Complex System Monitoring Based on Distributed Least Squares Method

Abstract

The distributed monitoring framework is undoubtedly more suitable for large-scale complex industrial systems. However, most existing distributed monitoring methods ignored the information interaction between the local system and its neighbors. In this article, an improved distributed fault detection framework that considering the communication between subsystems is present. The system decomposition is optimized based on the monitoring performance with mechanism knowledge as constraints. The integration of mechanism and data is helpful to find the appropriate common variables between subsystems. The distributed partial least squares (DPLSs) algorithm is proposed to address the local monitoring challenges caused by the propagation of a common variable. The local monitoring model takes full advantage of the information from neighbors to reduce the uncertainty of the local system. Bayesian fusion performance metrics strategy is implemented to detect system status. The simulation results of the Tennessee Eastman process verify the effectiveness of the proposed scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article attempted to tackle an issue derived from distributed process monitoring of industrial processes. Even in an era of big industrial data, the fusion idea of process data and mechanism knowledge also provides a solution to the process decomposition monitoring strategy. It reduces the computational complexity, corrects the misdirection caused by the false information hidden in the measurements, and further increases the monitoring accuracy. Considering the information flowing and spreading along with the process equipment, common variables are used to describe the interaction between different subsystems. Then, the pretrained monitoring model and the online monitoring strategy are given to promote automatic implementation. The operability and monitoring accuracy of the proposed method is verified. It is suitable for process monitoring of large-scale complex industrial systems.