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Open AccessArticle

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

Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Fraunhoferstraße 1, Karlsruhe 76131, Germany
Institute for Anthropomatics, Karlsruhe Institute of Technology, Adenauerring 4, Karlsruhe 76131, Germany
Author to whom correspondence should be addressed.
Academic Editor: David Mba
Machines 2014, 2(4), 255-274;
Received: 5 March 2014 / Revised: 5 August 2014 / Accepted: 13 October 2014 / Published: 4 November 2014
(This article belongs to the Special Issue Machinery Diagnostics and Prognostics)
In 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
Keywords: root cause localization; causal structure discovery; time series analysis root cause localization; causal structure discovery; time series analysis
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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.

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