Faults detection in gas turbine using hybrid adaptive network based fuzzy inference systems

• Autorzy: Hadroug N. , Hafaifa A. , Kouzou A. , Chaibet A.
• Języki publikacji: EN

Abstrakty

EN

The main aim of the present paper is the implementation of a fault detection strategy to ensure the fault detection in a gas turbine which is presenting a complex system. This strategy is based on an adaptive hybrid neuro fuzzy inference technique which combines the advantages of both techniques of neuron networks and fuzzy logic, where, the objective is to maintain the desired performance of the studied gas turbine system in the presence of faults. On the other side, the representation of fuzzy knowledge in the learning neural networks has to be accurate to provide significant improvements for modeling of the studied system dynamic behavior. The results presented in this paper proves clearly that the proposed detection technique allows the perfect detection of the studied gas turbine malfunctions, furthermore it shows that the use of the proposed technique based on the Adaptive Neuro-Fuzzy Interference System (ANFIS) approach which uses the adaptive learning mechanism of neuron networks and fuzzy inference techniques, can be a promising technique to be applied in several industrial application for faults detection.

Słowa kluczowe

EN

faults detection; gas turbine; dynamic behaviour; adaptive network based fuzzy inference systems; ANFIS

PL

detekcja błędów; turbina gazowa; zachowanie dynamiczne; ANFIS

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2016
• Tom: Vol. 17, No. 4
• Strony: 3--17
• Opis fizyczny: Bibliogr. 42 poz., rys., tab.

Twórcy

autor

Hadroug N.

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa 17000 DZ, Algeria

autor

Hafaifa A.

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa 17000 DZ, Algeria

autor

Kouzou A.

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Sciences and Technology, University of Djelfa 17000 DZ, Algeria

autor

Chaibet A.

• Aeronautical Aerospace Automotive Railway Engineering school, ESTACA Paris, France

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