涡扇发动机故障诊断中粒子滤波改进方法

Improved Method of Particle Filter in Turbofan Engine Fault Diagnosis

针对标准粒子滤波算法诊断步数多且诊断结果噪声水平高的问题,提出伪协方差和自适应似然分布结合的改进粒子滤波算法。该算法通过使用伪协方差代替了粒子集协方差,保证采样得到的粒子能够更真实地反映突变情况,减少诊断步数;通过对似然分布自适应调整,增加其与先验分布的重叠区域,提高抽样率,增加有效粒子数,降低诊断结果噪声水平。发动机健康参数估计仿真结果表明:与标准粒子滤波算法相比,改进的粒子滤波算法能使诊断速度提高约27%,诊断精度提高约38%,有效地减少了突变故障的诊断步数,并显著降低了诊断结果的噪声水平。

An improved particle filter algorithm combining pseudo covariance and adaptive likelihood distribution was proposed for standard particle filter algorithms with multiple steps and high noise level of diagnostic results. By replacing the particle set covariance with pseudo covariance, the algorithm ensured that the sampled particles could reflect the mutation more realistically and reduce the number of diagnostic steps. By adaptive adjustment of the likelihood distribution,the overlapping area with the prior distribution was increased,the sampling rate and the effective particle number were increased,and the noise level of the diagnosis result was reduced. The simulation results of engine health parameter estimation show that compared with the standard particle filter algorithm,the improved particle filter algorithm can improve the diagnostic speed by about 27% and the diagnostic accuracy by about 38%,which effectively reduces the number of diagnosis steps of sudden faults and significantly reduces the noise level of the diagnostic results.