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作者:

Liu, Sizhe (Liu, Sizhe.) | Qi, Yongsheng (Qi, Yongsheng.) | Gao, Xuejin (Gao, Xuejin.) | Liu, Liqiang (Liu, Liqiang.) | Ma, Ran (Ma, Ran.)

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摘要:

With the advancement of complex system diagnosis, prediction, and health management technologies, digital twin technology has become a prominent research area in the fields of intelligent manufacturing and system operation and maintenance. However, due to the high complexity of practical systems, the difficulty of data acquisition, and the low accuracy of modeling techniques, current digital twin modeling suffers from low accuracy, and the generalization ability of models is poor when applied in model transfer. To address this issue, a novel fault diagnosis method is proposed, which integrates a digital twin model based on transfer learning. The framework introduces an innovative approach to construct multiple digital twin models using both mechanistic and data-driven models. The mechanism twin constructs a universal simulation model based on physical equipment and updates it with system response measurement data. The data twin consists of a high-dimensional fully connected-generative adversarial network twin for extracting deep features from data and an long- and short-term memory twin for extracting time series features. Subsequently, transfer learning is introduced to achieve deep fusion in the multiple digital twins system. The mechanism twin is used to obtain source domain samples to construct a diagnostic network, and the data twin is used to extract target domain features to correct the diagnostic network, thereby improving the accuracy and reliability of fault diagnosis. Finally, the proposed framework is applied to the fault diagnosis of triplex pump equipment. The accuracy of diagnosis continuously improves as the system is updated and ultimately reaches 89.28%, demonstrating the effectiveness of the algorithm and providing a novel solution for the generalization limitations of current digital twin models.

关键词:

intelligent fault diagnosis long short-term memory networks generative adversarial networks digital twin transfer learning

作者机构:

  • [ 1 ] [Liu, Sizhe]Inner Mongolia Univ Technol, Inst Elect Power, Hohhot, Peoples R China
  • [ 2 ] [Qi, Yongsheng]Inner Mongolia Univ Technol, Inst Elect Power, Hohhot, Peoples R China
  • [ 3 ] [Liu, Liqiang]Inner Mongolia Univ Technol, Inst Elect Power, Hohhot, Peoples R China
  • [ 4 ] [Ma, Ran]Inner Mongolia Univ Technol, Inst Elect Power, Hohhot, Peoples R China
  • [ 5 ] [Liu, Sizhe]Coll & Univ Inner Mongolia Autonomous Reg, Intelligent Energy Technol & Equipment Engn Res Ct, Hohhot 010051, Peoples R China
  • [ 6 ] [Qi, Yongsheng]Coll & Univ Inner Mongolia Autonomous Reg, Intelligent Energy Technol & Equipment Engn Res Ct, Hohhot 010051, Peoples R China
  • [ 7 ] [Liu, Liqiang]Coll & Univ Inner Mongolia Autonomous Reg, Intelligent Energy Technol & Equipment Engn Res Ct, Hohhot 010051, Peoples R China
  • [ 8 ] [Liu, Sizhe]Minist Educ, Engn Res Ctr Large Energy Storage Technol, Hohhot 010051, Peoples R China
  • [ 9 ] [Qi, Yongsheng]Minist Educ, Engn Res Ctr Large Energy Storage Technol, Hohhot 010051, Peoples R China
  • [ 10 ] [Ma, Ran]Minist Educ, Engn Res Ctr Large Energy Storage Technol, Hohhot 010051, Peoples R China
  • [ 11 ] [Gao, Xuejin]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

通讯作者信息:

  • [Qi, Yongsheng]Inner Mongolia Univ Technol, Inst Elect Power, Hohhot, Peoples R China;;[Qi, Yongsheng]Coll & Univ Inner Mongolia Autonomous Reg, Intelligent Energy Technol & Equipment Engn Res Ct, Hohhot 010051, Peoples R China;;[Qi, Yongsheng]Minist Educ, Engn Res Ctr Large Energy Storage Technol, Hohhot 010051, Peoples R China

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来源 :

MEASUREMENT SCIENCE AND TECHNOLOGY

ISSN: 0957-0233

年份: 2024

期: 2

卷: 35

2 . 4 0 0

JCR@2022

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 12

ESI高被引论文在榜: 0 展开所有

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