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.