Deep learning has achieved remarkable success in the field of bearing fault diagnosis. However, this success comes with larger models and more complex computations, which cannot be transferred into industrial fields requiring models to be of high speed, strong portability, and low power consumption. In this paper, we propose a lightweight and deployable model for bearing fault diagnosis, referred to as BearingPGA-Net, to address these challenges. Firstly, aided by a well-trained large model, we train BearingPGA-Net via decoupled knowledge distillation. Despite its small size, our model demonstrates excellent fault diagnosis performance compared to other lightweight state-of-the-art methods. Secondly, we design an FPGA acceleration scheme for BearingPGA-Net using Verilog. This scheme involves the customized quantization and designing programmable logic gates for each layer of BearingPGA-Net on the FPGA, with an emphasis on parallel computing and module reuse to enhance the computational speed. To the best of our knowledge, this is the first instance of deploying a CNN-based bearing fault diagnosis model on an FPGA. Experimental results reveal that our deployment scheme achieves over 200 times faster diagnosis speed compared to CPU, while achieving a lower-than-0.4\% performance drop in terms of F1, Recall, and Precision score on our independently-collected bearing dataset. Our code is available at \url{https://github.com/asdvfghg/BearingPGA-Net}.

翻译：深度学习在轴承故障诊断领域取得了显著成功。然而，这种成功伴随着更大的模型规模和更复杂的计算，使其难以迁移到需要高速、强可移植性和低功耗的工业场景中。本文提出了一种轻量级且可部署的轴承故障诊断模型BearingPGA-Net，以应对上述挑战。首先，借助预训练的大模型，我们通过解耦知识蒸馏训练BearingPGA-Net。尽管模型规模较小，但与其他先进的轻量级方法相比，我们的模型仍展现出优异的故障诊断性能。其次，我们利用Verilog为BearingPGA-Net设计了FPGA加速方案。该方案包括定制化量化操作，并在FPGA上为BearingPGA-Net的每一层设计可编程逻辑门，重点通过并行计算和模块复用来提升计算速度。据我们所知，这是首次将基于CNN的轴承故障诊断模型部署在FPGA上。实验结果表明，相较于CPU，我们的部署方案实现了超过200倍的诊断速度提升，同时在我们自行采集的轴承数据集上，F1分数、召回率和精确率的性能下降均低于0.4%。我们的代码已开源在 \url{https://github.com/asdvfghg/BearingPGA-Net}。