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中文摘要：
针对滚动轴承故障诊断时所提取的特征值中可能含有较小相关性和冗余性特征,采用基于Wrapper模式的距离评价技术（distance evaluation technique,简称DET）进行特征选择。在分类器的设计中,提出了基于稳健回归的多变量预测模型（Robust regression-Variable predictive model based class discriminate,简称RRVPMCD）分类方法,以减小＂异常值＂对参数估计的影响,从而有望建立更加准确的预测模型。即根据Wrapper模式的特点,首先通过DET方法计算出各特征值对类的敏感度,并结合RRVPMCD分类器,选择敏感度最大的若干特征值组成特征向量矩阵;然后用RRVPMCD方法进行训练,建立预测模型;最后用所建立的预测模型进行模式识别。实验分析结果表明,基于Wrapper模式的特征选择方法和RRVPMCD分类方法相结合可以有效地对滚动轴承的工作状态和故障类型进行识别。
英文摘要：
Aiming at the disadvantage that the extraction features may contain smaller correlation and redundancy characteristics in the rolling bearing fault diagnosis, distance evaluation technique （DET） based on Wrapper was used for feature selection. In the design of the classifier, regression-Variable predictive mode based class discriminate （RRVPMCD） was put forward to reduce the effect of abnormal value in the estimation of parameters, therefore more accurate prediction models can he built up. According to the characteristics of the Wrapper mode, firstly, the sensitivities of each feature were calculated for class by using DET,and several features with the biggest sensitivity were chosen to establish feature vector matrix combined with RRVPMCD,then, a predictive model was built with the method of RRVPMCD, finally, the established predictive model was used for pattern recognition. Experimental results show that the model based on the Wrapper feature selection and RRVPMCD method can effectively identify work status and fault type of rolling bearing.
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