摘要
监测、诊断和优化是当今技术系统运作中面临的主要挑战。对磨损状况有价值的信息、失效原因和优化潜力均包含在独立的机器部件,甚至整套机器和系统的过程数据中。记录和评估这些数据,然后对设备状况做出准确的判断和结论,这种情况下所使用的系统监测、故障原因分析及机器和系统优化的方法,可用于预测性维护。预测性维护是工业4.0的重要创新之一,记录关键运行参数成为确定最佳维修次数和运行状态的决策工具。文中详述了如何收集这些数据,以及哪些参数对维护具有重要的意义。
Monitoring,diagnosis and optimization are core challenges in the operation of today's technical systems.Valuable information on wear conditions,causes of faults and optimization potential are contained in the process data of individual machine components or even entire machines and systems.The main task is to record and evaluate these data and then draw appropriate conclusions about the circumstances.The methods used in this context thus support the monitoring,fault cause analysis and optimization of machines and systems and can be used for predictive maintenance.This predictive maintenance is one of Industry 4.0's key innovations.Critical operating parameters are recorded as a decision-making tool for determining optimum maintenance times and operating conditions.The paper explains in detail how to collect this data and which parameters are important for maintenance.
出处
《传感器世界》
2022年第1期26-30,共5页
Sensor World
关键词
状态监控
预测性维护
传感器
condition monitorng
predictive maintenance
sensor
