Gas turbine vibration monitoring based on real data using neuro-fuzzy system
1
Faculty of Science and Technology, University of Djelfa 17000 DZ, Algeria.
2
Department of Computer, Automation and Management Engineering, Sapienza University of Rome, Italy.
3
Institute for Systems Analysis and Computer Science, Italian National Research Council, Italy.
4
Department of Computational Intelligence, Czestochowa University of Technology, Czestochowa, Poland.
5
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.
Department of Computer, Automation and Management Engineering, Sapienza University of Rome. Italy.
Diagnostyka 2024;25(1):2024108
KEYWORDS
TOPICS
ABSTRACT
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.
ACKNOWLEDGEMENTS
This work has been developed at is.Lab() Intelligent Systems Laboratory at the Department of Computer, Control, and Management Engineering, Sapienza University of Rome (https://islab.diag.uniroma1.it).
FUNDING
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.
REFERENCES (24)
1.
El-Shazly AA, Elhelw M, Sorour MM, El-Maghlany WM. Gas turbine performance enhancement via utilizing different integrated turbine inlet cooling techniques. Alexandria Engineering Journal 2016; 55(3): 1903–14. https://doi.org/10.1016/j.aej.....
2.
Pourbabaee B, Meskin N, Khorasani K. sensor fault detection, isolation, and identification using multiple-model-based hybrid Kalman filter for gas turbine engines. IEEE Transactions on Control Systems Technology 2016; 24(4): 1184–200. https://doi.org/10.1109/TCST.2....
3.
Sina Tayarani-Bathaie S, Khorasani K. Fault detection and isolation of gas turbine engines using a bank of neural networks. Journal of Process Control 2015; 36: 22–41. https://doi.org/10.1016/j.jpro....
4.
Tabkhi F, Pibouleau L, Hernandez-Rodriguez G, Azzaro-Pantel C, Domenech S. Improving the performance of natural gas pipeline networks fuel consumption minimization problems. AIChE Journal 2010; 56(4): 946–64. https://doi.org/10.1002/aic.12....
5.
Grange B, Dalet C, Falcoz Q, Ferrière A, Flamant G. Impact of thermal energy storage integration on the performance of a hybrid solar gas-turbine power plant. Applied Thermal Engineering 2016; 105: 266–75. https://doi.org/10.1016/j.appl....
6.
Dong X, Axinte D, Palmer D, Cobos S, Raffles M, Rabani A, i in. Development of a slender continuum robotic system for on-wing inspection/repair of gas turbine engines. Robotics and Computer-Integrated Manufacturing 2017; 44: 218–29. https://doi.org/10.1016/j.rcim....
7.
Lu F, Ju H, Huang J. An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring. Aerospace Science and Technology 2016; 58: 36–47. https://doi.org/10.1016/j.ast.....
8.
Igor O, Lyubomyr P, Vasyl Z, Andrii H, Liubov P, Andrij S, et.al. Impact of Long-Term Operation on the Reliability and Durability of Transit Gas Pipelines. Strojnícky časopis - Journal of Mechanical Engineering 2020; 70(1): 115–26.
9.
Ewins DJ. Control of vibration and resonance in aero engines and rotating machinery – An overview. International Journal of Pressure Vessels and Piping 2010; 87(9): 504–10. https://doi.org/10.1016/j.ijpv....
10.
Zhou X, Lu F, Zhou W, Huang J. An improved multivariable generalized predictive control algorithm for direct performance control of gas turbine engine. Aerospace Science and Technology 2020; 99: 105576. https://doi.org/10.1016/j.ast.....
11.
Bornassi S, Ghalandari M, Maghrebi SF. Blade synchronous vibration measurements of a new upgraded heavy duty gas turbine MGT-70(3) by using tip-timing method. Mechanics Research Communications 2020; 104: 103484. https://doi.org/10.1016/j.mech....
12.
Li J, Ying Y. Gas turbine gas path diagnosis under transient operating conditions: A steady state performance model based local optimization approach. Applied Thermal Engineering 2020; 170: 115025. https://doi.org/10.1016/j.appl....
13.
Yazdani S, Montazeri-Gh M. A novel gas turbine fault detection and identification strategy based on hybrid dimensionality reduction and uncertain rule-based fuzzy logic. Computers in Industry 2020; 115: 103131. https://doi.org/10.1016/j.comp....
14.
Agh SM, Pirkandi J, Mahmoodi M, Jahromi M. Development of a novel rotary flow control valve with an electronic actuator and a pressure compensator valve for a gas turbine engine fuel control system. Flow Measurement and Instrumentation 2020; 74: 101759. https://doi.org/10.1016/j.flow....
15.
Nail B, Bekhiti B, Puig V. Internal stability improvement of a natural gas centrifugal compressor system based on a new optimal output feedback controller using block transformation and grey wolf optimizer. Journal of Natural Gas Science and Engineering 2021; 85: 103697. https://doi.org/10.1016/j.jngs....
16.
Ishak KEHK, Ayoub MA, Predicting the Efficiency of the Oil Removal From Surfactant and Polymer Produced Water by Using Liquid–Liquid Hydrocyclone: Comparison of Prediction Abilities Between Response Surface Methodology and Adaptive Neuro-Fuzzy Inference System. IEEE Access 2019; 7: 179605-179619. https://doi.org/10.1109/ACCESS....
17.
Heo SJ, Chunwei Z, Yu E. Response Simulation, Data Cleansing and Restoration of Dynamic and Static Measurements Based on Deep Learning Algorithms. International Journal of Concrete Structures and Materials 2018; 82.
18.
Batrakov DO, Batrakova AG, Antyufeyeva MS. Combined GPR data analysis technique for diagnostics of structures with thin near-surface layers. Diagnostyka 2018; 19(3): 11-20. https://doi.org/10.29354/diag/....
19.
Wayan S, Kemal MA. Modeling of Tropospheric Delays Using ANFIS. Springer Cham.; 1(16): 109. https://doi.org/10.1007/978-3-....
20.
Bulnes F. Retracted: A Modern Review of Wavelet Transform in Its Spectral Analysis. Recent Advances in Wavelet Transforms and Their Applications. 2022. https://doi.org/10.5772/intech....
21.
Heo SJ, Chunwei Z, Yu E. Response Simulation, Data Cleansing and Restoration of Dynamic and Static Measurements Based on Deep Learning Algorithms. International Journal of Concrete Structures and Materials 2018; 12(1): 82. https://doi.org/10.1186/s40069....
22.
Lal M. Modeling and estimation of speed dependent bearing and coupling misalignment faults in a turbine generator system. Mech Syst Sig Process 2021; 151: 107365. https://doi.org/10.1016/j.ymss....
23.
Gazzar DME. An integrated study for solving high vibration problem of a deep well turbine pump. Diagnostyka 2023; 24(2): 1–7. https://doi.org/10.29354/diag/....
24.
Bouaouiche K, Menasria Y, Khalfa D. Detection of defects in a bearing by analysis of vibration signals. Diagnostyka 2023; 24(2): 1–7. https://doi.org/10.29354/diag/....
Share
RELATED ARTICLE
| eISSN: | 2449-5220 |
„Podniesienie poziomu naukowego i poziomu umiędzynarodowienia czasopisma Diagnostyka oraz upowszechniania informacji o wynikach badań lub prac rozwojowych poprzez udział uznanych zagranicznych recenzentów w ocenie publikacji - zadanie finansowane w ramach umowy 745/P-DUN/2017. ze środków Ministra Nauki i Szkolnictwa Wyższego przeznaczonych na działalność upowszechniającą naukę”
© 2006-2024 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
