Availability phase estimation in gas turbine based on prognostic system modeling

• Autorzy: Saadat B. , Kouzou A. , Guemana M. , Hafaifa A.
• Języki publikacji: EN

Abstrakty

EN

The present paper deals mainly with the improvement of the degradation indicators of a gas turbine. Therefore, to achieve this purpose a prognostic approach is used in order to provide an adequate diagnostic function of the studied gas turbine. In this context, this paper proposes a degradation modeling of the studied gas turbine system in order to increase its safety and to ensure accurate future decision making process that allow to enhance the operating state of this industrial equipment. Indeed, the prognostic system proposed in this work takes into account the eventual vibration impacts over all phases of the life cycle process of the studied system to provide a diagnostic function with the required availability at with lowest maintenance cost.

Słowa kluczowe

EN

availability time; fatigue prediction; fault prognosis; gas turbine; prognostic system

PL

czas dostępności; zmęczenie materiału; prognoza; prognozowanie uszkodzeń; turbina gazowa; system prognostyczny

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2017
• Tom: Vol. 18, No. 2
• Strony: 3--11
• Opis fizyczny: Bibliogr. 23 poz., rys., wykr.

Twórcy

autor

Saadat B.

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

autor

Kouzou A.

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

autor

Guemana M.

• Faculty of Science and Technology, University of Medea, 26000, Algeria

autor

Hafaifa A.

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

Bibliografia

• 1. Abbas Loghman, Mehdi Moradi. Creep damage and life assessment of thick-walled spherical reactor using Larson–Miller parameter. International Journal of Pressure Vessels and Piping, 2017;151:11-19.
• 2. Banjevic D. Remaining useful life in theory and practice. Metrika, 2009; 69(2-3):337-49.
• 3. Bernard Tirbonod. A fixed point in the Coffin- Manson law. International Journal of Fatigue. 2015;81:143-147.
• 4. Bindi Chen, Peter C. Matthews, Peter J. Tavner. Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS. Expert Systems with Applications, 2013; 40(17):6863-6876.
• 5. Byington CS, Roemer MJ, Galie T. Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft]. Proceedings of IEEE Aerospace Conference 2002; 6: 2815-2824.
• 6. Chunhui Z, Furong G. Online fault prognosis with relative deviation analysis and vector autoregressive modeling. Chemical Engineering Science, 2015;138:531-543.
• 7. David Tam. A Theoretical analysis of Cumulative Sum Slope (CUSUM-Slope) statistic for detecting signal onset (begin) and offset (end) trends from background noise level. The Open Statistics and Probability Journal, 2009;1:43-51.
• 8. Dezhi Li, Wilson Wang, Fathy Ismail. Enhanced fuzzy-filtered neural networks for material fatigue prognosis. Applied Soft Computing, 2013;13:283-291.
• 9. Dong Wan Shin, Eunju Hwang. A CUSUM test for panel mean change detection. Journal of the Korean Statistical Society, 2017;46(1):70-77.
• 10. 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): 504-510.
• 11. Fernando Cortez Sica, Frederico Gadelha Guimarães, Ricardo de Oliveira Duarte, Agnaldo JR. Reis. A cognitive system for fault prognosis in power transformers. Electric Power Systems Research, 2015;127:109-117.
• 12. Gwo-Chung Tsai. Rotating vibration behavior of the turbine blades with different groups of blades. Journal of Sound and Vibration 2004;271(3-5 and 6): 547-575.
• 13. Skima H, Medjaher K, Varnier C, Dedu E, Bourgeois J. A hybrid prognostics approach for MEMS: From real measurements to remaining useful life estimation. Microelectronics Reliability, 2016;65:79- 88.
• 14. Hadroug Nadji, Hafaifa Ahmed, Kouzou Abdellah, Chaibet Ahmed. Faults detection in gas turbine using hybrid adaptive network based fuzzy inference systems. Diagnostyka 2016;17(4):3-17.
• 15. Hector Sanchez, Teresa Escobet, Vicenç Puig, Peter Fogh Odgaard. Health-aware model predictive control of wind turbines using fatigue prognosis. IFAC-PapersOnLine, 2015;48(21):1363-1368.
• 16. Khanh Le Son, Mitra Fouladirad, Anne Barros. Remaining useful lifetime estimation and noisy gamma deterioration process. Reliability Engineering & System Safety, 2016;149:76-87.
• 17. Nguyen DN, Dieulle L, Grall A. Remaining useful lifetime prognosis of controlled systems: a case of stochastically deteriorating actuator. Mathematical Problems in Engineering, 2015;1:1-16.
• 18. Palmgren A. The service life of ball bearings. Zeitschrift des Vereines Deutscher Ingenieure, 1994; 68(14):339-341.
• 19. Philippe Castagliola, Petros E. Maravelakis. A CUSUM control chart for monitoring the variance when parameters are estimated. Journal of Statistical Planning and Inference, 2011;141(4):1463-1478.
• 20. Qi Li, Zhanbao Gao, Diyin Tang, Baoan Li. Remaining useful life estimation for deteriorating systems with time-varying operational conditions and condition-specific failure zones. Chinese Journal of Aeronautics, 2016; 29(3):662-674.
• 21. Yingyu Wang, Luca Susmel. The Modified Manson- Coffin Curve Method to estimate fatigue lifetime under complex constant and variable amplitude multiaxial fatigue loading. International Journal of Fatigue, 2016; 83(Part 2):135-149.
• 22. Zeyi Huang, Zhengguo Xu, Xiaojie Ke, Wenhai Wang, Youxian Sun. Remaining useful life prediction for an adaptive skew-Wiener process model. Mechanical Systems and Signal Processing, 2017;87(A):294-306.
• 23. Zhou C, Chen Z, Lee JW, Lee MG, Wagoner RH. Implementation and application of a temperaturedependent Chaboche model. International Journal of Plasticity, 2015; 75:121-140.