Details
| Original language | English |
|---|---|
| Article number | 111186 |
| Number of pages | 19 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 211 |
| Early online date | 3 Feb 2024 |
| Publication status | Published - 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
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 211, 111186, 01.04.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning
AU - Zhuang, Jichao
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.
PY - 2024/4/1
Y1 - 2024/4/1
N2 - 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.
AB - 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.
KW - Adversarial augmentation
KW - Autoregressive regression
KW - Meta learning
KW - Remaining useful life
KW - Semantic attention mechanism
UR - http://www.scopus.com/inward/record.url?scp=85183972482&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2024.111186
DO - 10.1016/j.ymssp.2024.111186
M3 - Article
AN - SCOPUS:85183972482
VL - 211
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 111186
ER -