ArticleSensors (Basel, Switzerland)2023
Intelligent Fault Diagnosis of Liquid Rocket Engine via Interpretable LSTM with Multisensory Data.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Physics-Topology-Anchored Learning: A Robust and Lightweight Framework for Time-Series Prediction and Anomaly Detection Under Data Scarcity.Sensors (Basel, Switzerland) · 2026Article
- Fault Detection and Isolation Based on Structural Analysis: Application to a Multi-Engine Propulsion Cluster.Sensors (Basel, Switzerland) · 2025Article
- Particle-Filter-Based Fault Diagnosis for the Startup Process of an Open-Cycle Liquid-Propellant Rocket Engine.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
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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Registered trials
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