 | In engineering products and systems development, manufacturers continue to generate data from their first conceptual ideas to the design, manufacturing, installation and operation phases. The concept of the digital twin has evolved and is used in many engineering facets with the increase in sophisticated developments of digital technology. However, the digital twin of a product is traditionally considered in terms of a discrete moment in time at a particular stage in its lifecycle. This limits the possibilities and usability of the digital twin, which has a lifecycle that reflects its counterpart (the product). Data is generated at every stage of a product or system’s life. This data can be used to embed predictive capabilities in the digital twin, which can provide valuable insights (for example, a system’s capacity to withstand a critical event throughout its operational life). This white paper will explore the value of building a predictive capability in the digital twin and describe science and mathematics-based approaches that can predict real-world behaviors of equipment and systems. Request Free! |