文件名称:cluster_VMDaFCM_casedat
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为了精准、稳定地提取滚动轴承故障特征，提出了基于变分模态分解和奇异值分解的特征提取方法，采用标准模糊C均值聚类(fuzzy C means clustering, FCM)进行故障识
别。对同一负荷下的已知故障信号进行变分模态分解，利用
奇异值分解技术进一步提取各模态特征，通过FCM形成标准聚类中心，采用海明贴近度对测试样本进行分类，并通过计算分类系数和“卜均模糊嫡对分类性能进行评价，将该方法
应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比，该方法对标准FCM初始化条件小敏感，在同负荷故障诊断中表现出更好的分类性能 变负荷故障诊断时，除外圈故障特征线发生明显迁移，其他测试样本故障特征线仍在原聚类中心附近，整体故障识别率保持在100 ，因此，该方法能精确、稳定提取故障特征，为实际滚动轴承智能故障诊断提供参考。- In order to extract fault features of rolling bearing precisely and steadily, a method which is based on variational mode decomposition(VMD) and singular value
decomposition was proposed for fault diagnosis using standard
fuzzy C means clustering(FCM). First of all, the known fault signals measured in the same load but with different faults were
decomposed by VMD, and the modes characteristics were
further extracted using singular value decomposition technique,forming the standard clustering centers by FCM, and then the
test samples were clustered by a Hamming nearness approach,
and the classification performance was uated by calculating classification coefficient and average fuzzy entropy. At last, the method was applied in rolling bearing fault diagnosis under
variable loads. By comparing with a method based on EMD,
this approach is not sensitive to the initialization of standard FCM, and exhibits better classification performance in the same load fault diagnosis Fo
别。对同一负荷下的已知故障信号进行变分模态分解，利用
奇异值分解技术进一步提取各模态特征，通过FCM形成标准聚类中心，采用海明贴近度对测试样本进行分类，并通过计算分类系数和“卜均模糊嫡对分类性能进行评价，将该方法
应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比，该方法对标准FCM初始化条件小敏感，在同负荷故障诊断中表现出更好的分类性能 变负荷故障诊断时，除外圈故障特征线发生明显迁移，其他测试样本故障特征线仍在原聚类中心附近，整体故障识别率保持在100 ，因此，该方法能精确、稳定提取故障特征，为实际滚动轴承智能故障诊断提供参考。- In order to extract fault features of rolling bearing precisely and steadily, a method which is based on variational mode decomposition(VMD) and singular value
decomposition was proposed for fault diagnosis using standard
fuzzy C means clustering(FCM). First of all, the known fault signals measured in the same load but with different faults were
decomposed by VMD, and the modes characteristics were
further extracted using singular value decomposition technique,forming the standard clustering centers by FCM, and then the
test samples were clustered by a Hamming nearness approach,
and the classification performance was uated by calculating classification coefficient and average fuzzy entropy. At last, the method was applied in rolling bearing fault diagnosis under
variable loads. By comparing with a method based on EMD,
this approach is not sensitive to the initialization of standard FCM, and exhibits better classification performance in the same load fault diagnosis Fo
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下载文件列表
cluster_VMD&FCM_casedat
.......................\EMI.m
.......................\FCMClus2t.m
.......................\KFCMClust.m
.......................\MI.m
.......................\Main.fig
.......................\Main.m
.......................\VMD.m
.......................\VMD_sin_center_f.m
.......................\VMD_singular.m
.......................\VMD_test.m
.......................\VMD_test_original.m
.......................\VMD_winddata.m
.......................\cluster_SAME_con_VMD_casedata.asv
.......................\cluster_SAME_con_VMD_casedata.m
.......................\cluster_var1_con_VMD_casedata.asv
.......................\cluster_var1_con_VMD_casedata.m
.......................\cluster_var2_con_VMD_casedata.asv
.......................\cluster_var2_con_VMD_casedata.m
.......................\cluster_var3_con_VMD_casedata.asv
.......................\cluster_var3_con_VMD_casedata.m
.......................\distfcm.m
.......................\emd_vmd_con.asv
.......................\emd_vmd_con.m
.......................\fuzzydist.m
.......................\hua_baoluo.m
.......................\hua_fft1.asv
.......................\hua_fft1.m
.......................\hua_xihua.m
.......................\imssdata.mat
.......................\initfcm.m
.......................\labview_data.m
.......................\matlab.dat
.......................\matlab1.dat
.......................\muting.m
.......................\plot_imf.m
.......................\stepfcm11.m
.......................\stepfcm_hxm.m
.......................\test.m
.......................\test2.m