CPS: Small: Mechanical Vibration Based Prognostic Monitoring of Machinery Health with Sub-millisecond Accuracy Using Backscatter Signals

CPS：小型：使用反向散射信号以亚毫秒精度对机械健康状况进行基于机械振动的预测监测

• 批准号: 1837146
• 负责人: Alex Liu
• 金额: $ 50万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837146&HistoricalAwards=false
• 关键词: CPS; Small; Mechanical; Vibration; Based

This project aims to develop non-intrusive and universal vibration sensing schemes that can detect the abnormal vibrations of a running machine. Towards this goal, the researchers propose a system that first uses the backscatter signals in commercial off the shelf RFID systems to accurately measure machine vibrations, and then uses machine learning and signal processing techniques to detect abnormal machine vibration patterns so that machine operators can be alerted to take actions before the machine fails. This project represents an emerging space driving new CPS and Internet of Things concepts for machinery safety. It can be used for the prognostic monitoring of not only indoor machines, but also outdoor appliances and civil infrastructures, such as drilling system monitoring, pumping system monitoring, pipeline system monitoring, and bridge monitoring. The proposed system is expected to impact manufacturing and economy. This project will bridge the communities between Computer Science and Mechanical Engineering; and foster interaction and communication among them. It will also facilitate the effort of the researchers on attracting and mentoring undergraduate students and underrepresented graduate students in research. Furthermore, the researchers will integrate the research results from this project into both undergraduate and graduate curricula. This project has two key technical objectives: to develop vibration measurement schemes using RFID systems and to develop abnormal vibration pattern recognition schemes based on the measured vibration signals. For vibration sensing, the basic idea is to measure the machine vibrations through random and low-frequency readings of the tag using the RFID reader, where each reading is viewed as one sampling of the vibration. For abnormal vibration pattern detection, the basic idea is to build base line models based on the measured vibration readings and then classify real time vibration readings of a running machine as being either normal or abnormal. The proposed system would have several advantages over prior art in machine health monitoring , e.g., nonintrusive, inexpensive, accurate, and easily deployable including in non-line-of-sight scenarios.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在开发非侵入式和通用的振动传感方案，可以检测运行机器的异常振动。为了实现这一目标，研究人员提出了一种系统，该系统首先使用商用现成RFID系统中的反向散射信号来准确测量机器振动，然后使用机器学习和信号处理技术来检测异常的机器振动模式，以便机器操作员可以在机器故障之前采取行动。该项目代表了一个新兴的空间，推动了新的CPS和物联网概念的机械安全。它不仅可以用于室内机器的预测监测，还可以用于室外设备和民用基础设施，如钻井系统监测，泵系统监测，管道系统监测和桥梁监测。预计拟议的系统将影响制造业和经济。该项目将架起计算机科学和机械工程之间的桥梁，并促进它们之间的互动和沟通。它还将促进研究人员努力吸引和指导本科生和研究人数不足的研究生。此外，研究人员将把该项目的研究成果纳入本科和研究生课程。该项目有两个关键的技术目标：开发使用RFID系统的振动测量方案和开发基于测量的振动信号的异常振动模式识别方案。对于振动感测，基本思想是使用RFID读取器通过标签的随机和低频读数来测量机器振动，其中每个阅读被视为振动的一个采样。对于异常振动模式检测，基本思想是基于测量的振动读数建立基线模型，然后将运行机器的真实的振动读数分类为正常或异常。所提出的系统在机器健康监测方面将具有优于现有技术的几个优点，例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。