Building on prior lightweight CNN model combined with LSTM-AM framework to guide fault detection in fixed-wing UAVs

Fixed-wing UAVs (FW-UAVs) are empowered to handle diverse civilian and military missions, but sensor failure scenarios are constantly rising. Rapid advancement in deep learning methods currently proposes state-of-the-art solutions for fault detection of UAVs. However, most recent deep learning-based detection models suffer from model size, high computational complexity, and high-power consumption, which are challenging for small-sized FW-UAVs with limited battery backup and computational power. Therefore, to overcome these problems, this article introduces a lightweight CNN model built on prior work combined with the LSTM-AM framework to obtain accurate fault detection of FW-UAVs with low power consumption and fast computations. First, lightweight CNN architecture aims to minimize computational complexity while maintaining high accuracy in fault detection. The LSTM model merged with Attention Mechanism (AM), allows the architecture to obtain temporal dependencies and concentrate on essential features for enhanced fault detection accuracy. The combined version of lightweight CNN, LSTM, and AM commits to more reliable and efficient fault detection in FW-UAV applications, improving UAV drones' overall performance and safety.