There is also one additional prerequisite going across all three levels of twin with
increasing complexity:
• People
For each of these prerequisites, we will provide a basic overview, insights on why
they are required, and illustrations of common roadblocks to success in industrial
environments. Obviously, funding, be it through private means within companies or
through public subsidies, is also a prerequisite in most cases but this topic goes
beyond the scope of our investigation.
3 Major Prerequisites
3.1 Sensors
The basics of a digital twin being to create a common state between a physical reality
and a virtual representation, we need to have sensors that can measure the state of the
physical assets and provide that data.
Such sensors exist on most modern equipment and the growth of industrial IoT is
generating a lot of innovation making it easier to add additional sensors to existing
equipment. There currently exists a wide range of sensors that will capture the state
of a physical environment (temperature, pressure, pH, movement, flow, etc.) and we
increasingly see so-called software sensors that will analyze basic physical parameters and convert them into higher level measurements [1]. This will typically be the
case for measuring biological processes or using computer vision to extract complex
information.
The common roadblocks in this area will usually be converting the signal
obtained by the sensor into some useful information for the digital twin. Many
older sensors will provide an analog signal where a digital signal is required for our
twin. Another common case is where sensors are managed by a PLC that no one has
the appropriate competencies to use and the sensor information remains locked
within the PLC. Critical approaches to avoid these kinds of issues include defining
corporate standards for PLC/sensors upon purchase and implementation that ensure
such black boxes do not happen.
Beyond sensors, we shall also usually need data from standard Information
Technology solutions such as MES (Manufacturing Execution System), LIMS
(Laboratory Information Management System), ELN (Electronic Lab Notebook),
etc. However, these are generalizable as sensors where we have a human intermediate between the measurement system and the data capture which we would
generally try to avoid as we develop more sophisticated levels of digital twins. It
should be noted, however, that the sensors and measurement system only need to be
as good as the goal given for the twin and in many cases the required sensor quality
required for observability of the model can be quite low.
170
M. Canzoneri et al.
increasing complexity:
• People
For each of these prerequisites, we will provide a basic overview, insights on why
they are required, and illustrations of common roadblocks to success in industrial
environments. Obviously, funding, be it through private means within companies or
through public subsidies, is also a prerequisite in most cases but this topic goes
beyond the scope of our investigation.
3 Major Prerequisites
3.1 Sensors
The basics of a digital twin being to create a common state between a physical reality
and a virtual representation, we need to have sensors that can measure the state of the
physical assets and provide that data.
Such sensors exist on most modern equipment and the growth of industrial IoT is
generating a lot of innovation making it easier to add additional sensors to existing
equipment. There currently exists a wide range of sensors that will capture the state
of a physical environment (temperature, pressure, pH, movement, flow, etc.) and we
increasingly see so-called software sensors that will analyze basic physical parameters and convert them into higher level measurements [1]. This will typically be the
case for measuring biological processes or using computer vision to extract complex
information.
The common roadblocks in this area will usually be converting the signal
obtained by the sensor into some useful information for the digital twin. Many
older sensors will provide an analog signal where a digital signal is required for our
twin. Another common case is where sensors are managed by a PLC that no one has
the appropriate competencies to use and the sensor information remains locked
within the PLC. Critical approaches to avoid these kinds of issues include defining
corporate standards for PLC/sensors upon purchase and implementation that ensure
such black boxes do not happen.
Beyond sensors, we shall also usually need data from standard Information
Technology solutions such as MES (Manufacturing Execution System), LIMS
(Laboratory Information Management System), ELN (Electronic Lab Notebook),
etc. However, these are generalizable as sensors where we have a human intermediate between the measurement system and the data capture which we would
generally try to avoid as we develop more sophisticated levels of digital twins. It
should be noted, however, that the sensors and measurement system only need to be
as good as the goal given for the twin and in many cases the required sensor quality
required for observability of the model can be quite low.
170
M. Canzoneri et al.