COST-EFFECTIVE PROGNOSTICS AND HEALTH MONITORING OF LOCALLY DAMAGED PIPELINES WITH HIGH CONFIDENCE LEVEL

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dc.contributor.advisorModarres, Mohammaden_US
dc.contributor.advisorAzarm, Shapouren_US
dc.contributor.authorAria, Aminen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2020-10-10T05:30:52Z
dc.date.available2020-10-10T05:30:52Z
dc.date.issued2020en_US
dc.description.abstractLocalized pipeline damages, caused by degradation processes such as corrosion, are prominent, can result in pipeline failure and are expensive to monitor. To prevent pipeline failure, many Prognostics and Health Monitoring (PHM) approaches have been developed in which sensor network for online, and human inspection for offline data gathering are separately used. In this dissertation, a two-level (segment- and integrated-level) PHM approach for locally damaged pipelines is proposed where both of these degradation data gathering schemes (i.e., detection methods) are considered simultaneously. The segment-level approach, in which the damage behavior is considered to be uniform, consists of a static and a dynamic phase. In the static phase, a new optimization problem for the health monitoring layout design of locally damaged pipelines is formulated. The solution to this problem is an optimal configuration (or layout) of degradation detection methods with a minimized health monitoring cost and a maximized likelihood of damage detection. In the dynamic phase, considering the optimal layout, an online fusion of high-frequency sensors data and low-frequency inspection information is conducted to estimate and then update the pipeline’s Remaining Useful Life (RUL) estimate. Subsequently, the segment-level optimization formulation is modified to improve its scalability and facilitate updating layouts considering the online RUL estimates. Finally, at the integrated-level, the modified segment-level approach is used along with Stochastic Dynamic Programming (SDP) to produce an optimal set of layouts for a long pipeline consisting of multiple segments with different damage behavior. Experimental data and several notional examples are used to demonstrate the performance of the proposed approaches. Synthetically generated damage data are used in two examples to demonstrate that the proposed segment-level layout optimization approach results in a more robust solution compared to single detection approaches and deterministic methods. For the dynamic segment-level phase, acoustic emission sensor signals and microscopic images from a set of fatigue crack experiments are considered to show that combining sensor- and image-based damage size estimates leads to accuracy improvements in RUL estimation. Lastly, using synthetically generated damage data for three hypothetical pipeline segments, it is shown that the constructed integrated-level approach provides an optimal set of layouts for several pipeline segments.en_US
dc.identifierhttps://doi.org/10.13016/mvmu-c1id
dc.identifier.urihttp://hdl.handle.net/1903/26572
dc.language.isoenen_US
dc.subject.pqcontrolledIndustrial engineeringen_US
dc.subject.pqcontrolledOperations researchen_US
dc.subject.pqcontrolledMechanical engineeringen_US
dc.subject.pquncontrolleddeep learningen_US
dc.subject.pquncontrolledhuman inspectionen_US
dc.subject.pquncontrolledlocalized corrosionen_US
dc.subject.pquncontrolledoptimizationen_US
dc.subject.pquncontrolledprognostics and health monitoringen_US
dc.subject.pquncontrolledsensors networken_US
dc.titleCOST-EFFECTIVE PROGNOSTICS AND HEALTH MONITORING OF LOCALLY DAMAGED PIPELINES WITH HIGH CONFIDENCE LEVELen_US
dc.typeDissertationen_US

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