Automatic identification of corrosive factors categories according to the environmental factors

Time of wetness, SO2 and Cl - pollutants are three key factors for the selection of metal materials in engineering applications and the determination of atmospheric corrosivity categories. In the past, when one or more corrosive factors data is missing, corrosive factors categories were often subjectively determinedaccording to expert experience. In order to overcome such difficulty, this paper presents a method to automaticallydetermine corrosive factors categories using detected environmental factors data instead of expert scoring. In this method, Bayesian network was used to build the mathematicalmodel. And the inference was obtained by clique tree algorithm. The validity of the model and algorithm was verified by the simulation results.

کلیدواژه ها:

Bayesian Network ، Clique tree algorithm ، Corrosive factors ، Environmental factors