%0 Journal Article
%@ 09576509
%A Fentaye, A.D.
%A Ul-Haq Gilani, S.I.
%A Baheta, A.T.
%A Li, Y.-G.
%D 2019
%F scholars:11379
%I SAGE Publications Ltd
%J Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy
%K 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
%N 6
%P 786-802
%R 10.1177/0957650918812510
%T Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method
%U https://khub.utp.edu.my/scholars/11379/
%V 233
%X 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.
%Z cited By 38