A Big Data Analysis Approach for Rail Failure Risk Assessment

Railway infrastructure monitoring is a vital task to ensure rail transportation safety. A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers. In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras. We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks. We measure the visual length of the squats and use them to model the failure risk. For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats. Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios. The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network. The results illustrate the practicality and efficiency of the proposed approach.

[1]  Qingyong Li,et al.  A Visual Detection System for Rail Surface Defects , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Min Xie,et al.  Accident risk assessment in marine transportation via Markov modelling and Markov Chain Monte Carlo simulation , 2014 .

[3]  D. Xu,et al.  Reliability prediction using multivariate degradation data , 2005, Annual Reliability and Maintainability Symposium, 2005. Proceedings..

[4]  Enrique López Droguett,et al.  Bayesian Methodology for Model Uncertainty Using Model Performance Data , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[5]  Rolf Dollevoet,et al.  Dutch research results on wheel/rail interface management: 2001–2013 and beyond , 2014 .

[6]  Junrui Xu,et al.  Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data , 2015, Risk analysis : an official publication of the Society for Risk Analysis.

[7]  J. Rosenthal,et al.  Markov Chain Monte Carlo , 2018 .

[8]  Desheng Dash Wu,et al.  Risk Intelligence in Big Data Era: A Review and Introduction to Special Issue , 2016, IEEE Trans. Cybern..

[9]  S. Walker.  Invited comment on the paper "Slice Sampling" by Radford Neal , 2003 .

[10]  Qingyong Li,et al.  A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads , 2012, IEEE Transactions on Instrumentation and Measurement.

[11]  Xiang Liu,et al.  Analysis of Derailments by Accident Cause , 2011 .

[12]  Karen Lowrie,et al.  Passenger Rail Security, Planning, and Resilience: Application of Network, Plume, and Economic Simulation Models as Decision Support Tools , 2013, Risk analysis : an official publication of the Society for Risk Analysis.

[13]  R. Koenker,et al.  Asymptotic Theory of Least Absolute Error Regression , 1978 .

[14]  Tsutomu Yamada,et al.  Detection of Damage and Crack in Railhead by Using Eddy Current Testing , 2011 .

[15]  James H Lambert,et al.  Decision Analysis and Risk Models for Land Development Affecting Infrastructure Systems , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[16]  Grzegorz Psuj.  Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements † , 2018, Sensors.

[17]  Stefano Mariani,et al.  Noncontact ultrasonic guided wave inspection of rails , 2013 .

[18]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[19]  Hareton K. N. Leung,et al.  Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[20]  Ying Li,et al.  Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection , 2014, IEEE Transactions on Intelligent Transportation Systems.

[21]  M. García-Fiñana,et al.  Imaging technologies for monitoring the safety, efficacy and mechanisms of action of cell-based regenerative medicine therapies in models of kidney disease , 2016, European journal of pharmacology.

[22]  Vikas Singh,et al.  Higher Pollution Episode Detection Using Image Classification Techniques , 2016, Environmental Modeling & Assessment.

[23]  Desheng Dash Wu,et al.  General Probability‐Time Tradeoff and Intertemporal Risk‐Value Model , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[24]  Patrick Hofstetter,et al.  Selecting Human Health Metrics for Environmental Decision‐Support Tools , 2002, Risk analysis : an official publication of the Society for Risk Analysis.

[25]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[26]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[27]  Desheng Dash Wu,et al.  Business intelligence in risk management: Some recent progresses , 2014, Inf. Sci..

[28]  Bart De Schutter,et al.  Deep convolutional neural networks for detection of rail surface defects , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[29]  Anders Ekberg,et al.  Predicting crack growth and risks of rail breaks due to wheel flat impacts in heavy haul operations , 2007 .

[30]  Saeed Kermani,et al.  Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis , 2016, Microscopy research and technique.

[31]  I. Swaine,et al.  Hypermobility and sports injury , 2018, BMJ Open Sport & Exercise Medicine.

[32]  Radford M. Neal.  Slice Sampling , 2003, The Annals of Statistics.

[33]  Nataliia Kussul,et al.  Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia , 2014, Risk analysis : an official publication of the Society for Risk Analysis.

[34]  Rolf Dollevoet,et al.  Robust and predictive fuzzy key performance indicators for condition-based treatment of squats in railway infrastructures , 2017 .

[35]  Yew-Soon Ong,et al.  Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks , 2016, Ann. Oper. Res..

[36]  Desheng Dash Wu,et al.  A risk analysis model in concurrent engineering product development. , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[37]  Davide Anguita,et al.  Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis , 2015, INNS Conference on Big Data.

[38]  Hing Kai Chan,et al.  Recent Development in Big Data Analytics for Business Operations and Risk Management , 2017, IEEE Transactions on Cybernetics.

[39]  Mohd Rapik Saat,et al.  Analysis of Causes of Major Train Derailment and Their Effect on Accident Rates , 2012 .

[40]  Valerio Cozzani,et al.  Cost‐Benefit Analysis of Passive Fire Protections in Road LPG Transportation , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[41]  Atiq Siddiqui,et al.  An expected consequence approach to route choice in the maritime transportation of crude oil. , 2013, Risk analysis : an official publication of the Society for Risk Analysis.

[42]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[43]  Ali Mosleh,et al.  Improving default risk prediction using Bayesian model uncertainty techniques. , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[44]  Luís Paulo Santos,et al.  A Spherical Gaussian Framework for Bayesian Monte Carlo Rendering of Glossy Surfaces , 2013, IEEE Transactions on Visualization and Computer Graphics.

[45]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[46]  Syed Irtiza Ali Shah,et al.  A novel approach to CAD system for the detection of lung nodules in CT images , 2016, Comput. Methods Programs Biomed..

[47]  Bart De Schutter,et al.  Probabilistic Defect-Based Risk Assessment Approach for Rail Failures in Railway Infrastructure , 2016, IFAC-PapersOnLine.

[48]  Do Thompson,et al.  Ultrasonic surface wave propagation and interaction with surface defects on rail track head , 2006 .

[49]  Christopher W. Karvetski,et al.  Flood Protection Diversification to Reduce Probabilities of Extreme Losses , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[50]  Zili Li,et al.  Squat growth—Some observations and the validation of numerical predictions , 2011 .

[51]  Tara N. Sainath,et al.  Deep Convolutional Neural Networks for Large-scale Speech Tasks , 2015, Neural Networks.

[52]  J.W. Sheppard,et al.  IEEE Standards for Prognostics and Health Management , 2008, IEEE Aerospace and Electronic Systems Magazine.