Wang Yudong, Guo Tingting, He Xiaodong, Rong Lihong, Li Juan
College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China.
School of Electromechanical and Automative Engineering, Yantai University, Yantai 264005, China.
Sensors (Basel). 2025 Sep 1;25(17):5396. doi: 10.3390/s25175396.
This paper investigates the joint state and fault estimation problem for a class of nonlinear systems subject to both measurement censoring (MC) and random missing measurements (MMs). Recognizing that state estimation for nonlinear systems in complex environments is frequently compromised by MMs, MC phenomena, and actuator faults, a novel joint estimation framework that integrates improved Tobit Kalman filtering and federated fusion is proposed, enabling simultaneous robust estimation of system states and fault signals. Among them, the Tobit measurement model is introduced to characterize the phenomenon of MC, a set of Bernoulli random variables is used to describe the MM phenomenon and common actuator faults (abrupt and ramp faults) are considered. In the fusion estimation stage, each sensor transmits observations to the local estimator for preliminary estimation, then sends the local estimated values to the fusion center for generating fusion estimates. The local filtering error covariance is ensured and the upper bound is minimized by reasonably determining the filter gain, while the fusion center performs fusion estimation based on the federated fusion criterion. In addition, this paper proves the boundedness of the filtering error of the designed estimator under certain conditions. Finally, the effectiveness of the estimation framework is demonstrated through two engineering experiments.
本文研究了一类同时受到测量删失（MC）和随机测量缺失（MMs）影响的非线性系统的联合状态与故障估计问题。认识到复杂环境中非线性系统的状态估计经常受到测量缺失、测量删失现象以及执行器故障的影响，提出了一种集成改进的Tobit卡尔曼滤波和联邦融合的新型联合估计框架，能够同时对系统状态和故障信号进行鲁棒估计。其中，引入Tobit测量模型来表征测量删失现象，使用一组伯努利随机变量来描述测量缺失现象，并考虑了常见的执行器故障（突变和斜坡故障）。在融合估计阶段，每个传感器将观测值传输到本地估计器进行初步估计，然后将本地估计值发送到融合中心以生成融合估计。通过合理确定滤波器增益来确保本地滤波误差协方差并使其上界最小化，而融合中心则基于联邦融合准则进行融合估计。此外，本文证明了在一定条件下所设计估计器的滤波误差的有界性。最后，通过两个工程实验验证了估计框架的有效性。
