Embedded real-time UAV sensor fault diagnosis with a multirate EKF based lightweight network

On-board sensor fault diagnosis and detection (FDD) is a critical component of unmanned aerial vehicle (UAV) health monitoring. Developing real-time FDD for UAVs requires overcoming the conflict between the high performance demanded by their dynamic characteristics and the limited on-board computational capabilities. Furthermore, the resulting lightweight FDD algorithms often face issues such as training instability and poor generalization, which are exacerbated by the imbalance and coupling of sensor fault data. To address these issues, this paper introduces a novel extended Kalman filter-based (EKF-based), lightweight FDD method for UAV inertial measurement units (IMUs). By integrating multi-rate filtering algorithm with deep learning, our approach accurately diagnoses multiple fault modes, achieving more than 93% precision and accuracy in offline evaluations. Beyond the core model, we propose a complete deployment pipelines for real-time sensor FDD, covering the entire process from offline dataset creation and lightweight model training to final on-board deployment. Online validation experiments were conducted using an IMU with a 100 Hz update rate. The proposed approach demonstrated effective real-time detection of mixed-mode faults, achieving a low error rate of 6.24%, an improvement of 71.24% over the baseline model. The single inference latency of this method is less than 11 ms, basically meeting the real-time and on-board generalization requirements for UAV applications.