Design and development of a LIVE Digital Twin methodology for predictive maintenance of bearings in rotary machine systems

Abstract

Digital Twin (DT) is a prominent focus for many predictive and prescriptive maintenance strategies. In maintenance, DT is used for connecting the physical and digital models of a maintenance monitoring system and proactively prescribing maintenance solutions to extend the products life. Many of the failures in modern DT can be attributed to the lack of defined structure, the unavailability of failure data to calibrate the systems, and poor connectivity between the physical and digital systems. LIVE provides a systematic approach to implement a DT system in 4 stages of Learn, Identify, Verify and Extend. This thesis uses LIVE DT for dynamic rotary systems connecting the physical asset to its DT to predict failure. In addition, this thesis covers the development of a device that will allow for emulating bearing defects in a controllable and repeatable way for calibrating virtual systems when historical or failure data is unavailable.