An Introduction To Intelligent Digital Twins Webinar
During this informative session, XMPro CEO, Pieter Van Schalkwyk will delve into the concept of Intelligent Digital Twins and discuss how they differ from traditional Digital Twins. This webinar aims to equip professionals across various domains with the knowledge necessary to harness the power of AI, accelerating the adoption and implementation of IDTs in diverse industries.
Don't miss this opportunity to gain valuable insights from an industry expert and stay ahead of the curve in this rapidly evolving field.
Transcript
hi everybody I'm Peter from Skull click
um the CEO of Ericsson Pro and the topic
for today is intelligent digital twins
um I wish I I could take the credit for
coming up with the concept of
intelligent digital twins but we're
really standing on the shoulders of
giants um Dr Michael Greaves who
is also known as the father of digital
twins actually started the concept or in
his work looking at where digital twins
are going
came up with it
concept of industrial of intelligent
digital Twins and you can also find
um a great paper that he where he
explains the whole concept
um at the digital
twin1.org and we'll also make the the um
the link available after the webinar and
I I was fortunate I had the opportunity
to meet with Dr Griggs at one of the
digital twin Consulting member meetings
and we discussed where things are going
with digital Twins and this is a diagram
that he's got in in the piper
um and he explains kind of the evolution
and right now we're seeing quite a lot
of ad hoc so sometimes people refer to
this as digital Shadows where there's
one-way communication so we've got the
physical
items and that updates a a digital
version so I'm creating a static
repository of data and we've seen more
and more digital twin platforms emerging
where there's some replication of data
going around
and and the future is moving towards uh
intelligent digital twins with things
like front running simulations and
everything so
really expensive paper that explains it
all my summary of the paper
um quickly ran through that is we're
moving from a focus on data to focus on
data flow so the difference between a
traditional digital twin where it's just
a passive repository and we kind of
taking data from my physical and just
representing it in in a virtual way to
something which is active and always on
so it continuously monitors the
environment and the the assets itself
where the traditional digital twin is
more of an offline and it writes for the
physical to actuate it so even if you
think of something like airbags there's
a a
um
it's as soon as an event happens on the
physical it updates the digital on the
online one this this continues running
on the side and it monitors scans the
environment and based on that also
creates actuation of of potential
actions that come out of it
with traditional digital twins we give
it a a goal and then we create kpis and
performance measures to see how well
we're doing based on the goals that we
are with that we've set for that
with intelligent digital twins we can
now use more intelligent approaches to
make it more goal-seeking to try and
optimize where we augment what humans
are doing with AI
and smarter digital twins to to support
that
and lastly we have predictive
capabilities so we can predict but it's
not really optimizing so it's not
looking for certain optimal set points
operating points
[Music]
maintenance intervals those kind of
things where going forward doing things
like front running
simulation we are able to speed up and
that's what we mean by manipulate time
we can take real-time data we can take
contextual data we can take historical
data and speed that all up and then
based on that anticipate what is going
to happen and see what are the better
responses for those and great example of
front running simulation is what happens
in Formula One car Rising
and example is a partner with Dell it's
this is not built on XM Pro but this is
work that Dell has done with McLaren
around things like front running
simulation so this is one of the fastest
Edge devices out there
um it goes uh 200 miles an hour or 360
km 360 uh
kilometers per hour
and at that it generates about a hundred
thousand data points per second so a lot
of information that you can put together
and based on that determine fuel
strategies and a whole bunch of other
things during race time so ability to
speed up so take the data run it faster
than real time to speed it up and then
decide and
um on on on certain actions so this is
the McLaren example and this also
great work done by Amazon AWS and others
on on on on similar things so things
like again tracking information on the
vehicles they can track information
combining all of that you can create
multiple use cases and I think that's
one of the the other elements of digital
twins that's that's quite uh that's not
often understood it's not about one
application or use case that I'm trying
to do I can now take that data and I can
actually facilitate a multiple number of
different things that I can do so
multiple use cases in this instance of
battle Focus pitch strategy Striker
Performance Tire performance exit speeds
a whole bunch of different front running
simulations that I can do in a specific
interesting one is undercut thread so
um great video on the AWS website as
well
um but with undercut thread it's
actually deciding when to bring your car
in and also looking at what are your
competitors potentially doing so are
they looking at undercutting you by
bringing in a vehicle by bringing in a
race car at a certain window of
opportunity now in order to do that you
need to meet you need to measure the
real-time speed of all your other
competitors look at their way by using
computer vision and a whole bunch of
things die away
um trying to figure out what their fuel
consumption is take all of that and run
it in your strategy so these are very
sophisticated and advanced examples of
front running simulation but we're
starting to see that move into the
industrial space and other areas where
we are trying to do this so
from a digital twin perspective how we
make decisions is changing as you saw in
those very sophisticated examples we can
bring a whole lot of information from
multiple different places
and that's really the essence of
decision intelligence
where we've got external information
we've got internal information and
traditionally what we've done with kind
of the more static digital Twins or or
conventional digital twins is really
decision support
so there's a business process there's a
human in that Loop and we are now
um trying to give them some decision
support and traditionally it's been
dashboards and business intelligence
condition monitoring basic predictions
as well
um so again the mind of of decision
support we're seeing more and more
decision augmentation where we can bring
in smarts from Ai and other tools
so um we still have the human in the
loop that make the decision but that
human is now the the decision process is
augmented with
um information that can be processed at
a speed that humans can't do it the
volume of information that that can be
processed again is done at a speed that
we can't do as as humans but
at the end the decision Still Remains
with us so this is where front running
simulations prescriptive recommendations
um
is is coming in and this provides us the
opportunity to create a closed loop a
feedback loop and learn from that and
kind of improve our decision making as
well as improve the models that we have
and this is kind of where a lot of um
focus is at the moment in terms of
moving towards more intelligent digital
twins
but there is a future where we also look
at decision automation so once we have a
high level of confidence on in some of
these decisions or or some of these
models we can actually let the machine
make the decision through business
process Automation and create this
distributed intelligence system
where we can maintain the rules and the
models and everything centrally and it
it it it provides us the opportunity to
get to algorithmic business where again
a certain number of these of of these
um
business processes that can be done in
an automated advice so if you think
about fly by wire which you can do with
aircraft
you might be able to operate by wire in
a certain envelope those decisions can
all be made by machines so that's
the future that we see in terms of where
this is all heading distributed
intelligence systems and now if you
combine that with the control
environments if you look at a
distributed control systems and you add
distributed intelligence systems my
personal views that's probably the
future of what operations will look like
now what does this mean for intelligent
digital Twins and I refer back to the
digital twin Consortium
um
definition of what a digital twin is
it's a virtual representation of
entities uh of real world entities and
processes to synchronized at a certain
frequency frequency and fidelity
interested in being on it's synchronized
and it helps us with decision making and
taking effective action again it's about
decision making it uses all sorts of
data and it is motivated by outcomes and
it's focused around use cases again that
example that I showed with the front
running simulation a whole bunch of
different
use cases
um that are being facilitated for that
and we implement it in it requires the
main knowledge and implemented through
this so
at a traditional digital twin has got
um that synchronization Sometimes some
of the information may go back
but it's really passive it's offline
um in the sense that it's waiting for
the thing on the on the left hand side
the physical twin something to happen
and that will then update that's not um
and it's called Givens I've got kpis and
things that I'm trying to to measure and
yeah we can predict but it's not focused
on optimization
going forward with intelligent digital
twins we see that decision intelligence
structure that I showed a little bit
earlier make its way into kind of
operating on the side so
we've got expert knowledge business
rules all the math and physics models
that we have and quite a few of those
are already being used in the uh more
decision support type digital twins that
we have we're now starting to see convey
what is regarded as conventional AI so
regression models and all of those but
then also the new generative AI that
we've seen lightly and large language
models that are making its way in
and some of the more sophisticated
techniques like deep learning neural
networks and those so that in
intelligence making is it's why
into digital Twins and then providing
the opportunity to Market goal seeking
and learning and doing this front
running simulations now the question
that we get is how do how do we make
this happen how do we do this because in
order in order to do this we need this
thing that intelligence that run on the
side almost and we go from where in the
previous one we had information at the
bottom there we now have prescriptions
and that is synchronized at a different
rate so it's not just the information
we've seen in back we actually send a
prescription in terms of what to do
um back now this could be augmented or
potentially automated as well but we
have to have this mechanism on the side
that continuously runs so now going from
data this requires a data flow where it
continuously run on the site and
interact with that
so in order to do this we came up with a
framework
as organizations are considering how do
we move to intelligent digital twins
well first of all it needs to be
integrated and it needs to be based on
standards models and have that
bi-directional integration that we that
I mentioned it needs to have
intelligence and we'll go into each of
these in a little bit more detail but
needs to be executable so it needs to be
able to run in real time we need some
way to make it Innovative and explore
experimentation and doing those
simulations but we also need to provide
an environment where we can bring in
the the the help from from the digital
Twin Side and augment so that we can
learn from that
and lastly we need to migrate
interactive so
um this is about the visualization so
how do we how do we provide
recommendations how do we make it in a
generative multi-experience user
interface
because all of this is becoming the
foundation for the industrial metaverse
whatever the metaverse looks like when
it comes out in order to do this at
scale we also need to do this on the
composable kind of
um
framework where we can reuse components
almost like the Lego blocks that kids
build
um toys with you can actually reuse
components and and and have a plugable
composable uh price for this
so if we briefly look at the integrated
side of things
um standards-based apis for these
capabilities that we package together a
model driven approach and bi-directional
in order to do this again this is about
data flow so example that we're showing
here this is our data stream designer is
being able to create standard
Integrations
to the apis of different systems then
being able to create a model now the
benefit of this is I can apply this to
um uh 100 wind turbines or a thousand
when turbines is exactly the same model
so in terms of the data model that
supports it and potentially the digital
twin model also model driven but in
terms of of creating The Logical data
flow structure making that model driven
and at the bottom right
it's not just about in uh have receiving
information one way but also sending
information back and potentially
changing set points um based on
recommendations that could come from Ai
and some of the other elements
so that's the the from integration
perspective making it more intelligent
where we now adding this capability
first of all in terms of those three
elements we need to make it executable
we need to provide an environment to to
to to for um to create these AI elements
and then we need to augment the user
experience with that and again different
audiences which this applies to so if I
look at that executable how do I bring
it into the data flow traditionally we
would take real-time data we would have
a model and
um so we've got the streaming agent that
with that with um
bring in real-time data we've got a
configuration of what the fly looks like
and that just gives us a result
with executable AI
um we can create a training model and
I'll briefly Show an example we'll
create a training model I'm using the in
this instance our XM Pro AI notebook
deploy that model into into an ml Ops
environment because again we need to now
look at how do we do this at scale if
I've got tens of thousands of models how
do I do the model management and as part
of the digital twin management as well
and then bring that in through bring
that model in through again an agent
that's got the intelligence bringing
live data on that can now run on that
training model and again in our in our
now code in environment you can
configure all of this and that will give
you the prediction and simulation
so what does it look like when you
actually do it
um what you can see here is a very basic
Way Reading uh tanks type and you'll see
where the yellow perform machine
learning analysis that actually calls a
beer quality model that sits in uh
uh the through um ml flow where where
which is the data repository for the
model so that's how we make the digital
twins
executable in the data streams that we
have the next part is
um
to be able to bring in an environment
where we can make it more Innovative so
we've embedded jupyter notebooks but
we've added some functionality to that
so you can wire it up into our data
streams but you can also integrate it
with things like giant GPT and others to
help you in the process
so again aimed at
on the one hand in analyzing the data
but also building models
um for things like front running
simulations and some of the others in
this instance I've got chat GPT
and um I I can ask it to tags to create
a code for me which is the next part
over here to create the guide for me to
represent this data
in this in a certain way so having the
the
um the request
um
having a request here through GPT
how do I visualize this data it then
writes the python code for me and it I
hope if I run that and it gives me the
visualization here's another example of
of embedding
um
or augmenting
um the the user experience in this
instance there's a copilot so we ran
this and based on the recommendations at
the top we can see that there is
potentially a impeller problem but I can
also see my discharge pressure is not
what it should be
so I can ask you know what are the top
five root causes for centrifugal pump if
there's a loss in discharge pressure and
you can see what it came up with if we
have time on briefly jump into and
showing you what that
is um towards the international fact let
me briefly let me quickly do that
um
okay
so
this is the Jupiter notebook with the
um
the
foreign
I can run through this it will generate
data for me
um
and
this is
machine generated data and now I ask
Chad GPT
to actually create the um
let me move down a little bit so I'm now
going to ask Jack GPT to create the code
for me
to visualize this data
and that's done that and now it's
created based on the code that it's
created for me in Python automatically
and also created the visualization
um and again this goes into more
actually putting this into into an email
slope on the the other example that I
briefly mentioned so
um
on the well Refinery operations
and I just go to that pump
we just got an issue
and you can see the normal information
that I have I've got all the contextual
information but I also want to ask it a
question now
um I'll just one of the top five root
causes and this thing interrogates using
um functionality of chat GPT to do that
so that's ways that we can bring
intelligence to it lastly on the
interactive side
to make it more accessible
in terms of providing recommendations
again uh
the intelligent digital twins have have
the opportunity to not just
um
tell you what is wrong but also give a
recommendation on on what you should be
doing triage instructions and some
additional context around what happened
in the past this gives you the
opportunity to also close the loop I can
create work orders and and things from
here and then be able to track that as
well to see how effective we are at
making certain decisions in terms of
creating a collaborative environment as
you can type as the previous example
which I briefly showed you
you can create very collaborative
environments and user experiences where
you now bring that intelligence
um into a front end that that users and
can use
and as I mentioned going forward you
know this will form the foundation of
the industrial nativist now the Brew the
Brewing example that you see here is is
actually an example that we built out
for the Dow validated
solution for the manufacturing Edge side
of things but this is what we see going
forward in terms of creating a meta
versus in a very interactive environment
where you know it doesn't matter what
your user experience is whether it's AR
VR desktops mobiles
um the intelligence is portable across
all of those
environments lastly and
the composable side
um
we came up with the the
um
positioning XM Pros that can composable
platform and
um
bringing in data from all the the the
underlying systems to be able to build
authorize various different use cases
and for that we have a whole bunch of
of
um modules that that support that so
in order to do that the basis for all of
that is is
um
capabilities and inside digital
Consortium we were instrumental in
creating the the
um capabilities periodic table these are
all capabilities that we see at a really
high level and that is used for digital
twins if you're wanting more information
on the digital Consortium website you
can download
the the whole
capabilities framework we also have a
webinar that we've done in the past
which you can have a look at where we go
into the capabilities and composability
and side for that so so here's our view
in terms of what the future looks like
oh sorry oh so the the the the
components that you need in order to
create intelligent digital twins which
we see as the future of where digital
twins are going we've created we've run
all that all into what we call
intelligent digital twin Suite example
idps consists of a number of modules and
and
happy to share more information on that
just lastly we also see that this way in
terms of where we are going and how
we're doing this at that line in future
may disappear and that intelligence all
being built into the
um digital twins of the future so with
that um there's a few minutes left any
questions that that everyone's got that
I can address
great presentation you do have a few
questions that came out there
um take it from the top uh do you one of
the questions that I that that we get is
you know do you supply the AI models
it's
it is the reason why we created that
environment so inside
um the XM Pro AI notebook
um you can create your own models there
are libraries of existing models
um there are Auto ml there's a whole
um uh kind of Continuum of opportunity
to use pre-made libraries of of models
we don't specifically do do models but
um there's a large library of of
simulation models of
all the standard
um traditional IR models as well as as
you saw being able to bring in
generative Ai and others pretty easily
how do you get started with this
um we're happy to run you through kind
of the the kind of three-step process
um on on how to get started with these
digital twins but hopefully that's been
been helpful in giving you an
understanding of you know what we see as
the future and what digital twin what
what intelligent digital twins
look like going forward
any additional questions
I have one question that was sent to me
around
um so yeah again this is I think the the
the way that we
um
that we look at how to how to create
this look at it it needs to be
integrated that needs to be intelligent
needs to be interactive all on a
composable approach and then in terms of
um the question is really do are they
starters or examples we have a
blueprints that we are creating they are
they are started
examples available in GitHub so we're
really easy to export into your
environment
um and then play with that we do have a
freemium option so if you go on a
website you can download premium option
um of us of our software and also some
of these libraries and examples that we
have
over that um thank you very much I
really appreciate you watching this and
we'll see you uh on the future webinar
we will send out the recording of this
um after the event thank you
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