Monitoring And Analysis Of The Operation Status Of Stamping Equipment And Improvement Of OEE

For a long time,China’s industry has achieved scale expansion and the main way to promote economic growth is through the massive consumption of resources and energy.Withthe introduction of the concept of Industry 4.0,industrial manufacturing wants to transform and upgrade,lean manufacturing has become a key step.OEE is a key indicator for evaluating lean manufacturing.As a simple and practical production equipment management tool,this indicator has been widely used in Europe,America,Japan and China.Enterprise management based on OEE analysis can accurately and clearly tell you the efficiency of the equipment,the reasons for the loss of production links and the corresponding improvement work,and then by reducing equipment failures,maintenance,quality control costs,improve productivity and efficiency.Improving OEE can not only improve equipment production efficiency,but also help companies find and maintain timely Production problems,improve the core competitiveness of enterprises.With the vigorous development of the Internet of Things technology,modern industry will deploy sensors that collect the working and operating status of its equipment on its important industrial equipment.While monitoring the operating status of the equipment,the sensors will periodically collect these data and transmit them to the data center.The analysis of equipment operation provides important data support,which can mine hidden information in the data and be used to identify the operation status of the equipment,thereby further improving enterprise equipment OEE.However,due to the enlargement and complexity of modern large-scale equipment,the disturbance of some components during the operation of the equipment will continue to be transmitted to other components,which makes the operation status of the equipment complicated.At the same time,the explosive growth of sensor monitoring data is difficult to store,transmit and calculate in time,and the traditional statistical analysis method is difficult to use a unified model to perform fusion analysis on the operating status data of these devices,Under this background,the artificial intelligence state analysis method based on big data provides a brand-new solution and technical means.This article aims at the most important production equipment in the automobile manufacturing industry-stamping equipment as the object,based on the background of big data,the use of deep learning technology to analyze the operation status of the equipment,and designed a stamping equipment operation status monitoring and analysis system.The system uses big data and artificial intelligence technology to solve the problems of storage,calculation and operation status analysis of massive industrial data.In this paper,first of all,through the research on the analysis method of equipment operation status,aiming at the shortcomings of the traditional LSTM-based equipment status analysis algorithm,combined with the characteristics of stamping equipment sensor data,an improved scheme is proposed for the original algorithm,and a fusion of attention is designed The CNN-LSTM state analysis algorithm model of the mechanism,which retains the advantage of LSTM’s processing of time-correlated sequences,uses convolution calculation to reduce data dimensions,capture local features,and consider the differences in the importance of features.This paper proves that the algorithm has more accurate and effective equipment status classification ability than the original algorithm through experimental simulation.After that,this paper proposes the design and implementation of the monitoring and analysis system for the operation status of the stamping equipment,and finally performs relevant functional tests on the system.After testing,the system implemented in this paper basically meets the needs of comprehensive efficiency analysis and improvement of stamping equipment,and can largely solve the pain points of lean management of industrial equipment.