you
operator a change in my mission is
causing damage to my HP turbine rotor
its win help me understand the problem
what's your operating profile in past
six months
my number of cold starts is for my
number of warm starts is eight my number
of hot starts is thirty nine the number
of start/stop cycles has increased by
twenty seven point five percent twin
tell me about your rotor damage my
damage rate has increased by four point
zero times over last six months if this
continues I would lose sixty nine point
nine percent of my useful life twin give
me options for mitigating that route of
damage based on weather forecasts
historical data feel caused electricity
pricing in my present condition I have
two options for you to optimize
operations option one is to manually
slow down my startup ramp rate so that
you can reduce the wear on my rotor
option two is to download the OP Flex up
and apply stress controls to minimize
wear and reduce fuel consumption two to
end
give me the factors you used a
calculated option two I used my past
fifteen years of historical data fleet
learning from 125 other d11 steam
turbines like me and fifty eight
thousand nine hundred sixty five
simulation runs to get this
recommendation I'm 95% confident of my
assumptions twin I need a financial
perspective tell me about the numbers
the numbers look good we can reduce
stress by 25 percent which brings the
damage rate back to normal range startup
fuel cost will go down by 40 percent in
start-up time will be cut by 50 percent
you will also avoid 12 million dollars
by preventing an unplanned outage twin
okay I select option 2 let's do it
I'm initiating option two which is flex
start thank you okay good morning
what you saw just now was not a glimpse
into the future this is happening now
today this is a representation of a d11
steam turbine that's currently running
in Southern California we call these
representations digital twins what you
saw was an example of the human mind
working with the mind of our machines
what's a digital twin it's a living
model that drives a business outcome so
this model gets data gets operational
data an environmental data and
constantly updates itself it also can
reach into the fleet the 125 the 11
turbines and it choir z' knowledge from
them and learning from them and it
incorporates this into the model and it
uses this to drive business outcomes it
can drive outcomes like it can predict I
can warn us and then predict failures to
reduce maintenance cost and unplanned
outages it can give us the ability to
optimize or provide mitigation events
when we find out that we have these type
of failures and it can also allow us to
forecast opportunities and actually
benefit from these opportunities so let
me step back and actually walk you
through what occurred in this digital
twin in a very simplified way so when
you think about the twin it really
operates in three stages see think and
do so in the seeing stage and I relate
this to what you saw previously in the
seeing stage what it's doing it's
actually gathering data of its situation
to give you a warning or prediction this
data that comes in is more operational
data and environmental data operational
data like the steam temperatures the
bowl temperature the rotor speeds it
takes that data in and it uses a model
at a living model this is a hybrid model
physical and digital capabilities so
it's a combination of metal fatigue
metal creep Bayesian learning and other
machine learning techniques and it uses
this to create a cumulative damage
understanding of what's happening on
that rotor as it does that when it hits
a threshold it can then warn us there's
a problem it then can actually predict
the problem it also uses similarity
learning techniques to reach into that
fleet of a hundred and twenty five other
the 11s and gather their knowledge as
well so this is a model it's constantly
updating itself to reflect the precise
conditions of the asset and then
learning from others in the fleet after
it does that it has the ability to give
us these insights at the right time
that's the first stage and it does that
true EPM and using the digital tread to
get all that data the second stage is a
think stage this is when I asked it to
him to give me options options that I
could pursue to do that it ran
simulations over 58,000 simulations
running them on the prereqs platform as
her l described in the cloud on those
edge boxes it's looking at historical
data it's looking at fleet data it's
looking at life in models it's looking
at forecasts for revenue and cost and
it's giving you a number of options it n
reasons across the option based upon the
risk and confidence that it can deliver
and with that it delivered us two
options one that was manual and one that
could be delivered by an application
that would run on a control system on
the edge exactly as described before now
we get to the dew stage the dew stage is
all about informing us and executing
what needs to be done so in option one
in the manual option what you could do
is you can have it prescribed to you
what's the load rates the ramp rates
steam temperatures needed for an
operator to actually manually input and
all that is logged in a PM and delivered
to an operator to manually input or you
can use an app app downloaded to the
edge running analytics on the edge one
of those boxes you saw previously
now that app can run much more precisely
it can precisely monitor the load and
the ramp rates aligning it to the steam
temperature so that the term of stresses
on the router are minimal that's how you
get to that 25 percent reduction number
it also by doing that allows you to
start a lot faster and building less
fuel that's where you see the other
benefit show up all of this executing on
an edge device that's see think and do
these are the digital twins working with
us with the minds of the machines
creating these capabilities now what if
I wanted to go in a much more immersive
environment and have the twin help me
understand the problem how would I use
one of these microsoft hololens which I
suddenly found here so what I'm going to
do is I'm actually going to use this
lens
to get a much more immersive environment
so what we would normally do is we would
normally go into the actual physical
plant and look at the steam turbine
itself the physical steam turbine and
from that physical steam turbine what I
would do there let's make sure we get
this to go right what I would do there
is I would overlay all of my digital
capabilities on the physical asset
seeing exactly where the damage was
occurring knows I can't do that I can't
take us all on that journey at six hours
away what I'll do is I'll ask the twin
to bring that to us take the exact CAD
dimensions of the asset from
manufacturing and maintenance and
simulated in this environment right now
as it begins to do that oh right you'll
see the steam turbine up here and it
should be appearing any second now or is
it one of these usual glitches when you
do these things that you see appearing
all right my big Lich there it is in the
audience now I can have the twin help me
twin open the steam turbine
okay I've been with twin open the steam
turbine let's see if that works better
let's use the hand gesture okay give it
a second here
ah okay
twin show me where the where is
twin show me the length of the where and
plot a baseline for me so I can see if
we did know mitigation what that would
be
okay twin close the steam turbine
staying still floating around what you
saw there was an experience with one a
five hundred and fifty one thousand
digital twins that we have currently
running this one can communicate with us
in a richness of voice and vision that
is usually unbelievable but quite real
but all of them deliver value several of
them deliver value in different ways so
what you've seen here is the list of the
other twins that we have so we have
parts twins which is what you just saw
to row the failure prediction and this
parts twins what that can do just like
we saw is deliver early warning about a
problem as well as tell you about the
actual failure that you'll encounter we
have product twins what product twins do
is they actually optimize the remaining
life of a steam turbine or any asset and
they do it against maintenance cost
what process twins do is they find ways
to optimize scheduling a field engineers
in this example to maximize the
utilization what the system twins do is
the system twins find a way to actually
optimize against a multiplicity of KPIs
balancing things like revenue against
remaining life against maintenance cost
all of these twins providing value so
when you think about the ear are we
going into of emergence this is the era
in which we have the minds of the humans
maybe 6 billion of them talking to the
minds of the machines 50 billion of them
that convergence is going to profoundly
change everything we know we believe
with the power that we have in predicts
the thread the twin and ATM we are
perfectly suited with our partners and
our customers to
to the next space thank you much very
much for your time and my thanks to the
twin