The Key Laboratory of Road and Traffic Engineering, Ministry of Education China School of Transportation Engineering, Tongji University Shanghai, 201804, P.R. China
2
Jinan Railway Bureau, Jinan, Shangdong, China, 250000, P.R. China
Publication date: 2018-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2018;20(4):514-522
Switches are one of the most important pieces of infrastructure in railway signal systems, and they significantly influence the efficiency and safety of train operation. Currently, the identification of switch failures mainly depends on the experience of railway
staff and the use of simple thresholding methods. However, these basic methods are highly inaccurate and frequently result in false
and missing alarms. This paper aims to develop a hybrid fault diagnosis (HFD) method for railway switches. The method is an
intelligent diagnosis method that uses massive current curves collected by microcomputer monitoring systems. We first divide the
switch operation current curves into three segments based on the three mechanical processes that occur during switch operation.
Then, a standard curve is selected from the fault-free curves, and common typical faults are ascertained through a microcomputer
monitoring system. Finally, derivative dynamic time warping and a quartile scheme are employed to identify fault curves. An
experiment based on current curves collected from the Guangzhou Railway Bureau in China demonstrates that the HFD method
is extremely accurate and has low false and missing alarm rates. HFD performs better than the studied support vector machine
(SVM) and dynamic time warping (DTW) methods, which are widely used for fault diagnosis.
REFERENCES(28)
1.
Ardakani H D, Lucas C, Siegel D, Chang S, Dersin P, Bonnet B, Lee J. PHM for railway system-a case study on the health assessment of the point machines. IEEE Conference on Prognostics and Health Management 2012: 1-5, https://doi.org/10.1109/ICPHM.....
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, https://doi.org/10.1016/j.trc.....
Atamuradov V, Camci F, Baskan S, Sevkli M. Failure diagnostics for railway point machines using expert systems. IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives 2009: 1-5, https://doi.org/10.1109/DEMPED....
Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002; 24: 603-619, https://doi.org/10.1109/34.100....
Comaniciu D, Ramesh V, Meer P. The variable bandwidth mean shift and data-driven scale selection. Proceedings 8th IEEE International Conference on Computer Vision ICCV 2001: 438-445, https://doi.org/10.1109/ICCV.2....
Eker O F, Camci F, Guclu A, Yilboga H, Sevkli M, Baskan S. A simple state-based prognostic model for railway turnout systems. IEEE Transactions on Industrial Electronics 2011; 58: 1718-1726, https://doi.org/10.1109/TIE.20....
Hartigan J A, Wong M A. Algorithm AS 136: A K-means clustering algorithm. Journal of the Royal Statistical Society Series C 1979; 28: 100-108, https://doi.org/10.2307/234683....
Keogh E J, Pazzani M J. Derivative dynamic time warping. Proceedings of the 2001 SIAM International Conference on Data Mining 2001: 1-11, https://doi.org/10.1137/1.9781....
Kim H, Sa J, Chung Y, Park D, Yoon S. Fault diagnosis of railway point machines using dynamic time warping. Electronics Letters 2016; 52: 818-819, https://doi.org/10.1049/el.201....
Lee J, Choi H, Park D, Chung Y, Kim H-Y, Yoon S. Fault detection and diagnosis of railway point machines by sound Analysis. Sensors 2016; 16: 549, https://doi.org/10.3390/s16040....
Letot C, Dersin P, Pugnaloni M, Dehombreux P, Fleurquin G, Douziech C, La-Cascia P. A data driven degradation-based model for the maintenance of turnouts: a case study. IFAC-PapersOnLine 2015; 48: 958-963, https://doi.org/10.1016/j.ifac....
Márquez F P G, Roberts C, Tobias A M. Railway point mechanisms: condition monitoring and fault detection. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2010; 224: 35-44, https://doi.org/10.1243/095440....
Marquez F P G, Schmid F, Collado J C. A reliability centered approach to remote condition monitoring. A railway points case study. Reliability Engineering & System Safety 2003; 80: 33-40, https://doi.org/10.1016/S0951-....
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V. Scikit-learn: machine learning in Python. Journal of Machine Learning Research 2011; 12: 2825-2830.
Ren Z, Sun S, Zhai W. Study on lateral dynamic characteristics of vehicle/turnout system. Vehicle System Dynamics 2005; 43: 285-303, https://doi.org/10.1080/004231....
Shaw D C. A universal approach to points condition monitoring. 4th IET International Conference on Railway Condition Monitoring 2008: 1-6, https://doi.org/10.1049/ic:200....
Wang G, Xu T, Tang T, Yuan T, Wang H. A Bayesian network model for prediction of weather-related failures in railway turnout systems. Expert Systems with Applications 2017; 69: 247-256, https://doi.org/10.1016/j.eswa....
Zhang K. The railway turnout fault diagnosis algorithm based on BP neural network. IEEE International Conference on Control Science and Systems Engineering 2014: 135-138, https://doi.org/10.1109/CCSSE.....
Zhang K, Du K, Ju Y. Algorithm of railway turnout fault detection based on PNN neural network. 7th International Symposium on Computational Intelligence and Design 2014: 544-547, https://doi.org/10.1109/ISCID.....
Zhao H, Chen H, Dong W, Sun X, Ji Y. Fault diagnosis of rail turnout system based on case-based reasoning with compound distance methods. Chinese Control and Decision Conference (CCDC) 2017: 4205-4210, https://doi.org/10.1109/CCDC.2....
Zhou F, Xia L, Dong W, Sun X, Yan X, Zhao Q. Fault diagnosis of high-speed railway turnout based on support vector machine. IEEE International Conference on Industrial Technology (ICIT) 2016: 1539-1544, https://doi.org/10.1109/ICIT.2....
Zhou J-L, Lei Y. Paths between latent and active errors: analysis of 407 railway accidents/incidents' causes in China. Safety Science 2017, https://doi.org/10.1016/j.ssci....
Artificial intelligence-based hybrid forecasting models for manufacturing systems Maria Rosienkiewicz Eksploatacja i Niezawodnosc - Maintenance and Reliability
A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction Qingzhou Meng, Weigang Wen, Yihao Bai, Yang Liu Entropy
On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview Moussa Hamadache, Saikat Dutta, Osama Olaby, Ramakrishnan Ambur, Edward Stewart, Roger Dixon Applied Sciences
A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation Mengyang Li, Xinhong Hei, Wenjiang Ji, Lei Zhu, Yichuan Wang, Yuan Qiu Sensors
A digital-twin-assisted fault diagnosis of railway point machine Shiyao Zhang, Hairong Dong, Ulrich Maschek, Haifeng Song 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI)
Railway Point Machine Control Automation Methods Sunnatillo Boltayev, Qamara Kosimova 2022 International Ural Conference on Electrical Power Engineering (UralCon)
Fault diagnosis of multistage centrifugal pump unit using non-local means-based vibration signal denoising Te Han, Dongxiang Jiang Eksploatacja i Niezawodność – Maintenance and Reliability
High-Confidence Fault Diagnosis for Train-Ground Wireless Communication Network of CBTC Lei Zhang, Yan Sun, Hongliang Pan, Lijuan Shi 2022 10th International Conference on Information Systems and Computing Technology (ISCTech)
Analysis, Modelling, and Implementation of Point Contact Reay in Railway Relay Interlocking System Raghuveer Chandaluri, UshaRani Nelakuditi 2023 IEEE 3rd International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET)
Cyber Threat Assessment in Monitoring Turnout Railway Systems Sara Abdellaoui, Emil Dumitrescu, Cédric Escudero, Eric Zamaï 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)
Pattern recognition based on statistical methods combined with machine learning in railway switches del Muñoz, Ramirez Segovia, Mayorkinos Papaelias, García Pedro Expert Systems with Applications
Fault diagnosis of railway point machines based on wavelet transform and artificial immune algorithm Xiaochun Wu, Weikang Yang, Jianrong Cao Transportation Safety and Environment
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.