Digital Twin
A live digital representation of a physical asset or system, kept current with real operating data.
A digital twin is a data model of a physical asset — or a whole system — that stays synchronized with reality through live telemetry. At minimum it mirrors the asset's current state: running or stopped, temperatures, loads, alarm conditions. Richer twins add history, engineering models, and simulation, allowing operators to ask what-if questions: how will this chiller plant respond to tomorrow's heat load, or what happens to throughput if line 2 slows 10 percent.
In maintenance contexts, the practical twin is less cinematic than the marketing version: it is the asset record enriched with real-time condition data, full work history, and predictive models — a single place where everything known about the machine converges. That convergence is what enables useful automation, because an AI agent reasoning about an asset needs its state, its history, and its context in one queryable object.
The term is used loosely across the industry. When evaluating claims, ask what data updates the twin, how often, and what decisions it actually informs.
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