Research On Fault Diagnosis And Quality Prediction Using Support Vector Machines In Cement Burning System

In the production process of the cement industry,effective process monitoring and quality control are crucial to guarantee production safety and increase product quality and economic gains.How to make full use of the data and information from the monitoring system in the production process to diagnose technological faults in the production process and predict the quality of the products is an urgent problem to be solved.Support Vector Machines(SVM) is an effective method applicable to the fault diagnosis and quality prediction when the samples are small.This study takes the technological fault diagnosis of the cement burning system,the prediction of the free calcium oxide content in the clinker,and the prediction of the fineness of raw mill finished products as its research objectives.We systematically studied in depth the SVM multi-class classification algorithm and the SVR estimation algorithm and constructed a model for the technological fault diagnosis of the cement burning system and a model for the prediction of product quality.In so doing,we hope to provide an effective method of guiding production and increasing product quality and production efficiency.To be brief,the main work of this study is as follows:1.In view of the characteristics of fault diagnosis under the circumstances of finite samples and the difficulties met by the methods based on traditional pattern recognition in the classification of fault patterns,with the technological faults in the cement burning system as the diagnostic objects,we studied the key issues of using SVM in fault diagnosis,presented the basic procedures for a SVM-based fault diagnosis,proposed a SVM multi-class classification algorithm on the basis of the interclass separability measure,a SVM multi-class classification algorithm on the basis of kernel clustering,a SVM multi-class classification algorithm on the basis of the semi-fuzzy hypersphere,and thereafter established their corresponding multi-class classification models,and then we analyzed the factors influencing the performance of the multi-class classifiers.2.In order to lower the computation complexity of the classifiers and the SVRs and increase the precision of classification and regression,considering the shortcomings of the principal component analysis(PCA) in feature extraction,through the computation of the inner product kernel function in the original feature space,we realized the nonlinear mapping from the original feature space to the high-dimension feature space,and in the high-dimension feature space,the nonlinear principal component of the original feature data are obtained through the PeA of the mapping data.Thus,we proposed and actualized an effective nonlinear feature extraction method on the basis of the kernel principal component analysis (KPCA).3.To lower the free calcium oxide content in the clinker,to reduce the computation complexity of predicting the fineness of raw mill finished products,and to increase the prediction precision,we proposed a nonlinear feature extraction method on the basis of the combination of rough sets(RS) reduction and KPCA,conducted preliminary treatments to the sample data by using the RS knowledge reduction algorithm,deleted the redundant attributes, reduced the number of the dimensions of the sample's input space,and then conducted the nonlinear feature extraction by using KPCA.On the basis of these procedures,we proposed an integrated RS-KPCA-SVM regression algorithm.By means of RS attribute reduction and the study of the KPCA feature extraction,we obtained the number to be used in guiding the selection of the kernel principal components by using the RS reduction results.4.We analyzed the causes to and the main phenomena of the common technological faults of the cement burning system.By using the fault-diagnosing method which combines the KPCA and the SVM,we proposed two kinds of binary tree SVM multi-class classification algorithm and the semi-fuzzy hypersphere multi-class classification algorithm for the diagnosis of the technological faults of the cement burning system,and we established a new fault diagnosis model for the new-style dry-process cement burning system.After that,we conducted simulation studies of the method of diagnosing the technological faults for the cement burning system when small samples were adopted.The testing results indicate that the proposed algorithms can distinctly reduce the time required for training and testing,and that the classification precision is relatively desirable.5.In view of the problem that it is impossible to monitor the cement quality online in the cement production process or there is a relatively long period of lagging,we analyzed the characteristics of the production process and the technological process of the 5000t/d cement, selected appropriate process technological parameters,extracted online process operational data,and on the basis of the proposed algorithms in this study,we proposed several methods of predicting the product quality in the process of cement production,and finally the prediction results verified the effectiveness and validity of the proposed multiple prediction methods.