面向边缘计算的机械装备状态监测系统研究

针对云计算框架的机械装备状态监测系统存在数据传输时延长、预警和诊断的实时性差等问题,提出一种面向边缘计 算的机械装备状态监测系统,具有设备层、边缘层和云层 3 层架构。 高实时性的计算任务部署在多个边缘计算节点,在边缘层 进行数据的特征提取、降维处理、智能诊断、数据保存与上传。 所提方法在高速机床主轴试验台进行验证,实验结果表明,基于 边缘计算的状态监测系统比基于云计算的状态监测系统减少 29. 5%的输出时延,并节省了 81. 3%的云层储存空间,在保证较高 诊断率的情况下,显著提高了系统实时性。

Research on mechanical equipment condition monitoring system for edge computing

There exist some problems in the mechanical equipment condition monitoring system based on cloud computing framework. The problems of the extension of data transmission, poor real-time performance of early warning and diagnosis etc. Usually occur in practical application. This paper presents a mechanical equipment condition monitoring system for edge computing, which has three-tier architecture: Equipment layer, edge layer and cloud layer. High real-time computing tasks are deployed in multiple edge computing nodes, and data feature extraction, dimensionality reduction, intelligent diagnosis, data saving and uploading are carried out in the edge layer. The proposed method is verified on the spindle test-bed of high-speed machine tool. The experimental results show that the condition monitoring system based on edge computing reduces the output delay by 29. 5% compared with the condition monitoring system based on cloud computing, saves 81. 3% cloud storage space, and significantly improves the real-time performance of the system under the condition of ensuring a high diagnosis rate.