起重机载荷谱回归预测的LSSVM模型优化研究

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (12) : 215-228.

论文

于燕南，戚其松，董青，徐格宁

作者信息 +

A study on optimization of the LSSVM model for crane load spectrum regression prediction

YU Yannan,QI Qisong,DONG Qing,XU Gening

Author information +

文章历史 +

摘要

准确获取在役起重机正常工作状态下的载荷谱是预测和评估起重机疲劳剩余寿命的必要条件，但由于起重机载荷的不确定性、多样性和随机性，以及现场实测环境的复杂性，导致起重机载荷谱数据获取仍然十分困难。为解决起重机载荷谱预测样本容量小和准确性低的问题，基于优化算法和机器学习技术，提出了一种改进的天牛须搜索算法优化最小二乘支持向量机（least square support vector machine，LSSVM）模型，建立IBAS-LSSVM载荷谱预测模型。在传统的天牛须搜索算法的基础上，通过反正切函数控制步长的更新，并设置能够跳出算法停滞的方法，避免算法陷入局部最优，提升了算法的全局寻优性能。以某型号通用桥式起重机为例，利用IBAS- LSSVM模型对起重机的小样本载荷谱进行回归预测和分析，结果表明，同其它回归预测模型相比，IBAS-LSSVM预测模型具有更高的预测精度、较快的收敛速度和更好的泛化性能，也避免了陷入局部极小值的问题，该方法对起重机载荷谱的回归预测和进一步的疲劳剩余寿命评估具有重要意义。

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.

导出引用

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[J]. Journal of Vibration and Shock, 2022, 41(12): 215-228

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