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Entropy 2018, 20(9), 667; https://doi.org/10.3390/e20090667

Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing

1,2,3
,
1,2,3,* , 1,2,3
and
1,2,3
1
School of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
Shanxi Province Mineral Fluid Controlling Engineering Laboratory, Taiyuan 030024, China
3
National-Local Joint Engineering Laboratory of Mining Fluid Control, Taiyuan 030024, China
*
Author to whom correspondence should be addressed.
Received: 7 August 2018 / Revised: 28 August 2018 / Accepted: 31 August 2018 / Published: 4 September 2018
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Abstract

In this paper, a novel weak fault features extraction scheme is proposed to extract weak fault features in head sheave bearings of floor-type multi-rope friction mine hoists in strong noise environments. A mutual information-based sample entropy (MI-SE) is proposed to select the effective intrinsic mode function (IMF). The numerical simulation presented in this paper has demonstrated that the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) has a poor performance on weak signals processing under a strong noise background, and fault features cannot be identified clearly. The de-noised signal is decomposed into several IMFs by the ICEEMDAN method, with the help of the minimum entropy deconvolution (MED), which works as a pre-filter to increase the kurtosis value by about 3.2 times. The envelope spectrum of the effective IMF selected by the MI-SE method shows almost all fault features clearly. An analogous experiment system was built to verify the feasibility of the proposed scheme, whose results have also shown that the proposed hybrid scheme has better performance compared with ICEEMDAN or MED on the weak fault features extraction under a strong noise background. This paper provides a novel method to diagnose the weak faults of the slow speed and heavy load rolling bearings in a strong noise environment. View Full-Text
Keywords: weak fault; hoist bearing; improved complete ensemble empirical mode decomposition; minimum entropy deconvolution; sample entropy; mutual information weak fault; hoist bearing; improved complete ensemble empirical mode decomposition; minimum entropy deconvolution; sample entropy; mutual information
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Yang, F.; Kou, Z.; Wu, J.; Li, T. Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing. Entropy 2018, 20, 667.

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