A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction

dc.contributor.authorEker, Ömer Faruk
dc.contributor.authorCamci, Faith
dc.contributor.authorJennions, Ian K.
dc.date.accessioned2016-06-23T15:46:04Z
dc.date.available2016-06-23T15:46:04Z
dc.date.issued2014-05-14
dc.description.abstractPhysics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimations on each time instance. The presented model is tested on; Virkler’s fatigue crack growth dataset, a drilling process degradation dataset, and a sliding chair degradation of a turnout system dataset. Prediction performances are compared utilizing an evaluation metric. Efficiency analysis of optimization results show that the modified similarity-based model performs better than the original definition.en_UK
dc.identifier.citationEker, O. F., Camci, F., Jennions, Ian K., A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction, 2nd European Conference of the Prognostics and Health Management Society, Nantes, France, 8-10 July 2014en_UK
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10016
dc.language.isoenen_UK
dc.publisherPHM Societyen_UK
dc.rightsAttribution 3.0 Unported (CC BY 3.0). You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. 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: 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.subjectData-driven prognosticsen_UK
dc.subjectAnomaly detectionen_UK
dc.subjectSimilarity-based modellingen_UK
dc.subjectMultivariate analysisen_UK
dc.titleA Similarity-Based Prognostics Approach for Remaining Useful Life Predictionen_UK
dc.typeConference paperen_UK

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