 Model selection for degradation modeling and prognosis with health monitoring dataKhanh T.P. Nguyen, Mitra Fouladirad and Antoine Grall Reliability Engineering and System Safety, 2018, vol. 169, issue C, 105-116 Abstract: Health monitoring data are increasingly collected and widely used for reliability assessment and lifetime prediction. They not only provide information about degradation state but also could trace failure mechanisms of assets. The selection of a deterioration model that optimally fits in with health monitoring data is an important issue. It can enable a more precise asset health prognostic and help reducing operation and maintenance costs. Therefore, this paper aims to address the problem of degradation model selection including goals, procedure and evaluation criteria. Focusing on continuous degradation modeling including some currently used Lévy processes, the performance of classical and prognostic criteria are discussed through numerous numerical examples. We also investigate in what circumstances which methods perform better than others. The efficiency of a new hybrid criterion is highlighted that allows to take into account the information of goodness-of-fit of observation data when evaluating prognostic measure. Keywords: Model selection; Prognostic prediction; Residual life prediction; Degradation process; Lévy process (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:169:y:2018:i:c:p:105-116 DOI: 10.1016/j.ress.2017.08.004 Access Statistics for this article Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares More articles in Reliability Engineering and System Safety from Elsevier |
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