Prognostics Aware Control Design for Extended Remaining Useful Life Application to Liquid Propellant Reusable Rocket Engine

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Feb 25, 2024
Dr. Julien Thuillier Mayank Shekhar Jha Sebastien Le Martelot Dr. Didier Theilliol

Abstract

As most of the safety critical industrial systems remain sensitive to functional degradation and operate under closed loop, it becomes imperative to take into account the state of health within the control design process. To that end, an effective assessment and extension of the Remaining Useful Life (RUL) of complex systems is a standing challenge that seeks novel solutions at the cross-over of Prognostics and Health Management (PHM) domain as well as automatic control. This paper considers a dynamical system subjected to functional degradation presents a novel control design strategy. Wherein the assessment of state of health of the system is taken into account leading to effective prediction of the RUL as well as its extension. To that end, the degradation model is considered unknown but input-dependent. The control design is formulated as an optimization problem wherein a suitable comprise is reached between the performance and desired RUL of the system. The main contribution of the paper remains in proposal of set-point modulation based approach wherein the control input at a given present time stage is modulated in such way that futuristic health of the system over a long time horizon is extended whilst assuring acceptable performance. The effectiveness of the proposed strategy is assessed in simulation using a numerical example as well as liquid propellant rocket engine case.

Abstract 148 | PDF Downloads 143

##plugins.themes.bootstrap3.article.details##

Keywords

Prognostics, closed loop prognostics, hybrid prognostics, Liquid Propellant Rocket Engine, remaining useful life

References
Bellani, L., Compare, M., Baraldi, P., & Zio, E. (2019). Towards developing a novel framework for practical phm: A sequential decision problem solved by reinforcement learning and artificial neural networks. International Journal of Prognostics and Health Management, 10(4).
Brown, D. W., Georgoulas, G., Bole, B., Pei, H. L., Orchard, M., Tang, L., & Vachtsevanos, G. (2009). Prognostics enhanced reconfigurable control of electro-mechanical actuators. papers.phmsociety.org, 1, 1.
Camci, F., Medjaher, K., Atamuradov, V., & Berdinyazov, A. (2019). Integrated maintenance and mission planning using remaining useful life information. Engineering optimization, 51(10), 1794–1809.
Chelouati, M., Jha, M. S., Galeotta, M., & Theilliol, D. (2021). Remaining useful life prediction for liquid propulsion rocket engine combustion chamber. In 2021 5th international conference on control and fault-tolerant systems (systol) (pp. 225–230).
Commault, C., Dion, J. M., & Perez, A. (1991). Disturbance rejection for structured systems. IEEE Transactions on Automatic Control, 36, 884-887.
Daigle, M., Saha, B., & Goebel, K. (2012). A comparison of filter-based approaches for model-based prognostics. In 2012 ieee aerospace conference (pp. 1–10).
Daigle, M. J., & Goebel, K. (2012). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, man, and cybernetics: systems, 43(3), 535–546.
Holmes, M., Tangirala, S., & Ray, A. (1997). Life-extending control of a reusable rocket engine. In Proceedings of the 1997 american control conference (cat. no. 97ch36041) (Vol. 4, pp. 2328–2332).
Hu, X., Zou, C., Tang, C., Liu, T., & Hu, L. (2020). Cost-optimal energy management of hybrid electric vehicles using fuel cell/battery health-aware predictive control. IEEE Transactions on Power Electronics, 35, 382-392.
Huang, Y., & Xue, W. (2014). Active disturbance rejection control: Methodology and theoretical analysis. ISA Transactions, 53, 963-976.
Jha, M. S., Bressel, M., Ould-Bouamama, B., & Dauphin-Tanguy, G. (2016). Particle filter-based hybrid prognostics of proton exchange membrane fuel cell in bond graph framework. Computers Chemical Engineering, 95, 216-230.
Jha, M. S., Dauphin-Tanguy, G., & Ould-Bouamama, B. (2016). Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework. Mechanical Systems and Signal Processing, 75, 301-329.
Jha, M. S., Theilliol, D., Biswas, G., &Weber, P. (2019). Approximate q-learning approach for health aware control design. In 2019 4th conference on control and fault tolerant systems (systol) (pp. 418–423).
Jha, M. S., Weber, P., Theilliol, D., Ponsart, J.-C., & Maquin, D. (2019). A reinforcement learning approach to health aware control strategy. In 2019 27th Mediterranean conference on control and automation (med) (pp. 171–176).
Kanso, S., Jha, M. S., Galeotta, M., & Theilliol, D. (2022). Remaining useful life prediction with uncertainty quantification of liquid propulsion rocket engine combustion chamber. IFAC-PapersOnLine, 55(6), 96–101.
Khelassi, A., Theilliol, D.,Weber, P., & Ponsart, J.-C. (2011). Fault-tolerant control design with respect to actuator health degradation: An lmi approach. In 2011 IEEE international conference on control applications (cca) (pp. 983–988).
Khelassi, M., Jiang, J., Theilliol, D., Weber, P., & Zhang, Y. M. (2011). Reconfiguration of control inputs for overactuated systems based on actuators health. IFAC Proceedings Volumes, 44, 13729-13734.
Kumar, D., Kalra, S., & Jha, M. S. (2022). A concise review on degradation of gun barrels and its health monitoring techniques. Engineering Failure Analysis, 142, 106791.
Li, S., Xie, X., Cheng, C., & Tian, Q. (2020). A modified coffin-manson model for ultra-low cycle fatigue fracture of structural steels considering the effect of stress triaxiality. Engineering Fracture Mechanics, 237, 107223.
Luo, J., Namburu, M., Pattipati, K., Qiao, L., Kawamoto, M., & Chigusa, S. (2003). Model-based prognostic techniques [maintenance applications]. In Proceedings autotestcon 2003. ieee systems readiness technology conference. (pp. 330–340).
Obando, D. R., Martinez, J. J., & Berenguer, C. (2021). Deterioration estimation for predicting and controlling rul of a friction drive system. ISA Transactions, 113, 97-110.
Paris, P., & Erdogan, F. (1963). A critical analysis of crack propagation laws. Journal of Basic Engineering, 85, 528-533.
Pecht, M. G. (2013). Prognostics and health management. Springer New York.
Pour, F. K., Theilliol, D., Puig, V., & Cembrano, G. (2021). Health-aware control design based on remaining useful life estimation for autonomous racing vehicle. ISA Transactions, 113, 196-209.
Ray, A., Wu, M.-K., Dai, X., Carpino, M., & LORENZO, C. (1993). Damage-mitigating control of space propulsion systems for high performance and extended life. In 29th joint propulsion conference and exhibit (p. 2080).
Reuben, L. C. K., & Mba, D. (2014). Diagnostics and prognostics using switching kalman filters. Structural Health Monitoring, 13(3), 296–306.
Rocco, J. A. F. F., Lima, J. E. S., Frutuoso, A. G., Iha, K., Ionashiro, M., Matos, J. R., & Su´arez-Iha, M. E. V. (2004). Thermal degradation of a composite solid propellant examined by dsc. Journal of Thermal Analysis and Calorimetry, 75, 551-557.
Rodriguez, D. J., Martinez, J. J., & Berenguer, C. (2018). An architecture for controlling the remaining useful lifetime of a friction drive system. IFAC-PapersOnLine, 51, 861-866.
Roemer, M. J., Kacprzynski, G. J., & Orsagh, R. F. (2001). Assessment of data and knowledge fusion strategies for prognostics and health management. In 2001 IEEE aerospace conference proceedings (cat. no. 01th8542) (Vol. 6, pp. 2979–2988).
Salazar, J. C., Weber, P., Nejjari, F., Sarrate, R., & Theilliol, D. (2017). System reliability aware model predictive control framework. Reliability Engineering System Safety, 167, 663-672.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Swanson, D. (2001). A general prognostic tracking algorithm for predictive maintenance. In 2001 ieee aerospace conference proceedings (cat. no.01th8542) (Vol. 6, p. 2971-2977 vol.6). doi: 10.1109/AERO.2001.931317
Thuillier, J., Galeotta, M., Jha, M. S., & Theilliol, D. (2022, June). Impact of control gain design on Remaining Useful Life for a liquid propellant reusable engine. In 9th European Conference for Aeronautics and Space Sciences, EUCASS 2022. Lille, France. Retrieved from https://hal.science/hal-03901551 doi: 10.13009/EUCASS2022-6113
Thuillier, J., Jha, M. S., Galeotta, M., & Theilliol, D. (2022). Control reconfiguration strategies for remaining useful life extension. IFAC-PapersOnLine, 55, 114-119.
Ure, N. K., Chowdhary, G., How, J. P., Vavrina, M. A., & Vian, J. (2013). Health aware planning under uncertainty for uav missions with heterogeneous teams. In 2013 european control conference (ecc) (pp. 3312–3319).
Wang, D., Tsui, K. L., & Miao, Q. (2018). Prognostics and health management: A review of vibration based bearing and gear health indicators. IEEE Access, 6, 665-676.
Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., & Xi, L. (2018). Recent advances in prognostics and health management for advanced manufacturing paradigms. Reliability Engineering System Safety, 178, 255-268.
Section
Technical Papers