Virtual sensing network for statistical process monitoring

Physical sensing is increasingly implemented in modern industries to improve information visibility, which generates real-time signals that are spatially distributed and temporally varying. These signals are often nonlinear and nonstationary in the high-dimensional space, which pose significant challenges to monitoring and control of complex systems. Therefore, this article presents a new virtual sensing approach that places imaginary sensors at different locations in signaling trajectories to monitor evolving dynamics within the signal space. First, we propose self-organizing principles to investigate distributional and topological features of nonlinear signals for optimal placement of imaginary sensors. Second, we design and develop the network model to represent real-time flux dynamics among these virtual sensors, in which each node represents a virtual sensor, while edges signify signal flux among sensors. Third, the establishment of a network model as well as the notion of transition uncertainty enable a fine-grained view into system dynamics and then extend a new Flux Rank (FR) algorithm for process monitoring. Experimental results show that the network FR methodology not only delineate real-time flux patterns in nonlinear signals, but also effectively monitor spatiotemporal changes in the dynamics of nonlinear dynamical systems.

Automated summary

This article presents a new virtual sensing approach that uses imaginary sensors placed at different locations in signaling trajectories to monitor evolving dynamics within the signal space. It proposes self-organizing principles to optimize the placement of these sensors and develops a network model to represent real-time flux dynamics among them. The establishment of the network model, along with the concept of transition uncertainty, enables a fine-grained view into system dynamics and introduces a new Flux Rank algorithm for process monitoring.