or respect of standards that basically make them unusable. Defining beforehand the
standards, naming conventions, hierarchies, etc., to follow are important elements of
success. The reality of today is that models are developed both by equipment
providers and by engineering companies, but nobody actually uses these models
beyond the short period of design and construction.
In other areas such as biological systems, 3D models are not the most efficient
way to represent the system and other approaches such as computational whole-cell
modeling are being used [4].
Having access to an open and programmable model where you can bring in and
display the measurements provided by sensors is a last and most important
constraint.
3.4 Asset Framework
As we move into more dynamic digital twins, we shall start seeing the need for an
additional prerequisite. In our basic solution, we would be manually making the
connection between a sensor and the corresponding objects of our virtual model.
However, this quickly becomes impractical as the number of sensors increases and
we start seeing evolutions of the different assets and models.
The key to overcome this challenge is through the use of an asset framework that
manages the overall plant and asset hierarchy and creates an abstraction layer
between the physical reality and sensors and the virtual models [5].
An asset framework is thus a hierarchical, contextualized, and digitized model to
describe all physical assets (including sensors) in the system and their relationships.
In this way, individual sensors and their measurements are not just data points but
acquire meaning within the digital twin system and become resilient to changes to
physical assets or models. A temperature sensor on a bioreactor remains as such even
if we move the bioreactor to another factory or upgrade the bioreactor.
Most solutions for connectivity include asset framework capabilities. The issue is,
however, double:
In many industrial environments, these asset frameworks were either not
implemented upon commissioning of the equipment or they have not been
maintained. Basic operations without digital twin initiatives can usually survive
without a good asset framework. Digital Twins without it will not remain operational
for long.
There will often be multiple asset frameworks, and these become inconsistent
over time if they were not so already from the start. It is not uncommon to find asset
frameworks in a historian, the ERP environment, MES, and an IoT platform
solution. All at the same time with variable standards.
The most obvious way out of this dead-end is to manage the asset framework not
as an element of a transactional system (Historian, ERP, etc.) but as true master data
within an MDM solution that will provide a single source of reference for other
systems. For most organizations, this will be a serious change.
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M. Canzoneri et al.
standards, naming conventions, hierarchies, etc., to follow are important elements of
success. The reality of today is that models are developed both by equipment
providers and by engineering companies, but nobody actually uses these models
beyond the short period of design and construction.
In other areas such as biological systems, 3D models are not the most efficient
way to represent the system and other approaches such as computational whole-cell
modeling are being used [4].
Having access to an open and programmable model where you can bring in and
display the measurements provided by sensors is a last and most important
constraint.
3.4 Asset Framework
As we move into more dynamic digital twins, we shall start seeing the need for an
additional prerequisite. In our basic solution, we would be manually making the
connection between a sensor and the corresponding objects of our virtual model.
However, this quickly becomes impractical as the number of sensors increases and
we start seeing evolutions of the different assets and models.
The key to overcome this challenge is through the use of an asset framework that
manages the overall plant and asset hierarchy and creates an abstraction layer
between the physical reality and sensors and the virtual models [5].
An asset framework is thus a hierarchical, contextualized, and digitized model to
describe all physical assets (including sensors) in the system and their relationships.
In this way, individual sensors and their measurements are not just data points but
acquire meaning within the digital twin system and become resilient to changes to
physical assets or models. A temperature sensor on a bioreactor remains as such even
if we move the bioreactor to another factory or upgrade the bioreactor.
Most solutions for connectivity include asset framework capabilities. The issue is,
however, double:
In many industrial environments, these asset frameworks were either not
implemented upon commissioning of the equipment or they have not been
maintained. Basic operations without digital twin initiatives can usually survive
without a good asset framework. Digital Twins without it will not remain operational
for long.
There will often be multiple asset frameworks, and these become inconsistent
over time if they were not so already from the start. It is not uncommon to find asset
frameworks in a historian, the ERP environment, MES, and an IoT platform
solution. All at the same time with variable standards.
The most obvious way out of this dead-end is to manage the asset framework not
as an element of a transactional system (Historian, ERP, etc.) but as true master data
within an MDM solution that will provide a single source of reference for other
systems. For most organizations, this will be a serious change.
172
M. Canzoneri et al.