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dc.contributor.authorShafiullah, G.
dc.contributor.authorSimson, S.
dc.contributor.authorThompson, A.
dc.contributor.authorWolfs, Peter
dc.contributor.authorAli, S.
dc.contributor.editorHamid R Arabnia and Youngsong Mun
dc.date.accessioned2017-01-30T12:59:33Z
dc.date.available2017-01-30T12:59:33Z
dc.date.created2010-04-20T20:03:01Z
dc.date.issued2008
dc.identifier.citationShafiullah, 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.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/27539
dc.description.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

dc.publisherCSREA Press
dc.subjectVehicle health monitoring
dc.subjectvetical acceleration
dc.subjectmachine learning
dc.subjectbogie dynamics
dc.titleMonitoring Vertical Accelerations of Railway Wagon using Machine Leaning Technique
dc.typeConference Paper
dcterms.source.startPage770
dcterms.source.endPage775
dcterms.source.titleInternational Conference on machine learning Models and Technologies, MLMTA'08
dcterms.source.seriesInternational Conference on machine learning Models and Technologies, MLMTA'08
dcterms.source.isbn1-60132-070-1
dcterms.source.conferenceInternational Conference on machine learning Models and Technologies, MLMTA'08
dcterms.source.conference-start-dateJul 14 2008
dcterms.source.conferencelocationLas Vegas
dcterms.source.placeUSA
curtin.note

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

curtin.accessStatusOpen access
curtin.facultyDepartment of Electrical and Computer Engineering
curtin.facultySchool of Engineering
curtin.facultyFaculty of Science and Engineering


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