Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder

In three linesAnomaly detection for Electro-Hydrostatic Actuators (EHAs) using LSTM autoencoder. Model achieves 99.0% average accuracy, up to 100% precision, and F1-scores of 93.1–99.8% on temperature and pressure sensor data. Outperforms classical methods (Z-score, Isolation Forest, k-means) by capturing temporal dependencies.

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