Research of UAV Engine Fault Prediction based on Particle Filter
This paper presents an UA V 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 UA V fault prediction. As UA V 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 UA V 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.
UAV fault prediction particle filter.
Li Baoan Liu Zhihua Li Xinjun
Beihang University Beijing China Beihang University of Chemical technology Beijing China
国际会议
2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)
北京
英文
4041-4045
2009-08-16（万方平台首次上网日期，不代表论文的发表时间）