摘要
设备定期维护是保证设备能够健康、长期运行的有效措施,但许多设备都是24小时不停工运行,停机维护耗时、耗力,一定程度上增加了维护的成本,影响了工厂的产量。基于大多数工厂设备的停工维护情况,结合现场实际生产需求,提出了基于边缘计算的设备健康预测系统,论述了系统的整体框架、系统特点以及系统实用性等,同时经现场论证,一定程度上改善了设备维护的及时性,降低了维护成本,提升了维护效率。
Regular equipment maintenance is an effective measure to ensure the health of the equipment and long-term operation.However,many equipments are operated 24 hours a day without downtime.Downtime maintenance is time-consuming and labor-intensive,which increases the cost of maintenance to a certain extent and affects the output of the factory.Based on the shutdown and maintenance of most factory equipment and the actual production needs on site,this paper proposes an edge computing-based equipment health prediction system,and discusses the overall framework,system characteristics,and system practicability of the system.It improves the timeliness of equipment maintenance,reduces maintenance costs,and improves maintenance efficiency.
出处
《工业控制计算机》
2020年第12期55-56,59,共3页
Industrial Control Computer
关键词
边缘计算
预测性维护
健康预测
edge computing
predictive maintenance
health prediction
