用LSTM自编码器检测电液作动器传感器异常，准确率超99%。
Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder
- 用LSTM自编码器捕捉传感器数据的时间依赖性，通过重构误差检测异常。
- 平均准确率99.0%，召回率90.2%~99.6%，误报率极低。
- 适合航空航天与工业系统中对可靠性要求高的故障监测场景。
电液作动器（EHAs）广泛应用于航空航天和工业系统中，及时检测传感器异常对保障安全可靠运行至关重要。然而，EHA传感器数据量大、采样频率高，传统统计与经典机器学习方法如Z-score、IQR、MAD、孤立森林、高斯混合模型和k-means难以捕捉信号中的时序依赖性，导致检测精度有限且误报率高。此外，针对EHA系统的数据驱动异常检测方法在不同工况下的系统性评估仍较缺乏。本研究提出一种离线异常检测框架，针对单变量传感器信号（温度与压力），基于控制实验台采集的数据，采用基于重构的长短期记忆（LSTM）自编码器，通过验证集的重构误差分布进行校准与评估。在多种故障注入场景下，以准确率、精确率、召回率和F1分数进行性能评价，并开展不同工况下的敏感性分析。结果表明，该方法平均准确率达99.0%，精确率最高达100%，召回率在90.2%至99.6%之间，F1分数介于93.1%至99.8%之间，展现出高检测灵敏度与极低误报率，验证了数据驱动离线异常检测在EHA系统中的可行性。未来工作将聚焦于该框架向在线（实时）环境的适配。
原文摘要 · Abstract (English)
Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation. However, the large volume and high sampling frequency of EHA sensor data pose challenges for accurate and efficient anomaly detection. Conventional statistical and classical machine-learning methods such as Z-score, Interquartile Range (IQR), Median Absolute Deviation (MAD), Isolation Forest, Gaussian Mixture, and k-means often fail to capture the temporal dependencies inherent in EHA signals, resulting in limited detection accuracy and elevated false-alarm rates. Furthermore, systematic evaluations of data-driven anomaly detection approaches for EHA systems remain scarce, particularly under varying operational conditions. This study presents an offline anomaly-detection framework for univariate EHA sensor signals, focusing on temperature and pressure data collected from a controlled test bench. The method employs a reconstruction-based Long Short-Term Memory (LSTM) autoencoder, calibrated and evaluated using validation-set reconstruction-error distributions. Performance is assessed across multiple fault-injection scenarios using accuracy, precision, recall, and F1-score, complemented by sensitivity analyses under varying operating conditions. The LSTM autoencoder achieved an average accuracy of 99.0\%, precision up to 100\%, recall between 90.2\% and 99.6\%, and F1-scores from 93.1\% to 99.8\%, demonstrating high detection sensitivity and a very low false-alarm rate across all evaluated sensors. These results highlight the feasibility of data-driven offline anomaly detection for EHAs. Future work will focus on adapting the developed framework for an online (real-time) environment.
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