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dc.contributor.authorShao, Yunfei
dc.contributor.authorSi, Wujun
dc.date.accessioned2022-03-22T20:22:44Z
dc.date.available2022-03-22T20:22:44Z
dc.date.issued2021-12-23
dc.identifier.citationY. Shao and W. Si, "Degradation Modeling With Long-Term Memory Considering Measurement Errors," in IEEE Transactions on Reliability, doi: 10.1109/TR.2021.3125958.en_US
dc.identifier.issn0018-9529
dc.identifier.issn1558-1721
dc.identifier.urihttps://doi.org/10.1109/TR.2021.3125958
dc.identifier.urihttps://soar.wichita.edu/handle/10057/22736
dc.descriptionAccepted version of the article is available in SOARen_US
dc.description.abstractWith the advancement of measurement technology, the long-term memory (LTM) effect within the degradation data of many assets has recently been detected, which implies that the future degradation process highly correlates with both the current degradation status and historical degradation trajectory across a long time period. To capture the LTM effect, several LTM-integrated degradation models have been developed in the literature. In practice, degradation data are often contaminated with measurement errors, which are ignored in most existing LTM-integrated degradation studies. Without considering measurement errors, the asset degradation modeling and reliability analysis may be biased. In this article, we propose a novel LTM-integrated degradation model that quantitatively incorporates measurement errors. Both fixed-effect and random-effect scenarios of degradation growth are considered. A maximum-likelihood estimation approach is developed to estimate the parameters of the proposed model. Based on this model, asset reliability analysis and lifetime prediction are developed. Simulation studies are implemented to evaluate the performance of the proposed model. A real case study using the capacity degradation data of lithium-ion pouch cells is conducted to illustrate the superiority of the proposed model. Results demonstrate that conventional LTM-integrated degradation models, which ignore the measurement errors, significantly misestimate the uncertainty of asset lifetime.en_US
dc.description.sponsorshipKansas NASA EPSCoR Research Infrastructure Development Program (Grant Number: 80NSSC19M0042) 10.13039/501100008982-National Science Foundation (Grant Number: OIA-1656006) 10.13039/100007160-Wichita State Universityen_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesIEEE Transactions on Reliability;
dc.subjectDegradation testen_US
dc.subjectFractional Brownian motion (FBM)en_US
dc.subjectLifetime predictionen_US
dc.subjectLong memoryen_US
dc.subjectObservational erroren_US
dc.subjectReliability analysisen_US
dc.titleDegradation modeling with long-term memory considering measurement errorsen_US
dc.typeArticleen_US
dc.typePostprint
dc.rights.holder© 2021 IEEEen_US


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  • ISME Research Publications
    Research works published by faculty and students of the Department of Industrial, Systems, and Manufacturing Engineering

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