摘要
针对汽车加工业机床状态数据传统的采集方式采样频率低、生产运行状态不可控、被动维修的现状,尝试采用高速采集架构及数据模型算法,对机床运行的数据进行采集、挖掘、利用,最终生成机床能效分析报告、健康诊断报告等。使设备的状态信息更直观可控,使维修更有预见性,维修工作者快速精准地定位故障信息。推进维修工作专业化,促进汽车制造业良性发展。
The traditional collection methods of machine tool state data in the automobile manufacturing industry have the disadvantages of low sampling frequency, uncontrollable production state and passive maintenance. In view of this situation, this paper attempts to adopt high-speed acquisition architecture and big data model algorithm to collect, excavate and utilize the machine tool running data. Finally, the machine tool energy efficiency analysis report and health diagnosis report were generated. These reports make the status information of the equipment more intuitive and controllable, which makes the maintenance more predictable, so that repair maintenance worker to quickly and accurately locate the fault information. Promote the maintenance of professional work and boost the healthy development of the automobile manufacturing industry.
出处
《计算机应用与软件》
2017年第12期154-157,共4页
Computer Applications and Software
基金
智能MES系统项目(150802)
关键词
机床
智能
能效
Machine tool
Intelligent
Energy efficiency
