论文标题
传感器故障检测和通过网络估计的隔离：全等级动力学系统
Sensor Fault Detection and Isolation via Networked Estimation: Full-Rank Dynamical Systems
论文作者
论文摘要
本文考虑了线性全级动力系统的同时传感器故障检测，隔离和网络估计的问题。提出的网络估计是单个时间尺度协议的变体，基于（i）在\ textit {a-priori}估计值和（ii）测量创新上的共识。传感器网络上的必要连接条件和稳定块对基因对基质的增益矩阵是根据我们以前的工作得出的。考虑到存在系统和测量噪声的附加故障，得出传感器处的估计误差，并定义了适当的残差以进行故障检测。与文献中的许多作品不同，没有考虑在噪声上简化上限条件，我们假设高斯系统/测量噪声。然后根据估计误差协方差规范定义概率阈值以进行故障检测。最后，提出了图理论传感器置换方案，以恢复由于删除故障传感器而导致的网络可观察性损失。我们在一个说明性的学术示例中检查了提出的故障检测和隔离方案，以验证结果并与相关文献进行比较研究。
This paper considers the problem of simultaneous sensor fault detection, isolation, and networked estimation of linear full-rank dynamical systems. The proposed networked estimation is a variant of single time-scale protocol and is based on (i) consensus on \textit{a-priori} estimates and (ii) measurement innovation. The necessary connectivity condition on the sensor network and stabilizing block-diagonal gain matrix is derived based on our previous works. Considering additive faults in the presence of system and measurement noise, the estimation error at sensors is derived and proper residuals are defined for fault detection. Unlike many works in the literature, no simplifying upper-bound condition on the noise is considered and we assume Gaussian system/measurement noise. A probabilistic threshold is then defined for fault detection based on the estimation error covariance norm. Finally, a graph-theoretic sensor replacement scenario is proposed to recover possible loss of networked observability due to removing the faulty sensor. We examine the proposed fault detection and isolation scheme on an illustrative academic example to verify the results and make a comparison study with related literature.
