自主飞行器的实时异常自动检测
Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles
摘要：
最近，飞行器的使用增加，引起了人们对自主操作的安全性和可靠性的担忧。越来越需要一种方法来监控这些飞机的状态，并向安全驾驶员或自动驾驶仪报告任何故障和异常，以应对紧急情况。在本文中，我们提出了一个实时的方法，使用递归最小二乘法检测异常的行为的飞机。该方法在线建模相关输入输出对之间的关系，并使用该模型来检测异常。其结果是一种易于部署的异常检测方法，该方法不假设特定的飞机模型，并且可以检测各种自主飞机中的多种类型的故障和异常。对22次以上的飞行试验结果表明，该方法的准确率为88.23%，召回率为88.23%，准确率为86.36%。另一个贡献是为自动驾驶飞机提供了一个新的故障检测开放数据集，其中包含22个固定翼飞行的完整数据和地面实况，其中有8种不同类型的飞行中执行器故障，以帮助未来的飞机故障检测研究。源代码和数据集可以从https://theairlab.org/fault-detection/访问。
英文摘要：
The recent increase in the use of aerial vehicles raises concerns about the safety and reliability of autonomous operations. There is a growing need for methods to monitor the status of these aircraft and report any faults and anomalies to the safety pilot or to the autopilot to deal with the emergency situation. In this paper, we present a real-time approach using the Recursive Least Squares method to detect anomalies in the behavior of an aircraft. The method models the relationship between correlated input-output pairs online and uses the model to detect anomalies. The result is an easy-to-deploy anomaly detection method that does not assume a specific aircraft model and can detect many types of faults and anomalies in a wide range of autonomous aircraft. The experiments on this method show a precision of 88.23%, recall of 88.23%, and 86.36% accuracy for over 22 flight tests. The other contribution is providing a new fault detection open dataset for autonomous aircraft, which contains complete data and the ground truth for 22 fixed-wing flights with eight different types of mid-flight actuator failures to help future fault detection research for aircraft. The source code and the dataset can be accessed from https://theairlab.org/fault-detection/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
