A Short Introduction to Digital Twins
based on physics or chemistry, engineering
or simulation models, data models based on
statistics, machine learning and Artificial
Intelligence (AI). It may also include 3-D
models and augmented reality models for
aiding human understanding of the
operational states or behaviors of real-world
objects.
Data
A digital twin contains data about its real-
world object that are required by the models
to represent and understand the states and
behaviors of the object. In many cases, it
may consist of data in the full lifecycle of the
object or—in the case of equipment—
consist of data during the design phase
(specifications, design models, production
process and engineering data), production
phase (data about workers, production
equipment, material and parts, production
methods and quality assurance), operation
phase (installation and configuration data,
real-time and historical state and status, as
well as maintenance records) and even end-
of-life procedural data. It may also contain
business data, such as transaction records.
Service (interface)
A digital twin contains a set of service
interfaces for industrial applications or other
digital twins to access its data and invoke its
capabilities.
With a common approach, we can construct
a digital twin for equipment from its
constituent component digital twins; a
production line digital twin from the
constituent equipment digital twins; and a
factory digital twin from its production line
constituent digital twins—just like with their
counterparts in the real world.
Models
A digital twin contains computational or
analytic models that are required to
describe, understand and predict the real-
world object’s operational states and
behaviors, as well as models that are used to
prescribe actions based on business logic
and objectives about the corresponding
object. These models may include models
IIC Journal of Innovation
Relying on a digital representation enabled
by the digital twin, we can enable a host of
smart industrial applications to run
operations in the real world, optimally
improving these operations.
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