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

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外文摘要：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 diag-nosis of RPMs under multiple noises.MTSVMD decomposes condition monitoring signals after coarse-grained pro-cessing 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 posi-tion,random type,random number,and random length(4R)noise-adding algorithm helps to verify the robustness of the approach.The adequate experimental results demonstrate the superiority of the proposed MTSVMD-based fault diagnosis.

外文关键词：

Railway point machinesFault diagnosisMulti-time-scaleVariational mode decomposition

作者：

Junqi LIU、Tao WEN、Guo XIE、Yuan CAO、Clive Roberts

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作者单位：

Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing,Xi'an University of Technology,Xi'an 710048,China

The School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China

Birmingham Centre for Railway Research and Education,University of Birmingham,Birmingham,B15 2TT,UK

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaOpening Project of Guangdong Provincial Key Lab of Robotics and Intelligent System

项目编号：

6212010601152172323

出版年：

2024

DOI：

10.23919/cje.2022.00.234

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(3)