Data-Driven Methods for the Detection of Causal Structures in Process Technology
AbstractIn modern industrial plants, process units are strongly cross-linked with eachother, and disturbances occurring in one unit potentially become plant-wide. This can leadto a flood of alarms at the supervisory control and data acquisition system, hiding the originalfault causing the disturbance. Hence, one major aim in fault diagnosis is to backtrackthe disturbance propagation path of the disturbance and to localize the root cause of thefault. Since detecting correlation in the data is not sufficient to describe the direction of thepropagation 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 comeinto consideration as being the root cause. In this paper, different data-driven methods areproposed, compared and combined that can detect causal relationships in data while solelyrelying on process data. The information of causal dependencies is used for localization ofthe root cause of a fault. All proposed methods consist of a statistical part, which determineswhether the disturbance traveling from one process variable to a second is significant, and aquantitative part, which calculates the causal information the first process variable has aboutthe second. The methods are tested on simulated data from a chemical stirred-tank reactorand on a laboratory plant. View Full-Text
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Kühnert, C.; Beyerer, J. Data-Driven Methods for the Detection of Causal Structures in Process Technology. Machines 2014, 2, 255-274.
Kühnert C, Beyerer J. Data-Driven Methods for the Detection of Causal Structures in Process Technology. Machines. 2014; 2(4):255-274.Chicago/Turabian Style
Kühnert, Christian; Beyerer, Jürgen. 2014. "Data-Driven Methods for the Detection of Causal Structures in Process Technology." Machines 2, no. 4: 255-274.