Fault diagnosis of rolling bearings based on IRCMNDE and NNCHC
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摘要: 针对多尺度散布熵(MDE)在粗粒化过程中易发生信息丢失、产生虚假信息,难以全面提取轴承故障信息的问题,提出了基于改进的精细复合多尺度归一化散布熵(IRCMNDE)和最近邻凸包分类(NNCHC)的滚动轴承故障诊断方法。引入精细复合多尺度散布熵(RCMDE),将其粗粒化过程中平均值替换为最大值来表示数据段信息,以克服传统粗粒化过程的不足并突出故障特征。通过归一化操作减弱熵值计算时不同参数选择导致的熵值波动幅度,得到IRCMNDE。将IRCMNDE作为故障特征,使用NNCHC分类器对故障特征进行分类。经实验验证,该方法可达到98.98%的故障识别准确率,相比基于MDE(故障识别准确率为95.99%)和RCMDE(故障识别准确率为97.60%)的方法,能够更准确地提取滚动轴承的故障特征信息,提高承故障分类的准确性。
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关键词:
- 滚动轴承 /
- 故障诊断 /
- 多尺度散布熵 /
- 精细复合多尺度散布熵 /
- 最近邻凸包分类(NNCHC)
Abstract: In view of the problem that information loss and false information may occur during the coarse-graining process of multi-scale dispersion entropy (MDE),which make it difficult to extract bearing fault information comprehensively,a rolling bearing fault diagnosis method based on improved refined composite multi-scale normalized dispersion entropy (IRCMNDE) and nearest neighbor convex hull classification (NNCHC) was proposed.The refined composite multi-scale dispersion entropy (RCMDE) was introduced,and the average value in the coarse-graining process was replaced by the maximum value to represent the data segments information,which can overcome the shortcomings of the traditional coarse-graining process and highlight the fault characteristics.Through the normalization operation to reduce the influence of the selection of different parameters on the entropy value,IRCMNDE was acquired as feature samples; NNCHC was used to classify the feature samples to realize bearing fault diagnosis.Experimental results showed that the proposed method can achieve 98.98% fault identification accuracy.Compared with the methods based on MDE (fault identification accuracy was 95.99%) and RCMDE (fault identification accuracy was 97.60%),the proposed method can extract the fault feature information of rolling bearings more accurately and improve the accuracy of fault classification. -
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