Predictive condition monitoring of industrial systems for improved maintenance and operation

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dc.contributor.advisor Mba, David
dc.contributor.advisor Cao, Yi Ruiz Cárcel, Cristóbal 2015-07-03T13:40:20Z 2015-07-03T13:40:20Z 2014-07
dc.description.abstract Maintenance strategies based on condition monitoring of the different machines and devices in an industrial process can minimize downtime, increase the safety of plant operations and help in the process of decision-taking for control and maintenance actions in order to reduce maintenance and operating costs. Multivariate statistical methods are widely used for process condition monitoring in modern industrial sites due to the quantity of data available and the difficulties of building analytical models in complex facilities. Nevertheless, the performance of these methodologies is still far away from being ideal, due to different issues such as process nonlinearities or varying operational conditions. In addition application of the latest approaches developed for process monitoring is not widely extended in real industry. The aim of this investigation is to develop new and improve existing methodologies for predictive condition monitoring through the use of multivariate statistical methods. The research focuses on demonstrating the applicability of multivariate algorithms in real complex cases, the improvement of these methods in terms of fault detection and diagnosis by means of data fusion and the estimation of process performance degradation caused by faults. en_UK
dc.description.sponsorship Marie Curie en_UK
dc.language.iso en en_UK
dc.publisher Cranfield University en_UK
dc.rights © Cranfield University 2014. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright owner. en_UK
dc.title Predictive condition monitoring of industrial systems for improved maintenance and operation en_UK
dc.type Thesis or dissertation en_UK
dc.type.qualificationlevel Doctoral en_UK
dc.type.qualificationname PhD en_UK
dc.identifier.grantnumber PITN-GA-2010-264940 en_UK

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