未知噪声下非线性系统的故障检测方法研究

摘要

In the production process, aiming at solving the problem of fault detection for nonlinear systems with unknown noise,an improved fault detection method based on particle filter is proposed.Firstly, the real-time estimation of the unknown noise characteristics was obtained by the Sage-Husa filter,introducing UKF to avoid the error caused by linearization of Sage-Hu-sa estimator,and taking full account of the latest measurement information to generate a new proposal distribution function.Then, the resampling process based on the weight-jittered firefly algorithm was optimized,which can reduce the problems of degeneracy and diversity in the particle filter.Finally,comparing the measured value based on improved particle filter with the actual meas-ured value,residual was generated,and the diagnosis was identified through analyzing the residual.Simulation results show the effectiveness of this method.%为解决生产过程中未知噪声背景下非线性系统的故障检测问题,提出了一种基于改进粒子滤波的故障检测方法.首先利用Sage-Husa估计器直接对未知噪声特性进行估计,引入UKF避免Sage-Husa估计器线性化带来的误差,并充分考虑最新量测信息产生新的建议分布函数;然后采用权值抖动的萤火虫算法优化重采样过程,缓解粒子退化和样本枯竭问题;最后依据改进粒子滤波估计的量测值与实际量测值比较产生残差,以残差为依据进行故障检测.仿真结果验证了该方法的有效性.