Root cause analysis for process industry using causal knowledge map under large group environment

Root cause analysis (RCA) is a powerful tool utilized to identify the underlying causes of an event or problem. However, due to the specificity of production requirements in the process industry, existing methods still face challenges such as insufficient or unreliable data, and intrinsic complexities. Therefore, RCA relies on decision making in the process industry which is actually a typical knowledge-intensive work. To address these challenges, we propose a new causal knowledge maps-based RCA method that combines a newly developed grey reasoning dynamic uncertain causality graph (GRDUCG) with an improved grey relation analysis (IGRA) technique. The proposed method utilizes GRDUCG to construct a causal knowledge map to represent the complex knowledge. Under the large group environment, the IGRA method is employed to integrate causal knowledge provided by domain experts, thus determining the parameters' values which take the form of interval grey numbers. Due to the importance of distinguishing coefficient in IGRA, a new decision method is proposed to improve GRA instead of subjective decision based on the maximum information entropy. Root causes are identified using the maximum posterior probability on the causal knowledge map. Finally, we demonstrate the effectiveness and practicality of our proposed approach by utilizing GRDUCG-IGRA to analyze abnormal conditions in a real-world aluminum electrolysis plant.

Automated summary

Root cause analysis (RCA) is a valuable tool for identifying the underlying causes of an event or problem. However, existing methods in the process industry face challenges due to limited or unreliable data and inherent complexities. To overcome these challenges, a new RCA method is proposed that combines a grey reasoning dynamic uncertain causality graph (GRDUCG) with an improved grey relation analysis (IGRA) technique. This method utilizes GRDUCG to construct a causal knowledge map and integrates causal knowledge provided by domain experts using IGRA.