Fixed Wing Unmanned Aerial Vehicles (FW-UAVs) are prone to faults when performing a variety of tasks, which can lead to mission failure and even pose a safety risk. These faults can be recorded by mission-specific time-series flight data, but are very limited. Traditional methods are usually difficult to process these data, which poses a huge challenge to FW-UAV fault diagnosis (FD). To address this problem, this paper proposed a novel Heterogeneous Deep Multi-Task Learning (HDMTL) framework with adaptive sharing and knowledge complementation for FW-UAV FD. Specifically, we first capture the temporal and spatial features in the flight data through sub-networks respectively. Then, we design a novel attention-based adaptive sharing strategy. The sharing strategy aims to transfer relevant knowledge to different sub-networks to improve their prediction accuracy through knowledge complementation. Finally, extensive experimental results show that HDMTL is significantly competitive with currently popular methods. The code and data are available at .

中文翻译：

---

小样本下基于知识互补网络的FW-UAV故障诊断

固定翼无人机（FW-UAV）在执行多种任务时容易出现故障，导致任务失败，甚至带来安全风险。这些故障可以通过特定任务的时间序列飞行数据来记录，但非常有限。传统方法通常难以处理这些数据，这对FW-UAV故障诊断（FD）提出了巨大的挑战。为了解决这个问题，本文提出了一种新颖的异构深度多任务学习（HDMTL）框架，具有自适应共享和知识补充的FW-UAV FD。具体来说，我们首先通过子网络分别捕获飞行数据中的时间和空间特征。然后，我们设计了一种新颖的基于注意力的自适应共享策略。共享策略旨在将相关知识转移到不同的子网络中，通过知识互补来提高其预测精度。最后，大量的实验结果表明 HDMTL 与当前流行的方法相比具有显着的竞争力。代码和数据可在 处获得。