Design And Implementation Of Equipment Capacity Monitoring And Analysis System Based On Industrial Internet Of Things

The main purpose of this thesis is to implement a monitoring and analysis system for the production capacity of a bearing factory production line.The special feature of this production line is that it has more than 20 production lines established in the early days.The numerical control system models are relatively complicated,and it is difficult and costly to implement machine networking transformation.For this batch of production line equipment,it is planned to use an external equipment monitoring module for data collection.The latest batch of more than ten production lines use Siemens PLC system and have direct networking capabilities.This system should realize the status monitoring of these two types of equipment and the collection and storage of capacity data;it should analyze the production data from the process,equipment,production line and other aspects;it should be able to visually display the results of the data analysis and assist field decision-makingThe thesis first analyzes the feasibility of the acquisition schemes required by the two devices.For the batch of machine tools using Siemens CNC,the communication scheme of Python-Snap7 is used to achieve data collection.Use specialized IoT modules for machine tools that cannot communicate directly.The PLC and inductive switches of the pipeline control equipment are used for counting and detection.The acquisition module and the production line connection equipment PLC implement data device summary through IO signals,and use the Internet of Things gateway and cloud platform to complete data collection.Then,the LED lights and buttons of the acquisition module are programmed to implement the functions of monitoring communication failures and manually reporting the status of the equipment.After that,the collected data was analyzed based on the hardware installation point number table and the PLC configuration table,and the data from two different sources were processed to achieve the same data structure output.Finally,establish a database to store production process data and analysis result data.Then,analyze the data from three perspectives:process,equipment and production line.In terms of process management,according to the characteristics of bearing grinding process,an analysis method of grinding machine process efficiency was proposed.Monitor the status of the equipment and calculate the time rate of the equipment.In terms of equipment management,the actual working hours of equipment production were statistically analyzed,and the stability of the production process was analyzed.The performance of the equipment and the overall efficiency of the equipment were calculated.In terms of production lines,a complete control plan for the assembly line is designed to track and monitor the balance of the assembly lineFinally,based on the configuration development platform,an electronic KANBAN was developed based on the data analysis results.Designed real-time production progress KANBAN,equipment monitoring KANBAN,product complete KANBAN,and production line management center KANBAN.And carried out field tests and verifications at the partner company.This thesis develops information collection method based on numerical control system and information collection based on Industrial Internet of Things,and solves the data collection problem of two types of typical equipment.Completed data analysis and visual KANBAN development from three aspects:process,equipment,and production line.In field use,this system provides good assistance for on-site decision management,reducing waste during production.The two data acquisition schemes and data analysis visualization schemes proposed in this paper have certain practical application value in the current digital transformation and application of actual factories.