Yun-Xia Chen

affiliation not provided to SSRN

No Address Available

SCHOLARLY PAPERS

4

DOWNLOADS

146

TOTAL CITATIONS

2

Scholarly Papers (4)

1.

Physics-Informed Deep Learning for Lithium-Ion Battery Diagnostics Using Electrochemical Impedance Spectroscopy

Number of pages: 49 Posted: 29 May 2023
Yan-Hui LIN, Sheng-Jia Ruan, Yun-Xia Chen and Yan-Fu Li
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and Tsinghua University
Downloads 88 (625,801)
Citation 1

Abstract:

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Lithium-ion batteries, diagnostics, physics-informed deep learning, Electrochemical Impedance Spectroscopy, multi-task learning

2.

Causal Graph-Based Spatial-Temporal Attention Network for Rul Prediction of Complex Systems

Number of pages: 40 Posted: 25 Jul 2024
Shuwen Zheng, Jie Liu, Yun-Xia Chen, Yu Fan and Dan Xu
affiliation not provided to SSRN, Beihang University (BUAA) - School of Reliability and Systems Engineering, affiliation not provided to SSRN, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 28 (1,059,352)
Citation 1

Abstract:

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Remaining useful life, spatial-temporal features, causality, complex systems, prognostics

3.

A Meta-Learning Lifetime Prediction Approach for Lithium-Ion Batteries Considering Label Noise

Number of pages: 19 Posted: 05 Dec 2023
Guisong Wang, Cong Wang and Yun-Xia Chen
affiliation not provided to SSRN, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 26 (1,082,892)

Abstract:

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Lithium-ion batteries, lifetime prediction, label noise, meta-learning, collaborative optimization

4.

Ensemble Learning-Based Fault Diagnosis of Aero-Engine Bearings with Limited Samples Under Unseen Variable Conditions

Number of pages: 64 Posted: 19 Apr 2025
Kunyu Dong, Dan Xu and Yun-Xia Chen
affiliation not provided to SSRN, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 4 (1,316,015)

Abstract:

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fault diagnosis, Ensemble learning, Wasserstein distance, Unseen conditions, Limited sample, Physically interpretable features