Conceptual framework of a digital twin to evaluate the degradation status of complex engineering systems

Degradation of engineering structures and systems often comes in the form of wear, corrosion, and fracture. These factors progressively bring about performance decay, until the system fails to function satisfactorily. Complex engineering systems (CES) need regular maintenance throughout their operation, along with continuous checks on the health status of components and equipment, within regulatory frameworks. A digital twin paradigm is able to continuously monitor CES, to use this data to update a virtual model of the CES and thus make real-time predictions about future functionality. The purpose of this paper is to introduce a conceptual framework of a digital twin to be applied within the degradation assessment process of a CES. The digital twin framework will aim to gather digital data through a network to plan through-life requirements of the system. Data-driven approaches can be used to predict how degradation evolves over time. The proposed framework will help the decision-making process to better handle maintenance operations and achieve targets such as asset availability and minimised cost.