典型文献
Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear
文献摘要：
Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety.Firstly,the existing methods con-cerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two cate-gories,i.e.,model-based and data-driven approaches.The advantages and disadvantages of these two categories of approaches are briefly summarized.Secondly,a 1D con-volution 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 perfor-mance 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 suspen-sion 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.
文献关键词：
中图分类号：
作者姓名：
Yunguang Ye;Ping Huang;Yongxiang Zhang
作者机构：
Institute of Land and Sea Transport Systems,Technical University of Berlin,Berlin 10587,Germany;National United Engineering Laboratory of Integrated and Intelligent Transportation,Southwest Jiaotong University,Chengdu 610031,China;Institute for Transport Planning and Systems,ETH Zurich,8093 Zurich,Switzerland
文献出处：
引用格式：
[1]Yunguang Ye;Ping Huang;Yongxiang Zhang-.Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear)[J].铁道工程科学（英文）,2022(01):96-116
A类：
bogie
B类：
Deep,learning,diagnostic,network,high,speed,secondary,suspension,systems,immunity,irregularities,wheel,wear,Fault,detection,isolation,critical,importance,guarantee,running,safety,Firstly,existing,methods,cerning,are,briefly,reviewed,divided,into,model,data,driven,approaches,disadvantages,these,categories,summarized,Secondly,volution,designed,To,improve,robustness,Gaussian,white,noise,strategy,GWN,edge,sample,training,EST,proposed,whole,called,1DCNN,Thirdly,show,perfor,mance,this,multibody,dynamics,simulation,built,generate,lateral,acceleration,frame,corresponding,different,profiles,faults,simulated,signals,then,inputted,results,correctness,superiority,Finally,further,validated,using,tracking,CRH3,railway,line
AB值：
0.463026
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
