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国外文献 > 1.轴承 > 7. 智能方法与计算 > 神经网络

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标 题： A FPGA Deployment System Based on Convolutional Neural Network for Rolling Bearing Diagnosis
基于卷积神经网络的滚动轴承诊断FPGA部署系统
作 者： Peixin Tan
Guijun Ma
Pengchao Hu
Shaolin Ran
关键词： Real-time  [实时]
Fault diagnosis  [ 故障诊断]
Convolutional neural network  [ 卷积神经网络]
Field programmable gate array  [ 现场可编程门阵列]
出版年： 2021
会议名： 6th IEEE International Conference on Cloud Computing and Big Data Analytics (ICCCBDA 2021)
第六届IEEE国际云计算与大数据分析会议（ICCCBDA 2021）
届 次： 6th
摘 要： Real-time fault diagnosis of rolling bearing is a challenging issue for industry. Although artificial intelligence-based technologies could be well used for fault diagnosis of rolling bearing, the factories may not take into account the deployment of diagnosis algorithms. To tackle the issue, this work proposes a flexible deployment system of diagnosis algorithm for rolling bearing, where the required Convolutional Neural Network (CNN) model is deployed on the Field Programmable Gate Array (FPGA) to identify the working conditions of rolling bearing using vibration signals. The experimental results show that the deployed system performs accurately and efficiently on the test set, while a real-time prediction of FPGA could be guaranteed, indicating its potential as a powerful auxiliary system of rotating machinery.

滚动轴承的实时故障诊断是工业界面临的一大难题。尽管人工智能技术可用于滚动轴承的故障诊断，但工厂可能并未考虑部署诊断算法。为解决这一问题，本文提出了一种灵活的滚动轴承诊断算法部署系统，将所需的卷积神经网络（CNN）模型部署在可编程门阵列（FPGA）上，通过振动信号识别滚动轴承的工作状态。实验结果表明，该部署系统在测试集上表现出高精度和高效性，同时能够实现对FPGA的实时预测，显示出其作为旋转机械强大辅助系统的潜力。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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