Electrodes' Center Distance Design And Fault Diagnosis Of Double Electrode DC Fused Magnesia Furnace

Fused magnesia is an important refractory material in industrial production.It is widely used in chemical,metallurgy,aerospace and other fields.Fused magnesia furnace is the main production equipment of fused magnesia.At present,more fused magnesia Furnace equipments are three-phase AC power supply systems.As the three-phase AC fused magnesia furnace has a series of problems such as poor AC arc stability,low AC power factor,unbalanced three-phase arc power,frequent grid voltage flicker due to reactive power fluctuations and so on.However,these problems caused by AC arc are unavoidable when heated.Therefore,in recent years,the application of DC arc furnace has been gradually increased.The development of DC arc furnace has become the inevitable trend of the development of the technology of fused magnesium furnace.In the first main part of this thesis,a double electrode DC fused furnace is studied.First,through the study of DC arc and the analysis of heat transfer,the distribution model of electric arc on the surface of the melting pool is obtained.Then,the electrical analysis of the double electrode DC fused furnace was carried out,and the influence of current and electrodes'center distance on the arc power distribution on the surface of the melting pool was obtained.Finally,based on the above analysis,find the best current for fused magnesium furnace operation,and in the best operating current to get the best electrode' center distance and the best furnace shape.The second main part of this thesis,a fault diagnosis method applied to double electrode DC melting furnace is presented.This method is based on Fisher discriminant analysis and improved it.This method optimizes the between-class scatter matrix by introducing the weights of various classes.This method reduces the dominant effect of edge classes on classification and improves the classification accuracy.In addition,this method has also been greatly improved in establishing scatter matrices.Based on the manifold distance,the within-class and the between-class scatter matrices are established,which is combined with the within-class and the between-class scatter matrices based on the Euclidean distance.Therefore,fully tap out the features of all kinds of data.The final classification effect has been greatly improved.