The Cubic Dynamic Uncertain Causality Graph: A Methodology for Temporal Process Modeling and Diagnostic Logic Inference

To meet the demand for dynamic and highly reliable real-time fault diagnosis for complex systems, we extend the dynamic uncertain causality graph (DUCG) by proposing novel temporal causality modeling and reasoning methods. A new methodology, the Cubic DUCG, is therefore developed. It exploits an efficient scheme for compactly representing and accurately reasoning about the dynamic causalities in the system fault-spreading process. The Cubic DUCG is characterized by: 1) continuous generation of a causality graph that allows for causal connections penetrating among any number of time slices and discards the restrictive assumptions (about the underlying graph structure) upon which the existing research commonly relies; 2) a modeling scheme of complex causalities that includes dynamic negative feedback loops in a natural and intuitive manner; 3) a rigorous and reliable inference algorithm based on complete causalities that reflect real-time fault situations rather than on the cumulative aggregation of static time slices; and 4) some solutions to causality simplification and reduction, graphical transformation, and logical reasoning, for the sake of reducing the reasoning complexity. A series of fault diagnosis experiments on a nuclear power plant simulator verifies the accuracy, robustness, and efficiency of the proposed methodology.