Model Based Machine Diagnostics Using Remote Sensing

使用遥感进行基于模型的机器诊断

• 批准号: 8605917
• 负责人: Jeffrey Stein
• 金额: $ 15.85万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-03-15 至 1990-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8605917&HistoricalAwards=false
• 关键词: Model; Based; Machine; Diagnostics; Using

This research will investigate developing a machine diagnostics system based on sensors that are typically found in a machine tool. This approach will be applied to machine tool drive systems. The signals that are readily available are motor voltage, current, velocity, and position; these signals will be employed in this technique. The research is concerned with detecting either a change in the load imposed on the drive system (e.g. load caused by a failed bearing, gear, or belt or a slide/weighs fault) or a change in the dynamic behavior of the drive system (e.g. caused by motor characteristics or slide/weighs changes). The research will concentrate on mechanical component faults. The major tasks of the research include development of a dynamic mathematical model of the system to quantify the relation between the load and the remotely measured load signal, identification of the number of sensors required to detect machine faults, and off-line algorithms to detect and identify faults.

这项研究将研究开发基于机床中常见传感器的机器诊断系统。 这种方法将应用于机床驱动系统。 容易获得的信号有电机电压、电流、速度和位置；这些信号将用于该技术。 该研究涉及检测施加在驱动系统上的负载变化（例如，由轴承、齿轮或皮带故障或滑动/称重故障引起的负载）或驱动系统动态行为的变化（例如，由电机特性或滑动/称重变化引起的）。 研究将集中于机械部件故障。 研究的主要任务包括开发系统的动态数学模型，以量化负载和远程测量的负载信号之间的关系，确定检测机器故障所需的传感器数量，以及检测和识别故障的离线算法。