relation: https://khub.utp.edu.my/scholars/11379/
title: Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method
creator: Fentaye, A.D.
creator: Ul-Haq Gilani, S.I.
creator: Baheta, A.T.
creator: Li, Y.-G.
description: An 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. © IMechE 2018.
publisher: SAGE Publications Ltd
date: 2019
type: Article
type: PeerReviewed
identifier:   Fentaye, A.D. and Ul-Haq Gilani, S.I. and Baheta, A.T. and Li, Y.-G.  (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, 233 (6).  pp. 786-802.  ISSN 09576509     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060049597&doi=10.1177%2f0957650918812510&partnerID=40&md5=dc164cdd371a480ca9450f6bd8ae7ccc
relation: 10.1177/0957650918812510
identifier: 10.1177/0957650918812510