A human-centered framework to update digital twins

Developing a digital twin (DT) involves establishing (1) a predictive capability (a model) relevant to the application, (2) means to collect data from the physical counterpart, and (3) means to apply the collected data to the model. Ideally, with these three goals achieved, long periods of steady-state use of the DT might be interrupted only by failure of the sensors used to collect data from the physical counterpart. In reality, however, it can be difficult to confirm that the DT system occupies this comfortable steady-state position. Assessing uncertainty in the predictive model, and ensuring the relevance of data collected from the physical counterpart are design-time activities with unclear termination points. Distinguishing sensed change in the physical counterpart from sensor failure is a persistent challenge. In this short paper we describe early work towards a human-centered framework to establish, refine, and update digital twins. Condition-based maintenance and gear backlash in production equipment are used as examples.