Dynamic causal modeling and efficient fuzzy temporal causality reasoning in industrial fault diagnosis: a C-DUCG approach

Online fault diagnosis is crucial for improving the reliability of safety–critical industrial systems. Fault diagnosis becomes increasingly complex if the time-variations, fuzziness, and uncertain causal relationships related to the various internal factors in the system are present. In view of the time-varying working conditions of industrial systems, as well as the fuzzy uncertainty of fault data and knowledge, a C-DUCG (Cubic dynamic uncertain causality graph) approach is proposed for fault diagnosis. Such a solution is intended to help develop a generative cubic causality graph modeling scheme and a FUTURE (fuzzy temporal causality reasoning) algorithm, both of which facilitate the representation and reasoning about complex fault situations (involving temporal causalities and uncertain evidence). In C-DUCG, the strategies of causality simplification and EELA (event-oriented early logical absorption) are proposed to mitigate the complexities of modeling and reasoning. Comparative experiments and a sequence of fault diagnosis tests on a nuclear power plant (NPP) simulator validate the efficiency, recall, accuracy, and interpretability of C-DUCG in large-scale dynamic systems. Experiments reveal that the proposed algorithm achieves 0.62 and 0.999 in terms of recall rate AC@1 and AC@k, respectively, with the average reasoning time being 10 ms, and the average time spent in NPP fault diagnosis tests is 9.42 ms.