Particle filter-based prognostic approach for railway track geometry

Track degradation of ballasted railway track systems has to be measured on a regular basis, and these tracks must be maintained by tamping. Tamping aims to restore the geometry to its original shape to ensure an efficient, comfortable and safe transportation system. To minimize the disturbance introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. This paper proposes a particle filter-based prognostic approach for railway track degradation; this approach is demonstrated by examining different railway switches. The standard deviation of the longitudinal track degradation is studied, and forecasts of the maintenance limit intersection are derived. The particle filter-based prognostic results are compared with the standard regression method results for four railway switches, and the particle filter method shows similar or better result for the four cases. For longer prediction times, the error of the proposed method is equal to or smaller than that of the regression method. The main advantage of the particle filter-based prognostic approach is its ability to generate a probabilistic result based on input parameters with uncertainties. The distributions of the input parameters propagate through the filter, and the remaining useful life is presented using a particle distribution.

[1]  Ernest T. Selig,et al.  PERMANENT STRAIN BEHAVIOR OF RAILROAD BALLAST , 1981 .

[2]  Gilles Foret,et al.  Experimental settlement and dynamic behavior of a portion of ballasted railway track under high speed trains , 2008 .

[3]  Per Hokstad,et al.  RAMS Management of Railway Tracks , 2008 .

[4]  C. Esveld.  Modern railway track , 1989 .

[5]  James V. Candy,et al.  Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods , 2009 .

[6]  Enrico Zio,et al.  Ensemble neural network-based particle filtering for prognostics , 2013 .

[7]  R. Frohling.  Deterioration of railway track due to dynamic vehicle loading and spatially varying track stiffness , 1997 .

[8]  Yu Peng,et al.  Comparison of resampling algorithms for particle filter based remaining useful life estimation , 2014, 2014 International Conference on Prognostics and Health Management.

[9]  H. Chang,et al.  A multi-stage linear prediction model for the irregularity of the longitudinal level over unit railway sections , 2010 .

[10]  Simon Iwnicki,et al.  TRACK SETTLEMENT PREDICTION USING COMPUTER SIMULATION TOOLS , 1999 .

[11]  G Riboulet,et al.  Maintenance optimization for a system with a gamma deterioration process and intervention delay: Application to track maintenance , 2009 .

[12]  Yunmin Chen,et al.  Accumulative deformation in railway track induced by high-speed traffic loading of the trains , 2010 .

[13]  Lin Ma,et al.  Prognostic modelling options for remaining useful life estimation by industry , 2011 .

[14]  Javad Sadeghi,et al.  Development of improved railway track degradation models , 2010 .

[15]  A.A. Ferri,et al.  An Intelligent Diagnostic/Prognostic Framework for Automotive Electrical Systems , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[16]  G. Vachtsevanos,et al.  Particle filter based anomaly detection for aircraft actuator systems , 2009, 2009 IEEE Aerospace conference.

[17]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[18]  George J. Vachtsevanos,et al.  A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .

[19]  Enrico Zio,et al.  Particle filtering prognostic estimation of the remaining useful life of nonlinear components , 2011, Reliab. Eng. Syst. Saf..

[20]  K. Goebel,et al.  An integrated approach to battery health monitoring using bayesian regression and state estimation , 2007, 2007 IEEE Autotestcon.

[21]  Bhaskar Saha,et al.  Battery health management system for electric UAVs , 2011, 2011 Aerospace Conference.

[22]  Sato Yoshihiko,et al.  Japanese Studies on Deterioration of Ballasted Track , 1995 .

[23]  Jorge F. Silva,et al.  Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena , 2013, IEEE Transactions on Instrumentation and Measurement.

[24]  Futian Wang,et al.  Markov-based model for the prediction of railway track irregularities , 2015 .

[25]  E Schnieder,et al.  Monte Carlo simulation of railway track geometry deterioration and restoration , 2012 .

[26]  Uday Kumar,et al.  Optimisation of maintenance track possession time: A tamping case study , 2015 .

[27]  Kai Goebel,et al.  Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .

[28]  Bhaskar Saha,et al.  Model Adaptation for Prognostics in a Particle Filtering Framework , 2011 .

[29]  B. Saha,et al.  Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques , 2008, 2008 IEEE Aerospace Conference.

[30]  Tore Dahlberg,et al.  Some railroad settlement models—A critical review , 2001 .

[31]  John V. Ringwood,et al.  A feasibility study into prognostics for the main bearing of a wind turbine , 2012, 2012 IEEE International Conference on Control Applications.

[32]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[33]  M. Shenton.  Ballast deformation and track deterioration , 1985 .

[34]  Stanislav Jovanovic,et al.  Railway track quality assessment and related decision making , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[35]  George Vachtsevanos,et al.  Filtering and prediction techniques for model-based prognosis and uncertainty management , 2010, 2010 Prognostics and System Health Management Conference.

[36]  A. J. Truscott,et al.  Adaptive Ride Control In Active Suspension Systems , 1995 .

[37]  Luis Ferreira,et al.  Modelling Rail Track Performance: An Integrated Approach , 2000 .

[38]  K. Goebel,et al.  Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.

[39]  Evert Andersson,et al.  Determining the deterioration cost for railway tracks , 2009 .

[40]  Kai Goebel,et al.  Prognostics Applied to Electric Propulsion UAV , 2013, Handbook of Unmanned Aerial Vehicles.

[41]  Michael Osterman,et al.  Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me , 2011 .

[42]  Peter Veit,et al.  Sustainability in Track: A Precondition for High Speed Traffic , 2010 .