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A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis
Condition monitoring and fault diagnosis of diesel engines are of great significance for safety production and maintenance cost control. The digital twin method based on data-driven and physical model fusion has attracted more and more attention. However, the existing methods lack deeper integration...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8758757/ https://www.ncbi.nlm.nih.gov/pubmed/35027591 http://dx.doi.org/10.1038/s41598-021-04545-5 |
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author | Jiang, Jiajie Li, Hui Mao, Zhiwei Liu, Fengchun Zhang, Jinjie Jiang, Zhinong Li, He |
author_facet | Jiang, Jiajie Li, Hui Mao, Zhiwei Liu, Fengchun Zhang, Jinjie Jiang, Zhinong Li, He |
author_sort | Jiang, Jiajie |
collection | PubMed |
description | Condition monitoring and fault diagnosis of diesel engines are of great significance for safety production and maintenance cost control. The digital twin method based on data-driven and physical model fusion has attracted more and more attention. However, the existing methods lack deeper integration and optimization facing complex physical systems. Most of the algorithms based on deep learning transform the data into the substitution of the physical model. The lack of interpretability of the deep learning diagnosis model limits its practical application. The attention mechanism is gradually developed to access interpretability. In this study, a digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis is proposed with considering its signal characteristics of strong angle domain correlation and transient non-stationary, in which a new soft threshold filter is designed to draw more attention to multi decentralized local fault information dynamically in real time. Based on this attention mechanism, the distribution of fault information in the original signal can be better visualized to help explain the fault mechanism. The valve failure experiment on a diesel engine test rig is conducted, of which the results show that the proposed adaptive sparse attention mechanism model has better training efficiency and clearer interpretability on the premise of maintaining performance. |
format | Online Article Text |
id | pubmed-8758757 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-87587572022-01-14 A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis Jiang, Jiajie Li, Hui Mao, Zhiwei Liu, Fengchun Zhang, Jinjie Jiang, Zhinong Li, He Sci Rep Article Condition monitoring and fault diagnosis of diesel engines are of great significance for safety production and maintenance cost control. The digital twin method based on data-driven and physical model fusion has attracted more and more attention. However, the existing methods lack deeper integration and optimization facing complex physical systems. Most of the algorithms based on deep learning transform the data into the substitution of the physical model. The lack of interpretability of the deep learning diagnosis model limits its practical application. The attention mechanism is gradually developed to access interpretability. In this study, a digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis is proposed with considering its signal characteristics of strong angle domain correlation and transient non-stationary, in which a new soft threshold filter is designed to draw more attention to multi decentralized local fault information dynamically in real time. Based on this attention mechanism, the distribution of fault information in the original signal can be better visualized to help explain the fault mechanism. The valve failure experiment on a diesel engine test rig is conducted, of which the results show that the proposed adaptive sparse attention mechanism model has better training efficiency and clearer interpretability on the premise of maintaining performance. Nature Publishing Group UK 2022-01-13 /pmc/articles/PMC8758757/ /pubmed/35027591 http://dx.doi.org/10.1038/s41598-021-04545-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Jiang, Jiajie Li, Hui Mao, Zhiwei Liu, Fengchun Zhang, Jinjie Jiang, Zhinong Li, He A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis |
title | A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis |
title_full | A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis |
title_fullStr | A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis |
title_full_unstemmed | A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis |
title_short | A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis |
title_sort | digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8758757/ https://www.ncbi.nlm.nih.gov/pubmed/35027591 http://dx.doi.org/10.1038/s41598-021-04545-5 |
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