Cargando…
Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing
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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513190/ https://www.ncbi.nlm.nih.gov/pubmed/33265756 http://dx.doi.org/10.3390/e20090667 |
_version_ | 1783586331218149376 |
---|---|
author | Yang, Fen Kou, Ziming Wu, Juan Li, Tengyu |
author_facet | Yang, Fen Kou, Ziming Wu, Juan Li, Tengyu |
author_sort | Yang, Fen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7513190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75131902020-11-09 Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing Yang, Fen Kou, Ziming Wu, Juan Li, Tengyu Entropy (Basel) Article 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. MDPI 2018-09-04 /pmc/articles/PMC7513190/ /pubmed/33265756 http://dx.doi.org/10.3390/e20090667 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Fen Kou, Ziming Wu, Juan Li, Tengyu Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing |
title | Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing |
title_full | Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing |
title_fullStr | Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing |
title_full_unstemmed | Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing |
title_short | Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing |
title_sort | application of mutual information-sample entropy based med-iceemdan de-noising scheme for weak fault diagnosis of hoist bearing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513190/ https://www.ncbi.nlm.nih.gov/pubmed/33265756 http://dx.doi.org/10.3390/e20090667 |
work_keys_str_mv | AT yangfen applicationofmutualinformationsampleentropybasedmediceemdandenoisingschemeforweakfaultdiagnosisofhoistbearing AT kouziming applicationofmutualinformationsampleentropybasedmediceemdandenoisingschemeforweakfaultdiagnosisofhoistbearing AT wujuan applicationofmutualinformationsampleentropybasedmediceemdandenoisingschemeforweakfaultdiagnosisofhoistbearing AT litengyu applicationofmutualinformationsampleentropybasedmediceemdandenoisingschemeforweakfaultdiagnosisofhoistbearing |