基于云平台的旋转机械轴承监测系统设计

针对旋转机械监测中无法随时随地查看其运行状态，监测产生的数据量逐渐加大，以及故障特征提取困难的问题，以轴承作为关键部件，提出一种基于云平台的旋转机械轴承监测系统。系统采用温度和加速度传感器、STM32单片机获得轴承监测所需的数据；然后利用窄带物联网完成数据远程传输，并将其存储到云端数据库中；在云平台利用相关时域频域分析对轴承状态进行监测，并利用设计的一种多尺度一维卷积神经网络模型实现轴承的故障诊断；然后由Web浏览器显示轴承的运行状态和故障诊断结果。实验结果表明提出的故障诊断方法诊断准确率高、效果好，系统能够良好地运行。

Design of Rotating Machinery Bearing Monitoring System Based on Cloud Platform

In view of the problems that the rotating machinery running state cannot be checked anytime and anywhere in monitoring,the data generated by monitoring is gradually increasing,and the fault features extraction is hard,a cloud platform-based monitoring system for rotating machinery bearings was proposed,taking bearings as the key component.Firstly,temperature and acceleration sensors were used in the system,and STM32 MCU was used to obtain the data needed for bearing monitoring.The data were then transferred remotely using the narrowband internet of things and stored in a cloud database.In the cloud platform,the bearing status was monitored by correlation time-domain and frequent-domain analysis,and a multi-scale attention convolution neural network diagnosis algorithm designed was used for bearing fault diagnosis.Finally,it is indicated that the fault diagnosis algorithm has a high diagnosis accuracy and the system can run well.