eprintid: 11379
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/13/79
datestamp: 2023-11-10 03:25:53
lastmod: 2023-11-10 03:25:53
status_changed: 2023-11-10 01:15:07
type: article
metadata_visibility: show
creators_name: Fentaye, A.D.
creators_name: Ul-Haq Gilani, S.I.
creators_name: Baheta, A.T.
creators_name: Li, Y.-G.
title: Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method
ispublished: pub
keywords: Aircraft propulsion; Failure analysis; Fault detection; Gases; Neural networks; Sensors; Support vector machines; Vectors, Artificial neural network methods; Autoassociative neural networks; Condition based maintenance; Gas path; Industrial gas turbines; Multiple fault diagnosis; Prediction accuracy; Steady-state operating conditions, Gas turbines
note: cited By 38
abstract: 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.
date: 2019
publisher: SAGE Publications Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060049597&doi=10.1177%2f0957650918812510&partnerID=40&md5=dc164cdd371a480ca9450f6bd8ae7ccc
id_number: 10.1177/0957650918812510
full_text_status: none
publication: Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy
volume: 233
number: 6
pagerange: 786-802
refereed: TRUE
issn: 09576509
citation:   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