Abstract:Accurately obtaining the load spectrum of an in-service crane under normal working conditions is a necessary condition for predicting and evaluating the fatigue remaining life of the crane. However, due to the uncertainty, diversity, and randomness of the crane load, and the complexity of the actual measurement environment on site, it is still very difficult to assess the fatigue life of cranes. In order to solve the problem of low accuracy and small sample size of crane load spectrum prediction, based on optimization algorithm and machine learning technology, an improved beetle antennae search algorithm was proposed to optimize the regression prediction model of least square support vector machine(LSSVM), and the IBAS-LSSVM model is established. Based on the traditional beetle antennae search algorithm, the update of the step size is controlled by the inverse tangent function, and the method that can jump out of the algorithm stagnation is set, so as to avoid the algorithm from falling into the local optimum, the global optimization performance of the algorithm is improved. A certain type of general bridge crane is taken as an example. The IBAS-LSSVM model is used to perform regression prediction and analysis on the small sample load spectrum of the crane. The results show that compared with other regression prediction models, the IBAS-LSSVM prediction model proposed has more high prediction accuracy, faster convergence speed and better generalization performance. In addition, it avoids the problem of falling into local minimums. This method is of great significance for the regression prediction of crane load spectrum and the evaluation of crane fatigue remaining life.
于燕南,戚其松,董青,徐格宁. 起重机载荷谱回归预测的LSSVM模型优化研究[J]. 振动与冲击, 2022, 41(12): 215-228.
YU Yannan,QI Qisong,DONG Qing,XU Gening. A study on optimization of the LSSVM model for crane load spectrum regression prediction. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(12): 215-228.
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