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- W3208485722 abstract "Rolling element bearing is the most important and critical mechanical device of rotating machinery. Identifying and trouble-shooting bearing faults in an early stage is necessary to prevent possible damage. The traditional intelligent bearing fault diagnosis method usually need preprocess the signal, manually extracting the features and pattern classification. The process of feature extraction requires much professional knowledge and complex feature extraction for this reason the model does not achieve satisfactory result. To overcome the complexity of traditional method, In this paper, the authors propose a deep learning algorithm for detection and classification of bearing faults based on Convolutional Neural Network, which does not need any feature extraction method. To evaluate the proposed CNN model, the bearing fault diagnosis experiment were carried out using transfer learning method and also compared with four other models. Finally, on CWRU's bearing dataset, we implemented the proposed model. The achieved accuracy indicates that the proposed CNN model is highly reliable in bearing fault diagnosis." @default.
- W3208485722 created "2021-11-08" @default.
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- W3208485722 date "2021-09-24" @default.
- W3208485722 modified "2023-09-30" @default.
- W3208485722 title "Performance Analysis of Bearing fault diagnosis using Convolutional Neural Network" @default.
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- W3208485722 doi "https://doi.org/10.1109/gucon50781.2021.9573710" @default.
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