LAUSR

With the wide applications of the unmanned aerial vehicle (UAV), operating safety becomes a critical issue. Thus, fault detection (FD) has been focused, which can realize fault alarm and schedule maintenance in time. Since the accurate physical model of UAV is usually difficult to obtain and flight data with random noise has both spatial and temporal correlation, a huge challenge is posed to FD. In this article, a data-driven multivariate regression approach based on long short-term memory with residual filtering (LSTM-RF) is proposed to fulfill UAV flight data FD and recovery. First, an LSTM network is designed as a regression model, which can extract the spatial–temporal features from the flight data and obtain an estimation of the monitored parameter. Second, a filter is utilized to smooth the residuals between real flight data and estimated values, which mitigates the effect of random noise and dramatically improves the detection performance. Finally, FD is achieved by comparing the smoothed residual with a statistical threshold. Then, fault recovery is fulfilled by replacing fault data with the estimated value. To validate the effectiveness of the proposed method, experiments are conducted based on simulation data and real flight data. The experimental results demonstrate that the proposed method has good performance in FD and recovery of UAV flight data.