Cubic Dynamic Uncertain Causality Graph: A New Methodology for Modeling and Reasoning About Complex Faults With Negative Feedbacks

Scientific modeling and analysis for fault spreading process is a promising way for guaranteeing the safe, reliable, and efficient operation of complex system. However, the representing and reasoning of uncertain, time-varying, and sophisticated dependences are difficult, especially for the complex issues of dynamic negative feedback loops in multivariate time series. Dynamic uncertain causality graph (DUCG) provides a dynamic inference method without causality propagation across time slices, the disadvantage of which lies in the interpretability and applicability. In order to overcome the shortcomings of DUCG and extend its capabilities of temporal causality representation and dynamic reasoning, this paper proposes a new methodology named Cubic DUCG. The fundamental idea is to continuously generate the cubic causality graph online according to the sequential observations by discarding the restrictive Markov and conditional independence assumptions. Based on the complete causal dependencies representing the real-time fault spreading behaviors, the efficient and rigorous inference algorithm is thus proposed. The method is validated on fault data of the secondary loop of two nuclear power plant simulators, concerning the effectiveness, and in particular, the capability of dealing with complex dynamics and negative feedback processes.