Time-series Independent Component Analysis-aided Fault Detection for Running Gear Systems

被引：0
作者：
Chao Cheng
Sheng Yang
Yu Song
Gang Liu
机构：
[1] Changchun University of Technology,School of Computer Science and Engineering
来源：
International Journal of Control, Automation and Systems | 2022年 / 20卷
关键词：
Fault detection (FD); non-Gaussian; running gear systems; time-series independent component analysis (TsICA);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
By dealing with the non-Gaussian measurement and slow-change faults in running gear systems, this paper presents a fault detection (FD) scheme named time-series independent component analysis (TsICA), where the time-series characteristic is taken into account. Time-series algorithms can extract slow-change information in the data. The advantages of the proposed method are: 1) it can improve the FD power; 2) it considers the information in the data 3) it is suitable for non-Gaussian systems; 4) it is sensitive to slow-change faults; 5) it can effectively shorten the first time of fault detection. The feasibility of the proposed scheme is verified through a case study on running gear systems.
引用
收藏
页码：2892 / 2901
页数：9
相关论文
共 117 条
[1]  
Chen H(2022)Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives IEEE Transactions on Intelligent Transportation Systems 23 1700-1716
[2]  
Jiang B(2014)A review on basic data-driven approaches for industrial process monitoring IEEE Transactions on Industrial Electronics 61 6418-6428
[3]  
Ding S X(2020)Review on diagnosis techniques for intermittent faults in dynamic systems IEEE Transactions on Industrial Electronics 67 2337-2347
[4]  
Huang B(2017)Fault detection and diagnosis for delay-range-dependent stochastic systems using output PDFs International Journal of Control, Automation, and Systems 15 1701-1709
[5]  
Yin S(2020)Data-driven incipient fault detection and diagnosis for the running gear in high-speed trains IEEE Transactions on Vehicular Technology 69 9566-9576
[6]  
Ding S X(2004)Nonlinear process monitoring using kernel principal component analysis Chemical Engineering Science 59 223-234
[7]  
Xie X(2018)A reduced-order fault detection filter design for polytopic uncertain continuous-time Markovian jump systems with time-varying delays International Journal of Control, Automation, and Systems 16 2021-2032
[8]  
Luo H(2017)A fault-injection strategy for traction drive control systems IEEE Transactions on Industrial Electronics 64 5719-5727
[9]  
Zhou D(2011)Torsional-vibration assessment and gear-fault diagnosis in railway traction system IEEE Transactions on Industrial Electronics 58 1707-1717
[10]  
Zhao Y(2020)Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection Neurocomputing 376 222-231
12345678910