Quality-Oriented Efficient Distributed Kernel-Based Monitoring Strategy for Nonlinear Plant-Wide Industrial Processes

This paper studies a novel quality-oriented efficient distributed framework for nonlinear plant-wide industrial quality-related process monitoring. In this strategy, process variables contained in the local unit are divided into quality-related and quality-unrelated parts using the elastic network. Then, the least absolute shrinkage and selection operator technique is utilized to select the communication variables that are highly relevant to the quality-related part of the local unit from neighboring units, which not only improves the quality-oriented process monitoring performance but also reduces redundant communications. Then, for the reorganized quality-related part of the local unit, a reasonable orthogonal decomposition is developed to cope with the inherent flaws of kernel partial least squares. This decomposition further divides the process variable space into two orthogonal parts. For the remaining quality-unrelated part of the local unit, the kernel principal component analysis with a combined index is used to monitor it. Finally, the Bayesian fusion is used to improve the monitoring efficiency. The proposed scheme and the existing methods are compared using the Tennessee Eastman benchmark process, demonstrating the superiority and effectiveness of the proposed method.Note to Practitioners-For plant-wide process monitoring, a novel quality-oriented efficient distributed monitoring strategy is developed in this paper, which not only considers the monitoring of the quality variables within systems but also emphasizes the communication efficiency between local units and neighboring units. By using the proposed strategy, local unit and neighboring unit variables can be initially filtered by applying a combination of the elastic network and the least absolute shrinkage and selection operator technique, which not only takes into account the correlation between local units and neighboring units but also reduces unnecessary information transfer from neighboring units. As a result, it improves the efficiency of distributed monitoring and ensures the accuracy of quality-related fault detection. Furthermore, the supervised process monitoring for quality variables is realized with the help of the proposed strategy. By utilizing the monitoring results, practitioners can accurately determine whether the fault type is quality-related or quality-unrelated. This information facilitates the design of a more targeted fault-tolerant control scheme, reducing unnecessary fault-tolerant control actions and enhancing the efficient utilization of the control system, ultimately leading to energy savings. Finally, the incorporation of the Bayesian fusion strategy enables the generation of both global fault and local fault detection indicators. This feature proves beneficial for designing subsequent visualization platforms, providing comprehensive information for fault analysis and system visualization.