Fault diagnosis of switch machine based on parameter-optimized VMD and LSSVM
ZHANG Guangjian
XIAO Yancai
MENG Yadong
ZENG Xiangfa
MA Shilun
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.
Keywords：fault diagnosisswitch machineTSO-VMDRCMDECPO-LSSVM
Publication Date：2025-12-30
Online Publishing Date：2026-02-02(First online date of this platform, not the publication date of the document)
Pages：16( 14-29 )