Towards a Digital Twin of a Robot Workcell to Support Prognostics and Health Management

Abstract

Current maintenance research often includes modeling equipment degradation to support determining when any degradation will exceed a specified threshold. Such models provide critical intelligence to determine an impending failure and promote the timely scheduling of maintenance, yet, the models require equipment data. While healthy state data can be readily captured from a system, degraded or failure state data is more difficult to acquire because equipment are normally operating in a healthy state. The degradation process can be modeled in a digital twin to generate failing health data. This paper presents work that is a step in the process of realizing a digital twin for this purpose. A procedure for modeling a robot workcell in a healthy state is described. We discuss how degradations will be incorporated into the robot to generate degraded data that can be used to predict future states of the robot and support decision-making.