Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method

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dc.contributor.authorFentaye, Amare
dc.contributor.authorUl-Haq Gilani, Syed Ihtsham
dc.contributor.authorBaheta, Aklilu Tesfamichael
dc.contributor.authorLi, Yi-Guang
dc.date.accessioned2018-12-05T14:58:59Z
dc.date.available2018-12-05T14:58:59Z
dc.date.issued2018-11-18
dc.description.abstractAn effective and reliable gas path diagnostic method that could be used to detect, isolate, and identify gas turbine degradations is crucial in a gas turbine condition-based maintenance. In this paper, we proposed a new combined technique of artificial neural network and support vector machine for a two-shaft industrial gas turbine engine gas path diagnostics. To this end, an autoassociative neural network is used as a preprocessor to minimize noise and generate necessary features, a nested support vector machine to classify gas path faults, and a multilayer perceptron to assess the magnitude of the faults. The necessary data to train and test the method are obtained from a performance model of the case engine under steady-state operating conditions. The test results indicate that the proposed method can diagnose both single- and multiple-component faults successfully and shows a clear advantage over some other methods in terms of multiple fault diagnosis. Moreover, 5-8 sets of measurements have been used to assess the prediction accuracy, and only a 2.3% difference was observed. This result indicates that the proposed method can be used for multiple fault diagnosis of gas turbines with limited measurements.en_UK
dc.identifier.citationFentaye AD, Ul-Haq Gilani SI, Baheta AT, Li YG. (2019) Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, Volume 233, Issue 6, September 2019, pp. 786-802en_UK
dc.identifier.issn0957-6509
dc.identifier.urihttps://doi.org/10.1177/0957650918812510
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13697
dc.language.isoenen_UK
dc.publisherSAGEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectSensoren_UK
dc.subjectgas turbineen_UK
dc.subjectartificial neural networken_UK
dc.subjectsupport vector machineen_UK
dc.subjectgas path diagnosticsen_UK
dc.titlePerformance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network methoden_UK
dc.typeArticleen_UK

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