UAV engine fault and diagnosis with parameter models based on telemetry data

It takes time to do feedback and control for the outer loop UAV system from failure to determine in the outer loop which will affect the stability of the system if not handled in time. As a large hysteresis delay complex systems it is the only way for UAV to master craft condition telemetry and remote data. UAV's prognostics and health management (PHM) technology, is to predict, monitor and manage the condition of UAV by using advanced sensor integration, real-time telemetry downlink data, and various algorithms and intelligent models. Based on the telemetry data and combined with actual engineering application demands UAVs, this paper analyzed the typical fault modes, established three UAV typical fault detection models, such as particle filter, K-Means cluster, Multi Layered Perceptron neural network.