Method of state identification of rolling bearings based on deep domain adaptation under varying loads
Author:
Affiliation:
1. School of Electrical and Electronic EngineeringHarbin University of Science and TechnologyHarbinPeople's Republic of China
2. Belarusian State UniversityMinskBelarus
Funder
National Natural Science Foundation of China
Natural Science Foundation of Heilongjiang Province
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-smt.2019.0043
Reference49 articles.
1. Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method;An Z.H.;Neurocomputing,2019
2. Rolling element bearing fault diagnosis based on non‐local means de‐noising and empirical mode decomposition
3. Cross‐domain fault diagnosis of rolling element bearings using deep generative neural networks;Li X.;IEEE Trans. Ind. Electron.,2019
4. Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis;Zheng J.D.;Mech. Syst. Signal Process.,2018
5. A positive energy residual (PER) based planetary gear fault detection method under variable speed conditions;Park J.;Mech. Syst. Signal Process.,2019
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1. Optimization of Deep Belief Network Based on Sparrow Search Algorithm for Rolling Bearing Fault Diagnosis;IEEE Access;2024
2. A Fast Classification Method of Rolling Bearing State Under Different Loads Based on Improved Broad Model Transfer Learning;J ELECTRON INF TECHN;2023
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4. Fault Diagnosis Method of Rolling Bearings Under Different Working Conditions Based on Federated Feature Transfer Learning;2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD);2022-11-30
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