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HEALTH ESTIMATION AND REMAINING USEFUL LIFE PREDICTION OF ELECTRONIC CIRCUIT WITH A PARAMETRIC FAULT

dc.contributor.advisorPecht, Michael Gen_US
dc.contributor.authorSai Sarathi Vasan, Arvinden_US
dc.date.accessioned2017-01-25T06:36:42Z
dc.date.available2017-01-25T06:36:42Z
dc.date.issued2016en_US
dc.identifierhttps://doi.org/10.13016/M2PR9V
dc.identifier.urihttp://hdl.handle.net/1903/19070
dc.description.abstractDegradation of electronic components is typically accompanied by a deviation in their electrical parameters from their initial values. Such parametric drifts in turn will cause degradation in performance of the circuit they are part of, eventually leading to function failure due to parametric faults. The existing approaches for predicting failures resulting from electronic component parametric faults emphasize identifying monotonically deviating parameters and modeling their progression over time. However, in practical applications where the components are integrated into a complex electronic circuit assembly, product or system, it is generally not feasible to monitor component-level parameters. To address this problem, a prognostics method that exploits features extracted from responses of circuit-comprising components exhibiting parametric faults is developed in this dissertation. The developed prognostic method constitutes a circuit health estimation step followed by a degradation modeling and remaining useful life (RUL) prediction step. First, the circuit health estimation method was developed using a kernel-based machine learning technique that exploits features that are extracted from responses of circuit-comprising components exhibiting parametric faults, instead of the component-level parameters. The performance of kernel learning technique depends on the automatic adaptation of hyperparameters (i.e., regularization and kernel parameters) to the learning features. Thus, to achieve high accuracy in health estimation the developed method also includes an optimization method that employs a penalized likelihood function along with a stochastic filtering technique for automatic adaptation of hyperparameters. Second, the prediction of circuit’s RUL is realized by a model-based filtering method that relies on a first principles-based model and a stochastic filtering technique. The first principles-based model describes the degradation in circuit health with progression of parametric fault in a circuit component. The stochastic filtering technique on the other hand is used to first solve a joint ‘circuit health state—parametric fault’ estimation problem, followed by prediction problem in which the estimated ‘circuit health state—parametric fault’ is propagated forward in time to predict RUL. Evaluations of the data from simulation experiments on a benchmark Sallen–Key filter circuit and a DC–DC converter system demonstrate the ability of the developed prognostic method to estimate circuit health and predict RUL without having to monitor the individual component parameters.en_US
dc.language.isoenen_US
dc.titleHEALTH ESTIMATION AND REMAINING USEFUL LIFE PREDICTION OF ELECTRONIC CIRCUIT WITH A PARAMETRIC FAULTen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentMechanical Engineeringen_US
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pquncontrolledCircuiten_US
dc.subject.pquncontrolledHealth Estimationen_US
dc.subject.pquncontrolledkernel learningen_US
dc.subject.pquncontrolledParametric Faulten_US
dc.subject.pquncontrolledPrognosticsen_US


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