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

Abstract

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.INDEX TERMS Root cause analysis, qualitative trend analysis, industrial alarm systems.

Understanding Overlap in Automatic Root Cause Analysis in Manufacturing Using Causal Inference

“…The goal of RCA is to determine the causal mechanism behind a change from a desirable state to an undesirable one in order to ultimately keep a problem from recurring (Ong et al 2015). Robust RCA solutions are required (Cai et al 2019) since diagnosing problems is very important for safe and efficient operations (Wang and Chen 2019). However, RCA can be very complicated, requiring extensive system and execution analysis (Steinhauer et al 2016).…”