Joint recurrence based root cause analysis of nonlinear multivariate chemical processes

A novel method of diagnosis and causality analysis of faults in chemical processes is developed based on the recurrence theory. By applying and adapting the joint recurrence plot (JRP), the effective feature or variables of each operating condition are examined and then they are used to detect and diagnose faults using an unsupervised method (the absence of fault labels and prior knowledge about different operating conditions), DBSCAN. Also, a method based on the concept of delayed joint recurrence plot is developed, which well reveals the ability to identify the main root cause of each fault and predict the propagation pathway of the fault affecting different variables. In order to reveal the capability of the proposed method in case of nonstationary, unstable, nonlinear data, two different multivariate chemical processes, i.e., Tennessee Eastman and chemical looping combustion, are used. Also, compared with other methods, it was found that the proposed method shows the best performance in fault diagnosis and root cause analysis of complex nonlinear processes even for unobservable faults. (C) 2021 Published by Elsevier Ltd.

Automated summary

A novel method based on recurrence theory is developed for fault diagnosis and causality analysis in chemical processes. By utilizing joint recurrence plot and delayed joint recurrence plot, the main root cause of faults can be identified and the propagation pathway of faults can be predicted. Experimental results demonstrate that the proposed method shows the best performance in fault diagnosis and root cause analysis of complex nonlinear processes.