Research of UAV engine fault prediction based on particle filter
Baoan Li, Zhihua Liu, Xinjun Li · 2009
This paper presents an UAV engine fault prediction approach which is based on particle filtering framework. As the UAV input and output response model is nonlinear and multi-parameters, it is needed to find an appropriate method of fault prediction for system maintenance and real-time command. Particle filters are sequential Monte Carlo methods based on point mass (or `particle') representations of probability densities, which can be applied to any state-space model. Their ability to deal with nonlinear and non-Gaussian statistics makes them suitable for application to the UAV fault prediction. As UAV is an extremely complex system, this paper mainly introduces the application on the engine speed. In this particle, the related works are: 1) Model based on the UAV high-altitude flight data; 2) depending on actual data, Analyse the model using particle filter for fault prediction. The experimental result indicates the effectiveness of this approach.