Pipeline fault simulation and control of a liquid rocket engine

Cheng, Yuqiang; Hu, Runsheng; Wu, Jianjun

doi:10.1177/09544100231157139

Cited by 1 publication

(1 citation statement)

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“…Valve control is a critical factor in the normal start-up of an engine. By adjusting the timing and response speed of five main valves, namely the main oxidizer valve (MOV), main fuel valve (MFV), fuel pre-burner oxidizer valve (FPOV), chamber coolant valve (CCV), and oxidizer pre-burner oxidizer valve (OPOV), valve failures can be simulated [ 22 ]. The specific formula is as follows, which mainly adjusts the flow rate through the control function to simulate valve failures such as valve not opening, slow valve opening, and valve blockage.…”

Section: Simulation System Constructionmentioning

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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Section: Simulation System Constructionmentioning

confidence: 99%