Reusing Model Validation Methods for the Continuous Validation of Digital Twins of Cyber-Physical Systems

TL;DR

This paper presents a generic validation approach for digital twins of cyber-physical systems, detecting anomalies and updating the twin using validation metrics and parameter estimation, demonstrated on a gantry crane case study.

Contribution

It introduces a reusable validation framework for digital twins that detects anomalies and updates models based on physical system changes.

Findings

Effective anomaly detection in digital twins.

Successful application to a gantry crane case study.

Improved model accuracy through parameter estimation.

Abstract

One of the challenges in twinned systems is ensuring the digital twin remains a valid representation of the system it twins. Depending on the type of twinning occurring, it is either trivial, such as in dashboarding/visualizations that mirror the system with real-time data, or challenging, in case the digital twin is a simulation model that reflects the behavior of a physical twinned system. The challenge in this latter case comes from the fact that in contrast to software systems, physical systems are not immutable once deployed, but instead they evolve through processes like maintenance, wear and tear or user error. It is therefore important to detect when changes occur in the physical system to evolve the twin alongside it. We employ and reuse validation techniques from model-based design for this goal. Model validation is one of the steps used to gain trust in the representativeness of a simulation model. In this work, we provide two contributions: (i) we provide a generic approach that, through the use of validation metrics, is able to detect anomalies in twinned systems, and (ii) we demonstrate these techniques with the help of an academic yet industrially relevant case study of a gantry crane such as found in ports. Treating anomalies also means correcting the error in the digital twin, which we do with a parameter estimation based on the historical data.