【摘要】 针对一类故障预测问题提出了一种基于粒子滤波的故障预测算法。在算法的状态估计阶段,采用联合估计和粒子滤波同时估计对象系统故障演化模型状态和未知参数的后验分布。在算法的状态预测阶段,采用了两种不同的计算方法:一种方法是对状态变量当前时刻的后验分布进行迭代采样,从而获得未来时刻的状态变量的先验分布;另一种方法是采用数据驱动的方法预测未来一段时间内对象系统的量测信息,从而将未来时刻状态变量的先验分布的预测问题转化为一个求解后验分布的估计问题。采用高斯混合模型近似随机变量分布密度,从而将两种方法的计算结果在一个统一的预测框架之下进行有效交互,进一步提高了预测的准确性和可靠性。在算法的决策阶段,在获取的故障演化模型状态变量分布基础上,结合一定的故障判据近似计算出对象系统剩余寿命分布。故障预测仿真实验结果证明了所提算法的有效性。更多还原

【Abstract】 To solve certain kinds of fault prognostic problems,an algorithm based on particle filter is presented.On its the state estimation stage,the algorithm estimates the posterior distribution of system fault progression model states and parameters based on joint estimation and particle filter.On the state prediction stage,two different approaches are used.One samples the current state posterior distribution iteratively and uses the sampled particles to form the state prior distribution for some future time.The other uses the data-driven prognostic algorithm to predict the system measurements,to convert the problem of predicting prior distribution to the problem of estimating posterior distribution.The algorithm also adopts Gaussian mixture model to approximate the distribution of random variables,to guarantee that the above two approaches can fuse efficiently under one prognostic frame and improve prediction precision and reliability gradually.On the prognostic decision stage,based upon the above calculated state distribution combined with certain fault criteria the distribution of system remaining useful lifetime can then be inferred.Simulation results demonstrate the validity and feasibility of the proposed algorithm.更多还原