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

被引：0

作者：

Liu, Junqi ^{[1
]}

Wen, Tao ^{[2
]}

Xie, Guo ^{[1
]}

Cao, Yuan ^{[2
]}

Roberts, Clive ^{[3
]}

机构：

[1] Xian Univ Technol, Shaanxi Key Lab Complex Syst Control & Intelligent, Xian 710048, Peoples R China

[2] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China

[3] Univ Birmingham, Birmingham Ctr Railway Res & Educ, Birmingham B15 2TT, England

来源：

CHINESE JOURNAL OF ELECTRONICS | 2024年 / 33卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Fault diagnosis; Condition monitoring; Noise; Signal processing algorithms; Aerospace electronics; Rail transportation; Robustness; Railway point machines; Multi-time-scale; Variational mode decomposition;

D O I：

10.23919/cje.2022.00.234

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

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 demonstrate the superiority of the proposed MTSVMD-based fault diagnosis.

引用

页码：814 / 822

页数：9