Anomaly Detection in Liquid Rocket Engines
Summary
Liquid rocket engines are critical propulsion systems in launch vehicles, operating under extreme thermal and mechanical conditions. Ensuring their reliable performance demands continuous health monitoring and rapid detection of anomalies—departures from normal operational signatures that may presage component degradation or failure. Anomaly detection in this context integrates high-fidelity sensor data, physical models and data-driven algorithms to identify subtle deviations in pressure, temperature, vibration and flow rates during start-up, steady-state and shutdown phases. Recent advances combine signal-processing techniques with physics-informed and machine-learning models to enhance sensitivity, reduce false alarms and accommodate the highly dynamic, non-linear behaviour of cryogenic propellant systems. Practical applications range from ground test stands to in-flight monitoring, where early warning enables preventative maintenance, minimises mission risk and supports designs for reusable engines.
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Anomaly Detection in Liquid Rocket Engines publication trend
The graph below shows the total number of articles in anomaly detection in liquid rocket engines across all publications each year (not limited to Nature Index journals).
Technical terms
Anomaly detection: The process of identifying patterns in data that do not conform to expected normal behaviour.
Convolutional autoencoder: A neural network that learns to compress and reconstruct sensor data to extract salient features.
One-class support vector machine: A classification algorithm trained exclusively on normal data to distinguish anomalies without requiring fault labels.
Long short-term memory (LSTM): A recurrent neural network architecture designed to capture long-range temporal dependencies in sequential data.
Generative adversarial network (GAN): A pair of neural networks that contest with each other to generate realistic data and detect deviations.
References
- Steady-State Process Fault Detection for Liquid Rocket Engines Based on Convolutional Auto-Encoder and One-Class Support Vector Machine. IEEE Access (2019).
- Fault Detection and Diagnosis for Liquid Rocket Engines Based on Long Short-Term Memory and Generative Adversarial Networks. Aerospace (2022).
- Intelligent Fault Diagnosis of Liquid Rocket Engine via Interpretable LSTM with Multisensory Data. Sensors (2023).
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