Major challenges in prognostics: study on benchmarking prognostic datasets
dc.contributor.author | Eker, Ömer Faruk | |
dc.contributor.author | Camci, Faith | |
dc.contributor.author | Jennions, Ian K. | |
dc.date.accessioned | 2016-06-22T10:47:30Z | |
dc.date.available | 2016-06-22T10:47:30Z | |
dc.date.issued | 2012-12-31 | |
dc.description.abstract | Even 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.citation | Eker 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.148-155 | en_UK |
dc.identifier.issn | 2325-016X | |
dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/9994 | |
dc.language.iso | en | en_UK |
dc.publisher | PHM Society | en_UK |
dc.rights | Attribution-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.title | Major challenges in prognostics: study on benchmarking prognostic datasets | en_UK |
dc.type | Conference paper | en_UK |
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