Gas turbine vibration monitoring based on real data using neuro-fuzzy system
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Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria.
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.
L’École Supérieure de Physique et de Chimie Industrielles, Paris, France.
These authors had equal contribution to this work
Submission date: 2023-06-02
Final revision date: 2023-12-20
Acceptance date: 2024-01-15
Online publication date: 2024-01-21
Publication date: 2024-01-21
Corresponding author
Imad Eddine Tibermacne   

Department of Computer, Automation and Management Engineering, Sapienza University of Rome. Italy.
Diagnostyka 2024;25(1):2024108
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 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.
This work has been developed at is.Lab() Intelligent Systems Laboratory at the Department of Computer, Control, and Management Engineering, Sapienza University of Rome (
This paper has been partially supported by the Age-It: Ageing Well in an ageing society 219 project, task 9.4.1 work package 4 spoke 9, within topic 8 extended partnership 8, under the National 220 Recovery and Resilience Plan (PNRR), Mission 4 Component 2 Investment 1.3 - Call for tender No. 221 1557 of 11/10/2022 of Italian Ministry of University and Research funded by the European Union - 222 NextGenerationEU, CUP B53C22004090006.
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