Major challenges in prognostics: study on benchmarking prognostic datasets

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dc.contributor.authorEker, Ömer Faruk
dc.contributor.authorCamci, Faith
dc.contributor.authorJennions, Ian K.
dc.date.accessioned2016-06-22T10:47:30Z
dc.date.available2016-06-22T10:47:30Z
dc.date.issued2012-12-31
dc.description.abstractEven though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression.en_UK
dc.identifier.citationEker OF, Camci F, Jennions IK (2012) Major challenges in prognostics: study on benchmarking prognostic datasets. Proceedings of the 1st European Conference of the Prognostics and Health Management Society, Dresden, Germany, 3-5 July 2012, PHM Society, pp.148-155en_UK
dc.identifier.issn2325-016X
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/9994
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.titleMajor challenges in prognostics: study on benchmarking prognostic datasetsen_UK
dc.typeConference paperen_UK

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