%P 786-802
%T Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method
%I SAGE Publications Ltd
%A A.D. Fentaye
%A S.I. Ul-Haq Gilani
%A A.T. Baheta
%A Y.-G. Li
%V 233
%D 2019
%N 6
%R 10.1177/0957650918812510
%O cited By 38
%L scholars11379
%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
%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.