Online Fatigue Life Prediction and Crack Length Estimation Based on LSTM-Reference-Cited by-同舟云学术

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Online Fatigue Life Prediction and Crack Length Estimation Based on LSTM

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

Zhu Yifeng¹,Zhang Jianzhao²,Luo Jiaxiang²,Guo Xinyan³,Liu Ziyu²,Zhang Ruonan¹

Affiliation:

1. China Ship Scientific Research Center,Taihu Laboratory of Deepsea Technological Science,Wuxi,China

2. South China University of Technology,School of Automation Science and Engineering,Guangzhou,China

3. South China University of Technology,School of Civil and Transportation Engineering,Guangzhou,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295757.pdf?arnumber=10295757

Reference15 articles.

1. Particle Filtering

2. Health indicator construction and remaining useful life estimation for mechanical systems using vibration signal prognostics

3. Long Short-Term Memory

4. Rate-dependent multiaxial life prediction for polyamide-6 considering ratchetting: Semi-empirical and physics-informed machine learning models

5. Neural network approach to fatigue-crack-growth predictions under aircraft spectrum loadings

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