In view of weak defect signals and large acoustic emission (AE) data in low speed bearing condition
monitoring, we propose a bearing fault diagnosis technique based on a combination of empirical mode decomposition
(EMD), clear iterative interval threshold (CIIT) and the kernel-based fuzzy c-means (KFCM) eigenvalue
extraction. In this technique, we use EMD-CIIT and EMD to complete the noise removal and to extract the
intrinsic mode functions (IMFs). Then we select the first three IMFs and calculate their histogram entropies as
the main fault features. These features are used for bearing fault classification using KFCM technique. The result
shows that the combined EMD-CIIT and KFCM algorithm can accurately identify various bearing faults based
on AE signals acquired from a low speed bearing test rig.
ZHANG Ke (张珂), LIN Tianran (林天然), JIN Xia (金霞)
. Low Speed Bearing Fault Diagnosis Based on EMD-CIIT Histogram Entropy and KFCM Clustering[J]. Journal of Shanghai Jiaotong University(Science), 2019
, 24(5)
: 616
-621
.
DOI: 10.1007/s12204-019-2108-0
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