Integrated gas turbine system diagnostics: components and sensor faults quantification using artificial neural networks

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dc.contributor.authorOsigwe, Emmanuel O.
dc.contributor.authorLi, Yi-Guang
dc.contributor.authorSuresh, Sampath
dc.contributor.authorJombo, Gbanaibolou
dc.date.accessioned2019-04-16T13:16:08Z
dc.date.available2019-04-16T13:16:08Z
dc.date.issued2017-09-11
dc.description.abstractThe role of diagnostic systems in gas turbine operations has changed over the past years from a single support troubleshooting maintenance to a more proactive integrated diagnostic system. This has become so, because detecting and fixing fault(s) on one gas turbine sub-system can trigger false fault(s) indication, on other component(s) of the gas turbine system, due to interrelationships between data obtained to monitor not only the GT single component, but also the integrated components and sensors. Hence, there is need for integration of gas turbine system diagnostics. The purpose of this paper is to present artificial neural network diagnostic system (ANNDS) as an integrated gas turbine system diagnostic tool capable of quantifying gas turbine component and sensor fault. A model based approach which consists of an engine model, and an associated parameter estimation algorithm that predicts the difference between the real engine data and the estimated output data is described in this paper. The ANNDS system was trained to detect, isolate and assess component(s) and sensor fault(s) of a single spool industrial gas turbine GT-PG9171ER. The ANN model was construed with multi-layer feed-forward back propagation network for component fault(s) and auto associative network for sensor fault(s). The diagnostic methodology adopted was a nested network structure, trained to handle specific objective function of detecting, isolating or quantifying faults. The data used for training, and testing purposes were obtained from a non-linear aero-thermodynamic model using PYTHIA; a Cranfield University in-house software. The data set analyzed in this paper represent samples of clean and faulty gas turbine components caused by fouling (0.5% - 6% degradation) and sensor fault(s) due to bias (±1% - ±7%). The results show the capability of ANN to detect, isolate (classification) and quantify multiple faults if properly trained.en_UK
dc.identifier.citationEmmanuel O. Osigwe, Yi-Guang Li, Sampath Suresh and Gbanaibolou Jombo. Integrated gas turbine system diagnostics: components and sensor faults quantification using artificial neural networks. 23rd International Symposium for Air-Breathing Engines (ISABE) 2017, Economy, Efficiency and Environment, 4-8 September 2017, Manchesteren_UK
dc.identifier.urihttps://www.isabe.org/
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14090
dc.language.isoenen_UK
dc.publisherInternational Society for Air Breathing Engines (ISABE)en_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectArtificial Neural Networken_UK
dc.subjectSensor Diagnosticsen_UK
dc.subjectSingle Component Faulten_UK
dc.titleIntegrated gas turbine system diagnostics: components and sensor faults quantification using artificial neural networksen_UK
dc.typeConference paperen_UK

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