Low Speed Bearing Fault Diagnosis Based on EMD-CIIT Histogram Entropy and KFCM Clustering

摘要/Abstract

摘要：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.

关键词:empirical mode decomposition - clear iterative interval threshold (EMD-CIIT),kernel-based fuzzy c-means (KFCM),acoustic emission (AE) signals,low speed machine,roller element bearing

Abstract: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.

Key words:empirical mode decomposition - clear iterative interval threshold (EMD-CIIT),kernel-based fuzzy c-means (KFCM),acoustic emission (AE) signals,low speed machine,roller element bearing

中图分类号:

TH 165

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 Jiao Tong University (Science), 2019, 24(5): 616-621.

参考文献20

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[16]	ZACHARY J, IYENGAR S S, BARHEN J. Contentbased image retrieval and information theory: Ageneral approach [J]. Journal of the American Societyfor Information Science and Technology, 2001, 52(10):840-852.
[17]	TAO X M, XU J, FU Q, et al. Kernel fuzzy C-meansalgorithm based on distribution density and its applicationin fault diagnosis [J]. Journal of Vibration andShock, 2009, 28(8): 61-64 (in Chinese).
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[19]	KOPSINIS Y, MCLAUGHLIN S. Development ofEMD-based denoising methods inspired by waveletthresholding [J]. IEEE Transactions on Signal Processing,2009, 57(4): 1351-1362.
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