The Adoption of Digital Twins in Integrated Vehicle Health Management EPR2023024

To many, a digital twin offers “functionality,” or the ability to virtually rerun events that have happened on the real system and the ability to simulate future performance. However, this requires models based on the physics of the system to be built into the digital twin, links to data from sensors on the real live system, and sophisticated algorithms incorporating artificial intelligence (AI) and machine learning (ML). All of this can be used for integrated vehicle health management (IVHM) decisions, such as determining future failure, root cause analysis, and optimized energy performance. All of these can be used to make decisions to optimize the operation of an aircraft—these may even extend into safety-based decisions.

The Adoption of Digital Twins in Integrated Vehicle Health Management, however, still has a range of unsettled topics that cover technological reliability, data security and ownership, user presentation and interfaces, as well as certification of the digital twin’s system mechanics (i.e., AI, ML) for use in safety-critical applications.