基于 MSK-SVM 的滚动轴承故障诊断方法

针对非线性支持向量机分类准确率受核函数影响的问题，提出一种多尺度核支持向量机(multi-scale kernel support vector machine, MSK-SVM)分类模型，并将该模型应用于滚动轴承故障诊断。该模型在常用的多项式核、高斯核和Sigmoid核函数基础上，引入了Morlet、Marr和DOG小波核函数。利用不同核函数的全局性和局部性以及核函数尺度参数不同作用范围不同的特点，组合具有不同特性及不同尺度参数的核函数作为多尺度核。基于梯度下降法，自适应地确定多尺度核函数权值，得到MSK-SVM滚动轴承故障诊断模型。为说明算法有效性，分别基于滚动轴承故障数据集和全寿命周期数据集进行了实验验证，并分析了基于不同特性MSK和相同特性MSK的SVM模型分类性能。结果表明本文所提模型较传统单个核函数SVM分类准确率更高，且具有良好的泛化能力。

Rolling bearing fault diagnosis method based on MSK-SVM

Aiming at the problem that the classification accuracy of nonlinear support vector machine is susceptible to kernel function, a multi-scale kernel support vector machine (MSK-SVM) classification model is proposed and applied to rolling bearing fault diagnosis. In this model, Morlet, Marr and DOG wavelet kernel functions are introduced on the basis of Polynomial, Gaussian and Sigmoid kernels. Using the global and local characteristics of various kernel functions, as well as the characteristics that kernel functions with different scale parameters have distinct influence range, kernel functions with different characteristics and scale parameters are combined as multiscale kernel. Based on the gradient descent method, the weights of multi-scale kernel function are adaptively determined, and the MSK-SVM rolling bearing fault diagnosis models are obtained. In order to illustrate the effectiveness of the algorithm, the rolling bearing fault data set and life cycle data set are selected for experimental verification, respectively. The classification performance of MSKSVM models based on different characteristic kernel functions and the same characteristic kernel function are analyzed. The results show that the proposed algorithm can achieve higher classification accuracy and better generalization ability than the traditional single kernel SVM.