Digital Twin Architecture and Standards
New industrial assets can be designed using
simulation tools and physical models to
precisely predict behavior. Physical
properties
(electromagnetic,
thermal,
pressure, stress, etc.) are mapped to the
design model to optimize the device’s
performance. This approach requires
knowledge of the environment and its
effects.
the production process, optimization re-
calibration and customization directives for
specific deliverables.
A heterogeneous ecosystem for processing
comes into play in all these phases and data
flows. Process measurement is associated
with its equipment type, converted to
engineering units and validated for accuracy.
Data is acquired using many different
protocols and temporary repositories. Each
component vendor has their own (legacy,
hosted) platform for historical data and
applications—for example, analysis that
interprets the measurements without
exposing proprietary algorithms. These
results guide business decisions and
continuous process improvement.
Digital twins are composable, where
components interact with each other in the
physical world. In discrete processes,
components are reasonably decoupled
which allows the combination of separate
behavioral simulators to build a larger
system. Components interact and influence
each other in continuous processes.
Equipment needs to be modeled in one
common simulation tool with a standardized
model format.
The keys to success for Industrial IoT are to
create value for end users and find business
models that allow various ecosystem players
to co-exist and successfully co-evolve. 2
Distributed data stores and analytics are
essential components that make this
ecosystem possible. One example is shown
in Figure 1, including use of a Distributed
Control System (DCS). Industrial IoT can be
organized in tiers or layers, with each layer
able to operate autonomously based on the
available data and services.
M OTIVATION FOR D IGITAL T WIN
Digital twins combine data and processing.
The necessary data capabilities for Industrial
IoT processing are provided in four
consecutive phases: data generation, data
acquisition, data storage and data
consumption. 1 Data also flows in the
opposite direction for set point control to
1
Hu, H., Wen, Y., Chua, T.S. and Li, X. 2014. Toward Scalable Systems for Big Data Analytics: A Technology Tutorial. In Access,
IEEE, vol.2, no., pp.652-687, DOI= http://dx.doi.org/10.1109/ACCESS.2014.2332453.
2
Toivanen, T., Mazhelis, O. and Luoma, E. 2015. Network Analysis of Platform Ecosystems: The Case of Internet of Things
Ecosystem. Software Business, DOI= http://dx.doi.org/10.1007/978-3-319-19593-3_3.
IIC Journal of Innovation
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