Selection of Root-Cause Process Variables Based on Qualitative Trends in Historical Data Samples

Root cause analysis helps industrial plant operators in finding possible root causes of alarms and their associated abnormalities, where one pre-requisite information is the set of root-cause process variables. This paper proposes a method to determine a set of root-cause process variables for a primary process variable based on their qualitative trends. According to requirements on time durations, amplitude changes and correlation coefficients, qualitative trends of process variables are extracted from historical data samples via a dynamic programming approach. A set of root-cause process variables is selected as the one whose qualitative trend combinations are associated with the largest ratio in explaining increasing and decreasing trends of the primary process variable. An industrial case study is provided to illustrate the effectiveness of the proposed method.