Monitoring Vertical Accelerations of Railway Wagon using Machine Leaning Technique

Shafiullah, G.; Simson, S.; Thompson, A.; Wolfs, Peter; Ali, S.

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Access Status

Open access

Authors

Shafiullah, G.

Simson, S.

Thompson, A.

Wolfs, Peter

Ali, S.

Date

2008

Type

Conference Paper

Metadata

Show full item record

Citation

Shafiullah, G. and Simson, S. and Thompson, A. and Wolfs, P. and Ali, S. 2008. Monitoring Vertical Accelerations of Railway Wagon using Machine Leaning Technique, in Arabnia, Hamid R. and Mun, Youngsong. (ed), Proceedings of the International Conference on Artificial Intelligence (ICAI'08) and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications (MLMTA'08), pp. 770-775. Las Vegas: CSREA Press.

Source Title

International Conference on machine learning Models and Technologies, MLMTA'08

Source Conference

International Conference on machine learning Models and Technologies, MLMTA'08

ISBN

1-60132-070-1

Faculty

Department of Electrical and Computer Engineering

School of Engineering

Faculty of Science and Engineering

Remarks

ISBN # for Set: 1-60132-072-8 (sponsors: http://www.world-academy-of-science.org/ )

URI

http://hdl.handle.net/20.500.11937/27539

Collection

Curtin Research Publications

Abstract

Wireless communications and modern machine learning techniques have jointly been applied in the recent development of vehicle health monitoring (VHM) systems. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviors of railway bogies especially in the cases of lateral instability and track irregularities. In this study we have proposed a system to monitor the vertical displacements of railway wagons attached to a moving locomotive. The system uses a classical linear regression machine learning technique with real wagon body acceleration data to predict vertical displacements of vehicle body motion. The system is then able to generate precautionary signals and system status which can be used by the locomotive driver for necessary actions. This VHM system provides forward-looking decisions on track maintenance that can reduce maintenance costs and inspection requirements of railway systems