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dc.contributor.authorSi, Wujun
dc.contributor.authorYang, Qingyu
dc.contributor.authorWu, Xin
dc.date.accessioned2018-11-26T21:23:17Z
dc.date.available2018-11-26T21:23:17Z
dc.date.issued2018-11-05
dc.identifier.citationWujun Si, Qingyu Yang & Xin Wu (2018) Material Degradation Modeling and Failure Prediction Using Microstructure Images, Technometricsen_US
dc.identifier.issn0040-1706
dc.identifier.urihttps://doi.org/10.1080/00401706.2018.1514327
dc.identifier.urihttp://hdl.handle.net/10057/15669
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractDegradation data, frequently along with low-dimensional covariate information such as scalar-type covariates, are widely used for asset reliability analysis. Recently, many high-dimensional covariates such as functional and image covariates have emerged with advances in sensor technology, containing richer information that can be used for degradation assessment. In this article, motivated by a physical effect that microstructures of dual-phase advanced high strength steel strongly influence steel degradation, we propose a two-stage material degradation model using the material microstructure image as a covariate. In Stage 1, we show that the microstructure image covariate can be reduced to a functional covariate while statistical properties of the image are preserved up to the second order. In Stage 2, a novel functional covariate degradation model is proposed, based on which the time-to-failure distribution in terms of degradation level passages is derived. A penalized least squares estimation method is developed to obtain the closed-form point estimator of model parameters. Analytical inferences on interval estimation of the model parameters, the mean degradation levels, and the distribution of the time-to-failure are also developed. Simulation studies are implemented to validate the developed methods. Physical experiments on dual-phase advanced high strength steel are designed and conducted to demonstrate the proposed model. The results show that a significant improvement is achieved for material failure prediction by using material microstructure images compared with multiple benchmark models.en_US
dc.language.isoen_USen_US
dc.publisherTaylor & Francisen_US
dc.relation.ispartofseriesTechnometrics;2018
dc.subjectDual-phase advanced high strength steelen_US
dc.subjectGeneralized cross-validationen_US
dc.subjectInterval estimationen_US
dc.subjectPenalized least squares estimationen_US
dc.subjectReliability analysisen_US
dc.subjectTwo-point correlation functionen_US
dc.titleMaterial degradation modeling and failure prediction using microstructure imagesen_US
dc.typeArticleen_US
dc.rights.holder© 2018 Taylor & Francisen_US


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