Degradation Modeling of a Robot Arm to Support Prognostics and Health Management
Deogratias Kibira, Helen Qiao
Robots are increasingly being adopted in manufacturing industries and this trend is projected to continue. However, robots like all equipment, degrade once in operation and eventually fail. Yet today's manufacturing systems are highly paced requiring high equipment availability. Tools and methods are being developed for monitoring, diagnostics, and prognostics to support maintenance activities. These tools require the presence of data representing both healthy and unhealthy states of the robot. This data is usually not available because robots are normally operated in a healthy state. A digital twin, which is a virtual real-time representation of a system, can support generating this data. This paper demonstrates the building of a digital twin of a robot workcell that uses data from the real system as input. The most frequent robot degradations are identified and modeled in the digital twin. Data representing degraded states of the workcell is generated and plotted to reveal patterns indicative of the type of failure. In our future work, the real system and digital twin will be synchronized. The digital twin will then be run as a simulation to generate data representing future heath state of the system. The results of analytics on the data will be used to support prognostics and health management.
Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering Conference (MSEC2023)
June 12-16, 2023
New Brunswick, NJ, US
Manufacturing Science and Engineering Conference 2023
and Qiao, H.
Degradation Modeling of a Robot Arm to Support Prognostics and Health Management, Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering Conference (MSEC2023)
, New Brunswick, NJ, US, [online], https://doi.org/10.1115/MSEC2023-105230, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935776
(Accessed December 3, 2023)