Buiding a Digital Twin for Robot Workcell Prognostics And Health Management

Abstract

The application of robot workcells increases the efficiency and cost effectiveness of manufacturing systems. However, during operation, robots naturally degrade leading to performance deterioration. Monitoring, diagnostics, and prognostics (collectively known as prognostics and health management (PHM)) capabilities enable required maintenance actions to be performed in a timely manner. Noting the importance of data-based decisions in many current systems, effective PHM should be based on the analysis of data. The main challenges with robot PHM are the difficulties of relating data to healthy and unhealthy states, and lack of models to fuse and analyze up-to-date data to predict the future state of the robot. This paper describes concepts of digital twin development to overcome the above challenges. A use case of a digital twin modeling robot tool center point accuracy is provided. The proposed procedure for this digital twin will be applicable to different use cases such as reduced repeatability or increased power consumption.