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Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN
Aiming to resolve the problem of redundant information concerning rolling bearing degradation characteristics and to tackle the difficulty faced by convolutional deep learning models in learning feature information in complex time series, a prediction model for remaining useful life based on multisc...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9321675/ https://www.ncbi.nlm.nih.gov/pubmed/35885128 http://dx.doi.org/10.3390/e24070905 |
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author | Wang, Yaping Wang, Jinbao Zhang, Sheng Xu, Di Ge, Jianghua |
author_facet | Wang, Yaping Wang, Jinbao Zhang, Sheng Xu, Di Ge, Jianghua |
author_sort | Wang, Yaping |
collection | PubMed |
description | Aiming to resolve the problem of redundant information concerning rolling bearing degradation characteristics and to tackle the difficulty faced by convolutional deep learning models in learning feature information in complex time series, a prediction model for remaining useful life based on multiscale fusion permutation entropy (MFPE) and a multiscale convolutional attention neural network (MACNN) is proposed. The original signal of the rolling bearing was extracted and decomposed by resonance sparse decomposition to obtain the high-resonance and low-resonance components. The multiscale permutation entropy of the low-resonance component was calculated. Moreover, the locally linear-embedding algorithm was used for dimensionality reduction to remove redundant information. The multiscale convolution module was constructed to learn the feature information at different time scales. The attention module was used to fuse the feature information and input it into the remaining useful life prediction module for evaluation. The appropriate network structure and parameter configuration were determined, and a multiscale convolutional attention neural network was designed to determine the remaining useful life prediction model. The results show that the method demonstrates effectiveness and superiority in degrading the feature information representation and improving the remaining useful life prediction accuracy compared with other models. |
format | Online Article Text |
id | pubmed-9321675 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93216752022-07-27 Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN Wang, Yaping Wang, Jinbao Zhang, Sheng Xu, Di Ge, Jianghua Entropy (Basel) Article Aiming to resolve the problem of redundant information concerning rolling bearing degradation characteristics and to tackle the difficulty faced by convolutional deep learning models in learning feature information in complex time series, a prediction model for remaining useful life based on multiscale fusion permutation entropy (MFPE) and a multiscale convolutional attention neural network (MACNN) is proposed. The original signal of the rolling bearing was extracted and decomposed by resonance sparse decomposition to obtain the high-resonance and low-resonance components. The multiscale permutation entropy of the low-resonance component was calculated. Moreover, the locally linear-embedding algorithm was used for dimensionality reduction to remove redundant information. The multiscale convolution module was constructed to learn the feature information at different time scales. The attention module was used to fuse the feature information and input it into the remaining useful life prediction module for evaluation. The appropriate network structure and parameter configuration were determined, and a multiscale convolutional attention neural network was designed to determine the remaining useful life prediction model. The results show that the method demonstrates effectiveness and superiority in degrading the feature information representation and improving the remaining useful life prediction accuracy compared with other models. MDPI 2022-06-30 /pmc/articles/PMC9321675/ /pubmed/35885128 http://dx.doi.org/10.3390/e24070905 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Yaping Wang, Jinbao Zhang, Sheng Xu, Di Ge, Jianghua Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN |
title | Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN |
title_full | Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN |
title_fullStr | Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN |
title_full_unstemmed | Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN |
title_short | Remaining Useful Life Prediction Model for Rolling Bearings Based on MFPE–MACNN |
title_sort | remaining useful life prediction model for rolling bearings based on mfpe–macnn |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9321675/ https://www.ncbi.nlm.nih.gov/pubmed/35885128 http://dx.doi.org/10.3390/e24070905 |
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