SSAE-AM: A Prediction Model for Fatigue Crack Growth-Reference-Cited by-同舟云学术

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SSAE-AM: A Prediction Model for Fatigue Crack Growth

Published:2024-07-01 Issue:13 Volume:11 Page:23032-23044
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhao Boyang¹^ORCID,Dai Wei¹^ORCID,Lin Yun²^ORCID,Liang Haoyang¹,Li Ning³

Affiliation:

1. School of Reliability and Systems Engineering, Beihang University, Beijing, China

2. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

3. Center of Testing Technology, AVIC Manufacturing Technology Institute, Beijing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10570342/10491590.pdf?arnumber=10491590

Reference47 articles.

1. High cycle fatigue S-N curve prediction of steels based on transfer learning guided long short term memory network

2. Application of the Gaussian process for fatigue life prediction under multiaxial loading;Sło´nski;Mech. Syst. Signal Process.,2022

3. Ultrasonic Lamb wave mixing based fatigue crack detection using a deep learning model and higher-order spectral analysis

4. A multi-axial vibration fatigue evaluation procedure for welded structures in frequency domain

5. Multisensor Fault Diagnosis Modeling Based on the Evidence Theory

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