北京交通大学学报2025，Vol.49Issue(6)：14-29,16.DOI:10.11860/j.issn.1673-0291.20250065
基于参数优化VMD与LSSVM的转辙机故障诊断
Fault diagnosis of switch machine based on parameter-optimized VMD and LSSVM
摘要
Abstract
To address the current limitation that switch machine health monitoring predominantly relies on power signals while vibration signals remain underutilized for fault diagnosis,this study proposes a fault diagnosis model that integrates Variational Mode Decomposition(VMD)optimized by Tuna Swarm Opti-mization(TSO)and Least Squares Support Vector Machine(LSSVM)optimized by the Crested Porcu-pine Optimizer(CPO).First,vibration data from eight typical operating conditions of the ZD6 switch ma-chine are collected through experiments.TSO is employed to optimize VMD and determine the optimal number of decomposition layers k and the penalty factor α,after which the optimized VMD decomposed the vibration signals into several Intrinsic Mode Functions(IMF).Second,IMFs are selected using a dual screening criterion based on envelope entropy and kurtosis,and the signals are reconstructed.The Re-fined Composite Multiscale Diversity Entropy(RCMDE)of the reconstructed signals is then extracted.Third,the RCMDE features are divided into training and testing data and used as feature vectors for LSSVM,which is configured with the optimal combination of penalty factor γ and kernel function param-eter σ obtained via CPO optimization.Finally,accuracy,macro-precision,macro-recall and macro-F1 are adopted as evaluation metrics for comparative analysis across multiple models.The results show that the proposed TSO-VMD-RCMDE-CPO-LSSVM,as the fault diagnosis model of switch machine,achieves an average runtime of 20.4 s over 10 iterations.The average accuracy of the training data is 99.68%with a standard deviation of 0.12.The average accuracy of the testing data is 99.25%with a stan-dard deviation of 0.07.Compared with other models,it attains the highest mean macro-precision,macro-recall and macro-F1 scores,along with the lowest standard deviations.These findings demonstrate the su-perior performance and feasibility of the proposed model for switch machine fault diagnosis.关键词
故障诊断/转辙机/金枪鱼群算法优化变分模态分解/精细复合多尺度散布熵/冠豪猪算法优化最小二乘支持向量机Key words
fault diagnosis/switch machine/TSO-VMD/RCMDE/CPO-LSSVM分类
交通工程引用本文复制引用
张光建,肖燕彩,孟亚东,曾祥发,马世伦..基于参数优化VMD与LSSVM的转辙机故障诊断[J].北京交通大学学报,2025,49(6):14-29,16.基金项目
国家自然科学基金(52305276) National Natural Science Foundation of China(52305276) （52305276）
