Unmanned Aerial Vehicle Health Evaluation Method Based on Cloud Model Depth Theory

The working conditions of diverse components within an unmanned aerial vehicle (UAV) significantly impact its overall performance. Given the intricate composition of UAVs, they have a higher likelihood of experiencing malfunctions. In this paper, the profound theory of the cloud model is employed to devise a health assessment methodology. By establishing a multi-tiered cloud evaluation index system, the adverse effects of random and ambiguous information data that emerge during the flight phase on the health reliability are mitigated, thereby effectively enabling the soft measurement of the operational state. Experimental validation demonstrates that, in comparison with conventional health assessment approaches, this proposed method substantially enhances the accuracy of the assessment.