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- W4307943437 abstract "In industrial practice, deploying a neural network for the real-time diagnosis of rolling bearings is a challenging task. To address this problem, this paper proposes a method for deploying Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) on a field programmable gate array (FPGA). Vibration signals can be used to predict the bearing condition directly by the accelerator. Using high-level synthesis tools to optimize the accelerator, the prediction time of a sample (2,048 points) has been reduced to milliseconds. Our research results indicate that the accelerator can be used to accurately and rapidly predict the bearing state." @default.
- W4307943437 created "2022-11-06" @default.
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- W4307943437 date "2022-11-02" @default.
- W4307943437 modified "2023-09-26" @default.
- W4307943437 title "A FPGA accelerator-based WDCNN for rolling bearing diagnosis" @default.
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- W4307943437 doi "https://doi.org/10.1117/12.2652344" @default.
- W4307943437 hasPublicationYear "2022" @default.
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