Component reliability in fault-diagnosis decision making based on dynamic Bayesian networks

Increasing effectiveness of model-based fault diagnosis: A Dynamic Bayesian Network design for decision making

This papers aims to design a new approach in order to increase the performance of the decision making in model-based fault diagnosis when signature vectors of various faults are identical or closed. The proposed approach consists on taking into account the knowledge issued from the reliability analysis and the model-based fault diagnosis. The decision making, formalised as a bayesian network, i...

Dynamic Bayesian Decision - Making :

Computational modeling of dynamic decision making using connectionist networks

In this research connectionist modeling of decision making has been presented. Important areas for decision making in the brain are thalamus, prefrontal cortex and Amygdala. Connectionist modeling with 3 parts representative for these 3 areas is made based the result of Iowa Gambling Task. In many researches Iowa Gambling Task is used to study emotional decision making. In these kind of decisio...

Dynamic Non-Bayesian Decision Making

The model of a non-Bayesian agent who faces a repeated game with incomplete information against Nature is an appropriate tool for modeling general agent-environment interactions. In such a model the environment state (controlled by Nature) may change arbitrarily, and the feedback/reward function is initially unknown. The agent is not Bayesian, that is he does not form a prior probability neithe...

Fault Diagnosis Based on Bayesian Petri Nets

This paper provides a kind of Bayesian Petri net model and presents a fault diagnosis method for power grid based on this model. Firstly, the BPN models for each component are established in power cut area separately, according to the fault spread directions. Secondly, the failed component is determined by the application of Petri net reasoning and Bayesian probability calculation. At last, the...