Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method

• Main Authors: Fentaye, A.D., Ul-Haq Gilani, S.I., Baheta, A.T., Li, Y.-G.
• Format: Article
• Published: SAGE Publications Ltd 2019
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060049597&doi=10.1177%2f0957650918812510&partnerID=40&md5=dc164cdd371a480ca9450f6bd8ae7ccc; http://eprints.utp.edu.my/24981/
• Institution: Universiti Teknologi Petronas

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.