Digital twin-driven operational CycleGAN-based multiple virtual-physical mappings for remaining useful life prediction under limited life cycle data.

Accurately predicting the remaining useful life (RUL) of complex equipment plays a vital role in maintaining modern manufacturing systems' operational safety and reliability. This challenge has attracted considerable interest within the domain of intelligent operation and maintenance. However, the lack of high-quality, comprehensive lifecycle data in industrial environments is a major barrier to developing and deploying intelligent RUL prediction algorithms.