Research On Crane Structural Health Online Monitoring System In Service Center Mode

With the continuous advancement of smart port planning,port enterprises have gradually increased the demand for online monitoring and health diagnosis technology of hoisting machinery.But at this stage,the establishment of a remote online monitoring technology system for hoisting machinery lacks a scientific guidance framework.The research on the processing method of monitoring data is still in its infancy,and monitoring data cannot be effectively sorted and used.To this end,this paper proposes a platform architecture and implementation method of the crane structure health online monitoring system under the service center mode,which is used to realize remote realtime online monitoring and comprehensive data processing services of the crane status,in order to eliminate the hidden dangers of crane operation and ensure Human-machine safety.The main contents of this paper is listed as follows:(1)Analyze the research status of existing crane online monitoring technology and related basic subject theories,fault diagnosis technology,and health assessment theory at home and abroad.Based on the needs of port enterprises,analyze and determine the functions and capabilities of the monitoring system.The overall framework.(2)In-depth investigation of the crane operation site and the application examples of its online monitoring system,combined with fiber grating sensing technology,finite element simulation,My SQL database and other technologies and engineering actual design of a theory of a crane structural health online monitoring system Architecture,construction method and corresponding implementation process.Based on this,a service center architecture is proposed,and its function and construction method are analyzed.(3)After researching on the preliminary processing method of monitoring big data,BP neural network is introduced into the preprocessing of crane monitoring data,then its processing effect is verified through MATLAB simulation.A modular data preliminary processing structure is proposed and implemented by programming in order to reduce the data storage pressure of the service center and prepare for subsequent health diagnosis analysis of crane monitoring data.(4)Research on the health diagnosis analysis method of crane monitoring data,determine the idea of intelligent diagnosis of crane state using deep convolutional neural network algorithm,design and compare experiments to identify the state of different convolutional neural networks in crane monitoring data.After verifying and analyzing,the residual neural network is determined as the main algorithm for intelligent diagnosis,and the program is developed to realize the intelligent diagnosis of crane online monitoring data.