中北大学学报（自然科学版）2024，Vol.45Issue(1)：58-65,8.DOI:10.3969/j.issn.1673-3193.2024.01.008
基于可视图特征与CatBoost的转辙机故障诊断
Fault Diagnosis of Switch Machine Based on Visibility Graph Feature and CatBoost
摘要
Abstract
Aiming at the problem that it is difficult to extract effective fault features from oil pressure sig-nals converted by turnout switch machine and the traditional fault diagnosis method is not effective,a fault diagnosis method turnout switch machine based on visibility graph features and CatBoost is pro-posed.Firstly,the visual graph algorithm is used to convert the time-domain signal into a complex net-work graph.Then,the five statistical features of complex network graphs are extracted(i.e.,network average degree,global clustering coefficient,average path length,transitivity feature and network graph density).Finally,the fault diagnosis of turnout switch machine is realized by CatBoost algorithm.This method is compared with other feature extraction methods and fault classification algorithms.The experi-mental results show that the visibility graph feature can more effectively represent the working state of turnout switch machine.The diagnostic accuracy of CatBoost algorithm for four working states of turnout switch machine reaches 97.5%,which verifies its effectiveness and superiority.关键词
可视图特征/道岔转辙机/CatBoost算法/故障诊断Key words
visibility graph feature/turnout switch machine/CatBoost algorithm/fault diagnosis分类
信息技术与安全科学引用本文复制引用
杨喜旺,王智超,黄晋英,荆丽澄,王宇轩..基于可视图特征与CatBoost的转辙机故障诊断[J].中北大学学报（自然科学版）,2024,45(1):58-65,8.基金项目
山西省回国留学人员科研教研资助项目(2022-141) （2022-141）
山西省基础研究计划资助项目(202203021211096) （202203021211096）
