A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction
dc.contributor.author | Eker, Ömer Faruk | |
dc.contributor.author | Camci, Faith | |
dc.contributor.author | Jennions, Ian K. | |
dc.date.accessioned | 2016-06-23T15:46:04Z | |
dc.date.available | 2016-06-23T15:46:04Z | |
dc.date.issued | 2014-05-14 | |
dc.description.abstract | Physics-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.citation | Eker, 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 2014 | en_UK |
dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/10016 | |
dc.language.iso | en | en_UK |
dc.publisher | PHM Society | en_UK |
dc.rights | Attribution 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.subject | Data-driven prognostics | en_UK |
dc.subject | Anomaly detection | en_UK |
dc.subject | Similarity-based modelling | en_UK |
dc.subject | Multivariate analysis | en_UK |
dc.title | A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction | en_UK |
dc.type | Conference paper | en_UK |
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