狼群优化LVQ神经网络的齿轮箱故障诊断应用研究

为了提高齿轮箱故障诊断的准确度,采用LVQ神经网络来完成齿轮箱故障定位及识别,并借助狼群优化算法来实现模型参数的优化。在齿轮箱故障诊断的建模过程中,引入狼群优化算法,将LVQ神经网络权重和阈值作为狼群个体,随机产生的多个权重和阈值组合个体构成狼群,并根据狼群游走、召唤和围攻等行为,不断更新狼群中个体狼的位置来获取全局适应度最大的头狼,得到最优权重和阈值,确定最优齿轮箱故障诊断模型。经过实验证明:采用基于狼群优化LVQ神经网络的齿轮箱故障分类,分类准确度更高。

Application of gearbox fault diagnosis by LVQ neural network optimized by wolves

In order to improve the accuracy of gearbox fault diagnosis, LVQ neural network is used to complete the gearbox fault location and identification, and the wolf pack optimization algorithm is used to optimize the model parameters. In the process of gearbox fault diagnosis, a wolf pack optimization algorithm is introduced. The LVQ neural network weights and thresholds are used as wolves. Individuals with multiple randomly generated weights and thresholds are combined to form wolves. According to the behaviors of wolves swimming, beckoning, and siege, the positions of individual wolves in the wolves are continuously updated to obtain the head wolf with the highest global fitness, in order to achieve the optimal weight and threshold, and determine the optimal gearbox fault diagnosis model. It is proved by experiments that the gearbox fault classification based on wolf pack optimization LVQ neural network has higher classification accuracy.