Data-Driven Methods for the Detection of Causal Structures in Process Technology

In modern industrial plants, process units are strongly cross-linked with each other, and disturbances occurring in one unit potentially become plant-wide. This can lead to a flood of alarms at the supervisory control and data acquisition system, hiding the original fault causing the disturbance. Hence, one major aim in fault diagnosis is to backtrack the disturbance propagation path of the disturbance and to localize the root cause of the fault. Since detecting correlation in the data is not sufficient to describe the direction of the propagation path, cause-effect dependencies among process variables need to be detected. Process variables that show a strong causal impact on other variables in the process come into consideration as being the root cause. In this paper, different data-driven methods are proposed, compared and combined that can detect causal relationships in data while solely relying on process data. The information of causal dependencies is used for localization of the root cause of a fault. All proposed methods consist of a statistical part, which determines whether the disturbance traveling from one process variable to a second is significant, and a quantitative part, which calculates the causal information the first process variable has about the second. The methods are tested on simulated data from a chemical stirred-tank reactor and on a laboratory plant.