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Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy
Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising an...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512669/ https://www.ncbi.nlm.nih.gov/pubmed/33265242 http://dx.doi.org/10.3390/e20030152 |
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author | Dang, Zhang Lv, Yong Li, Yourong Yi, Cancan |
author_facet | Dang, Zhang Lv, Yong Li, Yourong Yi, Cancan |
author_sort | Dang, Zhang |
collection | PubMed |
description | Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes. Then, multiscale permutation entropy calculation is performed to calculate the complexity of each DMD mode. Modes corresponding to the noise components are discarded by threshold technology, and we reconstruct the modes whose entropies are smaller than a threshold to recover the signal. By applying the algorithm to rolling bearing simulation signals and comparing with the result of wavelet transform, the effectiveness of the proposed method can be verified. Finally, the proposed method is applied to the experimental rolling bearing signals. Results demonstrated that the proposed approach has a good application prospect in noise reduction and fault feature extraction. |
format | Online Article Text |
id | pubmed-7512669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75126692020-11-09 Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy Dang, Zhang Lv, Yong Li, Yourong Yi, Cancan Entropy (Basel) Article Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes. Then, multiscale permutation entropy calculation is performed to calculate the complexity of each DMD mode. Modes corresponding to the noise components are discarded by threshold technology, and we reconstruct the modes whose entropies are smaller than a threshold to recover the signal. By applying the algorithm to rolling bearing simulation signals and comparing with the result of wavelet transform, the effectiveness of the proposed method can be verified. Finally, the proposed method is applied to the experimental rolling bearing signals. Results demonstrated that the proposed approach has a good application prospect in noise reduction and fault feature extraction. MDPI 2018-02-27 /pmc/articles/PMC7512669/ /pubmed/33265242 http://dx.doi.org/10.3390/e20030152 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 Dang, Zhang Lv, Yong Li, Yourong Yi, Cancan Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy |
title | Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy |
title_full | Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy |
title_fullStr | Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy |
title_full_unstemmed | Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy |
title_short | Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy |
title_sort | optimized dynamic mode decomposition via non-convex regularization and multiscale permutation entropy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512669/ https://www.ncbi.nlm.nih.gov/pubmed/33265242 http://dx.doi.org/10.3390/e20030152 |
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