Fault Diagnosis of Switch Machine Based on Visibility Graph Feature and CatBoost
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.
国家科技期刊平台
NSTL
万方数据
维普
