Autonomous robotic systems, such as quadrotors, are susceptible to actuator faults, and for the safe operation of such systems, timely detection and isolation of these faults is essential. Neural networks can be used for verification of actuator performance via online actuator fault detection with high accuracy. In this paper, we develop a novel model-free fault detection and isolation (FDI) framework for quadrotor systems using long-short-term memory (LSTM) neural network architecture. The proposed framework only uses system output data and the commanded control input and requires no knowledge of the system model. Utilizing the symmetry in quadrotor dynamics, we train the FDI for fault in just one of the motors (e.g., motor $\# 2$), and the trained FDI can predict faults in any of the motors. This reduction in search space enables us to design an FDI for partial fault as well as complete fault scenarios. Numerical experiments illustrate that the proposed NN-FDI correctly verifies the actuator performance and identifies partial as well as complete faults with over $90\%$ prediction accuracy. We also illustrate that model-free NN-FDI performs at par with model-based FDI, and is robust to model uncertainties as well as distribution shifts in input data.

翻译：自主机器人系统（如四旋翼无人机）易受执行器故障影响，为确保此类系统的安全运行，及时检测并隔离这些故障至关重要。神经网络可通过在线执行器故障检测实现高精度验证。本文提出一种基于长短期记忆（LSTM）神经网络架构的新型无模型故障检测与隔离（FDI）框架，用于四旋翼系统。该框架仅需使用系统输出数据和指令控制输入，无需任何系统模型知识。利用四旋翼动力学中的对称性，我们仅针对一个电机（如电机$\# 2$）训练FDI故障检测器，训练后的FDI即可预测任何电机的故障。这种搜索空间的缩减使得我们能够设计适用于部分故障及完全故障场景的FDI。数值实验表明，所提出的NN-FDI能正确验证执行器性能，并以超过$90\%$的预测精度识别部分故障与完全故障。我们还验证了无模型NN-FDI与基于模型的FDI性能相当，并对模型不确定性及输入数据分布偏移具有鲁棒性。