Detection and Classification of Faults in Aeronautical Gas Turbine Engine: a Comparison Between two Fuzzy Logic Systems
| Autor: | Ricardo Tanscheit, Ivan F. M. Menezes, Eduardo Pestana de Aguiar, Renan Piazzaroli Finotti Amaral, Pedro Henrique Souza Calderano, Marley M. B. R. Vellasco, Mateus Gheorghe de Castro Ribeiro |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Fuzzy logic system business.industry Computer science ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Propulsion computer.software_genre Fuzzy logic Data set 020901 industrial engineering & automation Software 0202 electrical engineering electronic engineering information engineering Method of steepest descent 020201 artificial intelligence & image processing Data mining business computer |
| Zdroj: | FUZZ-IEEE |
| DOI: | 10.1109/fuzz-ieee.2018.8491444 |
| Popis: | Gas turbines are the most common engine used in the majority of commercial aircraft. Due to its criticality, to detect and classify faults in a gas turbine is extremely important. In this work, a type-1 and singleton fuzzy logic system trained by steepest descent method is used for detecting and classifying gas turbine faults. The data set was obtained through simulations on the software Propulsion Diagnostic Method Evaluation Strategy created by the National Aeronautics and Space Administration. Results are compared to those obtained with a type-1 fuzzy classifier with rule extraction by Wang and Mendel method. Analysis of results shows the effectiveness of the proposed model. When compared to the Wang and Mendel fuzzy classifier, it requires fewer rules to achieve a better performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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