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Application of machine learning techniques for railway health monitoring

Shafiullah, GM., Thompson, A., Wolfs, P.J. and Ali, A.B.M.S. (2010) Application of machine learning techniques for railway health monitoring. In: Ali, A.B.M.S. and Xiang, Y., (eds.) Dynamic and Advanced Data Mining for Progressing Technological Development. IGI Global, pp. 396-421.

Link to Published Version: http://dx.doi.org/10.4018/978-1-60566-908-3.ch016
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Abstract

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies especially in the cases of lateral instability and track irregularities. In order to ensure safety and reliability of railway in this chapter, a forecasting model has been developed to investigate vertical acceleration behaviour of railway wagons attached to a moving locomotive using modern machine learning techniques. Initially, an energy-efficient data acquisition model has been proposed for WSN applications using popular learning algorithms. Later, a prediction model has been developed to investigate both front and rear body vertical acceleration behaviour. Different types of models can be built using a uniform platform to evaluate their performances and estimate different attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity for each of the algorithm. Finally, spectral analysis of front and rear body vertical condition is produced from the predicted data using Fast Fourier Transform (FFT) and used to generate precautionary signals and system status which can be used by the locomotive driver for deciding upon necessary actions.

Publication Type: Book Chapter
Publisher: IGI Global
Copyright: © 2010 IGI Global
URI: http://researchrepository.murdoch.edu.au/id/eprint/31858
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