This paper presents the introduction, investigation, and critical assessment of three data-driven methods for rotor failure detection and identification in a multicopter. These methods are based on aircraft attitude signals obtained from forward flight under turbulence and uncertainty. The knowledge-based method exploits the system rigid-body dynamics insight under the different rotor failures to construct a decision tree that detects and identifies the rotor failure simultaneously by how the roll, pitch, and yaw signals violate the statistical confidence limits immediately after failure. For the statistical time-series method, the development of stochastic time-series models and residual-based statistical hypothesis tests are discussed. Here, fault detection in the transient response is followed by identification after the signals reach a stationary state, after controller compensation, with the healthy and the different faulty models, respectively, in a two-step manner. The third method employs the healthy time-series model to extract a useful feature, which is the residual cross correlation, as an input to a neural network trained to achieve simultaneous rotor failure detection and identification. The time-series assisted neural network is capable of making decisions throughout the entire flight with an accuracy of 98.8%, with minimum computation time (less than 0.03 s) making it the best alternative for real-time monitoring.

本文介绍了用于多旋翼转子故障检测和识别的三种数据驱动方法的介绍、调查和关键评估。这些方法基于在湍流和不确定性下向前飞行获得的飞机姿态信号。基于知识的方法利用系统刚体动力学在不同转子故障下的洞察力来构建决策树，该决策树通过在故障后立即通过滚转、俯仰和偏航信号如何违反统计置信限制来同时检测和识别转子故障。对于统计时间序列方法，讨论了随机时间序列模型和基于残差的统计假设检验的发展。这里，瞬态响应中的故障检测之后，在信号达到稳定状态后进行识别，在控制器补偿后，分别以两步方式使用健康和不同的故障模型。第三种方法使用健康的时间序列模型来提取有用的特征，即残差互相关，作为输入到经过训练以实现同步转子故障检测和识别的神经网络的输入。时间序列辅助神经网络能够在整个飞行过程中以 98.8% 的准确率做出决策，最短的计算时间（小于 0.03 s）使其成为实时监控的最佳选择。第三种方法使用健康的时间序列模型来提取有用的特征，即残差互相关，作为输入到经过训练以实现同步转子故障检测和识别的神经网络的输入。时间序列辅助神经网络能够在整个飞行过程中以 98.8% 的准确率做出决策，最短的计算时间（小于 0.03 s）使其成为实时监控的最佳选择。第三种方法使用健康的时间序列模型来提取有用的特征，即残差互相关，作为输入到经过训练以实现同步转子故障检测和识别的神经网络的输入。时间序列辅助神经网络能够在整个飞行过程中以 98.8% 的准确率做出决策，最短的计算时间（小于 0.03 s）使其成为实时监控的最佳选择。