Research On On-line Health Monitoring Technology Of Instruments And Instruments Based On Industrial Interconnection

In the context of the rapid development of the Internet,the Internet of Things and artificial intelligence,the measurement industry is also undergoing revolutionary changes.In the investigation of the domestic metrology industry,more and more industries are extremely demanding on the detection status of the metering device and the health status of the instrument itself.Therefore,it is extremely important for the instrument to continuously sense the self-calibration system throughout the day.In this paper,the instrumentation of the water plant in the vaccine workshop is taken as the research background,and the monitoring system based on industrial interconnection is established to realize the real-time monitoring of the instrumentation in the production process.The health status of the production equipment parameters and related instruments is processed by intelligent algorithms.The corresponding data analysis results are obtained,and the liberation of the measurement personnel for the regular inspection of the corresponding instrumentation can facilitate the real-time understanding of the health status of the instrumentation.The main research points of this paper are as follows:(1)Research and design of data sensing and transmission system based on industrial interconnection.In different application scenarios,the required sensing module and the transmission module will be different.The sensing mode can be summarized as direct acquisition data and indirect acquisition,and the transmission mode can also be divided into wired and wireless.In this paper,the data is sensed and transmitted mainly for related instruments and meters in the water production workshop of the Biological Research Institute.According to the situation on the ground,the corresponding sensing methods and transmission methods are adopted.In this paper,the intelligent sensing module and GPRS intelligent transmission module for PLC will be adopted.(2)Hybrid diagnostic algorithm model based on big data and artificial intelligence.The characteristics of industrial metering faults are complex.In order to ensure the accuracy of the diagnosis of instrumentation health status,different artificial intelligence diagnosis methods are used for hybrid diagnosis.Among them,the expert system-based diagnosis method is to summarize and store the knowledge and experience of the corresponding workers and administrators in the workshop,and establish a corresponding expert knowledge base for direct diagnosis.At the same time,real-time data collection and long-term accumulation can be carried out for the data on the shop floor.Based on these large amounts of data,an artificial intelligence model such as artificial neural network is used to train the corresponding data to obtain a comparative diagnostic model.Finally,the two types of diagnostic models are mixedly diagnosed,and the final hybrid diagnostic model is built to realize real-time monitoring of the health status of the corresponding instrumentation.(3)Visual real-time warning software cloud platform.The corresponding monitoring system is deployed on the cloud server,and can be accessed through the web terminal or the APP terminal to realize real-time data visualization,visualizing the real-time diagnosis effect,and having an abnormal state push warning function.