RAVEN: Unsupervised Anomaly Detection in Multivariate Jet Engine Time Series using Residual Learning on Real Test Data
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Abstract
Jet engines operate under demanding conditions, subjecting critical components to gradual wear and degradation over time. Early identification of incipient faults is essential for maintaining performance, safety, and reliability. Detecting incipient faults early is essential but remains difficult due to two major challenges: the scarcity of faulty data and the strong variability in operating conditions that obscure fault signatures. Most existing anomaly detection approaches rely on simulated datasets or assume the availability of labeled faults, limiting their applicability to real-world engine monitoring. In this work, we introduce RAVEN, a fully unsupervised anomaly detection framework designed for jet engine monitoring under real test conditions. RAVEN integrates (i) a regression-based residual model to normalize sensor responses against varying operating regimes, with (ii) a deep LSTM autoencoder that captures subtle deviations in time-series behavior without requiring fault labels. By explicitly addressing operational variability, sensor noise, and label scarcity, RAVEN provides a robust pathway for early fault detection. We validate RAVEN on real jet engine test data, demonstrating its ability to detect anomalies under diverse operating conditions. Results show that our approach delivers reliable detection performance in scenarios where conventional approaches struggle, offering a practical and scalable solution for propulsion system health monitoring.
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Jet engines, Anomaly detection, Unsupervised learning, autoencoder, Engine health monitoring
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