A method to obtain prior knowledge in the fault diagnosis based on probability model

Network has uncertain characteristic as its appearances, which made the reasoning based on probability model become a hotspot in faults diagnosis field. For uncertainty reasoning methods, how to obtain the prior knowledge is very important. In the past, the knowledge usually obtained through artificial experience or random hypothesis, which is very random and not reasonable. In order to obtain prior knowledge effectively, a method using Bayesian networks learning to learn knowledge dynamically from previous sample has been put forward, which made the diagnosis system be self-adaptive. Experiment turns out that Bayesian networks learning can obtain prior knowledge effectively and increase the diagnosis accuracy.