A digital twin framework for prognostics and health management

Abstract

Despite rapid advances in modeling and analysis technology, the manufacturing industry has been slow to implement prognostic and health management strategies. A cause of this delay is the individualized focus of most health monitoring solutions, which makes it difficult to deploy and reuse modeling resources across manufacturing equipment fleets. This paper presents a digital twin-based framework that standardizes communication and organization of modeling resources used for health monitoring, a critical aspect of prognostics and health management. The framework is based on a novel, state-based model of mechanical system health that can be reused across manufacturing machines and components. A set of modular digital twin classes enables the creation of extensible digital twin hierarchies for monitoring the health of complex systems. A case study implements this framework to standardize fault detection results for the seal and bearing systems of an industrial pump. The framework’s standardized DT classes and aggregation relationships allow component-level models to be re-used and aggregated to predict faults in the pump’s bearing system.