Real-time prognostics and health management without run-to-failure data on railway assets

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dc.contributor.authorShimizu, Minoru
dc.contributor.authorPerinpanayagam, Suresh
dc.contributor.authorNamoano, Bernadin
dc.contributor.authorStarr, Andrew
dc.date.accessioned2023-04-18T09:11:26Z
dc.date.available2023-04-18T09:11:26Z
dc.date.issued2023-03-20
dc.description.abstractPrognosis is a challenging technology that aims to accurately predict and estimate the remaining useful life of a component or system in order to enhance its reliability and performance. Although prognosis research for predictive maintenance is a well-researched topic, practical examples of successful prognostic applications remain scarce. This is due to the lack of available run-to-failure data to build the prediction model as maintenance is usually conducted regularly to avoid significant defects. This paper proposes a novel prognosis method that can be applied to real-world railway maintenance planning without employing runto-failure data. The key idea is that the fault severity assessment and approximate remaining time prediction are often all that is needed in order to plan maintenance. Firstly, using motor current signals, a degradation indicator on railway door systems is generated based on the dynamic time warping method to measure similarity between typical normal and faulty behaviour. Then, the K-means algorithm is applied to assess fault severity, followed by the representative time estimation for each level of fault severity. This estimation thus allows the remaining time prediction until reaching the critical fault severity level without using runto-failure data. As a result, the proposed method enables predictive maintenance planning for railway door systems. In addition, the fault severity threshold can be updated by additional operational data, enabling the remaining time prediction to be more reliable. Furthermore, the proposed method can be applied to conventional railway assets and other electro-mechanical actuators as motor current signals are primarily available from the controller or motor drive without additional sensors.en_UK
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC): EP/T518104/1en_UK
dc.identifier.citationShimizu M, Perinpanayagam S, Namoano B, Starr A. (2023) Real-time prognostics and health management without run-to-failure data on railway assets, IEEE Access, Volume 11, March 2023, pp. 28724-28734en_UK
dc.identifier.issn2169-3536
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2023.3259221
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19494
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-Non 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectFault detectionen_UK
dc.subjectprognosisen_UK
dc.subjectprognostics and health managementen_UK
dc.subjectPHMen_UK
dc.subjectsignal processingen_UK
dc.subjectremaining useful lifeen_UK
dc.subjectrailwayen_UK
dc.subjectdoor systemsen_UK
dc.subjectlinear actuatoren_UK
dc.subjectelectro-mechanical actuatorsen_UK
dc.subjectEMAsen_UK
dc.titleReal-time prognostics and health management without run-to-failure data on railway assetsen_UK
dc.typeArticleen_UK

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