Spatio-Temporal Fusion Attention: A Novel Approach for Remaining Useful Life Prediction Based on Graph Neural Network-Reference-Cited by-同舟云学术

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Spatio-Temporal Fusion Attention: A Novel Approach for Remaining Useful Life Prediction Based on Graph Neural Network

Published:2022 Issue: Volume:71 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Kong Ziqian¹^ORCID,Jin Xiaohang²^ORCID,Xu Zhengguo¹^ORCID,Zhang Bin³^ORCID

Affiliation:

1. College of Control Science and Engineering, Zhejiang University, Hangzhou, China

2. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China

3. Department of Electrical Engineering, University of South Carolina, Columbia, SC, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China

Ningbo Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09801529.pdf?arnumber=9801529

Reference37 articles.

1. Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics

2. The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study

3. Cost-Effective Lebesgue Sampling Long Short-Term Memory Networks for Lithium-Ion Batteries Diagnosis and Prognosis

4. Damage propagation modeling for aircraft engine run-to-failure simulation

5. An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme

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