Description

The advent of digital twins has the potential to revolutionise engine health monitoring and predictive maintenance, interacting with available sensors to generate novel insights into engine dynamics. This is transforming operational risks and costs. But the underlying artificial intelligence for digital twins is itself being hugely disrupted. This presentation will show how new AI techniques can (literally) evolve physics-based digital twins for individual engines using tiny fragments of noise-polluted sensor data, radically altering the way engine OEMs and MRO teams can understand the engines they are building and maintaining.