基于鲸鱼算法优化LSSVM的滚动轴承故障诊断

针对轴承振动信号中的故障特征难以提取的问题,提出一种基于改进的鲸鱼算法优化最小二乘支持向量机(least square support vector machine, LSSVM)的故障分类方法.首先,利用变分模态分解(variational mode decomposition, VMD)对原始信号进行分解,使用中心频率法解决VMD中分解参数K值的选取问题;其次,计算每个IMF分量的多尺度排列熵值,提取信号故障特征;再次,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;最后,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,并利用美国凯斯西储大学提供的轴承数据集进行仿真实验.实验结果表明,所提方法的故障分类性能更好,准确率更高.

Fault diagnosis method of rolling bearing based on LSSVM optimized by whale optimization algorithm

Aiming at the problem that it is difficult to extract fault features from bearing vibration signals, a fault classification method based on the improved whale algorithm for optimizing the least square support vector machine(LSSVM) model is proposed. Firstly, the original signal is decomposed by variational modal decomposition, and the center frequency method is used to solve the problem of selecting the decomposition parameter $ K $ in VMD. Then, we calculate the multi-scale permutation entropy value of each IMF component and extract signal fault characteristics. Furthermore, aiming at the slow convergence speed and low accuracy of the whale optimization algorithm(WOA), the von-neumann and adaptive weights are introduced to improve the whale optimization algorithm, which can appropriately adjust the balance between global search ability and local search ability. Finally, by using an improved whale optimization algorithm, the penalty factor and kernel parameter of the LSSVM are optimized to establish the fault diagnosis model of rolling bearing, and the bearing data set provided by Case Western Reserve University is used to perform simulation experiments. The results show that the proposed method has better fault classification performance and higher accuracy.