College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, People's Republic of China.
Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taoyuan, 32023, Taiwan, Republic of China.
ISA Trans. 2019 Feb;85:247-261. doi: 10.1016/j.isatra.2018.10.015. Epub 2018 Oct 12.
A new approach to fault detection and diagnosis (FDD) is developed for nonlinear stochastic dynamic process systems in this paper. It is called PFs-IMM, which combines particle filters (PFs) and the interactive multiple model (IMM) estimation. In this method, a multiple-model estimation scheme is first formulated to describe the complex process system poorly represented by a single model. The IMM algorithm can deal with abrupt changes in the behavior of operating processes. The residuals of the multiple models are examined for the likelihood of each model. A decision rule is employed to adaptively determine which model is the most appropriate one at each time step. Then based on IMM, a set of PFs run in parallel is used to estimate the states and the reconciled measurements even when the operating mode changes. Each of the PFs utilizes a particular mode to derive the estimation of the state variables as well as the reconciliation of the measured variables based on the probabilistic weighting scheme. From the multiple filters, the interaction among PFs allows the fusing of dynamic estimates. To achieve higher sensitivity to faults and more robustness to disturbances and noises, a new fault index function is developed for FDD. The proposed PFs-IMM approach provides an integrated framework. It can estimate the current operational or faulty mode of the system and derive the overall state estimation and the measurement reconciliation as well. The simulation solutions to the problems are obtained to demonstrate the effectiveness of the proposed method in highly nonlinear dynamic processes.
本文提出了一种新的故障检测与诊断（FDD）方法，用于非线性随机动态过程系统。它被称为 PFs-IMM，结合了粒子滤波器（PFs）和交互式多模型（IMM）估计。在这种方法中，首先制定了一种多模型估计方案，以描述单一模型难以表示的复杂过程系统。IMM 算法可以处理操作过程行为的突然变化。检查多个模型的残差以确定每个模型的可能性。采用决策规则自适应地确定每个时间步的最合适模型。然后，基于 IMM，一组并行运行的 PFs 用于估计状态和协调测量，即使操作模式发生变化。每个 PFs 都利用特定的模式来根据概率加权方案得出状态变量的估计值以及测量变量的协调。从多个滤波器中，PFs 之间的交互允许融合动态估计值。为了提高对故障的敏感性和对干扰和噪声的鲁棒性，为 FDD 开发了一个新的故障指标函数。提出的 PFs-IMM 方法提供了一个集成框架。它可以估计系统的当前运行或故障模式，并推导出整体状态估计和测量协调。通过模拟解决问题，证明了该方法在高度非线性动态过程中的有效性。
