中文摘要：

针对滚动轴承故障诊断中普遍存在的小样本学习问题，采用支持向量机实现轴承故障的模式识别．为了解决时域统计参数对于轴承故障的多分类效果较差的问题，引入小波包分解（wavelet packet decomposition，WPD）技术，提取振动信号各频带的能量系数构造特征向量，并采用Fisher比率法对特征向量进行优化选取；然后利用支持向量机（support vector machine，SVM）进行故障模式识别，并与小波包分解及时域统计参数的分类效果进行对比分析．结果表明：支持向量机是实现轴承故障模式识别的一种有效手段；本方法的分类效果及时间效率明显优于传统的多维时域指标和小波能量系数分类方法；将Fisher比率法与SVM相结合可以提高轴承故障诊断的准确率．

英文摘要：

According to the widespread problem of small sample learning on rolling bearing fault diagnosis, support vector machine （SVM） is used to complete the pattern recognition of bearing fault. In order to solve the problem of poor effect of multi-classification bearing faults owing to time-domain statistical parameters, the Wavelet Packet Decomposition （WPD） technology is introduced to extract energy coefficient of each vibration signal frequency band to construct feature vector, optimize and select feature vector though Fisher ratio method, then the SVM is used for fault pattern recognition and comparative analysis of the classification results of WPD and time-domain statistical parameters. The comparative analysis results have indicated that the SVM technology is an effective classification method for fault identification of rolling bearings. When Fisher ratio method combines with the SVM, the fault classification accuracy and time efficiency is higher than that of the traditional multidimensional time-domain and WPD, the diagnosis precision can also be improved.