Turnout fault diagnosis method based on parameter optimization VMD and improved LSSVM

被引：0

作者：

Wang Y. ^{[1
]}

Meng J. ^{[2
]}

Zhang Y. ^{[3
]}

Yang J. ^{[4
]}

机构：

[1] School of Railway Technical, Lanzhou Jiaotong University, Lanzhou

[2] School of Mechatronic Engineering, Lanzhou Jiaotong University, Lanzhou

[3] State Grid Gansu Electric Power Research Institute, Lanzhou

[4] China Railway Signal & Communication Corp, Beijing

来源：

Journal of Railway Science and Engineering | 2024年 / 21卷 / 05期

关键词：

fault diagnosis; improved LSSVM; multi-feature fusion; parameter optimization VMD; turnout;

D O I：

10.19713/j.cnki.43-1423/u.T20231237

中图分类号：

学科分类号：

摘要：

In order to solve the problem that the feature index was difficult to extract and the model training time was long, the intelligent fault diagnosis of turnout equipment driven by ZDJ9 switch machine was taken as the research object. The power curve of switch machine was taken as the data basis, a turnout fault diagnosis method based on parameter optimization Variational Mode Decomposition (VMD) and improved Least Squares Support Vector Machines (LSSVM) was proposed. Firstly, the Whale Optimization Algorithm (WOA) was used to optimize the VMD parameters, and the optimal parameter combination of the number of Intrinsic Mode Functions (IMF) components and the penalty factor was obtained. Secondly, the correlation coefficient between the IMF component and the power curve was calculated. The first three IMF components with large correlation were selected. The power spectral entropy, fuzzy entropy and envelope entropy were calculated. The sample database of multi-feature fusion was constructed. Finally, aiming at the problem that Sparrow Search Algorithm (SSA) was easy to fall into local optimum, the population was randomly generated by the improved tent chaotic map initialization strategy, and the follower position was updated by the positive chord algorithm. The improved SSA algorithm was used to optimize the penalty factor and kernel function variance of the LSSVM algorithm, and the TSSSA-LSSVM fault diagnosis model of high speed railway turnout was constructed. The experimental results show that the turnout fault diagnosis method proposed in this paper is feasible. The multi-feature fusion can extract the typical fault features of the turnout more comprehensively, reflect the real operating state of the turnout, and improve the accuracy of fault diagnosis. Compared with TSSSA-SVM, PSO-LSSVM, GWO-LSSVM and SSA-LSSVM, it has higher fault diagnosis accuracy, recall rate and lower false negative rate, reduces the model training time, fully meets the principle of on-site turnout fault-oriented safety. It has better fault diagnosis performance, which has certain guiding significance for the fault maintenance of on-site turnout equipment. © 2024, Central South University Press. All rights reserved.

引用

页码：2072 / 2085

页数：13

共 22 条

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