Research On Structural Damage And Fatigue Life Analysis Technology Of Large Cranes

Due to the long working time,high frequency,heavy load and complex working environment,there are inevitably fatigue cracks,corrosion,wear,local instability of stressed components,local plastic deformation and other damages in the structure of large crane.Accurately identifying and assessing the structural state based on damage information is one of the necessary conditions to ensure the safe operation of crane.In order to solve the defect of non-destructive testing method that requires a lot of prior knowledge,this paper,based on vibration theory and fracture mechanics theory,studies the damage identification and fatigue life analysis technology of large crane metal structure.The general bridge crane is selected as the research object.First of all,the finite element model of metal structure is updated with the first four modal frequencies based on the response surface methodology(RSM).Besides,the static and dynamic characteristics of the updated finite element model under typical working conditions are analyzed to evaluate the mechanical properties and obtain information about the damage hotspots of metal structure.Secondly,in order to solve the problem that the single dynamic fingerprint is not sensitive to local damage of large-span metal structure of the bridge crane,a two-stage damage localization method based on acceleration frequency response analysis and curvature modal method is proposed.In the first stage,the crane metal structure is divided into several beam sections in the transverse direction to locate the damage using the acceleration frequency response amplitude.The second stage is to find out which plate at the damaged beams may crack based on the curvature mode shape before and after damage.Compared with the method using single dynamic fingerprint for damage identification,the proposed two-stage method can reduce the scope of damage search and the number of sensors needed in actual detection.What's more,according to the localization of damaged plate,particle swarm optimization support vector machine(PSO-SVM)is used to predict the damage severity based on the regression relationship between the crack size and relative variation rate of the curvature.The detection results of the crack size of the lower flange plate indicate that compared with the standard SVM,the PSO-SVM regression model increases the prediction accuracy.Finally,a fatigue analysis method combining the theory of fracture mechanics and the sequential law is proposed to reduce the calculation bias of linear fatigue cumulative damage rule.Compared with the fatigue life results of Miner method,the feasibility and accuracy of the proposed method is demonstrated.This paper starts from the modeling analysis,then studies the damage identification method and evaluates the residual fatigue life of the crane metal structure.The proposed ideas can be used as the basis for the damage identification and structural health monitoring of large cranes.