Investigation of intelligent classification of current technical condition of the gas turbine engine

The structure of diagnosing the technical condition of the gas turbine engine (GTE) is given. It is proposed a method of training the intellectual automated system of diagnostics and control reconfiguration (IASDCR) by GTE modes based on integration of fuzzy logic and neural networks. The theoretica...

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Main Author: Микола Петрович Кравчук
Format: Article
Language:English
Published: PC Technology Center 2015-01-01
Series:Tehnologìčnij Audit ta Rezervi Virobnictva
Subjects:
Online Access:http://journals.uran.ua/tarp/article/view/38073
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spelling doaj-69b5e7fa9383402e89f4b4b2368e055a2020-11-25T02:03:09ZengPC Technology CenterTehnologìčnij Audit ta Rezervi Virobnictva2226-37802312-83722015-01-0113(21)535710.15587/2312-8372.2015.3807338073Investigation of intelligent classification of current technical condition of the gas turbine engineМикола Петрович Кравчук0National Aviation University, Komarova Ave, 1, Kyiv, Ukraine, 03058The structure of diagnosing the technical condition of the gas turbine engine (GTE) is given. It is proposed a method of training the intellectual automated system of diagnostics and control reconfiguration (IASDCR) by GTE modes based on integration of fuzzy logic and neural networks. The theoretical and experimental capabilities of IASDCR classification of current condition of GTE in specific operational situations are proposed. It is designed and synthesized the structure of IASDCR GTE, based on the proposed model. The method provides the ability to customize such systems for the diagnosis and management of different types of GTE reconfiguration during their operation, thereby increasing the reliability of classification and prediction of residual life, and prevents the transition of emergency situation in catastrophic situation. The expediency of using hybrid IASDCR based on radial basis networks and fuzzy logic theory, which allowed to classify the vibrational state GTE DR-59L with a probability of 0,96 and GTE DT-71P with a probability of 0,92.http://journals.uran.ua/tarp/article/view/38073gas turbine engineintelligent systemcontrol reconfigurationdiagnostic systemtechnical condition
collection DOAJ
language English
format Article
sources DOAJ
author Микола Петрович Кравчук
spellingShingle Микола Петрович Кравчук
Investigation of intelligent classification of current technical condition of the gas turbine engine
Tehnologìčnij Audit ta Rezervi Virobnictva
gas turbine engine
intelligent system
control reconfiguration
diagnostic system
technical condition
author_facet Микола Петрович Кравчук
author_sort Микола Петрович Кравчук
title Investigation of intelligent classification of current technical condition of the gas turbine engine
title_short Investigation of intelligent classification of current technical condition of the gas turbine engine
title_full Investigation of intelligent classification of current technical condition of the gas turbine engine
title_fullStr Investigation of intelligent classification of current technical condition of the gas turbine engine
title_full_unstemmed Investigation of intelligent classification of current technical condition of the gas turbine engine
title_sort investigation of intelligent classification of current technical condition of the gas turbine engine
publisher PC Technology Center
series Tehnologìčnij Audit ta Rezervi Virobnictva
issn 2226-3780
2312-8372
publishDate 2015-01-01
description The structure of diagnosing the technical condition of the gas turbine engine (GTE) is given. It is proposed a method of training the intellectual automated system of diagnostics and control reconfiguration (IASDCR) by GTE modes based on integration of fuzzy logic and neural networks. The theoretical and experimental capabilities of IASDCR classification of current condition of GTE in specific operational situations are proposed. It is designed and synthesized the structure of IASDCR GTE, based on the proposed model. The method provides the ability to customize such systems for the diagnosis and management of different types of GTE reconfiguration during their operation, thereby increasing the reliability of classification and prediction of residual life, and prevents the transition of emergency situation in catastrophic situation. The expediency of using hybrid IASDCR based on radial basis networks and fuzzy logic theory, which allowed to classify the vibrational state GTE DR-59L with a probability of 0,96 and GTE DT-71P with a probability of 0,92.
topic gas turbine engine
intelligent system
control reconfiguration
diagnostic system
technical condition
url http://journals.uran.ua/tarp/article/view/38073
work_keys_str_mv AT mikolapetrovičkravčuk investigationofintelligentclassificationofcurrenttechnicalconditionofthegasturbineengine
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