An Intelligent Fault Diagnosis Method Based on FastDTW for Railway Turnout
JI Wenjiang1, ZUO Yuan1, HEI Xinhong1, SEI Takahashi2, HIDEO Nakamura2
1. Faculty of Computer Science and Engineering,Xi′an University of Technology,Xi′an 710048; 2. Department of Computer Engineering,Nihon University,Funabashi 274-8501; 3. Graduate School of Frontier Science,The University of Tokyo,Tokyo 113-8656
Abstract The turnout handles the direction of the train.It is a key equipment for the safety of railway transportation system.An intelligent fault diagnosis method based on fast dynamic time warping(FastDTW) for railway turnout is proposed in this paper.It is testified by the real action current data obtained from switch machine model No.ZD7.Firstly,the original current curve is segmented according to wave form features.Then,the warp path distance between the standard sample and the tested current curve is obtained by FastDTW algorithm.Finally,a dynamic optimized threshold is exploited to confirm whether there is a fault in the turnout.The experimental results show the proposed method works well with both single and double action type turnout machines with only 200 turnout action current samples.The proposed method is suitable for the train control system of new generation as well due to its high diagnosis accuracy and low time cost.
Corresponding Authors:
HEI Xinhong,Ph.D.,professor.His research interests include machine learning,system safety and train ru-nning control.
About author:: JI Wenjiang,Ph.D.,lecturer.His research interests include intelligent rail system and VANET.ZUO Yuan,master student.His research interests include machine learning and pattern recognition.SEI Takahashi,Ph.D.,professor.His research interests include soft computing and its industrial application,intelligent rail system.HIDEO Nakamura,Ph.D.,professor.His research interests include intelligent rail system.
[1] ASADA T,ROBERTS C,KOSEKI T.An Algorithm for Improved Performance of Railway Condition Monitoring Equipment:Alternating-Current Point Machine Case Study.Transportation Research Part C(Emerging Technologies),2013,30:81-92. [2] VILEINISKIS M,REMENYTE-PRESCOTT R,RAMA D.A Fault Detection Method for Railway Point Systems.Proceedings of the Institution of Mechanical Engineers,Part F(Journal of Rail and Rapid Transit),2015,230(3).DOI:10.1177/0954409714567487. [3] ZHOU D H,ZHAO Y H,WANG Z D,et al. Review on Diagnosis Techniques for Intermittent Faults in Dynamic Systems.IEEE Tran-sactions on Industrial Electronics,2020,67(3):2337-2347. [4] 周东华,纪洪泉,何 潇.高速列车信息控制系统的故障诊断技术,自动化学报,2018,44(7):1153-1164. (ZHOU D H,JI H Q,HE X.Fault Diagnosis Techniques for the Information Control System of High-Speed Trains.Acta Automatica Sinica,2018,44(7):1153-1164.) [5] LEI Y G,YANG B,JIANG X W,et al.Applications of Machine Learning to Machine Fault Diagnosis:A Review and Roadmap.Mechanical Systems and Signal Processing,2020,138.DOI:10.1016/j.ymssp.2019.106587. [6] YANG J,XIE G,YANG Y X.An Improved Ensemble Fusion Autoencoder Model for Fault Diagnosis from Imbalanced and Incomplete Data.Control Engineering Practice,2020,98.DOI:10.1016/j.conengprac.2020.104358. [7] YANG J,YANG Y X,XIE G.Diagnosis of Incipient Fault Based on Sliding-Scale Resampling Strategy and Improved Deep Autoencoder.IEEE Sensors Journal,2020,20(15):8336-8348. [8] 李小宝,郭立君,张 荣,等.混合l2/l1/2范数的局部组稀疏表示方法.模式识别与人工智能,2018,31(9):773-785. (LI X B,GUO L J,ZHANG R,et al.Local Group Sparse Representation with Mixed l2/l1/2 Norm.Pattern Recognition and Artificial Intelligence,2018,31(9):773-785.) [9] ZHAO C H,GAO F R.Fault Subspace Selection Approach Combined with Analysis of Relative Changes for Reconstruction Modeling and Multifault Diagnosis.IEEE Transactions on Control Systems Technology,2016,24(3):928-939. [10] ZHAO C H,GAO F R.Critical-to-Fault-Degradation Variable Analy-sis and Direction Extraction for Online Fault Prognostic.IEEE Transactions on Control Systems Technology,2017,25(3):842-854. [11] CAO Y,SUN Y K,MA L C.A Fault Diagnosis Method for Train Plug Doors via Sound Signals [J/OL].[2020-04-12].https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9005200. [12] CAO Y,SUN Y K,XIE G,et al. Fault Diagnosis of Train Plug Door Based on a Hybrid Criterion for IMFs Selection and Fractional Wavelet Package Energy Entropy.IEEE Transactions on Vehicular Technology,2019,68(8):7544-7551. [13] CAO Y,WANG Z C,LIU F,et al. Bio-inspired Speed Curve Optimization and Sliding Mode Tracking Control for Subway Trains.IEEE Transactions on Vehicular Technology,2019,68(7):6331-6342. [14] XIE G,SUN L L,WEN T,et al.Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm[J/OL].[2020-04-12].https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8747402. [15] 谢 国,金永泽,黑新宏,等.列车动力学模型时变环境参数自适应辨识.自动化学报,2019,45(12):2268-2280. (XIE G,JIN Y Z,HEI X H,et al.Adaptive Identification of Time-Varying Environmental Parameters in Train Dynamics Model.Acta Automatica Sinica,2019,45(12):2268-2280.) [16] JIN W J,SHI Z,SIEGEL D,et al.Development and Evaluation of Health Monitoring Techniques for Railway Point Machines//Proc of the IEEE Conference on Prognostics and Health Management.Washington,USA:IEEE,2015.DOI:10.1109/ICPHM.2015.7245016. [17] 赵林海,陆 桥.基于灰关联的道岔故障诊断方法.铁道学报,2014,36(2):69-74. (ZHAO L H,LU Q.Method of Turnout Fault Diagnosis Based on Grey Correlation Analysis.Journal of the China Railway Society,2014,36(2):69-74.) [18] 董 炜,刘明明,王良顺,等.基于群决策的道岔控制电路故障诊断方法.自动化学报,2018,44(6):1005-1014. (DONG W,LIU M M,WANG L S,et al.Fault Diagnosis for Railway Turnout Control Circuit Based on Group Decision Making.Acta Automatica Sinica,2018,44(6):1005-1014.) [19] 许庆阳,刘中田,赵会兵.基于隐马尔科夫模型的道岔故障诊断方法.铁道学报,2018,40(8):98-106. (XU Q Y,LIU Z T,ZHAO H B,Method of Turnout Fault Diagnosis Based on Hidden Markov Model.Journal of the China Railway Society,2018,40(8):98-106.) [20] 万里鹏,兰旭光,张翰博,等.深度强化学习理论及其应用综述.模式识别与人工智能,2019,32(1):67-81. (WAN L P,LAN X G,ZHANG H B,et al.A Review of Deep Reinforcement Learning Theory and Application.Pattern Recognition and Artificial Intelligence,2019,32(1):67-81.) [21] EKER O F,CAMCI F,GUCLU A,et al.A Simple State-Based Prognostic Model for Railway Turnout Systems.IEEE Transactions on Industrial Electronics,2012,58(5):1718-1726. [22] DINDAR S,KAEWUNRUEN S,AN M,et al.Derailment-Based Fault Tree Analysis on Risk Management of Railway Turnout Systems[J/OL].[2020-04-12].https://iopscience.iop.org/article/10.1088/1757-899X/245/4/042020/pdf. [23] DE AGUIAR E P,AMARAL R P F,VELLASCO M M B R,et al. An Enhanced Singleton Type-2 Fuzzy Logic System for Fault Classification in a Railroad Switch Machine.Electric Power Systems Research,2018,158:195-206. [24] KIM C,SEO K D.Robust DTW-Based Recognition Algorithm for Hand-Held Consumer Devices.IEEE Transactions on Consumer Electronics,2005,51(2):699-709. [25] 叶燕清,杨克巍,姜 江,等.基于加权动态时间弯曲的多元时间序列相似性匹配方法.模式识别与人工智能,2017,30(4):314-327. (YE Y Q,YANG K W,JIANG J,et al. Multivariate Time Series Similarity Matching Method Based on Weighted Dynamic Time Warping Algorithm.Pattern Recognition and Artificial Intelligence,2017,30(4):314-327.) [26] KIM J,KIM S,SCHAUMBURG E,et al. Calculating and Using Second-Order Accurate Solutions of Discrete Time Dynamic Equilibrium Models.Journal of Economic Dynamics and Control,2008,32(11):3397-3414. [27] AJTAY D,WEILENMANN M,SOLTIC P.Towards Accurate Instantaneous Emission Models.Atmospheric Environment,2005,39(13):2443-2449. [28] 梅寒蕾,蔡青林,陈 岭,等.一种基于动态时间弯曲的数据流子序列匹配系统.计算机研究与发展,2015,52(S1):112-117. (MEI H L,CAI Q L,CHEN L,et al.A Subsequence Matching System Over Data Stream under Dynamic Time Warping.Journal of Computer Research and Development,2015,52(S1):112-117.) [29] SALVADOR S,CHAN P K.Toward Accurate Dynamic Time Warping in Linear Time and Space.Intelligent Data Analysis,2007,11(5):561-580. [30] XU H,WANG P,LIU D,et al.Maintenance Technologies Research of High Speed Turnout.Applied Mechanics and Materials,2011,97/98:114-120. [31] 黄世泽,陈 威,张 帆,等.基于弗雷歇距离的道岔故障诊断方法.同济大学学报(自然科学版),2019,46(12):1690-1695. (HUANG S Z,CHEN W,ZHANG F,et al. Method of Turnout Fault Diagnosis Based on Fréchet Distance.Journal of Tongji University(Natural Science),2019,46(12):1690-1695.) [32] 钟志旺,唐 涛,王 峰.基于PLSA和SVM 的道岔故障特征提取与诊断方法研究.铁道学报,2018,40(7):80-87. (ZHONG Z W,TANG T,WANG F.Research on Fault Feature Extraction and Diagnosis of Railway Switches Based on PLSA and SVM.Journal of the China Railway Society,2018,40(7):80-87.)
2020, Vol. 33 
