A selfadaptive neural network weight adjustment particle filter algorithm was proposed for aeroengine gas path fault diagnosis of the nonlinear and nonGaussian properties of aeroengine. Number of particles split and adjusted was determined by the distribution of particles. Then particles were spilt by the way of normal distribution and adjusted by back propagation (BP) neural network

which avoided the degradation and impoverishment of particles and had stronger selfadaptive and tracking ability. The simulation results of onedimensional nonlinear tracking model and aeroengine gas path fault diagnosis show that selfadaptive neural network weight adjustmentparticle filter (SANNWAPF) algorithm has a good nonGaussian performance. Compared with normal particle filter

SANNWAPF improved 21% in accuracy of onedimensional nonlinear tracking model

30% with Gaussian noise and 26% with nonGaussian noise in aeroengine gas path fault diagnosis; and the diagnosis speed improved about 7 times with Gaussian noise and 10 times with nonGaussian noise.