Research On Fault Diagnosis Technology Of Cement Firing System

The cement firing system is the key link in the process of cement production,and it's gre at significance to the safe and stable operation.In order to improve the recognition accuracy,r eal-time and robustness of the cement production process conditions,and realize the safety,lo w consumption,high yield,high quality of the cement production,improve the existing fault diagnosis methods and the development of new fault diagnosis system is imperative.This pap er use the intelligent diagnosis technology to combine with the existing technology of fault di agnosis method,which study on the cement production process with fault monitoring,predicti on and diagnosis methods.The main research contents are as follows:(1)Research contents,background and significance of selecting the topic are firstly introduced,then the process of cement production?the existing process fault characteristics and fault diagnosis methods are elaborated in the paper.(2)Through analyzing production process and the change of parameters when the fault occurs,the influence of the related process parameters to the specific process failure that may occur is analyzed.The wavelet analysis method,BP neural network and genetic algorithm are also used in the study.For example,the change of rotary kiln host current has a strong affinity with the whole thermal system,which has obvious relevance with cement kiln,such as red kiln.Through the comparative analysis,it proves that the wavelet analysis of kiln current signal processing has a good effect.This paper identifies rotary kiln current by wavelet methods and then analysis the discriminant on the working condition of rotary kiln.It is found that preheater gas temperature change has a important influence on the preheater conditions of firing system,such as crust of preheater smoke chamber,preheater pipe plug,and blockage of the preheater by wavelet analysis for rotary kiln current identification and then the discriminant analysis on the working condition of rotary kiln.this paper uses the BP neural network to forecast the preheater gas temperature anomaly or not,and uses the genetic algorithm to optimize the BP neural network to predict.It has been verified by the simulation that this method has a good effect for the recognition of the working conditions of firing system.(3)There has been a relation between many calcining faults and parameters in kiln,so we can identify on the basis of the change of the parameters during failure and the mutual relationship.For example,the formation of ring is correlated with the production of ball in kiln,at this time,air flow and secondary air parameters such as temperature,kiln hood pressure change,and five cyclone tube tremie pipe blockage and decomposition furnace bottom plug main bring about the change of temperature and pressure in all levels of the cyclone tube outlet,In addition,the change of these parameters may also be a result of formation of ring in kiln,so it is difficult to discriminate.Through analyzing the change of all parameters during the fault,This paper adopts clustering and genetic algorithm to improve dimension of the fault samples respectively,then extract the fault samples parameters and classify various fault diagnosis.For the classification of fault samples,the BP neural network learning algorithm has low convergence speed and no definitive explanation of classification rules,lack of transparency,however Probabilistic neural network(Probabilistic neural network,PNN)process is simple,having fast convergence rate and high stability,which totally converge in bayesian optimization solution.In this paper,PNN is used to classify diagnosis to fault samples.Verified by the simulation analysis,PNN neural network has the characteristics of clear classification ? high classification precision and less error rate,therefore it has a good application in classified fault diagnosis of the cement firing system...