摘要
针对当前工业现场数据远程查询灵活性不足、传统故障诊断方式效率较低和扩展性较差、故障报警信息形式单一等问题,设计了基于阿里云的大型臭氧发生器远程监测及故障诊断系统。该系统通过工业无线的方式实时采集臭氧发生器的运行数据并上传至阿里云,在阿里云上利用BP神经网络进行故障诊断。终端采用B/S架构实现远程数据监测及手机短信报警等功能。该系统增强了现场数据查询的灵活性,提高了故障诊断的准确性,改善了现场故障处理的及时性。
Aiming at a series of existing problems, such as the lack of flexibility for remote query of industrial field data, low efficiency and poor expansibility of traditional fault diagnosis methods, and single form of fault diagnosis messages, a large-scale ozone generator remote monitoring and fault diagnosis system based on Alibaba Cloud is designed. The system realizes data acquisition of ozone generator in real time through industrial wireless and uploads them to Alibaba Cloud, where BP neural network is adopted for fault diagnosis, and the terminal of which uses B/S framework to achieve remote data monitoring and SMS alarm. The system strengthens the flexibility for remote query of industrial field data, improves the accuracy of fault diagnosis and perfects the timeliness of field fault handling.
作者
李超
李锦龙
孙德辉
雷振伍
于运渌
LI Chao;LI Jin-long;SUN De-hui;LEI Zhen-wu;YU Yun-lu(Beijing Key Laboratory of Fieldbus Technology and Automation,North China University of Technology,Beijing 100144,China)
出处
《组合机床与自动化加工技术》
北大核心
2020年第1期76-78,共3页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家重点研发计划(2018YFC0809700)
北京市科技计划课题(Z171100000717002)
关键词
阿里云
远程监测
故障诊断
BP神经网络
alibaba cloud
remote monitoring
fault diagnosis
BP neural network
