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中文摘要：
针对滚动轴承故障振动信号的强噪声背景以及现实中不易获取大量典型故障样本的特点，提出一‘种基于柔性形态滤波和支持矢量机（Supportvectormachine，SVM）的滚动轴承故障诊断方法。柔性形态滤波既可以有效地提取出信号的边缘轮廓和信号的形状特征，同时又具育稳健性；SVM具有良好的分类件能，特别在小样本、非线性及高维特征空间中具有较好的推广能力；SVM分类器的惩罚因子和核函数参数采用经典粒子群优化算法进行优化，避免传统方法对初始点和样本的依赖。首先对振动信号进行柔性形态滤波，然后提取滤波后信号的故障特征频率的归一化能量为特征矢量作为SVM分类器的输入参数，用于区分滚动轴承的外圈、内圈和滚动体故障，SVM分类器的参数采用标准粒子群优化算法进行优化。试验结果表明了方法的有效性。
英文摘要：
Based on soft morphological filters and support vector machine （SVM）, a roller bearing fault diagnosis method is proposed. It is very difficult to filter the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification. Soft morphology filter can not only identify the features of fringe and shape of the signals but also give improved performance under certain conditions. Support vector machine has good classification performance especially in the small-sample, nonlinear and high dimensional features and so on. The penalty factors and kernel parameters of SVM are optimized by using particle swarm optimization to avoid dependence on initial parameters and training samples. Firstly, vibration signals are filtered by the soft morphological filters. Secondly, the normalized energy of the different characteristic frequencies is utilized to identify the fault features of input parameters of SVM classifier. The SVM parameters are optimized by using the canonical particle swarm optimization. The experiment results indicate that the modeling method is correct.
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