Abstract
Predictive Maintenance (PdM) requires methods and tools to convey process information to maintenance planners allowing for data-driven repair decisions. Cyber-Physical Systems (CPS) and Digital Twins (DT) are current tools that transform data for informed decision making; however, the successful deployment of these tools is hampered by missing or low levels of training data for machine specific events such as failure. This paper proposes a standardized framework for adapting data from offline environments to train online systems without real world failure training data. This novel process, the Surrogate Digital Triplet (SDTr) framework, incorporates a third system, the surrogate triplet, to transfer data between the lab (offline) and production (online) environment. SDTr standardizes the data, information, and knowledge interfaces between systems to pass offline learning to the real world in a traceable manner.
Accepted Version (
Free)
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call