Evaluation Engineering - 17

EE: What about regulatory issues?
PW: I haven't noticed major regulations
for the Industrial IoT yet. It might still be
too early-this whole area is still developing. However, we do see requirements
relating to the sending of sensitive data
out of your closed environment. This issue
could require a stronger focus on the security aspect. We see that come up more
often in our conversations with customers. For instance, when it comes to industrial protocols like OPC UA, support for
security methods is definitely a key topic.
EE: Apart from security, what are other
issues related to data?
PW: It's obvious that the Industrial IoT
only really provides its full value if you
have enough data and the right data in
place. Here, we also see that many companies don't have a clear picture yet on
which data they need for the respective
uses or applications they have.
Let's talk about applications with
some examples where these applications
could be used-for instance, predictive
maintenance, operations optimization,
or performance management. Here, one
requirement of course is again to find out,
"Do I have the right data in place, do I
need some additional sensors elsewhere
in my facilities or on my equipment, is the
resolution of my data in the right shape?"
These are all additional requirements that
we see becoming more and more important with Industrial IoT solutions.
EE: What is the role of simulation software in the Industrial IoT?
PW: We see a couple of areas where
MATLAB and Simulink can be used in
Industrial IoT systems. One very good
example is predictive maintenance. Here,
it's really about data being collected at
the outset. Typically, an edge system
collects all the data from assets in the
same neighborhood and then runs algorithms that pre-process this data-for
instance, eliminating outliers or filtering
the data and then sending this preprocessed data to an OT system in the cloud
or on-premises where machine-learning

or deep-learning algorithms predict remaining useful lifetime. That's a common-use case that we see, and these
algorithms are typically implemented
in tools like MATLAB and then tested
in simulation.
We also see that methods based on machine learning or deep learning require a
lot of data-especially failure data. Very
often, this failure data is not available,
because obviously your asset doesn't fail
that often. Here, simulation models can
play another significant role. You can use
the simulation models to generate failure
data for training your algorithms.
EE: What about the operations optimization example?
PW: In the BuildingIQ case study posted
on our website,1 simulation models are actually used for optimizing the overall behavior of the system. What they do is they
have these intelligent edge-like systems
that take into account factors like what's
the date or the time, when do people typically come to work, what's the weather
outside now, what's the weather forecast,
what's the price of energy, et cetera.
Based on the simulation model that
runs in the Industrial IoT, they continuously update the center point in the system and optimize the behavior.
In another case study, Transpower
uses big Simulink models that they run
on their OT systems. 2 They collect data
from the assets and they feed this measured data optimizing the grid load to
make sure that the grid can still operate. Every 30 minutes they feed data
from the grid and then run hundreds
of different scenarios in the respective
Simulink models that are deployed on
the OT system. The results will either
show that everything is okay and that
the grid will be stable for another 30
minutes, or human operators will get
feedback that something won't work,
and then the human operators will intervene. That's how it works today, but
obviously one future step for systems like
these will be to take the human out of
this loop, in general, and then have the
system adopt the different parameters
of the grid automatically.

EE: How can people get started with the
Industrial IoT? What would the first basic step that they can take?
PW: That's a really good question, because for all the potential that Industrial
IoT and digital-twin technologies offer,
people very easily get lost. So, they could
get scared off. Or it could be that people
don't get scared off, but they jump into
different projects and don't really think
them through upfront and then don't get
a solution that pays off.
What we offer at MathWorks is a consulting crew that has good experience
from a number of customer projects. We
ramp up customers with getting their first
Industrial IoT, digital-twin application up
and running. Of course, we also discuss
with them whether they have a good use
case or not. And, of course, we help make
sure that customers have data available.
If you don't have any data or any essential
equipment in your facilities, then it's difficult to get any benefit from the Industrial
IoT and digital twins. It sounds very clear
if you say it like that, but interestingly,
most of the cases where the people struggle with getting started in adopting this
technology, the biggest issue is that they
don't have enough data or they have the
wrong data available. Again, this is also
something where our consulting team
can give them some guidance and some
ideas to help improve that.
Maybe the key takeaway for what role
MathWorks plays here is that we do have
building blocks for developing a customer's
IoT application. We don't believe that anyone could come up with a closed Industrial
IoT solution or digital-twin solution. What
is important is that we have these building
blocks and we do work with other solutions
and vendors to complement our building
blocks to get a good Industrial IoT application for our customers in place. We're more
than happy to get into conversations with
people and support them as well.
REFERENCES

1. "BuildingIQ Develops Proactive Algorithms
for HVAC Energy Optimization in Large-Scale
Buildings," User Stories, MathWorks.
2. "Transpower Ensures Reliability of New
Zealand National Grid with Reserve Management
Tool," User Stories, MathWorks.

DECEMBER 2019 EVALUATIONENGINEERING.COM

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Evaluation Engineering

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