Prognostics with autoregressive moving average for railway turnouts

dc.contributor.authorGuclu, Adem
dc.contributor.authorYilboga, Halis
dc.contributor.authorEker, Ömer Faruk
dc.contributor.authorCamci, Faith
dc.contributor.authorJennions, Ian K.
dc.date.accessioned2016-06-22T15:02:26Z
dc.date.available2016-06-22T15:02:26Z
dc.date.issued2010-12-31
dc.description.abstractTurnout systems are one of the most critical systems on railway infrastructure. Diagnostics and prognostics on turnout system have ability to increase the reliability & availability and reduce the downtime of the railway infrastructure. Even though diagnostics on railway turnout systems have been reported in the literature, reported studies on prognostics in railway turnout system is very sparse. This paper presents autoregressive moving average model based prognostics on railway turnouts. The model is applied to data collected from real turnout systems. The failure progression is obtained manually using the exponential degradation model. Remaining Useful Life of ten turnout systems have been reported and results are very promising.en_UK
dc.identifier.citationGuclu, A. et al., Prognostics with autoregressive moving average for railway turnouts, Annual Conference of the Prognostics and Health Management Society, 2010en_UK
dc.identifier.isbn9781936263011
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/9997
dc.language.isoenen_UK
dc.publisherPHM Societyen_UK
dc.rightsAttribution-Non-Commercial-No Derivatives 3.0 Unported (CC BY-NC-ND 3.0). You are free to: Share — copy and redistribute the material in any medium or format. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: Non-Commercial — You may not use the material for commercial purposes. No Derivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.en_UK
dc.titlePrognostics with autoregressive moving average for railway turnoutsen_UK
dc.typeConference paperen_UK

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