A Hybrid Prognostic Methodology and its Application to Well-Controlled Engineering Systems

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dc.contributor.advisorCamci, Fatih
dc.contributor.advisorJennions, Ian K.
dc.contributor.authorEker, Ömer Faruk
dc.date.accessioned2015-06-18T14:26:37Z
dc.date.available2015-06-18T14:26:37Z
dc.date.issued2015-01
dc.description.abstractThis thesis presents a novel hybrid prognostic methodology, integrating physics-based and data-driven prognostic models, to enhance the prognostic accuracy, robustness, and applicability. The presented prognostic methodology integrates the short-term predictions of a physics-based model with the longer term projection of a similarity-based data-driven model, to obtain remaining useful life estimations. The hybrid prognostic methodology has been applied on specific components of two different engineering systems, one which represents accelerated, and the other a nominal degradation process. Clogged filter and fatigue crack propagation failure cases are selected as case studies. An experimental rig has been developed to investigate the accelerated clogging phenomena whereas the publicly available Virkler fatigue crack propagation dataset is chosen after an extensive literature search and dataset analysis. The filter clogging experimental rig is designed to obtain reproducible filter clogging data under different operational profiles. This data is thought to be a good benchmark dataset for prognostic models. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. This comparison has been based on the most recent prognostic evaluation metrics. The results show that the presented methodology improves accuracy, robustness and applicability. The work contained herein is therefore expected to contribute to scientific knowledge as well as industrial technology development.en_UK
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/9269
dc.language.isoenen_UK
dc.publisherCranfield Universityen_UK
dc.rights© Cranfield University 2015. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner.en_UK
dc.subjectIntegrated Vehicle Health Managementen_UK
dc.subjectPrognostics and Health Managementen_UK
dc.subjectCondition Based Maintenanceen_UK
dc.subjectHybrid Prognosticsen_UK
dc.subjectPhysics-based Prognosticsen_UK
dc.subjectData-driven Prognosticsen_UK
dc.subjectSimilarity-based Prognosticsen_UK
dc.subjectFilter Clogging Modellingen_UK
dc.subjectFatigue Crack Growth Modellingen_UK
dc.titleA Hybrid Prognostic Methodology and its Application to Well-Controlled Engineering Systemsen_UK
dc.typeThesis or dissertationen_UK
dc.type.qualificationlevelDoctoralen_UK
dc.type.qualificationnamePhDen_UK

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PhD, EngD and MSc by research theses (SATM)