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面向工业资产的数字孪生驱动的预测与健康管理。

Digital twin-driven prognostics and health management for industrial assets.

作者信息

Xiao Bin, Zhong Jingshu, Bao Xiangyu, Chen Liang, Bao Jinsong, Zheng Yu

机构信息

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

College of Mechanical Engineering, Donghua University, Shanghai, 200240, China.

出版信息

Sci Rep. 2024 Jun 11;14(1):13443. doi: 10.1038/s41598-024-63990-0.

DOI:10.1038/s41598-024-63990-0
PMID:38862621
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11166927/
Abstract

As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented.

摘要

作为智能升级的推动者，数字孪生（DT）正在成为预测与健康管理（PHM）领域的一股驱动力。故障可能导致工业资产的性能下降或出现故障。因此，开展了基于数字孪生驱动的预测与健康管理研究，以提高工业资产的可靠性并降低维护成本。然而，目前缺乏对现有基于数字孪生驱动的工业资产预测与健康管理应用及方法进行分析和总结的系统性研究。因此，本文首先从应用层的应用领域、方面和层次分析了数字孪生在预测与健康管理中的应用。接着在理论层深入探讨了数字孪生在预测与健康管理中的核心及机制。然后在实施层研究并总结了数字孪生建模和数字孪生系统的使能技术与工具。最后给出了相关见解和未来研究建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/822ae3174838/41598_2024_63990_Fig15_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/822ae3174838/41598_2024_63990_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/d07ec6ef6ea6/41598_2024_63990_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/27f338bdf313/41598_2024_63990_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/ec0ed3cf3f99/41598_2024_63990_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/e8e20b81d7a6/41598_2024_63990_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/2436b6b323d6/41598_2024_63990_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/c35643e7457a/41598_2024_63990_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/a996577c90bf/41598_2024_63990_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/512b2d3da512/41598_2024_63990_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/76a10aed535c/41598_2024_63990_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/ceda6d85b580/41598_2024_63990_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/fdf736117888/41598_2024_63990_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/930a784b3f47/41598_2024_63990_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/91359cbd6ff3/41598_2024_63990_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/0355ef08e1ae/41598_2024_63990_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c5/11166927/822ae3174838/41598_2024_63990_Fig15_HTML.jpg

相似文献

[1]
Digital twin-driven prognostics and health management for industrial assets.

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[2]
Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead.

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[3]
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[4]
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[6]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Multimodal data generative fusion method for complex system health condition estimation.

Sci Rep. 2025-6-6

[2]
Research and Prospects of Digital Twin-Based Fault Diagnosis of Electric Machines.

Sensors (Basel). 2025-4-21

[3]
Intelligent fault diagnosis and operation condition monitoring of transformer based on multi-source data fusion and mining.

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本文引用的文献

[1]
Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance.

Sensors (Basel). 2023-2-21

[2]
Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics.

IEEE trans Intell Transp Syst. 2021-9-29

[3]
A Novel Fault Diagnosis Method for Denoising Autoencoder Assisted by Digital Twin.

Comput Intell Neurosci. 2022-7-21

[4]
A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis.

Sci Rep. 2022-1-13

[5]
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.

ISA Trans. 2022-1

[6]
The 'Digital Twin' to enable the vision of precision cardiology.

Eur Heart J. 2020-12-21

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