In recent years, a large number of edge computing devices have been used to monitor the operating state of industrial equipment and perform fault diagnosis analysis. Therefore, the fault diagnosis algorithm in the edge computing device is particularly important. With the increase in the number of device detection points and the sampling frequency, mechanical health monitoring has entered the era of big data. Edge computing can process and analyze data in real time or faster, making data processing closer to the source, rather than the external data center or cloud, which can shorten the delay time. After using 8 bits and 16 bits to quantify the deep measurement learning model, there is no obvious loss of accuracy compared with the original floating-point model, which shows that the model can be deployed and reasoned on the edge device, while ensuring real time. Compared with using servers for deployment, using edge devices not only reduces costs but also makes deployment more flexible.

中文翻译：

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齿轮箱在边缘设备上运行的多故障诊断方法

近年来，大量的边缘计算设备已用于监视工业设备的运行状态并执行故障诊断分析。因此，边缘计算设备中的故障诊断算法特别重要。随着设备检测点数量和采样频率的增加，机械健康监测已进入大数据时代。边缘计算可以实时或更快速地处理和分析数据，从而使数据处理更接近源，而不是外部数据中心或云，从而可以缩短延迟时间。使用8位和16位量化深度测量学习模型后，与原始浮点模型相比，精度没有明显下降，这表明该模型可以在边缘设备上部署和推理。同时确保实时。与使用服务器进行部署相比，使用边缘设备不仅可以降低成本，而且可以使部署更加灵活。