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- W4319082012 abstract "Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher diagnostic accuracy, but degradation problems of deep learning models often occur; and (2) the use of multiscale features can easily be ignored, which makes the extracted data features lack diversity. To deal with these problems, a novel multiscale feature fusion deep residual network is proposed in this paper for the fault diagnosis of rolling bearings, one which contains multiple multiscale feature fusion blocks and a multiscale pooling layer. The multiple multiscale feature fusion block is designed to automatically extract the multiscale features from raw signals, and further compress them for higher dimensional feature mapping. The multiscale pooling layer is constructed to fuse the extracted multiscale feature mapping. Two famous rolling bearing datasets are adopted to evaluate the diagnostic performance of the proposed model. The comparison results show that the diagnostic performance of the proposed model is superior to not only several popular models, but also other advanced methods in the literature." @default.
- W4319082012 created "2023-02-04" @default.
- W4319082012 creator A5004002278 @default.
- W4319082012 creator A5034088838 @default.
- W4319082012 creator A5047138391 @default.
- W4319082012 date "2023-02-03" @default.
- W4319082012 modified "2023-09-25" @default.
- W4319082012 title "Fault Diagnosis for Rolling Bearings Based on Multiscale Feature Fusion Deep Residual Networks" @default.
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- W4319082012 doi "https://doi.org/10.3390/electronics12030768" @default.
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