Fault Prognostics for Machinery with Multi-Degradation Stage Based on Deep Residual Network and Multi-Task Learning-Reference-Cited by-同舟云学术

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Fault Prognostics for Machinery with Multi-Degradation Stage Based on Deep Residual Network and Multi-Task Learning

Published:2023-09-22 Issue: Volume: Page:
ISSN:
Container-title:2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
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Author:

Wang Mingxian¹,Cui Langfu¹,Jiang Jiahe¹,Wang Junle¹,Jin Yang²,Xiang Gang³,Yang Jianbing⁴,Lin Ruishi³

Affiliation:

1. Beihang University,School of Automation Science and Electrical Engineering,Beijing,China

2. Shanghai Zhenhua Heavy Industries Co., Ltd,Shanghai,China

3. Shanghai Electro-Mechanical Engineering Institute,Shanghai,China

4. Beijing Aerospace Automatic Control Institute,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295667.pdf?arnumber=10295667

Reference18 articles.

1. Fuzzy Clustering: A Historical Perspective

2. LSTM networks based on attention ordered neurons for gear remaining life prediction

3. Deep Residual Learning for Image Recognition

4. A review of clustering techniques and developments

5. Journal bearing seizure degradation assessment and remaining useful life prediction based on long short-term memory neural network

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