| With the widespread application of big data,industrial Internet of Things and data acquisition technologies in industrial control systems,the equipment status monitoring based on big data is also concerned by the manufacturers of brick machine.Equipment maintenance personnel can accurately and directly grasp the real-time running status of the brick machine in the production process on remote computers,which can realize the remote monitoring and maintenance of the brick machine.Existing brick machine manufacturers generally only perform simple data collection and statistics on the brick machine.However,the operating state of the brick machine is not monitored and analyzed,and the utilization of the data is not sufficient.Therefore,how to effectively use the big data of brick machine for status monitoring is still a big challenge.This paper designs a set of status monitoring mechanism based on big data of brick machine.In view of the challenges of little knowledge accumulation and poor data quality of brick machine,this paper analyses the big data of brick pressing equipment based on the method of machine learning cluster analysis.What’s more,this paper constructs a process of brick machine data acquisition,data analysis and data modeling,which can distinguish the health status of the brick pressing equipment.The specific research contents are described as follow:1.Designed a scheme of data acquisition and communication for brick pressing equipment.In this scheme,the existing brick pressing equipment is updated and the data of brick-pressing equipment is collected by the industrial Internet of Things module.The data is uploaded to the server through Wi Fi or 4G.Thereby,it realizes the acquisition and transmission of the state and parameter characteristics of the industrial field device.The data which collected from the original industrial field of brick machine will be used for subsequent data analysis.2.Designed a data processing method for brick pressing equipment.The method integrates the original data source of the brick machine and then synchronizes the data to the Aliyun Cloud platform.The data will be processed in several steps,such as variable separation,data cleaning,feature extraction,integration and so on.Combining with the actual production impact,the current and current difference of the double-vibration motor are extracted as the main data features,which provide reliable data for the subsequent analysis of the brick equipment.3.Designed a method of analyzing the health status of brick machine based on cluster analysis.The method selects a clustering method which is suitable for the data characteristics of the brick pressing equipment for data analysis,and synchronizes the pre-processed brick pressing equipment data into the Aliyun Cloud Machine Learning Platform.Then a data clustering analysis model is built on the platform to realize big data analysis and data visualization.4.Completed the cluster analysis of the status of brick machine based on big data.This method uses K-means clustering analysis method to cluster the state of the brick pressing equipment,which realizes the identification of the several status of brick machine,such as standby,shutdown,abnormal state and normal working condition.This method can determine whether the data is in a fault condition and remind the operators to maintain the equipment,which can ensure the healthy operation of the brick machine. |
