Dynamic Uncertain Causality Graph Applied to Dynamic Fault Diagnoses and Predictions With Negative Feedbacks

Abstract

Intelligent systems are desired in dynamic fault diagnoses for large and complex systems such as nuclear power plants. Dynamic uncertain causality graph (DUCG) is such a system presented previously. This paper extends the DUCG methodology to deal with negative feedbacks, which is one of the most difficult problems in fault diagnosis, and predicts the fault development online. Two methods are presented. One does not involve causality propagation across time slices. Another involves causality propagation through time slices. A nuclear power plant simulator located at Tsinghua University is used to test the DUCG methodology. A typical experiment involving negative feedback is given, which jointly applies the methods separately presented in different papers, such as how to deal with dynamic fault diagnosis and directed cyclic graphs (DCGs), and the methods presented in this paper. Results show that DUCG is powerful in knowledge representation, diagnosing possible faults, and predicting developments of faults. It also demonstrates that DUCG is robust, which means that the DUCG inference does not rely much on the accuracy of probability parameters. The logics of diagnoses and predictions are graphically displayed, so that users know not only inference results, but also why they are correct.