ARMAX-based identification and diagnosis of vibration behavior of gas turbine bearings

• Autorzy: Mahroug Youcef , Khaldi Belgacem Said , Guemana Mouloud , Hafaifa Ahmed , Iratni Abdelhamid , Colak Ilhami

Identyfikatory

DOI

10.29354/diag/171277

• Języki publikacji: EN

Abstrakty

EN

Parametric identification approaches play a crucial role in the control and monitoring of industrial systems. They facilitate the identification of system variables and enable the prediction of their evolution based on the input-output relationship. In this study, we employ the ARMAX approach to accurately predict the dynamic vibratory behavior of MS5002B gas turbine bearings. By utilizing real input-output data obtained from their operation, this approach effectively captures the vibration characteristics of the bearings. Additionally, the ARMAX technique serves as a valuable diagnostic tool for the bearings, enhancing the quality of identification of turbine variables. This enables continuous monitoring of the bearings and real-time prediction of their behavior. Furthermore, the ARMAX approach facilitates the detection of all potential vibration patterns that may occur in the bearings, with monitoring thresholds established by the methodology. Consequently, this enhances the availability of the bearings and reduces turbine downtime. The efficacy of the proposed ARMAX approach is demonstrated through comprehensive results obtained in this study. Robustness tests are conducted, comparing the real behavior observed through various probes with the reference model, thereby validating the approach.

Słowa kluczowe

EN

system identification; ARMAX; parametric estimation; gas turbine; vibration modelling

PL

turbina gazowa; modelowanie drgań; estymacja parametryczna; identyfikacja systemu; ARMAX

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 3
• Strony: art. no. 2023310
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Mahroug Youcef

• Laboratory of Mechanics, Physics and Mathematical Modelling, University of Medea, 26000, Medea, Algeria

autor

Khaldi Belgacem Said

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

• https://orcid.org/0000-0002-0534-7711

autor

Guemana Mouloud

• Laboratory of Mechanics, Physics and Mathematical Modelling, University of Medea, 26000, Medea, Algeria
• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Djelfa, Algeria

• https://orcid.org/0000-0003-3126-1441

autor

Hafaifa Ahmed

• Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and Technology, University of Djelfa 17000 DZ, Djelfa, Algeria
• Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, İstanbul Nisantasi University, Sarıyer, 34398 Istanbul, Turkey

• https://orcid.org/0000-0002-7812-7429

autor

Iratni Abdelhamid

• Department of Electrical Engineering, Faculty of Science and Technology, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj, 34030 DZ, El Anasser, Bordj Bou Arreridj, Algeria

• https://orcid.org/0000-0003-3548-7675

autor

Colak Ilhami

• Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, İstanbul Nisantasi University, Sarıyer, 34398 Istanbul, Turkey

• https://orcid.org/0000-0002-6405-5938

Bibliografia

• 1. Benyounes A, Iratni A, Hafaifa A, Colak I. A comparative investigation of modeling and control approaches for gas turbines: fuzzy logic, neural network and ANFIS. International Journal of Smart Grid 2023; 7(2): 89-101. https://doi.org/10.20508/ijsmartgrid.v7i2.288.g275.
• 2. Djeddi AZ, Hafaifa A, Iratni A, Kouzou A. Gas turbine reliability estimation to reduce the risk of failure occurrence with a comparative study between the twoparameter Weibull distribution and a new modified Weibull distribution. Diagnostyka 2022: 23(1): 2022107. http://dx.doi.org/10.29354/diag/146240.
• 3. Djeddi AZ, Hafaifa A, Hadroug N, Iratni A. Gas turbine availability improvement based on long shortterm memory networks using deep learning of their failures data analysis. Process Safety and Environmental Protection Journal 2022; 159: 1-25. https://doi.org/10.1016/j.psep.2021.12.050.
• 4. Djaidir B, Hafaifa A, Guemana M, Kouzou A. Detection of vibrations defects in gas transportation plant based on input / output data analysis: Gas turbine investigations. International Journal of Applied Mechanics and Engineering 2020; 25(4): 42-58. https://doi.org/10.2478/ijame-2020-0048.
• 5. Djeddi C, Hafaifa A, Iratni A, Hadroug N, Chen X. Robust diagnosis with high protection to gas turbine failures identification based on a fuzzy neuro inference monitoring approach. Journal of Manufacturing Systems 2021; 59: 190-213. https://doi.org/10.1016/j.jmsy.2021.02.012.
• 6. Combescure D, Lazarus A. Refined finite element modelling for the vibration analysis of large rotating machines: Application to the gas turbine modular helium reactor power conversion unit. Journal of Sound and Vibration 2008; 318(4–5): 1262-1280. https://doi.org/10.1016/j.jsv.2008.04.025.
• 7. Duan Y, Sun L, Wang G, Wu F. Nonlinear modeling of regenerative cycle micro gas turbine. Energy 2015; 91: 168-175. https://doi.org/10.1016/j.energy.2015.07.134.
• 8. Ju J, Li W, Wang Y, Fan M, Yang X. Dynamics and nonlinear feedback control for torsional vibration bifurcation in main transmission system of scraper conveyor direct driven by high-power PMSM. Nonlinear Dynamics 2018; 93: 307-321. https://doi.org/10.1007/s11071-018-4193-2.
• 9. Wei LY. A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Applied Soft Computing 2016; 42,: 368-376. https://doi.org/10.1016/j.asoc.2016.01.027.
• 10. Lyantsev OD, Breikin TV, Kulikov GG, Arkov VY. Optimal multi-variable control of gas turbine engines. International Journal of Systems Science 2004; 35(2): 79-86. https://doi.org/10.1080/00207720310001657036.
• 11. Madhavan S, Jain R, Sujatha C, Sekhar AS. Vibration based damage detection of rotor blades in a gas turbine engine. Engineering Failure Analysis 2014; 46: 26-39. https://doi.org/10.1016/j.engfailanal.2014.07.021.
• 12. Greidanus MDR, Heldwein ML. Model-Based control strategy to reduce the fault current of a gas turbine synchronous generator under short-circuit in isolated networks. Electric Power Systems Research 2022; 204: 107687. https://doi.org/10.1016/j.epsr.2021.107687.
• 13. Alaoui M, Iratni A, Alshammari OS, Hafaifa A, Colak I, Guemana M. Stability and analysis of vibrations bifurcation based on dynamic modeling of a Solar Titan 130 gas turbine. Journal of Mechanical Engineering 2022; 72(2): 1-14. https://doi.org/10.2478/scjme-2022-0013.
• 14. Alaoui M, Alshammari OS, Iratni A, Hafaifa A, Jerbi H. Gas turbine speed monitoring using a generalized predictive adaptive control algorithm. Studies in Informatics and Control 2022; 31(3): 87-96.
• 15. Rahmoune MB, Iratni A, Hafaifa A, Colak I. Gas turbine vibration detection and identification based on dynamic artificial neural networks. Electrotehnica, Electronica, Automatica (EEA) 2023; 71(2): 19-27. https://doi.org/10.46904/eea.23.71.2.1108003.
• 16. Rahmoune MB, Hafaifa A, Kouzou A, Chen X, Chaibet A. Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling. Mathematics and Computers in Simulation 2021; 179: 23-47. https://doi.org/10.1016/j.matcom.2020.07.017.
• 17. Benrahmoune M, Hafaifa A, Abdellah K, Chen X. Monitoring of high-speed shaft of gas turbine using artificial neural networks: Predictive model application. Diagnostyka 2017; 18(4): 3-10.
• 18. Shabanian M, Montazeri M. A neuro-fuzzy online fault detection and diagnosis algorithm for nonlinear and dynamic systems. International Journal of Control, Automation and Systems 2011; 9: 665-670. https://doi.org/10.1007/s12555-011-0407-9.
• 19. Syed MM, Lemma TA, Vandrangi SK, Ofei TN. Recent developments in model-based fault detection and diagnostics of gas pipelines under transient conditions. Journal of Natural Gas Science and Engineering 2020; 83: 103550. https://doi.org/10.1016/j.jngse.2020.103550.
• 20. Aslam MS, Chaudhary NI, Raja MAZ. A slidingwindow approximation-based fractional adaptive strategy for Hammerstein nonlinear ARMAX systems. Nonlinear Dynamics 2017; 87: 519-533. https://doi.org/10.1007/s11071-016-3058-9.
• 21. Hadroug N, Hafaifa A, Iratni A, Guemana M. Reliability modeling using an adaptive neuro-fuzzy inference system: Gas turbine application. Fuzzy Information and Engineering 2021; 13(2): 154-183. https://doi.org/10.1080/16168658.2021.1915451.
• 22. Hadroug N, Hafaifa A, Alili B, Iratni A, Chen X. Fuzzy diagnostic strategy implementation for gas turbine vibrations faults detection: Towards a characterization of symptom–fault correlations. Journal of Vibration Engineering & Technologies 2022; 10: 225-251. https://doi.org/10.1007/s42417- 021-00373-z.
• 23. Sadough Vanini ZN, Khorasani K, Meskin N. Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach. Information Sciences 2014; 259: 234-251. https://doi.org/10.1016/j.ins.2013.05.032.
• 24. Saeed RA, Galybin AN, Popov V. 3D fluid-structure modelling and vibration analysis for fault diagnosis of Francis turbine using multiple ANN and multiple ANFIS. Mechanical Systems and Signal Processing 2013; 34(1-2): 259-276. https://doi.org/10.1016/j.ymssp.2012.08.004.
• 25. Sanjay Barad G, Ramaiah PV, Giridhar RK, Krishnaiah G, Neural network approach for a combined performance and mechanical health monitoring of a gas turbine engine. Mechanical Systems and Signal Processing 2012; 27: 729-742. https://doi.org/10.1016/j.ymssp.2011.09.011.
• 26. Tadayoshi S. Nonlinear vibration of saturated water journal bearing and bifurcation analysis. Journal of Vibration and Acoustics 2019; 141(2): 021016. https://doi.org/10.1115/1.4042041.
• 27. Aissat S, Hafaifa A, Iratni A, Guemana M, Chen X. Exploitation of multi-models identification with decoupled states in twin shaft gas turbine variables for its diagnosis based on parity space approach. International Journal of Dynamics and Control 2022; 10: 25-48. https://doi.org/10.1007/s40435-021-00804-5.
• 28. Aissat S, Hafaifa A, Iratni A, Hadroug N, Chen X. Fuzzy decoupled-states multi-model identification of gas turbine operating variables through the use their operating data. ISA Transactions 2023; 133: 384-396. https://doi.org/10.1016/j.isatra.2022.07.005.
• 29. Sun W, Yan Z, Tan T, Zhao D, Luo X. Nonlinear characterization of the rotor-bearing system with the oil-film and unbalance forces considering the effect of the oil-film temperature. Nonlinear Dynamics 2018; 92: 1119-1145. https://doi.org/10.1007/s11071-018-4113-5.
• 30. Chiker Y, Bachene M, Attaf B, Hafaifa A, Guemana M. Uncertainly influence of nanofiller dispersibilities on the free vibration behavior of multi-layered functionally graded carbon nanotube-reinforced composite laminated plates. Acta Mechanica 2023; 234: 1687-1711. https://doi.org/10.1007/s00707-022-03438-6.

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).