TY  - JOUR
ID  - scholars11379
KW  - Aircraft propulsion; Failure analysis; Fault detection; Gases; Neural networks; Sensors; Support vector machines; Vectors
KW  -  Artificial neural network methods; Autoassociative neural networks; Condition based maintenance; Gas path; Industrial gas turbines; Multiple fault diagnosis; Prediction accuracy; Steady-state operating conditions
KW  -  Gas turbines
IS  - 6
N2  - 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.
VL  - 233
JF  - Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy
A1  - Fentaye, A.D.
A1  - Ul-Haq Gilani, S.I.
A1  - Baheta, A.T.
A1  - Li, Y.-G.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060049597&doi=10.1177%2f0957650918812510&partnerID=40&md5=dc164cdd371a480ca9450f6bd8ae7ccc
Y1  - 2019///
TI  - Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method
SP  - 786
N1  - cited By 38
AV  - none
EP  - 802
PB  - SAGE Publications Ltd
SN  - 09576509
ER  -