University of Hertfordshire

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Original languageEnglish
Pages1-16
Publication statusPublished - Sep 2017
Event23rd International Symposium on Air Breathing Engines (ISABE) - Manchester, United Kingdom
Duration: 3 Sep 2017 → …

Conference

Conference23rd International Symposium on Air Breathing Engines (ISABE)
CountryUnited Kingdom
CityManchester
Period3/09/17 → …

Abstract

The role of diagnostic systems in gas turbine operations has changed over the past years from a singlesupport 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 triggerfalsefault(s) indication, on other component(s) of thegas 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 quantifyinggas turbinecomponent 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 datais 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 quantifyingfaults. The data used for training, and testing purposeswere obtained from a non-linear aero-thermodynamic modelusing PYTHIA; aCranfieldUniversityin-housesoftware.The data set analyzed in this paper representsamplesofclean and faulty gas turbine componentscaused 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

Integrated Gas Turbine System Diagnostics: Components and Sensor Faults Quantification using Artificial Neural Network | Request PDF. Available from: https://www.researchgate.net/publication/319645027_Integrated_Gas_Turbine_System_Diagnostics_Components_and_Sensor_Faults_Quantification_using_Artificial_Neural_Network [accessed Oct 24 2018].

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