Damage Precursor Based Structural Health Monitoring and Prognostic Framework Using Dynamic Bayesian Network

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dc.contributor.advisorLopez Droguett, Enriqueen_US
dc.contributor.advisorModarres, Mohammaden_US
dc.contributor.authorRabiei, Elahehen_US
dc.contributor.departmentReliability Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2017-06-22T05:46:02Z
dc.date.available2017-06-22T05:46:02Z
dc.date.issued2016en_US
dc.description.abstractStructural health monitoring (SHM), as an essential tool to ensure the health integrity of aging structures, mostly focus on monitoring conventional observable damage markers such as fatigue crack size. However, degradation starts and progressively evolves at microstructural levels much earlier than detection of such indicators. This dissertation goes beyond classical approaches and presents a new SHM framework based on evolution of Damage Precursors, when conventional direct damage indicator, such as crack, is unobservable, inaccessible or difficult to measure. Damage precursor is defined in this research as “any detectable variation in material/ physical properties of the component that can be used to infer the evolution of the hidden/ inaccessible/ unmeasurable damage during the degradation”. Accordingly, the degradation process is to be expressed based on progression of damage precursor through time and the damage state assessment would be updated by incorporating multiple different evidences. Therefore, this research proposes a systematic integration approach through Dynamic Bayesian Network (DBN) to include all the evidences and their relationships. The implementation of augmented particle filtering as a stochastic inference method inside DBN enables estimating both model parameters and damage states simultaneously in light of various evidences. Incorporating different sources of information in DBN entails advance techniques to identify and formulate the possible interaction between potentially non-homogenous variables. This research uses the Support Vector Regression (SVR) in order to define generally unknown nonparametric and nonlinear correlation between some of the variables in the DBN structure. Additionally, the particle filtering algorithm is studied more fundamentally in this research and a modified approach called “fully adaptive particle filtering” is proposed with the idea of online updating not only the state process model but also the measurement model. This new approach improves the ability of SHM in real-time diagnostics and prognostics. The framework is successfully applied to damage estimation and prediction in two real-world case studies of 1) crack initiation in a metallic alloy under fatigue and, 2) damage estimation and prognostics in composite materials under fatigue. The proposed framework is intended to be general and comprehensive such that it can be implemented in different applications.en_US
dc.identifierhttps://doi.org/10.13016/M2PG5K
dc.identifier.urihttp://hdl.handle.net/1903/19324
dc.language.isoenen_US
dc.subject.pqcontrolledEngineeringen_US
dc.subject.pqcontrolledMechanical engineeringen_US
dc.subject.pquncontrolledDamage precursoren_US
dc.subject.pquncontrolledDynamic Bayesian Networken_US
dc.subject.pquncontrolledParticle filtering/ Fully adaptive particle filteringen_US
dc.subject.pquncontrolledPrognosticsen_US
dc.subject.pquncontrolledStructural Health Monitoringen_US
dc.subject.pquncontrolledSupport Vector Regressionen_US
dc.titleDamage Precursor Based Structural Health Monitoring and Prognostic Framework Using Dynamic Bayesian Networken_US
dc.typeDissertationen_US

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