Rocket engines are complex and critical systems mostly relying on simple redlines strategies for monitoring the main functional parameters. This approach is typical on expendable rockets with non-adjustable valves because in case of failure the only possible action is to cut off the engine. Anyway years of experiments on engine firings or subsystem benches show that there is space for an update of the monitoring strategies because this would lead to a reduction of false alarm rates and to an improved exploitation of test hardware. Moreover real-time diagnosis methods will be necessary in case of design of intelligent rocket engine controllers for next generation reusable launchers. The work presented in this paper is part of a demonstration project of new diagnosis tools for rocket engines applied to the cryogenic combustion bench Mascotte. This bench developed by ONERA and CNES is used to analyze combustion and nozzle expansion characteristics of cryogenic fuels such as oxygen and hydrogen or methane. Model-based diagnosis tools have been developed for the combustion chamber and nozzle water cooling circuit. The basis was the setup of simplified expressions for modeling the functional behavior of the water circuit and then the development of predictive strategies such as parameter identification and Kalman filters. Anomalous event detection is obtained via residual analysis based on a CUSUM test. This paper presents the new automatic tuning strategies for the CUSUM threshold setting and the detection results obtained on Mascotte firing data.
How to Cite
anomaly detection, CUSUM, rocket motors, automatic threshold setting
Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., (2003). Diagnosis and Fault-Tolerant Control. Springer Verlag, Berlin Heidelberg.
Chow, E., Willsky, A.S., (1984), Analytical redundancy and the design of robust failure detection systems, IEEE Transactions on Automatic Control, Vol. 29(7), pages 603-614.
Cicanek, H., (1985), Space Shuttle main engine failure detection, American Control Conference, Boston MA, USA.
Ding, S.X., (2008), Model-based fault diagnosis techniques, Springer Verlag, Berlin Heidelberg.
Iannetti, A., (2014). Overview on European efforts on health monitoring/management systems for rocket engines. Space Propulsion Conference, Köln, Germany.
Iannetti, A., Marzat, J., Piet-Lahanier, H. et al., (2014), Development of model based fault diagnosis algorithms for the Mascotte cryogenic test bench. IOP Journal of Physics: Conference Series, Vol 570, number 7.
Iannetti, A., Marzat, J., Piet-Lahanier, H. et al., (2015), Fault diagnosis benchmark for a rocket engine demonstrator. 9th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, Paris, France, IFAC-PapersOnLine 48(21), pages 895-900.
Iannetti, A., Marzat, J., Piet-Lahanier, H., Ordonneau, G. et al., (2015), HMS developments for the rocket engine demonstrator Mascotte. 51st AIAA/SAE/ASEE joint Propulsion Conference.
Isermann, R., (1984). Process fault detection based on modeling and estimation methods—a survey. Automatica, Vol. 20(4), pages 387-404.
Marzat, J., Walter, E., Piet-Lahanier, H., Damongeot, F., (2010), Automatic tuning via kriging-based optimization of methods for fault detection and isolation, IEEE Conference on Control and Fault-
Tolerant systems, Nice, France, pages 505-510.
Marzat, J., Piet-Lahanier, H., Damongeot, F., Walter, E., (2012), Model based fault diagnosis for aerospace systems: a survey, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of aerospace engineering, Vol. 226(10), pages 1329-1360.
M. Basseville and I. V. Nikiforov. Detection of Abrupt Changes: Theory and Application. Prentice Hall Englewood Cliffs, NJ, 1993.
Ordonneau, G., Hervat, P., Vingert, L., Petitot, S., Pouffary, B., (2013), First results of heat transfer measurements in a new water-cooled combustor on the Mascotte facility, 4th European conference for aerospace sciences (EUCASS), Munich, Germany.
Wu, J., (2005), Liquid propellant rocket engines health monitoring- a survey, ACTA Astronautica, Vol. 56, pages 347-356.
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