Predicting Vertical Acceleration of Railway Wagons Using Regression Algorithms

The performance of rail vehicles running on railway tracks is governed by the dynamic behaviors of railway bogies, particularly in cases of lateral instability and track irregularities. To ensure reliable, safe, and secure operation of railway systems, it is desirable to adopt intelligent monitoring systems for railway wagons. In this paper, a forecasting model is developed to investigate the vertical-acceleration behavior of railway wagons that are attached to a moving locomotive using modern machine-learning techniques. Both front- and rear-body vertical-acceleration conditions are predicted using popular regression algorithms. Different types of models can be built using a uniform platform to evaluate their performance. The estimation techniques' performance has been measured using a set of attributes' correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE), and computational complexity for each of the algorithms. Statistical hypothesis analysis is applied to determine the most suitable regression algorithm for this application. Finally, spectral analysis of the front- and rear-body vertical condition is produced from the predicted data using the fast Fourier transform (FFT) and is used to generate precautionary signals and system status that can be used by a locomotive driver for necessary actions.

[1]  Samia Nefti-Meziani,et al.  A neural network approach for railway safety prediction , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[2]  Sankaran Mahadevan,et al.  Improving railroad wheel inspection planning using classification methods , 2007, Artificial Intelligence and Applications.

[3]  A. Gyasi-Agyei,et al.  Survey of Wireless Communications Applications in the Railway Industry , 2007, The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless 2007).

[4]  Ian Witten,et al.  Data Mining , 2000 .

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

[6]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[7]  Roger M. Goodall,et al.  Concepts and Techniques for Railway Condition Monitoring , 2006 .

[8]  Guy Lebanon,et al.  Linear Regression , 2010 .

[9]  David W. Aha,et al.  Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms , 1992, Int. J. Man Mach. Stud..

[10]  Ian H. Witten,et al.  Induction of model trees for predicting continuous classes , 1996 .

[11]  Cen Li,et al.  Classifying imbalanced data using a bagging ensemble variation (BEV) , 2007, ACM-SE 45.

[12]  Sakdirat Kaewunruen,et al.  Response and Prediction of Dymanic Characteristics of Worn Rail Pads Under Static Preloads , 2007 .

[13]  George D. Magoulas,et al.  Neural network-based colonoscopic diagnosis using on-line learning and differential evolution , 2004, Appl. Soft Comput..

[14]  C. S. Bonaventura,et al.  Intelligent system for real-time prediction of railway vehicle response to the interaction with track geometry , 2000, Proceedings of the 2000 ASME/IEEE Joint Railroad Conference (Cat. No.00CH37110).

[15]  Tin-Kin Ho,et al.  Optimal Track Access Rights Allocation for Agent Negotiation in an Open Railway Market , 2008, IEEE Transactions on Intelligent Transportation Systems.

[16]  Shawkat Ali,et al.  Forecasting Vertical Acceleration Railway Wagons - A comparative study , 2008, DMIN.

[17]  B. Walker.  I. Introduction , 2020 .

[18]  Peter Wolfs,et al.  A distributed low cost device for the remote observation of track and vehicle interactions , 2006 .

[19]  Masahiro Miwa,et al.  Measurement and analysis of track irregularity on super-high speed train - TRIPS , 2000 .

[20]  A. Sykes.  An Introduction to Regression Analysis , 1993 .

[21]  V K Garg,et al.  Dynamics of railway vehicle systems , 1984 .

[22]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[23]  P. Ackroyd,et al.  Remote ride quality monitoring of Acela train set performance , 2002, ASME/IEEE Joint Railroad Conference.

[24]  Jason Smith,et al.  Security as a Safety Issue in Rail Communications , 2003, SCS.

[25]  G. Holmes,et al.  Developing innovative applications in agriculture using data mining , 1999 .

[26]  P.J. Wolfs,et al.  An autonomous, low cost, distributed method for observing vehicle track interactions , 2006, Proceedings of the 2006 IEEE/ASME Joint Rail Conference.

[27]  The Math Works, Inc..  The Math Works Inc , 1991, International Conference on Advances in System Simulation.

[28]  Kate Smith-Miles,et al.  On learning algorithm selection for classification , 2006, Appl. Soft Comput..

[29]  J. E. Glynn,et al.  Numerical Recipes: The Art of Scientific Computing , 1989 .

[30]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .