A double inference engine belief rule base for oil pipeline leakage

Abstract

Accurate prediction of oil pipeline leakage is important for energy security and environmental protection. The belief rule base (BRB) is a rule-based modeling approach that can make use of information with uncertainty to describe causal relationships in predicting oil pipeline leaks. Due to the complexity and uncertainty of oil pipeline systems, traditional BRB models produce a large rule base, which reduces the modeling capability and interpretability of BRB. Therefore, this paper proposes a double inference engine BRB (BRB-DI) model. Compared with the traditional BRB models, the new model diminishes the number of the rules from 56 to 25, while maintaining the accuracy. In the BRB-DI model, firstly, rule reduction is used to reduce the complexity of the model by comprehensively analyzing the importance of rules. Secondly, to ensure the completeness of the model rule base, a double inference engine consisting of the evidence reasoning (ER) algorithm and ER rule is proposed, and a new reasoning computation process is designed. Finally, an optimization algorithm based on projection covariance matrix adaptation evolution strategy (P-CMA-ES) is proposed to prevent the diminishing interpretability of the optimized model. In order to verify the effectiveness of the proposed method, an oil pipeline leakage prediction problem is studied as a numerical example.