Research on decision-level fusion method based on structural causal model in system-level fault detection and diagnosis

Abstract

At present, system-level fault detection and diagnosis (FDD) research often uses correlation-based machine learning methods combined with multiple heterogeneous diagnosis methods to improve the fault detection rate (FDR), that is, decision-level fusion. Since it does not take into account the causal direction of the decision relationship, it will affect the realization of the fusion objectives, and lead to the reduction of the fusion range and the decrease of the global decision on FDR. In this regard, the structural causal model (SCM), a commonly used causal model in causal science, can use the causal graph to ensure causal direction of fusion, and the structural equation can be used to achieve fusion objectives to increase FDR, which can improve this problem. In this paper, we propose seven fusion objectives according to the diagnostic advantage interval of each preliminary method, and use SCM to construct causal graph and structural equation to achieve decision-level fusion according to the proposed seven fusion objectives, thereby improving FDR. The proposed method is validated through the simulation platform Tennessee Eastman process. We choose to combine the prediction results of Linear Discriminant Analysis method and Gaussian Naive Bayes method to achieve decision-level fusion. The results show that compared with the single method and the Bayesian network decision-level fusion method, the proposed method can achieve the best results in the FDR of each single system state and average FDR, and the above indicators are significantly improved.