Abstract
This paper addresses sensors’ analytical redundancy by a decision-level data fusion approach to improve the aero-engine control system reliability in the cases of impulsive disturbance and performance degradation. To bridge this gap, we propose a data-driven and model-based decision-level fusion framework to provide analytical redundancies of aero-engine sensors in dynamic behavior. On the one hand, a meta-learning model of the online sequential extreme learning machine (OSELM) algorithm is developed to support data-driven redundancy, whose architecture consists of several ELM units and one supervisor OSELM. The flight data are used to train the ELMs offline and then adjust the OSELM weights online, so the data information can be fully captured to increase data-driven regression accuracy. On the other hand, the decision integration strategy is designed and formulated as a convex optimization problem rather than a multivariate linear regression based on individual decisions from data-driven and model-based calculations. Although the model’s inputs are intentionally perturbing, the model-based decision can improve the fused signal’s dependability. For the data-driven method, several ELM units in meta-OSELM can alleviate the impact of impulsive noise and reflect sensitive signal changes without a physical model. We aim to provide an efficient analytical redundancy of the aero-engine sensors in the flight envelope. Simulations on benchmark databases and aircraft engine flight data show that the meta-OSELM yields better generalization performance and stability than the OSELM and the ensemble of OSELM. Furthermore, the analytical redundancy of engine sensors provided by the proposed decision-level fusion strategy performs superior in prediction accuracy compared to data-driven or model-based at typical operation points in the flight envelope.
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References
Burston, M., Ranasinghe, K., Sabatini, R.M.: Design principles and digital control of advanced distributed propulsion systems. Energy 241, 122788 (2021)
Gou, L.F., Shen, Y.W., Zheng, H., et al.: Multi-fault diagnosis of an aero-engine control system using joint sliding mode observers. IEEE Access 8, 10186–10197 (2020)
Zhu, M.Y., Wang, X., Pei, X.T., et al.: Modified robust optimal adaptive control for flight environment simulation system with heat transfer uncertainty. Chin. J. Aeronaut. 34(2), 420–331 (2021)
Bertin, M., Plummer, A., Bowen, C., et al.: A dual lane piezoelectric ring bender actuated nozzle-flapper servo valve for aero engine fuel metering. Smart Mater. Struct. 28(11), 115015 (2019)
Amin, A.A., Mahmood-Ul-Hasan, K.: Robust active fault-tolerant control for internal combustion gas engine for air–fuel ratio control with statistical regression-based observer model. Meas. Control 52(9–10), 1179–1194 (2019)
Qiu, X.J., Chang, X.D., Chen, J., et al.: Research on the analytical redundancy method for the control system of variable cycle engine. Sustainability 14(10), 5905 (2022)
Liu, T.J., Du, X., Sun, X.M., et al.: Robust tracking control of aero-engine rotor speed based on switched LPV model. Aerosp. Sci. Technol. 91, 382–390 (2019)
Yuan, Y., Liu, X.F., Ding, S.T., et al.: Fault detection and location system for diagnosis of multiple faults in aeroengines. IEEE Access 5, 17671–17677 (2017)
Lu, F., Jin, P., Huang, J.Q., et al.: Aircraft engine hot-section virtual sensor creation and gas path performance monitoring. Proc. Inst. Mech. Eng., Part G J. Aeros. Eng. 236(5), 879–899 (2022)
De Ceil, R., Cadarso, L.: GNSS/IMU laser quadrant detector hybridization techniques for artillery rocket guidance. Nonlinear Dyn. 91(4), 2683–2698 (2018)
Lu, S.W., Zhou, W.X., Huang, J.Q., et al.: A novel performance adaptation and diagnostic method for aero-engines based on the aerothermodynamic inverse model. Aerospace 9(1), 16 (2022)
Zhou, D.J., Huang, D.W.: Stochastic response analysis and robust optimization of nonlinear turbofan engine system. Nonlinear Dyn. 110(3), 2225–2245 (2022)
Zhou, X., Huang, J.Q., Lu, F.: HNN-based generalized predictive control for turbofan engine direct performance optimization. Aerosp. Sci. Technol. 112, 106602 (2021)
Tayarani-Bathaie, S.S., Khorasani, K.: Fault detection and isolation of gas turbine engines using a bank of neural networks. J. Process. Control 36, 22–41 (2015)
Lu, J.J., Huang, J.Q., Lu, F.: Kernel extreme learning machine with iterative picking scheme for failure diagnosis of a turbofan engine. Aerosp. Sci. Technol. 96, 105539 (2020)
Nyulaszi, L., Andoga, R., Butka, P., et al.: Fault detection and isolation of an aircraft turbojet engine using a multi-sensor network and multiple model approach. Acta Polytech. Hung. 15(2), 189–209 (2018)
Huang, G.B., Zhou, H., Ding, X., et al.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. B 42(2), 513–529 (2012)
Termenon, M., Grana, M., Barros-Loscertales, A., et al.: Extreme learning machines for feature selection and classification of cocaine dependent patients on structural MRI data. Neural Process. Lett. 38(3), 375–387 (2013)
Lu, J.J., Huang, J.Q., Lu, F.: Time series prediction based on adaptive weight online sequential extreme learning machine. Appl. Sci. 7(3), 217 (2017)
Liang, N.Y., Huang, G.B., Saratchandran, P., et al.: Fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17(6), 1411–1423 (2006)
Lu, X.J., Zhou, C., Huang, M.H., et al.: Regularized online sequential extreme learning machine with adaptive regulation factor for time-varying nonlinear system. Neurocomputing 174, 617–626 (2016)
Ganguli, R.: Jet engine gas-path measurement filtering using center weighted idempotent median filters. J. Propuls. Power 19(5), 930–937 (2003)
Zhao, H., Liao, Z.B., Liu, J.X., et al.: A highly robust thrust estimation method with dissimilar redundancy framework for gas turbine engine. Energy 245, 123255 (2022)
Wang, H.B., Wang, Y., Hu, Q.H.: Self-adaptive robust nonlinear regression for unknown noise via mixture of Gaussians. Neurocomputing 235, 274–286 (2017)
Lan, Y., Soh, Y.C., Huang, G.B.: Ensemble of online sequential extreme learning machine. Neurocomputing 72(13), 3391–3395 (2009)
Zou, W.D., Yao, F.X., Zhang, B.H., et al.: Improved meta-ELM with error feedback incremental ELM as hidden nodes. Neural Comput. Appl. 30, 3363–3370 (2018)
Liu, Y., Chen, Q., Liu, S.Y., et al.: Intelligent fault-tolerant control system design and semi-physical simulation validation of aero-engine. IEEE Access 8, 217204–217212 (2020)
Lu, F., Wu, J.D., Huang, J.Q., et al.: Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm. Aerosp. Sci. Technol. 84, 661–671 (2019)
Fang, L.Y., Li, T.S., Li, Z.F., et al.: Adaptive terminal sliding mode control for anti-synchronization of uncertain chaotic systems. Nonlinear Dyn. 74, 991–1002 (2013)
Li, Y.F., Chang, J.T., Kong, C., et al.: Recent progress of machine learning in flow modeling and active flow control. Chin. J. Aeronaut. 35(4), 14–44 (2022)
Liu, X.L., Xiao, J.W., Chen, D.X., et al.: Dynamic consensus of nonlinear time-delay multi-agent systems with input saturation: an impulsive control algorithm. Nonlinear Dyn. 97(2), 1699–1710 (2019)
Zhou, D.J., Jia, X.Y., Ma, S.X., et al.: Dynamic simulation of natural gas pipeline network based on interpretable machine learning model. Energy 253, 124068 (2022)
Tidriri, K.: Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges. Annu. Rev. Control 42, 63–81 (2016)
Huang, Y.F., Sun, G., Tao, J., et al.: A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine. Meas. Sci. Technol. 33, 085105 (2022)
Duyar, A., Merrill, W.: Fault diagnosis for the space shuttle main engine. J. Guid. Control Dyn. 15(2), 384–389 (1992)
Guo, H.N., Li, Y.G., Liu, C.Q., et al.: A deformation force monitoring method for aero-engine casing machining based on deep autoregressive network and Kalman filter. Appl. Sci. 12, 7014 (2022)
Peng, C.C., Chen, Y.H.: Digital twins-based online monitoring of TFE-731 turbofan engine using fast orthogonal search. IEEE Syst. J. 16(2), 3060–3071 (2022)
Tidriri, K., Tiplica, T., Chatti, N., et al.: A generic framework for decision fusion in fault detection and diagnosis. Eng. Appl. Artif. Intell. 71, 73–86 (2018)
Chen, Q.J., Huang, J.Q., Pan, M.X., et al.: A novel real-time mechanism modeling approach for turbofan engine. Energies 12(19), 1–18 (2019)
Zhou, D.J., Zhang, H.S., Weng, S.L.: A novel prognostic model of performance degradation trend for power machinery maintenance. Energy 78, 740–746 (2014)
Jin, P., Lu, F., Huang, J.Q., et al.: Life cycle gas path performance monitoring with control loop parameters uncertainty for aeroengine. Aerosp. Sci. Technol. 115, 106775 (2021)
Jonathan A., Jonathan S., Dean K.: A modular aero-propulsion system simulation of a large commercial aircraft engine. NASA/TM-2008, 215303, pp. 1–6 (2008)
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Funding was provided by National Natural Science Foundation of China (Grant Number 91960110), National Major Science and Technology Projects of China (Grant Number 2017-I-0006-0007), Fundamental Research Funds for the Central Universities (Grant Number 2022418).
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Jin, P., Zhou, X., Lu, F. et al. A novel analytical redundancy method based on decision-level fusion for aero-engine sensors. Nonlinear Dyn 111, 13215–13234 (2023). https://doi.org/10.1007/s11071-023-08561-0
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DOI: https://doi.org/10.1007/s11071-023-08561-0