Root cause analysis of industrial faults based on binary extreme gradient boosting and temporal causal discovery network

Abstract

As the modern industry develops, fault identification and root cause analysis in industries have become a significant problem. In this work, a new framework is developed to analyze the root cause of faults in the absence of historical fault information. First, binary-extreme gradient boosting (Bi-Xgboost) is proposed to analyze the fault contribution of variables. When a new fault occurs, the changes in the importance of variables before and after the fault occurrence are compared, and the contribution of the process variables to the fault is calculated. Secondly, a fault variables screening method based on the number of variables called mean contribution threshold (MCT) is proposed for screening the appropriate number of fault variables. In addition, a temporal causal discovery network (TCDN) is introduced for root cause analysis with causal time lag information. The proposed framework was validated in the Tennessee Eastman process, and results show that it can identify exact root causes and propagation paths of faults without historical fault data modeling.