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中文摘要：
为了解决智能监测和故障诊断中故障样本缺乏的问题,提出一种支持向量数据描述（support vector data description,SVDD）和经验模态分解（empirical mode decomposition,EMD）相结合的单分类方法。该方法在只有正常状态数据样本而无需故障样本的情况下可以建立起单值分类器,从而区分出机器的运行状态。采用经验模态分解对数据进行预处理,提取信号在不同频带的能量特征作为SVDD的输入参数进行分类。将该方法应用于滚动轴承的故障诊断中,试验结果表明,该方法可以比传统的SVDD方法更有效地识别轴承的运行状态。
英文摘要：
In order to solve the problem of insufficient fault samples in intelligent monitoring and fault diagnosis, a one-class classification method combined support vector data description （SVDD） and empirical mode decomposition （EMD） is proposed. With this method, one-class classifier can be built when only the object of normal condition is available, and the abnormal condition can be distinguished from normal condition. Empirical mode decomposition （EMD） is used as data preprocessing to extract the energy varies in different frequencies bands, and the energy features extracted by EMD can be served as the input parameters of SVDD for classification. Applying this method to the wiling bearing fault diagnosis, the test result shows that this method is superior to traditional SVDD method and would identify roiling bearing fault patterns more effectively.
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