Zenghui An
Orcid: 0000-0001-8482-5234
According to our database1,
Zenghui An
authored at least 14 papers
between 2019 and 2024.
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Bibliography
2024
Mode-Decoupling Auto-Encoder for Machinery Fault Diagnosis Under Unknown Working Conditions.
IEEE Trans. Ind. Informatics, March, 2024
2023
Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large-Speed-Fluctuation Conditions.
IEEE Trans. Ind. Informatics, July, 2023
2022
Multilayer Extreme Learning Convolutional Feature Neural Network Model for the Weak Feature Classification and Status Identification of Planetary Bearing.
J. Sensors, 2022
2021
Self-learning transferable neural network for intelligent fault diagnosis of rotating machinery with unlabeled and imbalanced data.
Knowl. Based Syst., 2021
2020
Enhanced sparse filtering with strong noise adaptability and its application on rotating machinery fault diagnosis.
Neurocomputing, 2020
A novel geodesic flow kernel based domain adaptation approach for intelligent fault diagnosis under varying working condition.
Neurocomputing, 2020
Neurocomputing, 2020
A renewable fusion fault diagnosis network for the variable speed conditions under unbalanced samples.
Neurocomputing, 2020
A Novel Data-Driven Fault Feature Separation Method and Its Application on Intelligent Fault Diagnosis Under Variable Working Conditions.
IEEE Access, 2020
Adaptive Cross-Domain Feature Extraction Method and Its Application on Machinery Intelligent Fault Diagnosis Under Different Working Conditions.
IEEE Access, 2020
2019
Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines.
Neurocomputing, 2019
Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method.
Neurocomputing, 2019
Adaptive Reinforced Empirical Morlet Wavelet Transform and Its Application in Fault Diagnosis of Rotating Machinery.
IEEE Access, 2019
Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks.
IEEE Access, 2019