Year: 2021
Type: article
Abstract: Manufacturers have long faced a choice between reactive maintenance, which wastes money on unplanned downtime, and fixed-schedule preventive maintenance, which wastes good component life, and the digital twin has emerged as the organizing concept for a third option: a continuously synchronized virtual counterpart of a physical asset that can forecast a specific failure mode far enough in advance to act on it. This article synthesizes the literature on digital twin frameworks for predictive maintenance in smart manufacturing, tracing the concept from Grieves and Vickers' original physical-virtual-connection model through Kritzinger et al.'s influential three-tier taxonomy distinguishing a digital model, a digital shadow, and a true digital twin, into the reference architectures -- Lee et al.'s cyber-physical 5C pipeline, Tao and Zhang's shop-floor paradigm, and Lu et al.'s broader reference model -- that operationalize it on the factory floor. We review the physics-based, data-driven, and hybrid methods used to turn a digital twin into a working prognosis, and we examine the comparatively rare field evaluations, including a recent CNC-machining framework reporting R² above 0.94 for two prediction tasks and an 18-month steel-industry field trial with a measured, transparent detection record, that ground the field's architectural claims in evidence. We conclude that the conceptual and architectural foundations are mature and convergent across independent research groups, while the empirical base for sustained field performance, the closing of the automated loop that separates a digital twin from a digital shadow, and the integration of asset-level prognoses into system-level production planning remain open engineering problems.
Institution: Manipal Academy of Higher Education
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7213630139