Fault analysis of industrial big data based on random forest algorithm
With the development of information technology,industrial Internet technology has been applied to all aspects of industrial big data production.The technology applications of data acquisition,data storage,data processing,data analysis and data visualization based on big data technology are becoming more and more mature and high-end.However,the risk of data anomalies in the production process is always a problem that enterprises cannot ignore.It is provided in this paper for performing feature extraction and dats processing to real-time data of industrial big data.The random forest algorithm is adopted to train industrial big data and build the model.The real-time data are inputted into the model and the parameters are updated dynamically to improve the classification accuracy of the model,from which the classification results are output.Finally,the fault warning and fault analysis of industrial big data are provided during the industrial production process.
万方数据
维普
