Self-attention enhanced GraphSAGE for UAV fault diagnosis using vibration signals

Traditional data-driven fault diagnosis methods for unmanned aerial vehicles (UAVs) often struggle to in effectively extract fault features from complex vibration signals. To address this challenge, this paper proposes an enhanced fault diagnosis framework based on GraphSAGE, incorporating a self-attention mechanism (GraphSAGE-SA). Initially, a customized vibration data acquisition system was developed using MPU6050 sensors to capture vibration data across various operational conditions. Subsequently, the K-Nearest Neighbors (KNN) algorithm was employed to convert one-dimensional vibration signals into graph-structured data, revealing explicit topological relationships. The proposed GraphSAGE-SA model aggregates information from both nodes and neighbors via a hierarchical sampling process. The integrated self-attention mechanism then adaptively adjusts the importance of different neighbors, facilitating more precise feature extraction. Experimental validation conducted on a quadrotor UAV platform demonstrated that the proposed method achieves an impressive fault classification accuracy of 98%, surpassing conventional GraphSAGE variants by 3-12% and other graph-based approaches. This superior performance is attributed to the model's ability to capture local structural patterns and global dependencies simultaneously, enabled by the attention mechanism. This research presents a novel and effective solution for intelligent fault diagnosis in UAVs and other complex mechanical systems. In real-world applications, timely and accurate fault diagnosis is essential for ensuring UAV operational safety, mission reliability, and preventing catastrophic failures.