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

Abstract

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.