{"title":"Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear","authors":"Ye, Yunguang, Huang, Ping, Zhang, Yongxiang","doi":"10.1007/s40534-021-00252-z","DOIUrl":null,"url":null,"abstract":"<p>Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for high-speed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy (GWN-strategy) for immunity to track irregularities and an edge sample training strategy (EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally, the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line.</p>","PeriodicalId":41270,"journal":{"name":"Railway Engineering Science","volume":"43 1","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Railway Engineering Science","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s40534-021-00252-z","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 1
Abstract
Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for high-speed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy (GWN-strategy) for immunity to track irregularities and an edge sample training strategy (EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally, the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line.
期刊介绍:
Railway Engineering Science is an international, peer-reviewed, and free open-access journal that publishes original research articles and comprehensive reviews related to fundamental engineering science and emerging technologies in rail transit systems, focusing on the cutting-edge research in high-speed railway, heavy-haul railway, urban rail transit, maglev system, hyperloop transportation, etc. The main goal of the journal is to maintain high quality of publications, serving as a medium for railway academia and industry to exchange new ideas and share the latest achievements in scientific research, technical innovation and industrial development in railway science and engineering. The topics include but are not limited to Design theory and construction technology System dynamics and safetyElectrification, signaling and communicationOperation and maintenanceSystem health monitoring and reliability Environmental impact and sustainabilityCutting-edge technologiesThe publication costs for Railway Engineering Science are fully covered by Southwest Jiaotong University so authors do not need to pay any article-processing charges.