A combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines and signal fault isolation

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dc.contributor.authorTogni, Simone
dc.contributor.authorNikolaidis, Theoklis
dc.contributor.authorSampath, Suresh
dc.date.accessioned2020-08-19T14:39:57Z
dc.date.available2020-08-19T14:39:57Z
dc.date.issued2020-06-18
dc.description.abstractThe target of this paper is the performance-based diagnostics of a gas turbine for the automated early detection of components malfunctions. The paper proposes a new combination of multiple methodologies for the performance-based diagnostics of single and multiple failures on a two-spool engine. The aim of this technique is to combine the strength of each methodology and provide a high success rate for single and multiple failures with the presence of measurement malfunctions. A combination of KF (Kalman Filter), ANN (Artificial Neural Network) and FL (Fuzzy Logic) is used in this research in order to improve the success rate, to increase the flexibility and the number of failures detected and to combine the strength of multiple methods to have a more robust solution. The Kalman filter has in his strength the measurement noise treatment, the artificial neural network the simulation and prediction of reference and deteriorated performance profile and the fuzzy logic the categorization flexibility, which is used to quantify and classify the failures. In the area of GT (Gas Turbine) diagnostics, the multiple failures in combination with measurement issues and the utilization of multiple methods for a 2-spool industrial gas turbine engine has not been investigated extensively. This paper reports the key contribution of each component of the methodology and brief the results in the quantification and classification success rate. The methodology is tested for constant deterioration and increasing noise and for random deterioration. For the random deterioration and nominal noise of 0.4%, in particular, the quantification success rate is above 92.0%, while the classification success rate is above 95.1%. Moreover, the speed of the data processing (1.7 s/sample) proves the suitability of this methodology for online diagnostics.en_UK
dc.identifier.citationTogni S, Nikolaidis T, Sampath S. (2020) A combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines and signal fault isolation. Chinese Journal of Aeronautics, Volume 34, Issue 2, February 2021, pp. 124-135en_UK
dc.identifier.issn1000-9361
dc.identifier.urihttps//doi.org/10.1016/j.cja.2020.04.015
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15705
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDiagnosticsen_UK
dc.subjectData Filteringen_UK
dc.subjectData Analyticsen_UK
dc.subjectKalman filteren_UK
dc.subjectFuzzy logicen_UK
dc.subjectPerformance-Based Diagnostics Artificial Neural Networken_UK
dc.subjectGas Turbineen_UK
dc.titleA combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines and signal fault isolationen_UK
dc.typeArticleen_UK

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