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- W4367016410 abstract "We propose an improved end-to-end Channel Attention based Large Kernel Convolutional Neural Network (CALKCNN) in this paper. The CALKCNN can automatically extract features from the original vibration signal and accurately diagnose bearing fault without any manual feature selection operations. We break the stereotype and innovatively use a large kernel in the first layer of CALKCNN to extract the long-time scale characteristics and suppress high-frequency noise, which improves the anti-noise and generalization ability. The CALKCNN can adaptively adjust the weights of feature maps generated by large kernel convolutions based on the channel attention mechanism, selectively focus on essential features and ignore unimportant features, which improves the adaptability and robustness. Compared with five excellent baseline models, the CALKCNN achieves state-of-the-art performance in bearing fault diagnosis by experiments using CWRU bearing fault dataset." @default.
- W4367016410 created "2023-04-27" @default.
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- W4367016410 date "2023-02-24" @default.
- W4367016410 modified "2023-10-01" @default.
- W4367016410 title "A Channel Attention based Large Kernel Convolutional Neural Network for Bearing Fault Diagnosis" @default.
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- W4367016410 doi "https://doi.org/10.1109/nnice58320.2023.10105739" @default.
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