A fast prediction method of fatigue life for crane structure based on Stacking ensemble learning model

Abstract

To quickly obtain the fatigue life of cranes in service, the metal structure that determines the crane life is anchored. Meanwhile, the fast prediction method of fatigue life of crane metal structures based on the Stacking ensemble learning model is proposed. Firstly, in line with the structural stress method, the global rough model of the metal structure is established by the co-simulation technology to obtain the fatigue damage regions of the structure. The local fine model is constructed by local cutting and boundary condition transplantation to determine the critical weld at the failure regions. Secondly, through weld definition, equivalent structural stress acquisition, and fatigue life calculation, the sample data set with lifting load and trolley running position as input and fatigue life cycle times as output is constructed. Then, the Stacking integrated learning model combining gradient boosting, ridge regression, Extra Trees, and linear is built. On this basis, combined with the Miner theory, the rapid prediction of crane fatigue life is realized. Finally, the proposed method is applied to the QD40t × 22.5 m × 9 m general bridge crane. The results show that the life sample set constructed by the structural stress method is more accurate and reasonable than the nominal, hot spot, and fracture mechanics methods. The life prediction results of the Stacking integration model were improved by 6.3 to 49.2% compared to the single model. The method has theoretical and practical significance in reducing accidents and ensuring the safe operation of cranes.