School of Mathematics and Statistics, Xidian University
针对轴承振动信号中的故障特征难以提取的问题,提出了一种基于改进的鲸鱼算法优化最小二乘支持向量机模型的故障分类方法.该方法使用变分模态分解和多尺度排列熵提取信号故障特征.针对鲸鱼算法(Whale Optimization Algorithm ,WOA)收敛速度慢和精度低的问题,引入冯诺依曼拓扑结构和自适应权重进行改进,可以适当地调整全局搜索能力和局部搜索能力之间的平衡.采用改进后的鲸鱼算法优化LSSVM核函数的参数和惩罚因子,建立滚动轴承故障诊断模型.结果表明,该方法的故障分类性能更好,准确率更高.
This Aiming at the problem that it was difficult to extract fault features from bearing vibration signal, a fault classification method based on the improved whale algorithm for optimizing the least square support vector machine model was proposed. The method combined the variational mode decomposition and multi-scale permutation entropy to extract fault signal characteristics. Aiming at the slow convergence speed and low accuracy of whale optimization algorithm (WOA), the von-neumann and adaptive weights were introduced to improve the whale optimization algorithm, which could appropriately adjust the balance between global search ability and local search ability. By using an improved whale optimization algorithm, the penalty factor and kernel parameter of the LSSVM were optimized to establish a fault diagnosis model of the rolling bearing. The results show that the proposed method has better fault classification performance and higher accuracy. article is designed to help in the contribution for Control and Decision. It is divided into several sections.