Meticulous process monitoring with multiscale convolutional feature extraction

Abstract

Due to the interaction of process variables, process data is in essence graph-structured with non-Euclidean nature. Hence, learning the graph representation in a low-dimensional Euclidean space will be helpful for gaining the true insights underlying the industrial process. In this study, a meticulous process monitoring method (PM-MCF) is proposed based on multiscale convolutional feature extraction. For the proposed method, the interactions between different process variables are identified using causality inference. According to the obtained graph structure, convolutional filters are specifically designed for each process variable. In this way, the local correlation within directly related variables and their corresponding dynamic information can be effectively extracted. Besides, with the increasing of convolutional layers, more variables can be involved through the interaction relationship to explore a larger reception field. Based on the obtained feature matrices, sub-models are developed to calculate the monitoring statistics and their corresponding control limits. Finally, the decisions of all the sub-models are integrated to identify the operation status of the process system. It is noted that the proposed PM-MCF can be readily generalized to other existing methods by replacing the selected filter and the developed sub-models. The monitoring performance of the proposed method is illustrated using process data collected from a thermal power plant. Experimental results show that the proposed method can accurately detect the process anomalies using the extracted causal relationship.