Detection and Modeling Vibrational Behavior of a Gas Turbine Based on Dynamic Neural Networks Approach

Open access

Abstract

During the gas turbine exploitation the presence of small defects can cause very high vibration amplifications, localized on the components of this rotating machine. For this, a diagnostic process is necessary for decision-making during the monitoring of failures caused by vibration phenomena, which consists in observing the system by comparing its current data with the data coming from a normal operation. These indicators help engineer to determine the symptoms for the failing components of the system. This work deals with problems related to these vibrations, with the aim of developing a system of detection of failures using dynamic neural networks approach. The originality of this contribution is to calculate the various alarms based on this system which used the determined vibration models in order to ensure a reliable and safe operation of the gas compression installation using the examined gas turbine.

[1] A. Zadeh Shirazi, M. Hatami, M. Yaghoobi, S. J. S. M. Chabok. An intelligent approach to predict vibration rate in a real gas turbine. Intelligent Industrial Systems 2016 (2), No. 3, 253 - 267.

[2] A. Hafaifa, M. Guemana, A. Daoudi. Vibration supervision in gas turbine based on parity space approach to increasing efficiency. Journal of Vibration and Control 2015 (21), 1622 - 1632.

[3] S. Madhavan, J. Rajeev, C. Sujatha, A. S. Sekhar. Vibration based damage detection of rotor blades in a gas turbine engine. Engineering Failure Analysis 2014 (46), 26 - 39.

[4] F. Z. Sierra-Espinosa, J. C. García. Vibration failure in admission pipe of a steam turbine due to flow instability. Engineering Failure Analysis 2013 (27), 30 - 40.

[5] V. S. Luk'yanov, G. A. Khodakov. Mathematical model for evaluating the natural vibration frequency of gas-turbine engine disks with small change of web thickness. Strength of Materials 1987 (19), No. 3, 430 - 434.

[6] S. Delvecchio, P. Bonfiglio, F. Pompoli. Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques. Mechanical Systems and Signal Processing 2018 (99), 661 - 683.

[7] Y. Wu, H. Zhang, L. Zuo. Thermoelectric energy harvesting for the gas turbine sensing and monitoring system. Energy Conversion and Management 2018 (157), 215 - 223.

[8] Z. Guo, K. Feng, T. Liu, P. Lyu, T. Zhang. Nonlinear dynamic analysis of rigid rotor supported by gas foil bearings: Effects of gas film and foil structure on subsynchronous vibrations. Mechanical Systems and Signal Processing 2018 (107), 549 - 566.

[9] L. Pesaresi, L. Salles, A. Jones, J. S. Green, C. W. Schwingshackl. Modelling the nonlinear behaviour of an underplatform damper test rig for turbine applications. Mechanical Systems and Signal Processing 2017 (85), 662 - 679.

[10] N. Hadroug, A. Hafaifa, K. Abdellah, A. Chaibet. Dynamic model linearization of two shafts gas turbine via their input / output data around the equilibrium points. Energy 2017 (120), 488 - 497.

[11] M. Tahan, E. Tsoutsanis, M. Muhammad, Z.A. Abdul Karim. Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied Energy 2017 (198), 122 - 144.

[12] M. Amozegar, K. Khorasani. An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines. Neural Networks 2016 (76), 106 -121.

[13] B. Djaidir, A. Hafaifa, K. Abdallah. Faults detection in gas turbine rotor using vibration analysis under varying conditions. Journal of Theoretical and Applied Mechanics 2017 (55), No. 2, 393 - 406.

[14] R. B. Joly, S. O. T. Ogaji, R. Singh, S. D. Probert. Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine. Applied Energy 2004 (78), No. 4, 397 - 418.

[15] P. Kin Wong, Z. Yang, Ch. M. Vong, J. Zhong. Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Neurocomputing 2014 (128), 249 - 257.

[16] S. Sina Tayarani-Bathaie, K. Khorasani. Fault detection and isolation of gas turbine engines using a bank of neural networks. Journal of Process Control 2015 (36), 22-41.

[17] A. V. Kochergin, i. V. Ivshin, A. R. Sabirov, V. A. Gavrilov. Diagnostics of gas turbine engine blades using the forced vibration method. Russian Aeronautics 2007 (50), No. 3, 330 - 332.

[18] M. Šofer, R. Fajkoš, R. Halama. Influence of Induction Hardening on Wear Resistance in Case of Rolling Contact. Journal of Mechanical Engineering - Strojnícky časopis 2016 (66), No. 1, 17 - 26.

[19] A. Benyounes, A. Hafaifa, M. Guemana. Gas turbine modelling based on fuzzy clustering algorithm using experimental data. Journal of Applied Artificial Intelligence 2016 (30), No. 1, 29 - 51.

[20] A. K. Mohapatra, J. Sanjay Arab. Analytical investigation of parameters affecting the performance of cooled gas turbine cycle with evaporative cooling of inlet air. Arabian Journal for Science and Engineering 2013 (38), No. 6, 1587 - 1597.

[21] G. Sanjay Barad, P. V. Ramaiah, R. K. Giridhar, G. Krishnaiah. 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.

[22] J. Jablonská, M. Kozubková, B. Zavadilová, L. Zavadil, S. Fialová. The Investigation of the Cavitation Phenomenon in the Laval Nozzle with Full and Partial Surface Wetting. Journal of Mechanical Engineering - Strojnícky časopis 2017 (67) 67, No. 1, 55 - 68.

[23] Md Sazzad Hossain, Zhi Chao Ong, Zubaidah Ismail, Siamak Noroozi, Shin Yee Khoo. Artificial neural networks for vibration based inverse parametric identifications: A review. Applied Soft Computing 2017 (52), 203 - 219.

[24] Fethi Bellamine, A. Almansoori, A. Elkamel. Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge. Applied Mathematics and Computation 2015 (266), 515 - 526.

[25] E. Swiercz. Classification of parameter changes in a dynamic system with the use of wavelet analysis and neural networks. Advances in Engineering Software 2012 (45), No. 1, 28 - 41.

[26] H. Nikpey, M. Assadi, P. Breuhaus. Development of an optimized artificial neural network model for combined heat and power micro gas turbines. Applied Energy 2013 (108), 137 - 148.

[27] M. Pástor, P. Frankovský, M. Hagara, P. Lengvarský. The use of optical methods in the analysis of the areas with stress concentration. Journal of Mechanical Engineering - Strojnícky časopis 2018 (68), No. 2, 61 - 76.

[28] H. Asgari, X. Chen, M. Morini, M. Pinelli, R. Sainudiin, P. Ruggero Spina, M. Venturini. NARX models for simulation of the start-up operation of a single-shaft gas turbine. Applied Thermal Engineering 2016 (93), 368 - 376.

[29] Z. N. Sadough Vanini, K. Khorasani, N. Meskin. 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.

Journal Information

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 148 148 28
PDF Downloads 96 96 13