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

Published

September 29, 2016

Author(s)

Guixiu Qiao, Brian Weiss

Abstract

From the moment a robot system is put into service to enable a manufacturing process, the overall process, its constituent sub-systems, and components begin to degrade. Without maintenance, this degradation will lead to faults and/or failures impacting the process. These faults and/or failures ultimately lead to unexpected downtime and lost production if they are not remedied. Unexpected downtime and lost production are 'pain points' for manufacturers, especially in that they usually translate to financial losses. To minimize these pain points, 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. As these new techniques emerge, the National Institute of Standards and Technology (NIST) is playing a key role in developing the necessary measurement science to enable the verification and validation of these robot system PHM technologies. NIST has created the Prognostics, Health Management, and Control (PHMC) project to develop the necessary measurement science, including performance metrics, test methods, reference datasets, and supporting tools, across several manufacturing operations domains. This test bed will promote the development of innovative sensing technology and prognostic decision algorithms, to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy.

Citation

International Journal of Prognostics and Health Management (IJPHM) – Special Issue: PHM for Smart Manufacturing Systems

Volume

7

Issue

2153-2648

Pub Type

Journals

Download Paper

Local Download

Keywords

Maintenance strategy, Prognostics and health management, Preventive and predictive maintenance, robotics

Citation

Qiao, G. and Weiss, B. (2016), Advancing Measurement Science to Assess Monitoring, Diagnostics,and Prognostics for Manufacturing Robotics, International Journal of Prognostics and Health Management (IJPHM) – Special Issue: PHM for Smart Manufacturing Systems, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920748 (Accessed April 25, 2024)