Digital Twin Applications for Predictive Maintenance of Industrial Machinery

Abstract

Predictive maintenance depends on how faithfully a monitoring system tracks an individual machine's actual condition, and the digital twin -- a continuously updated virtual counterpart synchronized with a physical asset's live operating data -- has become the architecture most credited with improving that fidelity. This short communication synthesizes the literature on digital twin applications for predictive maintenance of industrial machinery, tracing the concept from its origins in product lifecycle management and aerospace structural health monitoring through Kritzinger et al.'s influential digital model/digital shadow/digital twin taxonomy, to applied frameworks for rotating-machinery fault diagnosis and remaining-useful-life estimation. Particular attention is given to two complementary strategies for the historical-failure-data scarcity that maintenance-focused machine learning routinely faces: generating synthetic fault data from a high-fidelity simulation to pre-train a diagnostic model, and modeling only healthy operating behavior so that deviation from it becomes the diagnostic signal. Applied case studies extend these architectures to specific, named industrial equipment, while the review literature is candid that the field has not converged on a shared implementation methodology and that many systems described as digital twins meet only the weaker, one-way-data-flow bar of a digital shadow. Read together, the literature supports digital twins as a genuinely productive predictive-maintenance architecture, particularly where failure data is scarce, while leaving implementation standardization and closed-loop automation as open problems.