基于Monte Carlo方法的自适应多模型诊断

Adaptive multi-model diagnosis using Monte Carlo method

多模型混合系统的模型切换服从有限状态的Markov链,其转移概率通常假定是已知的.当模型转移概率未知的时候,本文基于Monte Carlo粒子滤波器给出了混合系统状态估计的一种自适应算法.该算法假定未知的转移概率先验分布为Dirichlet分布,首先通过采样得到一组模型序列的随机样本,利用其中状态的转移次数计算先验转移概率,使用量测信息对样本更新选择后,获得模型转移概率的一种迭代的后验估计值,同时由粒子滤波器得到系统状态和模型概率的后验估计.将该方法用于混合系统的状态监测和故障诊断,仿真结果表明了算法的有效性.

Commonly,the models of hybrid system switch according to a finite state Markov chain with known transition probabilities.For state estimation of hybrid system with unknown transition probabilities,an adaptive estimation algorithm is proposed based on Monte Carlo particle filtering. The proposed algorithm assumes that the prior distribution of unknown transition probabilities follows Dirichlet distribution. Fast, a set of random samples of model sequence is achieved by sampling. Second, the prior transition probabilities are calculated by the frequency of model transitions in model sequence samples. Third,the posterior estimation of transition probabilities is achieved via measurement update and selection. Finally, the posterior estimation of state and model probability is obtained by particle filtering. In the state monitoring and multiple faults diagnosis of a class of hybrid system,the proposed method has been proved to be very effective.