基于边缘智能的机电设备故障检测服务研究

Research on fault detection service of electromechanical equipment based on edge intelligence

文章引进边缘智能技术,研究基于边缘智能的机电设备故障检测服务,以高性能GPU为基础构建的实验环境,采用Pycharm集成开发环境和Tensorflow深度学习系统,利用Python语言对机电设备进行故障检测。相较传统方法,此方法进行机电设备故障检测时可以将检测精准率控制在95%以上,说明该故障检测方法可以提高故障检测结果的准确率,实现对机电设备故障的精准排查,保障机电设备可靠运行。

The article introduces edge intelligence technology and studies the fault detection service for electromechanical equipment based on edge intelligence.The experimental environment is built on the basis of high-performance GPU,and the Pycharm integrated development environment and Tensorflow deep learning system are used to detect faults in electromechanical equipment using Python language.Compared to traditional methods,this method can control the detection accuracy of mechanical and electrical equipment faults to over 95%,indicating that this fault detection method can improve the accuracy of fault detection results,achieve accurate troubleshooting of mechanical and electrical equipment faults,and ensure the reliable operation of mechanical and electrical equipment.