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Application of Machine Learning Techniques for Railway Health Monitoring

Application of Machine Learning Techniques for Railway Health Monitoring

G.M. Shafiullah, Adam Thompson, Peter J. Wolfs, A.B.M. Shawkat Ali
Copyright: © 2010 |Pages: 26
ISBN13: 9781605669083|ISBN10: 1605669083|ISBN13 Softcover: 9781616924461|EISBN13: 9781605669090
DOI: 10.4018/978-1-60566-908-3.ch016
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MLA

Shafiullah, G.M., et al. "Application of Machine Learning Techniques for Railway Health Monitoring." Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches, edited by A B M Shawkat Ali and Yang Xiang, IGI Global, 2010, pp. 396-421. https://doi.org/10.4018/978-1-60566-908-3.ch016

APA

Shafiullah, G., Thompson, A., Wolfs, P. J., & Ali, A. S. (2010). Application of Machine Learning Techniques for Railway Health Monitoring. In A. Ali & Y. Xiang (Eds.), Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches (pp. 396-421). IGI Global. https://doi.org/10.4018/978-1-60566-908-3.ch016

Chicago

Shafiullah, G.M., et al. "Application of Machine Learning Techniques for Railway Health Monitoring." In Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches, edited by A B M Shawkat Ali and Yang Xiang, 396-421. Hershey, PA: IGI Global, 2010. https://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.

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