Statistical Degradation Models for Electronics

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dc.contributor.advisorPecht, Michaelen_US
dc.contributor.advisorSlud, Ericen_US
dc.contributor.authorSotiris, Vasilis A.en_US
dc.contributor.departmentApplied Mathematics and Scientific Computationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2012-02-17T06:56:23Z
dc.date.available2012-02-17T06:56:23Z
dc.date.issued2011en_US
dc.description.abstractWith increasing presence of electronics in modern systems and in every-day products, their reliability is inextricably dependent on that of their electronics. We develop reliability models for failure-time prediction under small failure-time samples and information on individual degradation history. The development of the model extends the work of Whitmore et al. 1998, to incorporate two new data-structures common to reliability testing. Reliability models traditionally use lifetime information to evaluate the reliability of a device or system. To analyze small failure-time samples within dynamic environments where failure mechanisms are unknown, there is a need for models that make use of auxiliary reliability information. In this thesis we present models suitable for reliability data, where degradation variables are latent and can be tracked by related observable variables we call markers. We provide an engineering justification for our model and develop parametric and predictive inference equations for a data-structure that includes terminal observations of the degradation variable and longitudinal marker measurements. We compare maximum likelihood estimation and prediction results obtained by Whitmore et. al. 1998 and show improvement in inference under small sample sizes. We introduce modeling of variable failure thresholds within the framework of bivariate degradation models and discuss ways of incorporating covariates. In the second part of the thesis we investigate anomaly detection through a Bayesian support vector machine and discuss its place in degradation modeling. We compute posterior class probabilities for time-indexed covariate observations, which we use as measures of degradation. Lastly, we present a multistate model used to model a recurrent event process and failure-times. We compute the expected time to failure using counting process theory and investigate the effect of the event process on the expected failure-time estimates.en_US
dc.identifier.urihttp://hdl.handle.net/1903/12312
dc.subject.pqcontrolledApplied mathematicsen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledDegradation modelsen_US
dc.subject.pquncontrolledFirst hitting timeen_US
dc.subject.pquncontrolledLikelihooden_US
dc.subject.pquncontrolledLongitudinal marker processen_US
dc.subject.pquncontrolledParametric Inferenceen_US
dc.subject.pquncontrolledWiener processen_US
dc.titleStatistical Degradation Models for Electronicsen_US
dc.typeDissertationen_US

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