Considered by many to be the holy grail of predictive maintenance, transfer learning (TL) is the ability to identify failure symptoms (ISO-13379) from one asset and apply them automatically to another. Applied across the thousands of connected plant items in the Industrial Internet of Things (IIoT), it could unleash the sector's potential adding $14.2tn to the global economy by 2030 \[Accenture\].

Breakthroughs in deep learning (DL) solving Big-Data problems, such as accurate image recognition, might provide the impression that DL would enable asset failure predictions in much the same way. However asset failure data is scarce, every asset has unique data signatures, and therefore IIoT is not Big-Data \[Uniper, 2017\].

This is an industrial research programme building upon a successful novel proof-of-concept technology. The AI-Analyst provides automatic modelling, early-fault detection, and diagnosis using TL, practical for O&M requirements; delivering a genuinely unique offering which can be readily commercialised and exported globally to all IIoT connected assets