Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises  

作　　者：Junqi LIU Tao WEN Guo XIE Yuan CAO Clive Roberts 

机构地区：[1]Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing,Xi’an University of Technology,Xi’an 710048,China [2]The School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China [3]Birmingham Centre for Railway Research and Education,University of Birmingham,Birmingham,B152TT,UK

出　　处：《Chinese Journal of Electronics》2024年第3期814-822,共9页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos.62120106011 and 52172323);the Opening Project of Guangdong Provincial Key Lab of Robotics and Intelligent System。

摘　　要：The fault diagnosis of railway point machines(RPMs)has attracted the attention of engineers and researchers.Seldom have studies considered diverse noises along the track.To fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD)is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple noises.MTSVMD decomposes condition monitoring signals after coarse-grained processing in varying degrees.In this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single scale.In the experimental validation,a random position,random type,random number,and random length(4R)noise-adding algorithm helps to verify the robustness of the approach.The adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关 键 词：Railway point machines Fault diagnosis Multi-time-scale Variational mode decomposition 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置] U284.92[自动化与计算机技术—控制科学与工程]