Ensuring operational safety and reliability in Unmanned Aerial Vehicles (UAVs) necessitates advanced onboard fault detection. This paper presents a novel, mobility-aware multi-sensor health monitoring framework, uniquely fusing visual (camera) and vibration (IMU) data for enhanced near real-time inference of rotor and structural faults. Our approach is tailored for resource-constrained flight controllers (e.g., Pixhawk) without auxiliary hardware, utilizing standard flight logs. Validated on a 40 kg-class UAV with induced rotor damage (10% blade loss) over 100+ minutes of flight, the system demonstrated strong performance: a Multi-Layer Perceptron (MLP) achieved an RMSE of 0.1414 and R² of 0.92 for rotor imbalance, while a Convolutional Neural Network (CNN) detected visual anomalies. Significantly, incorporating UAV mobility context reduced false positives by over 30%. This work demonstrates a practical pathway to deploying sophisticated, lightweight diagnostic models on standard UAV hardware, supporting real-time onboard fault inference and paving the way for more autonomous and resilient health-aware aerial systems.

1. Introduction
2. Related Work
    2.1 Vibration-Based Fault Detection
    2.2 Vision-Based Anomaly Detection
    2.3 Multi-sensor Fusion for UAV Health Monitoring
    2.4 Summary and Research Gap
3. System Overview
    3.1 Architecture Overview
    3.2 Hardware and Deployment Scope
    3.3 Operational Workflow
    3.4 Design Objectives
4. Methodology
    4.1 Vibration-Based Fault Detection
    4.2 Vision-Based Anomaly Detection
5. Experimental Setup and Results
    5.1 UAV Platform and Hardware Configuration
    5.2 Flight Testing and Data Collection
    5.3 Dataset Construction and Labeling
    5.4 Evaluation Metrics
    5.5 Results: Vibration-Based Fault Detection(MLP)
    5.6 Results: Vision-Based Fault Detection (CNN)
    5.7 Fusion-Based Fault Detection (Rule-BasedStrategy)
    5.8 Real-Time Performance Summary
    5.9 Ablation Insights
6. Discussion
    6.1 Benefits of Multi-sensor Sensing
    6.2 Role of Mobility Awareness
    6.3 Real-Time Viability and Deployment
    6.4 Limitations
    6.5 Future Work
    6.6 Practical Implications
7. Conclusion
Acknowledgement
References
Appendix

• Berdibayev Yergali(AI & BigData Department, R&D Center, Gaion Ltd.) | 볘르드바에브 예르갈리 (㈜가이온 AI & 드론 연구소)
• Heon Gyu Lee(AI & BigData Department, R&D Center, Gaion Ltd.) | 이헌규 (㈜가이온 AI & 드론 연구소) Corresponding author