@article{scholars11379,
            year = {2019},
       publisher = {SAGE Publications Ltd},
         journal = {Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy},
           pages = {786--802},
          number = {6},
            note = {cited By 38},
          volume = {233},
             doi = {10.1177/0957650918812510},
           title = {Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method},
        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. {\^A}{\copyright} IMechE 2018.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060049597&doi=10.1177\%2f0957650918812510&partnerID=40&md5=dc164cdd371a480ca9450f6bd8ae7ccc},
        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},
          author = {Fentaye, A. D. and Ul-Haq Gilani, S. I. and Baheta, A. T. and Li, Y.-G.},
            issn = {09576509}
}