Gas turbine vibration monitoring based on real data and neuro-fuzzy system

• Autorzy: Nail Bachir , Djaidir Benrabeh , Tibermacine Imad Eddine , Napoli Christian , Haidour Nabil , Abdelaziz Rabehi

Identyfikatory

DOI

10.29354/diag/181190

• Języki publikacji: EN

Abstrakty

EN

The gas turbine is considered to be a very complex piece of machinery because of both its static structure and the dynamic behavior that results from the occurrence of vibration phenomena. It is required to adopt monitoring and diagnostic procedures for the identification and localization of vibration flaws in order to ensure the appropriate operation of large rotating equipment such as gas turbines. This is necessary in order to avoid catastrophic failures and deterioration and to ensure that proper operation occurs. Utilizing an approach that is based on spectrum analysis, the purpose of this study is to provide a model for the monitoring and diagnosis of vibrations in a GE MS3002 gas turbine and its driven centrifugal compressor. This will be done by utilizing the technique. Following that, the collection of vibration measurements for a model of the centrifugal compressor served as a suggestion for an additional method. This method is based on the neuro-fuzzy approach type ANFIS, and it aims to create an equivalent system that is able to make decisions without consulting a human being for the purpose of detecting vibratory defects. In spite of the fact that the compressor that was investigated has flaws, this procedure produced satisfactory results.

Słowa kluczowe

EN

vibration analysis; gas turbine; centrifugal compressor; neuro-fuzzy system; ANFIS

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2024
• Tom: Vol. 25, No. 1
• Strony: art. no. 2024108
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Nail Bachir

• Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria

• https://orcid.org/0000-0002-2542-8391

autor

Djaidir Benrabeh

• Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria

• https://orcid.org/0000-0002-5835-7817

autor

Tibermacine Imad Eddine

• Department of Computer, Automation and Management Engineering, Sapienza University of Rome, Italy

• https://orcid.org/0009-0004-4729-7128

autor

Napoli Christian

• Department of Computer, Automation and Management Engineering, Sapienza University of Rome, Italy
• Institute for Systems Analysis and Computer Science, Italian National Research Council, Italy
• Department of Computational Intelligence, Czestochowa University of Technology, Czestochowa, Poland

• https://orcid.org/0000-0002-5775-2276

autor

Haidour Nabil

• L’École Supérieure de Physique et de Chimie Industrielles, Paris, France

• https://orcid.org/0009-0004-5406-0604

autor

Abdelaziz Rabehi

• Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria

• https://orcid.org/0000-0001-8684-4754

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).