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• W4385540609 startingPage "109537" @default.
• W4385540609 abstract "Due to the tough environment in which rotating machinery is located, the extraction of features for its fault signals has been plaguing researchers. Therefore, this study proposed a novel feature enhancement framework, which combines signal reconstruction with multi-scale channel attention residual network. Firstly, the signal is decomposed into a series of components by CEEMDAN and the kurtosis-correlation coefficient criterion is used to select effective components. Secondly, ineffective components are denoised by wavelet packets and reconstructed with the effective component. Then, a novel multi-scale channel attention network is designed to enhance feature learning. Finally, experimental data from the QPZZ-II bearing fault with balanced datasets, imbalanced datasets, and small sample datasets are utilized to confirm the validity of the proposed approach. Experimental results demonstrate that the suggested approach outperforms other widely used intelligent fault detection methods by achieving the best diagnostic accuracy." @default.
• W4385540609 created "2023-08-04" @default.
• W4385540609 creator A5005909676 @default.
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• W4385540609 date "2023-08-01" @default.
• W4385540609 modified "2023-10-13" @default.
• W4385540609 title "A novel feature enhancement framework for rotating machinery fault identification under limited datasets" @default.
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• W4385540609 doi "https://doi.org/10.1016/j.apacoust.2023.109537" @default.
• W4385540609 hasPublicationYear "2023" @default.
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