Abstract
Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules. Because the health parameters are unmeasurable, researchers estimate them only based on the available measurement parameters. Kalman filter-based approaches are the most commonly used estimation approaches; however, the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty, and their ability to track the mutation condition is influenced by historical data. Therefore, in this paper, an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter (STEKF) approach is proposed to estimate the gas turbine health parameters. The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches. The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.
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References
Borguet S, Léonard O (2010) Comparison of adaptive filters for gas turbine performance monitoring. Journal of Computational & Applied Mathematics 234(7):2202–2212. https://doi.org/10.1016/j.cam.2009.08.075
Brotherton T, Volponi A, Luppold R, Simon DL (2003) eSTORM: enhanced self-tuning on-board real-time engine model. In: Proceedings of the 2003 IEEE aerospace conference, Big Sky, 3075–3086. https://doi.org/10.1109/AERO.2003.1234150
Camporeale SM, Fortunato B, Mastrovito M (2006) A modular code for real time dynamic simulation of gas turbines in Simulink. J Eng Gas Turbines Power 128(3):506–517. https://doi.org/10.1115/1.2132383
Chang X, Huang J, Lu F, Sun H (2016) Gas-path health estimation for an aircraft engine based on a sliding mode observer. Energies 9(8):598. https://doi.org/10.3390/en9080598
Johansen TA, Hunt KJ, Gawthrop PJ, Fritz H (1998) Off-equilibrium linearisation and design of gain-scheduled control with application to vehicle speed control. Control Eng Pract 6(2):167–180. https://doi.org/10.1016/S0967-0661(98)00015-X
Kerr LJ, Nemec TS, Gallops GW (1992) Real-time estimation of gas turbine engine damage using a control-based Kalman filter algorithm. J Eng Gas Turbines Power 114(2):187–195. https://doi.org/10.1115/1.2906571
Klapproth J, Miller M, Parker D (1979) Aerodynamic development and performance of CF6-6/LM2500 compressor. In: Proceedings of 4th international symposium on air breathing engines, Orlando, 243–249. https://doi.org/10.2514/6.1979-7030
Kobayashi T, Simon DL, Litt JS (2005) Application of a constant gain extended Kalman filter for in-flight estimation of aircraft engine performance parameters. In: Proceedings of ASME Turbo expo 2005: power for land, sea, and air, Reno, 617–628. https://doi.org/10.1115/GT2005-68494
Lambert HH (1991) A simulation study of turbofan engine deterioration estimation using Kalman filtering techniques. NASA Technical Memorandum 104233
Li YG (2010) Gas turbine performance and health status estimation using adaptive gas path analysis. J Eng Gas Turbines Power 132(4):109–121. https://doi.org/10.1115/1.3159378
Li YG, Korakiantis T (2012) Nonlinear weighted least squares estimation approach for gas-turbine diagnostic applications. Journal of Propulsion & Power 27(2):337–345. https://doi.org/10.2514/1.47129
Li YG, Pilidis P (2010) Ga-based design-point performance adaptation and its comparison with icm-based approach. Appl Energy 87(1):340–348. https://doi.org/10.1016/j.apenergy.2009.05.034
Lu F, Huang J, Lv Y (2013) Gas path health monitoring for a turbofan engine based on a nonlinear filtering approach. Energies 6(1):492–513. https://doi.org/10.3390/en6010492
Luppold RH, Roman JR, Gallops GW (1989) Estimating in-flight engine performance variations using Kalman filter concepts. Proceeding of the 25th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, Monterey, 1–10. https://doi.org/10.2514/6.1989-2584
Pu X, Liu S, Jiang H, Yu D (2013) Adaptive gas path diagnostics using strong tracking filter. Proc IMechE, Part G: Journal of Aerospace Engineering 228(4):577–585. https://doi.org/10.1177/0954410013478514
Rahme S, Meskin N (2015) Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine. Control Eng Pract 38:57–74. https://doi.org/10.1016/j.conengprac.2015.01.006
Simon D (2006) H∞ filtering with inequality constraints for aircraft turbofan engine health estimation. In: Proceedings of the 45th IEEE conference on decision and control, San Diego, CA, 3291–3296. https://doi.org/10.1109/CDC.2006.376880
Simon D (2008) A comparison of filtering approaches for aircraft engine health estimation. Aerosp Sci Technol 12(2):276–284. https://doi.org/10.1016/j.ast.2007.06.002
Simon DL, Garg S (2010) Optimal tuner selection for Kalman filter-based aircraft engine performance estimation. J Eng Gas Turbines Power 132(3):659–671. https://doi.org/10.1115/GT2009-59684
Simon D, Simon DL (2005) Aircraft turbofan engine health estimation using constrained Kalman filtering. J Eng Gas Turbines Power 127(2):323–328. https://doi.org/10.1115/1.1789153
Simon D, Simon DL (2006) Kalman filter constraint switching for turbofan engine health estimation. Eur J Control 12(3):331–343. https://doi.org/10.3166/ejc.12.341-343
Simon D, Simon DL (2010) Constrained Kalman filtering via density function truncation for turbofan engine health estimation. Int J Syst Sci 41(2):159–171. https://doi.org/10.1080/00207720903042970
Tsoutsanis E, Meskin N, Benammar M, Khorasani K (2013) Dynamic performance simulation of an aeroderivative gas turbine using the Matlab Simulink environment. In: Proceedings of ASME 2013 international mechanical engineering congress and exposition, San Diego, CA, 1–10. https://doi.org/10.1115/IMECE2013-64102
Volponi AJ (2013) Gas turbine engine health management: past, present and future trends. J Eng Gas Turbines Power 58(136):433–455. https://doi.org/10.1115/1.4026126
Volponi A, DePold H, Ganguli R, Daguang C (2003) The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study. J Eng Gas Turbines Power 125(4):917–924. https://doi.org/10.1115/2000-GT-0547
Yang X, Shen W, Pang S, Li B, Jiang K, Wang Y (2014) A novel gas turbine engine health status estimation approach using quantum behaved particle swarm optimization. Math Probl Eng 2014(3):1–11. https://doi.org/10.1155/2014/302514
Yang Q, Li S, Cao Y (2017) A new component map generation method for gas turbine adaptation performance simulation. Journal of Mechanical Science & Technology 31(4):1947–1957. https://doi.org/10.1007/s12206-017-0344-5
Zhou DH, Frank PM (1996) Strong tracking Kalman filtering of nonlinear time-varying stochastic systems with coloured noise: application to parameter estimation and empirical robustness analysis. Int J Control 65(2):295–307. https://doi.org/10.1080/00207179608921698
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Article Highlights
• An improved strong tracking extended Kalman filter approach is proposed to estimate marine gas turbine health parameters in uncertainty and mutation condition.
• The analytical expression of Jacobian matrix is deduced by non-equilibrium point analytical linearization.
• The proposed method is compared with the extended Kalman filter (EKF) and the unscented Kalman filter (UKF).
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Yang, Q., Li, S. & Cao, Y. A Strong Tracking Filtering Approach for Health Estimation of Marine Gas Turbine Engine. J. Marine. Sci. Appl. 18, 542–553 (2019). https://doi.org/10.1007/s11804-019-00103-8
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DOI: https://doi.org/10.1007/s11804-019-00103-8