Support Vector Machine Optimized by Improved Beetle Antennae Search

SVM Diagnosis of Gear Fault based on Improved Feature Selection Method

Related Author

Guo Panpan

Zhang Wenbin

Cui Ben

Xu Han

Hongmei Li

Wei Lian

Nie Yongjun

Liu Zhijun

Related Institution

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

College of Engineering， Honghe University， Key Laboratory of Mechanical Performance Analysis and Optimization of Plateau in Yunnan Province， Mengzi

Department of Mechanical Engineering， Tianjin Polytechnic College

Chongqing University， College of Mechanical Engineering

Department of Mechanical Engineering, Guangzhou Maritime University

⁰