HMS developments for the rocket engine demonstrator Mascotte

Iannetti, A.; Palerm, Sandrine; Marzat, Julien; Piet-Lahanier, Hélène; Ordonneau, G.

doi:10.2514/6.2015-3992

Cited by 4 publications

(4 citation statements)

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“…Adaptive tuning strategies are at the basis of any modelbased diagnosis system. The algorithms developed in previous works (Iannetti, Marzat, Piet-Lahanier, Ordonneau 2015), allowed calculation of detection residuals that have to be analyzed via a decision logic. The CUSUM test was chosen but it requires setting of a detection threshold.…”

Section: Discussionmentioning

confidence: 99%

“…A cryogenic test bench was selected as benchmark application of this research. The previous works (Iannetti, Marzat, Piet-Lahanier, Ordonneau, 2015) resulted in the development of model-based detection algorithms that have been validated for the cooling system of the Mascotte bench. Anomalous event detection is obtained via residual analysis based on a CUSUM test.…”

Section: Introductionmentioning

confidence: 99%

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

Iannetti¹,

Marzat

Piet-Lahanier

et al. 2016

PHME_CONF

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Iannetti¹,

Marzat

Piet-Lahanier

et al. 2016

PHME_CONF

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“…Efficient HMS techniques are able to detect the potential faults and decrease the probability of unexpected failures [2], which contributes to improving the safety and reliability of complex systems, such as liquid-propellant rocket engines (LREs) [3]. Furthermore, to follow with the new ambitions of being cost-effective to explore space through both expendable and reusable vehicles, it accentuates the need for advanced HMS algorithms [4]. * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive physics-encoded graph neural network for health stage assessment of liquid-propellant rocket engines

Ma,

Wang,

et al. 2024

Meas. Sci. Technol.

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The improvement of reliable health monitoring system (HMS) for liquid-propellant rocket engines (LREs) is a crucial part for reusable launch vehicle, which contributes to providing competitive and cost-effective propulsion systems. Thus, it accentuates the need for reliable and quick health stage assessment of system and follow-up damage-mitigating control. In this paper, we propose a novel adaptive physics-encoded graph neural network (APGNN) for health stage assessment of LREs. Our approach embeds the relations of different sensors obtained through expert experience, which contributes to constructing a physical graph layer. To better capture the information contained in all the sensor data, a novel convolutional layer of adaptive auto-regressive moving average (ARMA) filters is designed, which considers the personalized information propagation needs of each neural network layer. The performance of the proposed method is quantified with data obtained from physics simulations and real-world engineering systems. The results show that our model has potential applicability for the health stage assessment of LREs with high accuracy.

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“…Liquid rocket engines (LREs) are the highest energy systems as they produce up to thousands kN of thrust. The energy levels reach up to several GW of power by converting the high energy of the combustion product into high speed ejected mass flows [1]. To provide the high energy levels, LREs have to operate in a harsh thermal and mechanical environment, which may result in catastrophic anomalies due to unexpected events.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Liquid Rocket Engine via Interpretable LSTM with Multisensory Data

Xiao-guang¹,

Hua

Zhu

et al. 2023

Sensors

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Fault diagnosis is essential for high energy systems such as liquid rocket engines (LREs) due to harsh thermal and mechanical working environment. In this study, a novel method based on one-dimension Convolutional Neural Network (1D-CNN) and interpretable bidirectional Long Short-term Memory (LSTM) is proposed for intelligent fault diagnosis of LREs. 1D-CNN is responsible for extracting sequential signals collected from multi sensors. Then the interpretable LSTM is developed to model the extracted features, which contributes to modeling the temporal information. The proposed method was executed for fault diagnosis using the simulated measurement data of the LRE mathematical model. The results demonstrate the proposed algorithm outperforms other methods in terms of accuracy of fault diagnosis. Through experimental verification, the method proposed in this paper was compared with CNN, 1DCNN-SVM and CNN-LSTM in terms of LRE startup transient fault recognition performance. The model proposed in this paper had the highest fault recognition accuracy (97.39%).

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HMS developments for the rocket engine demonstrator Mascotte

Cited by 4 publications

References 1 publication

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

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

Adaptive physics-encoded graph neural network for health stage assessment of liquid-propellant rocket engines

Intelligent Fault Diagnosis of Liquid Rocket Engine via Interpretable LSTM with Multisensory Data

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