Study And Practice On The Machine Adaptability Of Cigarette Auxiliary Materials Based On Industrial Big Data Analysis

The machine applicability of cigarette auxiliary materials is the key to the effective operation rate of cigarette production equipment,cigar ette product quality and materials cost of production,which has become the focus of cigarette production.The practical machine applicability problems affecting the quality of cigarette products are usually reflected in the form of machine failure phenomen on.Categorizing different types of machine failure phenomenon,forecasting and controlling the elimination of cigarette products caused by the machine adaptability problems of cigarette auxiliary materials,parametric designing key indexes of cigarette auxiliary materials,are the prerequisite for effective measures to improve production efficiency,and have important theoretical research value and practical significance.SVM(Support Vector those,SVM),as a new machine learning method,to solve the practical classification and prediction of industrial high dimension and nonlinear data,has a strong advantage over other big data analysis and mining techniques in training learning ability and generalization ability.Participated in the national bureau project?The research and application of the technology adaptability of cigarette packaging based on big data analysis ?of H company,combined the production data and simulation experimental data of the big data platform of the cigarette dynamic diagnosis system(MES),this paper carries out the following three main research work based on the SVM theory method:Firstly,focusing on the classification of cigarette auxiliary materials of machine adaptability,two classification algorithms(Self-training algorithm and Co-training algorithm)based on semi-supervised SVM are proposed in this paper.The semi-supervised SVM classifier group of machine adaptability under the key index system of cigarette trademark paper is obtained.Combined with data validation and compared with traditional second-order clustering method,the two classification algorithms have good learning ability.The influence of the number of training data on the classification accuracy is discussed.Secondly,a support vector regression machine based on ?-insensitive loss function is proposed for the prediction of cigarette packet rejection rate caused by the machine adaptability problems of cigarette label paper.The SVR prediction model of the key index system of label paper is obtained by training.The predictive accuracy of test data of SVR based on ?-insensitivity loss function and of traditional multiple regression is compared with numerical results.In addition,statistical methods for predicting model test and training data processing are also discussed.Thirdly,based on SVR,a predictive control theory is proposed to solve the problem of parameter design of key indexes of cigarette label paper and quality control of machine adaptability.The multivariate nonlinear model is transformed into a linear regression model by combining kernel trick.Using kernel principal component analysis(KPCA)to secondary extraction of key features,the parameter design of friction coefficient and indentation force of cigarette label paper is realized,and the fluctuation of the cigarette packet rejection number caused by the machine adaptability problems of cigarette label paper is predicted.Currently,the participating research project has been successfully finished and recommended to declare the science and technology award of China national tobacco corporation.The main research results have been applied in the production of H company since this year.Compared with previous years,the rejection rate of cigarette products decreased from 0.051 to 0.032,which decreased by 37.3%.The effective operation rate of cigarette equipment and the utilization ratio of cigarette materials are greatly improved.