Data Monitoring And Analysis Of Polypropylene Production Line

In recent years,manufacturing enterprises in China have gradually realized the transformation and development to automation and intellectualization of the industry.Compared with the vigorous development of commercial big data and Internet big data,industrial big data lags far behind in scale and popularity.Various forms of industrial data generated in industrial line and production process have not been processed and used scientifically and effectively.The scientific utilization of data analysis and mining technology in industry has lagged far behind other industries.This situation makes manufacturers unable to respond quickly to changes in production and market,and indirectly causes problems such as high manufacturing costs.In the paper,the industrial raw data stream is converted into a signal file form that the computer can accept and processed,and the information data is collected through the collection and analysis tool.Use the application to perform data processing operations on target datasets(such as missing value interpolation,dirty data processing,data correction,white noise recognition,etc.),data analysis operations,and data visualization operations.In the visualization process,select the analytical visualization model that is most suitable for experimental research,and perform visual processing analysis for different types of products.Through the analysis and visualization methods and ideas of the data set with limited time span,it provides a reference for the future time span and the data size is complete.In the paper,in order to achieve the purpose of predicting future changes in production trends in the short term.In view of the offline data of production line products in recent years,the ARMA model was selected for time series modeling prediction,and some target sequences were selected to predict the gray model GM(1,1) of short-term small samples.The prediction data of this period are obtained by two different models,and different prediction values are combined with specific weights to obtain the predicted values of the ARMA-GM combination model.By comparing different models,a prediction model with higher accuracy and more realistic conditions is obtained.Through this paper,the product production change information with time as the influencing factor is obtained,which assists the production line staff to adjust the deviation fluctuation of the weight and modify the nominal deviation amount under the corresponding circumstances.Assist product line management personnel to understand the operating status of the equipment and even the adjustment of personnel working hours.Assist product manager to adjust the production plan of the entire production line and judge the trend of market demand changes.