This paper proposes a similarity-based Particle Filter (PF) method for Remaining Useful Life (RUL) prediction with improved performance. In the proposed methodology, Maximum Mean Discrepancy (MMD) and Kernel Two Sample Test are firstly adopted to query similar Run-To-Failure (R2F) profiles from historical data library. The states and parameters of degradation are initialized based on the similar R2F profiles. Next, Rao–Blackwellized​ Particle Filter (RBPF) is employed to update the degradation states based on the initialization. The RUL prediction results are obtained by extrapolating the degradation states updated by RBPF. The proposed RUL prediction method holds several advantages: (1) compared with other PF methods, the proposed model includes historical knowledge from similar R2F profiles; (2) compared with similarity-based methods, the proposed model presents good probabilistic interpretation of prediction uncertainties based on RUL distribution. The effectiveness and superiority over other peer algorithms are justified based on a public aero-engine dataset for prognostics.

本文提出了一种基于相似度的粒子滤波（PF）方法，用于改进剩余寿命（RUL）预测。在所提出的方法中，首先采用最大平均差异（MMD）和核两个样本测试从历史数据库中查询相似的运行失败（R2F）配置文件。基于类似的R2F配置文件初始化降级的状态和参数。接下来，使用Rao-Blackwellized粒子滤波器（RBPF）根据初始化来更新退化状态。通过外推RBPF更新的退化状态获得RUL预测结果。提出的RUL预测方法具有以下优点：（1）与其他PF方法相比，该模型包括来自相似R2F轮廓的历史知识；（2）与基于相似度的方法相比，所提出的模型基于RUL分布提供了对预测不确定性的良好概率解释。基于公共航空发动机数据集的预测结果，证明了其相对于其他对等算法的有效性和优越性。