Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning

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Authors

Research Organisations

External Research Organisations

  • Southeast University (SEU)
  • University of Electronic Science and Technology of China
  • University of Liverpool
  • Tongji University
  • Xi'an Jiaotong-Liverpool University
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Details

Original languageEnglish
Article number111186
Number of pages19
JournalMechanical Systems and Signal Processing
Volume211
Early online date3 Feb 2024
Publication statusPublished - 1 Apr 2024

Abstract

Data-driven prognostic and health management technologies are instrumental in accurately monitoring the health of mechanical systems. However, the availability of few-shot source data under varying operating conditions limits their ability to predict health. Also, the global feature extraction process is susceptible to temporal semantic loss, resulting in reduced generalization of extracted degradation features. To address these challenges, a transferable autoregressive recurrent adaptation method is proposed for bearing health prognosis. In the enhancement of few-shot data, a novel sample generation module with attribute-assisted learning, combined with adversarial generation, is introduced to mine data that better matches the source sample distribution. Additionally, a deep autoregressive recurrent model is designed, incorporating a statistical mode to consider the degradation processes more comprehensively. To complement the semantic loss, a semantic attention module is developed, embedded into the basic model of meta learning. To validate the effectiveness of this approach, extensive bearing prognostics are conducted across six tasks. The results demonstrate the clear advantages of this proposed method in bearing prognosis, especially when dealing with limited bearing data.

Keywords

    Adversarial augmentation, Autoregressive regression, Meta learning, Remaining useful life, Semantic attention mechanism

ASJC Scopus subject areas

Cite this

Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning. / Zhuang, Jichao; Jia, Minping; Huang, Cheng Geng et al.
In: Mechanical Systems and Signal Processing, Vol. 211, 111186, 01.04.2024.

Research output: Contribution to journalArticleResearchpeer review

Zhuang J, Jia M, Huang CG, Beer M, Feng K. Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning. Mechanical Systems and Signal Processing. 2024 Apr 1;211:111186. Epub 2024 Feb 3. doi: 10.1016/j.ymssp.2024.111186
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title = "Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning",
abstract = "Data-driven prognostic and health management technologies are instrumental in accurately monitoring the health of mechanical systems. However, the availability of few-shot source data under varying operating conditions limits their ability to predict health. Also, the global feature extraction process is susceptible to temporal semantic loss, resulting in reduced generalization of extracted degradation features. To address these challenges, a transferable autoregressive recurrent adaptation method is proposed for bearing health prognosis. In the enhancement of few-shot data, a novel sample generation module with attribute-assisted learning, combined with adversarial generation, is introduced to mine data that better matches the source sample distribution. Additionally, a deep autoregressive recurrent model is designed, incorporating a statistical mode to consider the degradation processes more comprehensively. To complement the semantic loss, a semantic attention module is developed, embedded into the basic model of meta learning. To validate the effectiveness of this approach, extensive bearing prognostics are conducted across six tasks. The results demonstrate the clear advantages of this proposed method in bearing prognosis, especially when dealing with limited bearing data.",
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author = "Jichao Zhuang and Minping Jia and Huang, {Cheng Geng} and Michael Beer and Ke Feng",
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AU - Jia, Minping

AU - Huang, Cheng Geng

AU - Beer, Michael

AU - Feng, Ke

N1 - Funding Information: The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (No. 52075095 ) and the China Scholarship Council. And the authors would like to appreciate the anonymous reviewers and the editor for their valuable comments.

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