Abstract: The Causality Diagram methodology, which was based on Belief Network, overcomes some shortages in knowledge expressing and reasoning of Belief Network and has evolved into a mixed causality diagram methodology coping with discrete and continuous variables[1][2][3], and it was very useful for industrial fault diagnosis application. However, it is still confronted with a problem as Belief Network is, of high computation complexity. By Comparing several Markov Chain Monte Carlo(MCMC) simulating algorithms, and analyzing the requirement for stable-condition, the principle of sampling sequence and the criterion of sampling ending, this paper puts forward an improved simulating reasoning algorithm based on Gibbs simulation. The simulating algorithm will improve the diagnosis speed and accuracy, which has an important significance for the application in industrial online fault diagnosis.

Key words: causality diagram, belief network, gibbs simulation, fault diagnosis

关键词: ~{Rr9{M<, ~}, ~{PE6HMx~}, Gibbs~{7BUf~}, ~{9JUOUo6O~}