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Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft

机译:使用支持向量回归用于商用飞机的安全监测的实时异常检测框架

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The development of an automated health monitoring framework is critical for aviation system safety, especially considering the expected increase in air traffic over the next decade. Conventional approaches such as model-based and exceedance methods have a low detection accuracy and are limited to specific applications. This paper proposes a robust real-time health monitoring framework for detecting performance anomalies, which may impact system safety during flight operations, with high accuracy and generalized applicability. The proposed monitoring framework utilizes sensor data from commercial flight data recorders to predict possible flight performance anomalies. Decimation, a signal processing technique, in conjunction with Savitzky-Golay filtering is utilized to preprocess the dataset and mitigate sampling rate and noise issues that prevent direct usage of historical flight data. Correlation-based feature subset selection is subsequently performed, and these features are used to train a support vector machine that predicts flight performance. With this model, performance anomalies in the test data are automatically detected based on deviations from the predicted flight behavior. The proposed monitoring framework was demonstrated to detect performance anomalies in real-time and exhibited accurate detection capabilities with high computational efficiency.
机译:自动化健康监测框架的开发对于航空系统安全至关重要,特别是考虑到未来十年内预期的空中流量增加。常规方法,如模型为基础和超标方法具有低检测精度,并且仅限于特定应用。本文提出了一种稳健的实时健康监测框架,用于检测性能异常,可能影响飞行运营期间的系统安全性,具有高精度和广义适用性。所提出的监测框架利用来自商业飞行数据记录器的传感器数据来预测可能的飞行性能异常。 DECIMATION是一种与SAVITZKY-GOLAY滤波结合的信号处理技术,用于预处理数据集并减轻防止防止历史飞行数据的直接使用的采样率和噪声问题。随后执行基于相关的特征子集选择,并且这些特征用于训练预测飞行性能的支持向量机。通过该模型,基于与预测飞行行为的偏差自动检测测试数据中的性能异常。拟议的监测框架被证明是实时检测性能异常,并具有高计算效率的准确检测能力。

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