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中文摘要：
支持向量数据描述是一种单值分类方法,该方法能够在缺少故障样本的情况下,仅仅利用采集到的正常状态数据样本建立起单值分类器,从而区分出机器的运行状态。正交小波变换对提取非平稳信号的冲击成分具有良好的性能。提出了一种基于正交小波变换和支持向量数据描述的状态评估方法,利用正交小波变换方法提取各细节信号的峰峰值作为分类器的输入参数,用支持向量数据描述方法建立起分类模型对机器运行状态进行定量评估。对滚动轴承内圈不同程度的点蚀故障进行了试验分析,建立起了对滚动轴承性能退化程度评估的定量指标。
英文摘要：
The support vector data description （SVDD） is a kind of single-value classification method, by which a single-value classifier can be built by using its normal state data samples even if the fault samples are lacking, thus revealing its normal operation. The orthogonal wavelet transform （OWT） has good performance for extracting the shock elements of a non-stable signal. We propose a new state evaluation method that uses the SVDD and the OWT and use the OWT to extract the peak-peak values of various detail signals, which are in turn used as input parame- ters of the classifier. We build the classification model of the classifier with the SVDD method to carry out the quan- titive evaluation of the state of the machine. We also use our method to do experimental analysis of the pitting faults on the inner ring of a rolling bearing and establish the quantitive indicators for evaluating its worsening perform- ance; the experimental results show that our method is effective.
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