Fault Diagnosis of Railway Point Machine Based on Improved Time- Domain Multiscale Dispersion Entropy and Support Vector Machine

CAO Yuan; SONG Di; HU Xiao-xi; SUN Yong-kui

doi:10.12263/DZXB.20210773

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Fault Diagnosis of Railway Point Machine Based on Improved Time- Domain Multiscale Dispersion Entropy and Support Vector Machine

PAPERS | 更新时间：2025-07-02

- Fault Diagnosis of Railway Point Machine Based on Improved Time- Domain Multiscale Dispersion Entropy and Support Vector Machine
- ACTA ELECTRONICA SINICA Vol. 51, Issue 1, Pages: 117-127(2023)
- 作者机构：
  
  1.北京交通大学轨道交通运行控制系统国家工程研究中心,北京 100044
  2.北京交通大学轨道交通控制与安全国家重点实验室,北京 100044
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210773
  CLC： U284.92
- Received：22 June 2021，
  
  Revised：2021-09-01，
  
  Published：25 January 2023
- 稿件说明：
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曹源,宋迪,胡小溪等.基于改进时域多尺度散布熵与支持向量机的转辙机故障诊断[J].电子学报,2023,51(01):117-127.

CAO Yuan,SONG Di,HU Xiao-xi,et al.Fault Diagnosis of Railway Point Machine Based on Improved Time- Domain Multiscale Dispersion Entropy and Support Vector Machine[J].ACTA ELECTRONICA SINICA,2023,51(01):117-127.
曹源,宋迪,胡小溪等.基于改进时域多尺度散布熵与支持向量机的转辙机故障诊断[J].电子学报,2023,51(01):117-127. DOI： 10.12263/DZXB.20210773.

CAO Yuan,SONG Di,HU Xiao-xi,et al.Fault Diagnosis of Railway Point Machine Based on Improved Time- Domain Multiscale Dispersion Entropy and Support Vector Machine[J].ACTA ELECTRONICA SINICA,2023,51(01):117-127. DOI： 10.12263/DZXB.20210773.

摘要

为充分挖掘转辙机振动信号的有效故障信息，提高故障诊断准确率，提出了一种基于集合经验模态分解（Ensemble Empirical Mode Decomposition，EEMD）、改进时域多尺度散布熵（Improved Time-domain Multiscale Dispersion Entropy，TMDE）与粒子群优化算法（Particle Swarm Optimization algorithm，PSO）优化支持向量机（Support Vector Machine，SVM）的故障诊断方法.首先，通过EEMD方法将不同故障类型的振动信号分解成若干个模态函数（Intrinsic Mode Functions，IMFs）；其次，采用相关系数与峭度的混合筛选准则筛选IMFs并重构信号；再次，应用所提ITMDE算法提取重构信号的多尺度故障特征；最后将得到的特征向量输入经PSO搜索最优参数后的SVM进行训练和测试.实验分类准确率为100%，分析表明所提方法优于传统的多尺度排列熵、多尺度散布熵的故障诊断方法，能精确地识别转辙机故障类型.

Abstract

To fully mine the effective fault information and improve the fault diagnosis accuracy

a fault diagnosis approach for railway point machine is proposed by ensemble empirical mode decomposition (EEMD)

improved time-domain multiscale dispersion entropy (ITMDE) and support vector machine (SVM) optimized by particle swarm optimization algorithm (PSO). Firstly

vibration signals with different fault types are decomposed into several intrinsic mode functions (IMFs) by EEMD. Subsequently

the hybrid screening criteria of correlation coefficient and kurtosis (CCKC) are used to screen IMFs and reconstruct the signal. Afterwards

the proposed ITMDE algorithm is employed to extract multiscale fault features from the reconstructed signal. Finally

the PSO is devoted to search the optimal parameters of SVM

with which the obtained feature vectors are trained and tested. The experimental classification accuracy reaches 100%. The results show that the proposed method is superior to the traditional multiscale permutation entropy and multiscale dispersive entropy fault diagnosis methods

and can accurately identify the fault type of the railway point machine.

关键词

Keywords

references

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