Fault diagnostics for advanced cycle marine gas turbine using genetic algorithm

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dc.contributor.advisor Singh, R.
dc.contributor.author Sampath, Suresh
dc.date.accessioned 2016-08-01T13:47:39Z
dc.date.available 2016-08-01T13:47:39Z
dc.date.issued 2003-08
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/10204
dc.description.abstract The major challenges faced by the gas turbine industry, for both the users and the manufacturers, is the reduction in life cycle costs , as well as the safe and efficient running of gas turbines. In view of the above, it would be advantageous to have a diagnostics system capable of reliably detecting component faults (even though limited to gas path components) in a quantitative marmer. V This thesis presents the development an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using advanced concepts in genetic algorithm. The diagnostics model operates in three distinct stages. The rst stage uses response surfaces for computing objective functions to increase the exploration potential of the search space while easing the computational burden. The second stage uses the heuristics modification of genetics algorithm parameters through a master-slave type configuration. The third stage uses the elitist model concept in genetic algorithm to preserve the accuracy of the solution in the face of randomness. The above fault diagnostics model has been integrated with a nested neural network to form a hybrid diagnostics model. The nested neural network is employed as a pre- processor or lter to reduce the number of fault classes to be explored by the genetic algorithm based diagnostics model. The hybrid model improves the accuracy, reliability and consistency of the results obtained. In addition signicant improvements in the total run time have also been observed. The advanced cycle Intercooled Recuperated WR2l engine has been used as the test engine for implementing the diagnostics model. en_UK
dc.language.iso en en_UK
dc.publisher Cranfield University en_UK
dc.rights © Cranfield University, 2003. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. en_UK
dc.title Fault diagnostics for advanced cycle marine gas turbine using genetic algorithm en_UK
dc.type Thesis or dissertation en_UK
dc.type.qualificationlevel Doctoral en_UK
dc.type.qualificationname PhD en_UK
dc.description.prize SOE Prize winner en_UK

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