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- W4367308124 abstract "In the processing, manufacturing, and production of modern fields, rolling bearings, the most basic module of most mechanical equipment, have a key role that cannot be ignored. This paper proposes three fault diagnosis model architectures for rolling bearings based on the deep convolutional neural network. Three models were tested on the industry-common Case Western Reserve University Dataset (CWRU). The original vibration signal acquisition and processing module mainly uses the vibration signal window translation method to complete the segmentation of overlapping signals and uses the Inception network to efficiently complete one-dimensional signal preprocessing. Finally, we use t-distributed stochastic neighbor embedding (t-SNE) to reduce the dimension and visualize the learned fault data distribution." @default.
- W4367308124 created "2023-04-29" @default.
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- W4367308124 date "2023-04-28" @default.
- W4367308124 modified "2023-09-30" @default.
- W4367308124 title "Rolling bearing intelligent faults diagnosis based on deep convolutional neural network" @default.
- W4367308124 doi "https://doi.org/10.1117/12.2674425" @default.
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