基于自适应粒子滤波的涡扇发动机故障诊断

针对涡扇发动机非线性、非高斯的特点,提出了一种自适应的粒子滤波算法用于涡扇发动机气路部件突变故障的诊断.为了减小算法的计算量并且保证滤波精度,分析了滤波精度和样本数目的关系,提出根据滤波过程中状态的方差自适应地调整粒子数,在保证一定的滤波精度下可以有效地减少滤波过程中使用的粒子数,提高了算法的实时性.同时,引入扩展卡尔曼滤波(EKF)用于更新粒子,产生重要概率密度函数,在一定程度上避免了粒子的退化.通过某型涡扇发动机的仿真分析表明:改进的算法相比标准粒子滤波算法用于涡扇发动机气路部件故障诊断时,参数估计的方均根误差减小了50%左右,且算法的计算量减小了30%.

Turbo-fan engine fault diagnosis based on adaptive particle filtering

An adaptive particle filter was proposed for the gas path component abrupt fault diagnosis of turbo-fan engine characterized by a nonlinear non-Gaussian system. In order to reduce the computational burden and ensure the filtering accuracy, the relation between the filtering accuracy and the sampling number was analyzed. The number of particles was adjusted in the filtering process according to the variance of the state variables. The proposed method could reduce the number of particles in the filtering process and computation time while guaranteed the filtering accuracy. The extended Kalman filter (EKF) was introduced to update the particles and generate the importance probability density function helping to avoid the particle degeneracy to some extent. A series of simulations on a turbo-fan engine indicates that the root mean square error of the improved particle filter for the turbo-fan engine fault diagnosis is reduced by 50% than the conventional particle filter, and the computational burden is also reduced by 30%.