Automatic tuning strategies for model-based diagnosis methods applied to a rocket engine demonstrator

Alessandra Iannetti, Julien Marzat, Helene Piet-Lahanier, and Gerard Ordonneau
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phmec_16_046.pdf608.26 KBJuly 5, 2016 - 12:29am

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 a residual analysis based on a CUSUM test. This paper presents the results obtained when comparing the detection performances of the developed tools with different automatic tuning strategies for the CUSUM threshold settings. Detection results on MASCOTTE firing data are presented to support the analysis.

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Submission Keywords: 
rocket motors
anomaly detection
automatic threshold setting
Submission Topic Areas: 
Industrial applications
Model-based methods for fault detection, diagnostics, and prognosis
Modeling and simulation
Verification and validation
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