基于改进核平滑辅助粒子滤波的失效预测方法

针对系统模型存在多个未知参数的情况,提出了一种基于改进核平滑辅助粒子滤波(improved kernel smoothing auxiliary particle filtering, IKS-APF)的失效预测方法。首先,在已有核平滑辅助粒子滤波基础上引入增益因子和加速因子,使其具有参数方差双向调节能力和更快的参数估计收敛速度。然后,使用ISK-APF进行状态和参数的联合估计,为确保参数估计的准确性同时减少参数的不确定性,设计了方差监视和短期预测误差匹配相结合的自适应粒子方差控制方案。最后,使用最新估计到的状态和参数粒子进行迭代预测,并通过统计状态粒子首达失效状态空间的时间计算出剩余使用寿命（remaining useful life, RUL）。仿真结果证明了本文方法的有效性和优越性。

Failure prognostics using improved kernel smoothing auxiliary particle filtering

A failure prognostics approach based on improved kernel smoothing auxiliary particle filtering (IKS-APF) is proposed for systems with multiple unknown parameters. Firstly, a gain factor and an acceleration factor are employed in the kernel smoothing APF to bi-directionally control the parameter variance and accelerate the parameter convergence. Secondly, the IKS-APF method is used to jointly estimate the states and parameters. In order to ensure the accuracy of parameter estimation and reduce its uncertainty, an adaptive control scheme for the particle variance of parameters is presented, combining the variance monitoring and the short term prediction errors. Finally, iterative prediction is implemented based on the latest estimated state and parameter particles, and then the remaining useful life (RUL) is calculated by the time of each propagated state particle first entering the failure zone. Simulation results demonstrate the effectiveness and superiority of the proposed approach.