Enhanced multicorrelation block process monitoring and abnormity root cause analysis for distributed industrial process: A visual data-driven approach

Abstract

With the rapid expansion of the scale of modern industrial processes, more and more machine learning approaches using process variables for process monitoring and alarm analysis. The complex correlation of these variables makes a purely process knowledge-based variable division method unsatisfactory for process monitoring. To address this problem, a distributed process monitoring and abnormity root cause analysis model is built from a data-driven perspective. The proposed hierarchical clustering-based multicorrelation block partial least squares (HCMCB-PLS) divides the whole process into several blocks by using hierarchical clustering (HC), and the maximum information coefficient (MIC) is performed to select the correlation variables between the sub-blocks. PLS is conducted in each sub-block for process monitoring. Besides, a modified contribution-based abnormity root cause analysis strategy is developed, which uses an online distributed contribution analysis method to track the root cause variables. The effectiveness of proposed HCMCB-PLS is validated through a case study on the Tennessee-Eastman process. Comparative simulation results indicate that the HCMCB-PLS methodology outperforms other models in both industrial process monitoring and abnormity root cause analysis.