Research On Data-driven Diagnosis Mechanism Of Abnormal Energy Consumption Of Large-scale Equipment

Since the reform and opening up,China’s manufacturing industry has gradually shined in the world.Today,when China’s manufacturing industry is highly developed,people’s huge demand for high-quality products is driving the continued development of China’s manufacturing industry.Today,intelligent manufacturing has become an important direction for the development of the manufacturing industry.Various domestic manufacturing enterprises are trying to use various information technology to upgrade their own enterprises in an intelligent industry.In order to obtain high-quality products,a standardized production process is indispensable,so the equipment status monitoring of the production process becomes particularly important,and the energy consumption abnormal diagnosis is an important application of formal equipment status monitoring.In the production process,energy consumption is an important status indicator.The current of each module of the equipment can be used to reflect the energy consumption status.During production,problems such as excessive energy consumption and equipment idling may be encountered,which will negatively affect product quality.At the same time,the waste of energy will also bring some unnecessary losses to the enterprise;and the insufficient energy supply will cause insufficient processing of the products during the operation,resulting in poor product quality.In order to effectively solve the above problems,so that when relevant abnormalities occur,the company staff can quickly respond and make improvements,this dissertation studies the status data of the large-scale PVC rolling equipment used by an enterprise,and proposes a large-scale data-driven equipment Diagnosis plan for abnormal energy consumption,this article mainly covers:First,for the data collected by calendering,an efficient data transmission module is designed for data transmission.In order to meet the needs of big data analysis,the system also selects a data terminal suitable for big data visualization and big data calculation and query.According to different data terminals,select the appropriate transmission components.The second is to obtain data that meets the requirements of the algorithm and preprocess the data.The data processing module introduces the concept of data warehouse,and realizes data preprocessing and hierarchical storage of data of different specifications at the same time.Finally,big data mining technology is used to diagnose abnormal energy consumption.In the diagnosis process,various basic data analysis methods,such as variance analysis,data distribution curve drawing,etc.,are used to carry out related analysis;according to the characteristics of the equipment,modules such as single machine energy consumption abnormality diagnosis and associated energy consumption abnormality diagnosis are designed,and according to The timing characteristics of the data,select RNN,CNN algorithm to predict,and through experiments to prove the effectiveness of the above methods.The above methods meet the operating status monitoring and abnormal diagnosis of various manufacturing equipment,and can effectively help enterprises to upgrade intelligently,making production equipment more efficient in production and operation,thereby improving the quality and competitiveness of enterprise products.