Anomaly detection for flight data is crucial in maintaining the safety and stability of unmanned aerial vehicles (UAVs), making it a topic of significant research and attention. However, existing anomaly detection methods often ignore the random noise of UAV flight data and lack effective parameter selection, resulting in inadequate anomaly detection performance. Furthermore, current methods generally face the problem of insufficient feature extraction capability. In this paper, a spatio-temporal correlation based on one-dimensional convolutional neural network (1D CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM) hybrid neural network with residual filtering (STC-1D CBiAM-RF) data-driven multiple regression framework is proposed for anomaly detection and recovery of UAV flight data. First, a correlation analysis method is used for parameter selection to reduce the dependence on expert knowledge. Second, a multiple regression model fusing attention mechanism is designed. It utilizes 1D CNN-BiLSTM as a feature extractor, guided by the attention mechanism, to enhance the learning of crucial information from UAV flight data. Then, to effectively mitigate the impact of random noise, a residual filtering method is introduced to smooth the residuals, thereby improving anomaly detection performance. Finally, anomaly detection is achieved by comparing the square of the smoothed residuals with the statistical threshold, and data recovery is achieved by replacing the anomalous data with the predicted data. The effectiveness of the proposed method is verified through a series of experiments using real UAV flight data injected with different anomaly types.

Fixed‐wing unmanned aerial vehicles (FW‐UAVs) play an essential role in many fields, but the faults of FW‐UAV components lead to severe accidents frequently; so, there is a need to continuously explore more intelligent fault detection methods to improve the safety and reliability of FW‐UAVs. Deep learning provides advanced solution ideas for future UAV fault detection, but the current lack of UAV monitoring data limits the advantages of deep learning in UAV fault detection, which are both a challenge and an opportunity. In this paper, we mainly consider the data availability of deep learning under various practical flight conditions of FW‐UAVs and propose a fault detection framework based on hybrid deep domain adaptation BiLSTM networks and the Hampel filter (HDBNH), the main purpose of which is to learn the knowledge of acquired data for detecting FW‐UAV faults in other unknown operating conditions. HDBNH consists of three modules: feature extractor, domain adaptor, and fault detector. The feature extractor is two BiLSTM networks constructed to extract the past and future state features from the time‐series flight data. The discrepancy of feature distribution between different domains is effectively reduced in the domain adaptor by a hybrid adversarial and the maximum mean discrepancy (MMD) domain adaptation method. The fault detector consists of a fault classification module and a Hampel filter. According to the continuous and dynamic characteristics of FW‐UAV state changes, the Hampel filter is used to detect and correct the predicted values of the fault classification module. Meanwhile, a new state sample preparation strategy is proposed to support the work of HDBNH better. Finally, the effectiveness of HDBNH is confirmed by conducting extensive experiments in real FW‐UAV flight data.