Learning phase in a LIVE Digital Twin for predictive maintenance

Abstract

Digital Twins are essential in establishing intelligent asset management for an asset or machine. They can be described as the bidirectional communication between a cyber representation and a physical asset. Predictive Maintenance is dependent on the existence of three data sets: Fault history, Maintenance/Repair History, and Machine Conditions. Current Digital Twin solutions can fail to simulate the behaviour of a faulty asset. These solutions also prove to be difficult to implement when an asset’s fault history is incomplete. This paper presents the novel methodology, LIVE Digital Twin, to develop Digital Twins with the focus of Predictive Maintenance. The four phases, Learn, Identify, Verify, and Extend are discussed. A case study analyzes the relationship of component stiffness and vibration in detecting the health of various components. The Learning phase is implemented to demonstrate the process of locating a preliminary sensor network and develop the faulty history of a Sand Removal Skid assembly. Future studies will consider fewer simplifying assumptions and expand on the results to implement the proceeding phases.