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推进测量科学以评估制造机器人的监测、诊断和预测。

Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics.

作者信息

Qiao Guixiu, Weiss Brian A

机构信息

National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.

出版信息

Int J Progn Health Manag. 2016;7(Spec Iss on Smart Manufacturing PHM).

Abstract

Unexpected equipment downtime is a 'pain point' for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system.

摘要

意外的设备停机是制造商的一个“痛点”,尤其是因为这种情况通常会转化为经济损失。为了将这个痛点降到最低,制造商们正在开发新的健康监测、诊断、预测和维护(统称为预测与健康管理,即PHM)技术,以提升其维护策略的技术水平。制造业界在推进和整合PHM技术以增强制造机器人系统能力方面有广泛的需求。包括美国国家标准与技术研究院(NIST)人员在内的众多研究人员已经确定了推进PHM技术存在的一系列障碍和挑战。其中一个挑战是通过开发性能指标、测试方法、参考数据集和支持工具来对PHM技术进行验证和确认。除了记录和展示研究情况外,NIST人员还在积极研究机器人的PHM,以促进创新传感技术和预测决策算法的发展,并制定一种强调识别静态和动态位置精度的位置精度测试方法。测试方法的开发将为制造商提供一种方法,使他们能够快速评估其机器人系统的位置健康状况,同时支持对机器人系统的PHM技术进行验证和确认。

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