Prediction Method And Software Development Of Fatigue Life For Main Girder Of Quayside Container Crane

As a main transport tool for port terminals,quayside container cranes are developing towards large scale and high speed of handling.As the frequency of use increases,the possibility of accidents is also increasing.The investigation shows that most of the accidents of the quayside container bridge crane are caused by the fatigue failure of the main girder,so it is required that people pay more attention to the fatigue life of the crane.In this thesis,a fatigue life prediction method for the main girder of quayside container crane is presented,and fatigue life is analyzed with monitoring data.Then we develop a cloud platform for the safety and health management of the heavy equipment,and move the monitoring,management and life prediction of the lifting equipment to the line to simplify and visualize the whole process,thus reducing the cost and the safe and effective prevention of the accident.The specific contents of the paper are as follows.First of all,this paper uses SolidWorks to model the quayside container crane,and simplifies the structure without affecting the stress analysis.Then,the finite element analysis of the main beam of the quayside container bridge crane is carried out.The finite element analysis is carried out on the main beam under the condition of general conditions without considering the environmental impact.The dangerous position of the main beam in the working process.Then,stress data are monitored on the dangerous position of the main girder.Through data collection of two months,the data are arranged into time stress curve,and the data preprocessing methods such as equivalent compression,peak valley value extraction and invalid stress amplitude are removed,and the invalid data are eliminated.Then,the statistical histogram of cyclic stress mean and amplitude is sorted by the method of rain flow counting.The residual fatigue life is calculated by combining the Goodman mean stress correction method and the miner law.Finally,a cloud platform for safety and health management is designed and developed.The platform mainly monitors and manages the crane,and makes the fault diagnosis and life prediction of the crane,which not only facilitates the monitoring of the crane,but also reduces the cost for the daily management of the crane.