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.

自动健康监控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空中交通的预期增长。诸如基于模型的方法和超越方法之类的常规方法具有较低的检测精度，并且限于特定的应用。本文提出了一个鲁棒的实时健康监测框架，用于检测性能异常，该异常可能会影响飞行操作期间的系统安全性，并且具有较高的准确性和广泛的适用性。拟议的监视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。抽取，一种信号处理技术，与Savitzky-Golay滤波结合使用可对数据集进行预处理，并减轻采样率和噪声问题，这些问题会阻止直接使用历史飞行数据。随后执行基于相关性的特征子集选择，并将这些特征用于训练支持向量机以预测飞行性能。使用此模型，可以根据与预测的飞行行为之间的偏差自动检测测试数据中的性能异常。所提出的监视框架被证明可以实时检测性能异常，并具有高计算效率的精确检测能力。这些功能用于训练支持向量机，以预测飞行性能。使用此模型，可以根据与预测的飞行行为之间的偏差自动检测测试数据中的性能异常。所提出的监视框架被证明可以实时检测性能异常，并具有高计算效率的精确检测能力。这些功能用于训练支持向量机，以预测飞行性能。使用此模型，可以根据与预测的飞行行为之间的偏差自动检测测试数据中的性能异常。所提出的监视框架被证明可以实时检测性能异常，并以高计算效率展现出准确的检测能力。