Design of UAV ground auxiliary warning system based on data mining

This paper presents an UAV fault and state detection system which is based on data mining. In the UAV system, on account of its dynamic environment, mechanical complexity and other factors, it is difficult to avoid all potential faults. So in order to early detect the potential fault, fault forecast is necessary so as to avoid enormous losses. As the input and output response model is nonlinear and multi-parameters, it is need to find an appropriate way to of fault detection for system maintenance and real-time command. Data mining (DM) is the process of posing queries and extracting patterns, often previously unknown from large quantities of data using pattern matching or other reasoning techniques. Nowadays, DM technologies have been widely used in security including for national security as well as for machine security. Their ability to deal with nonlinear and multi-parameters makes them suitable for application to the UAV fault detection. UAV is an extremely complex system, two important aspects of monitoring are focused on this paper: 1) Engine condition monitoring and fault detection; 2) flight attitude monitoring. The experimental result indicates the effectiveness of this system.