A Novel Hybrid Neural Network with Attentive Feature Selection for Degradation Status Identification of Aircraft Self-locking Nuts-Reference-Cited by-同舟云学术

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A Novel Hybrid Neural Network with Attentive Feature Selection for Degradation Status Identification of Aircraft Self-locking Nuts

Published:2022-12-15 Issue: Volume: Page:
ISSN:
Container-title:2022 4th International Conference on System Reliability and Safety Engineering (SRSE)
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Author:

Wenjing Zhang¹,Yulin Ma²,Yanwei Xu³,Xinfu Liang¹,Le Qi⁴,Jun Yang²,Lei Li²

Affiliation:

1. Technology Enterprises Aerospace Precision Co., Ltd,Tianjin Key Laboratory of Fastening and Connection,Tianjin,China

2. School of Reliability and System Engineering, Beihang University,Beijing,China

3. Aerospace Precision Co., Ltd.,Tianjin,China

4. The Second Military Representative Office of the Air Force Equipment Department,Tianjin,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10067247/10067267/10067512.pdf?arnumber=10067512

Reference10 articles.

1. Convolutional kernel aggregated domain adaptation for intelligent fault diagnosis with label noise

2. Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery

3. Failure analysis of hook end self locking nuts of an aero-engine

4. Mechanical Behavior of Elastic Self-Locking Nails for Intramedullary Fracture Fixation: A Numerical Analysis of Innovative Nail Designs

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