Methodology for enabling dynamic digital twins and virtual model evolution in industrial robotics - a predictive maintenance application

Abstract

A methodology for enabling the implementation of the Digital Twins (DTs) of industrial robots and complex machines is presented. The enabled DT can provide a series of benefits, for instance when combined with the degradation curves of critical health indicators can allow predicting the robots’ RUL. This paper suggests a systematic and generic methodology to address challenges such as (1) the definition of the physics-based model parameters for accurate DTs, (2) selection of the parameters to tune and the frequency of their tuning to capture the physical changes in the digital world, (3) calculation of new modeling parameters values based on the collected filed data and their automated propagation to the digital model. A software module to implement these functionalities was developed, whereas the testing and validation of the module were held for an industrial robotic manipulator for a manufacturing application. The results prove that the methodology is generic to accommodate the desired granularity per case, and that the tuning of the digital model ensures realistic DTs, which sets the foundation for physics-based predictive maintenance tools.