Xmpro Ai End To End Use Case
XMPro End To End AI Use Case Webinar
Join us as we delve deep into XMPro's integrated platform that seamlessly ties in data streams with machine learning models for predictive asset maintenance and management. In this session, we explore:
At-Risk Asset Monitoring: Discover how XMPro identifies assets like pumps, predicting their health, and providing actionable insights.
Recommendations & Event Analysis: Dive into the event data, maintenance history, and personalized recommendations for each at-risk asset.
-Visualization & Customization: Experience the adaptability of XMPro's interface, with tools ranging from calendars to detailed asset timelines.
-Scheduling Optimization: Understand the power of integrating scheduling models for efficient maintenance planning.
-Model Management & Deployment: Uncover the platform's prowess in supporting model registries, versioning, annotations, and the flexibility to deploy across diverse platforms like AWS.
-Iterative Development & Refinement: Grasp the iterative nature of XMPro, allowing for continuous model refinements and application improvements.
-Through this webinar, get a comprehensive view of how XMPro offers a holistic solution for organizations venturing into predictive analytics for asset management. From investigation to deployment and continuous refinement, walk through every stage of the AI use case lifecycle.
Stay till the end for an engaging Q&A segment where we address some burning questions about XMPro's capabilities, integrations, and more!
Whether you're a data enthusiast, an AI professional, or someone keen on digital transformations, this session has insights for everyone. Register now and step into the future of asset management with XMPro!
#XMPro #PredictiveAnalytics #AssetManagement #AIUseCase #MachineLearning #DataStreams #AssetMaintenance #DigitalTransformation #AIWebinar #PredictiveMaintenance #OptimizationModels #SchedulingModels #DigitalTwin #AIIntegration #ModelDeployment
Transcript
hello everyone welcome to this webinar
on an end-to-end Solution by example my
name is John Van hedden I'm part of the
Strategic Solutions team as an engineer
so today we'll be talking around the
exam Pro AI part of our four pillars of
X and Pro
uh that's this guy right here
and it is a distributed intelligence
that is achieved through AI engineering
where people and Technology meet to
innovate execute and augment with AI
uh so I'll just note as well we are
currently in our XM Pro notebook here so
it is part of our suite of applications
that's this guy here
and we'll step into our end-to-end
solution example
so going through this end-to-end
solution we will Define over the pro go
through the problem definition
look at some of the related data the
characteristics of that data visualize
the data identify what is um
the suit more suitable machine learning
models that we might use develop these
models all within the notebook
and then we'll step into the deployment
of uh sending these models to our data
streams
running live data through them
getting some results
pushing that up to applications to
recommendations and also touch on how to
manage your models
so looking at the problem definition for
this example
we have a subject matter expert that has
multiple pumps in remote areas
this subject matter expert wants to
analyze some sensor data and we actually
have a lot of sense of data I think
plus 50 50 plus sensors and the goal for
this subject matter expert is to be able
to identify which pumps are likely to
fail and estimate their remaining use of
life
the key for this subject matter expert
is to be able to create prioritize and
create a maintenance schedule
ultimately reducing the optimal downtime
as the key Focus
so
breaking this up into the components we
can see that we have a need for a
classification model for being able to
identify pumps that are likely to fail
and a regression model for the
estimating riding use of life and we'll
be stepping through that part
um at first through our uh stepping
through data analysis first to get to
those two models
so the Crux of this problem is that we
need the right people at the right place
at the right time
the data analysis part
utilizes our data streams to bring in
our data and our example notebook to do
the analysis so this is a Jupiter
notebook style notebook so it's very
familiar to data scientists and
Engineers who commonly use this platform
so stepping into the data analysis we'll
be loading in our data so this data is
coming from our data streams so
if I click into this guide here
where we have a listener so we have a
set of a whole bunch of listeners in our
toolbox this is
XM Pros set you can also build your own
through our extended school library
so you can bring in any types of
live data like mgtt your OPC UA
Etc we can then filter some invalid data
points out so if your sensors go offline
we might not want to bring that into our
model and we can choose to write this to
a set of
action agents is what we call them a set
of data storages so we also have a wide
array of choices here
in this example we'll be choosing just
to write it to CSV and pull it into our
notebook
put the data now in our notebook we can
go through
extensive or basic in this example basic
analysis looking at some of the
characteristics looking at the the top
top 10 rows of data summarizing our data
and
something to note about this data as
well is from our maintenance system
we've now labeled this data with our
machine status so we can see that
there's a there's three states normal
broken issue so ideally we want to
identify these issue States for our
classification model
yeah
the next step is to visualize our data
get a good idea of
how our data relates to each other since
we have a lot of sensors we're going to
push our data to a principal component
analysis space that brings it down to
two dimensions that's a different
transformation of the data for us to
visualize and identify clusters for our
classification
so here is the code for for visualizing
the data so there's just a contour plot
with the principle component space and
here's our results so we've got a nice
interactive uh
plots here I've got a console plot on
the right side and and a
density Contour on the on the left side
and if we actually just pull away all
our normal points we can see there's a
nice clustering of our issue
issue States and where we find our
machine breaks down
so we can see that there's significant
groupings for the issue States closely
related to the broken States and this is
a nice indication for us that this could
be suitable for our classification of
course we can go a lot deeper into this
and um
look look at a deeper analysis and
Transformations on this data but this is
just a simple example
um
please also take note that this type of
data that works really well is not it's
not always viable it depends on the data
characteristics and how we transform the
data how we clean the data how we bring
uh do feature engineering Etc
so now that we've had a nice idea look
at our data we've got a good idea of
what we're working with we're jumping
into our development of our two models
so this will be achieved through our
data streams so this is where we keep
bringing in that data and our notebook
that we're in currently
so the first step classification
classification of the at-risk pumps
choosing a model that's uh up to you so
I'm choosing a random Forest classifier
typically someone might start off with
something that's that's very basic or
use an automl library is optimized your
model selection and the hyper parameter
tuning it gives you a good starting
block so this one will just be a vanilla
random Forest classifier nothing too
special in the hyper parameters just
something that gets us nice basic clean
results
so
this is the code that does the
classification we do a test train split
for for our data that we've now pulled
out of we're using our data data streams
into that CSV
and we run this classification model and
we look at some of the evaluation
metrics and we can see it's actually got
a very very nice score broken it doesn't
have uh there's not many cases we've got
heavy class in Balance there and we're
not too wide we are we would like to
know that there's a good distinction
between our issue cases and normal so
this gives us great results for for that
in the accounting that this model will
do a good job at classifying our at-risk
pumps
so once we've created these models we
can now save the model files and use
them into our data streams which I'll
show ahead on the deployment stage
so this is our classification model we
can now jump into our regression model
so we've now taken our data
and creating these two models uh to
bring into our example application
Fuller regression model what I've done
here is I've just uh in this first block
uh
created a calculation to identify the
number of hours until the next broken
time
and in the second part similar to the
first I've chosen just a gradient
boosting regressor just a Model A very
basic I have a testing train split and
I've run this and it got some evaluation
metrics at the end here again very nice
scores on on for this data set so we can
see that there are squared score is is
above 0.9 which is very a very good
indication so we can see this will give
us a nice uh prediction for the
remaining use of life
once again you save these model files
you know just recap on that one
so for the deployment stage we now have
our models and
we will want our subject matter expert
to be able to interact with these models
so I'll click through here
um
sorry where this slide
so this is what we have at the moment so
we have our two models and we've brought
that in from our data
now we want to be able to apply live
data to these models and surface this to
a interface that our subject matter
expert can open up on is a laptop
wherever he's at
DLC
um so looking at the problem definition
we've got our models but we would now
like to create the maintenance schedule
and start getting value out of these
models
your deployment options are wide and uh
there are many options available through
XM Pro
there might be various reasons why you
might need Edge for for computational
Speed or security cloud or a hybrid and
we've got a range of methods to to apply
these models and um
and so as our entire set of data streams
can be configured
through through these options
so taking our models we will now put
them into our data stream
and we're actually going to model change
these models until you save on some
computational power if needed apply live
data to them
and this live data will then
um the results from this live data will
now go into our recommendation engine
and also surface into our application uh
giving us insights and and of for event
intelligence
so I will just open up our data streams
and recommendations
and applications just to show how the
three
um
gel into each other
so in our data stream
this is where we now we are now applying
our two models so we're using the python
agent here and
we've got our model scripts in there
and we can point this to any python
instance we like so if we want to go
execute this on the GPU we can set that
up
and
this is the other model there
so how this data stream works is we
bring in our live data
we prepare our Json package it goes into
our first at-risk pump classifier this
will give us a true or a sorry a normal
or issue classification so I'm just
looking at the live viewer here this is
just primarily used for for visualizing
what's going on here it's not our main
point of
of analysis
just to get the the flows working so
this data is coming through we'll be
waiting for a data set to come through
now so we can see we've got some some
data come in
uh we've got a range of sensor values
and it's gone into our model and we've
got a normal result so
at this point here
the normal result gets filtered out and
that's the end of that
um
of that
set of data
the model the the data points that
identified as a fault or their sort of
issue they go into our prediction
remaining use of Life model this will
now on that app on that pump give us our
remaining use of life
we extract our asset name and then we
broadcast this to our recommendations
and our application our recommendations
so that we can write some rules for um
for some events intelligence alerts and
our applications so that we can
visualize the type of data that's coming
through
here's our recommendations so
this is a recommendation rule I've set
up called pump at risk
I've added one rule here for the
remaining use of life
and in my alert headline I can
um
push live data into this alert headline
so from a glance you can see how many
hours it has left
uh I've created a set of
um
alert description so this rule is around
if I have a pump that has a remaining
use of Life less than 100 hours so these
are the ones that maybe I have a couple
of pumps that have a certain level of
urgency but these ones are imminent
failure critical risk
so I've set this as a high ranking and
the rule said for this recommendation is
when this remaining use of life is less
than a hundred
I can now add additional triage
instructions and information around this
recommendation and I'll show you how the
this configuration surfaces in the
application as well
so clicking into application what we
have here is we've got an interactive
map on the left hand side here
a list of our Assets in a table and
these recommendation rules that have
come up from our recommendation we've
set up so also note here that we can
we can set up
notifications for this recommendation as
well for more uh specific
uh urgent for more urgent action so we
can set this up as
soon as the basic name
and we can
send this out to the email or SMS
coming back to application so here we
have our list of pumps so this data is
getting fed live from our data stream
and we can see here we've got a remote
location very remote uh potentially
requires a helicopter for this type of
servicing and we can identify just
visually our issue pumps and how many
how the remaining use of Life on them
so I can now zoom in here and just uh
this by visual inspections can start
doing my scheduling and mapping out
there so looking at the recommendations
that I get for these at-risk files
I can now click into a recommendation
so this is the recommendation we set up
in the previous configuration
and I can look at some of the the event
data coming through so I can see okay
I've got 94 hours left on this guy this
is a critical one
um I can start writing notes on on this
pump and
uh submitting work requests
my maintenance crew can now go to this
pump
follow some triage instructions
uh looking at how to diagnose this pump
and how what is the best course of
action
we can build a discussion around this
pump uh look at a timeline of
what has been done to this
recommendation
look through some analytics for this
specific recommendation so if we see
this pump continually it has a low
remaining use of Life there may be a
persistent issue there
after we've resolved our issue we've
done our diagonal
excuse me
um
analysis on the diagnosis on the pump
and we've amended an issue or identified
the issue we can come down to the bottom
here
and we can either mark this uh pump as
resolved or mark it as a false positive
and this can then come back into our
data set and for a refraining and
refining our models
so that's a very nice feature there
so from point of view from our subject
matter expert
we want to open up our laptop in the
morning after our of our coffee or tea
and we come to this map here
and now we can have a look in look at
our pumps set up our scheduling
and this might be manual for this first
instance and I'll show you just in a
second how we can bring optimization
into this and we can keep iterating on
this application as as our as our models
become more refined and advanced so just
going into the edit mode for this
application we see we've got a range
similar to our data streams we also have
a toolbox for our visualization elements
so I can bring in something like a
calendar
rearrange and adjust my layout as well
as additionally creating template pages
and additional pages that I can drill
down into so I can dive into specific
assets
I'll just cancel that one
fantastic so this is uh what I've shown
here so we've got our two models from
before we've brought them into our data
stream here
and um we're we're pushing that data
through we've got our chained models so
that only the at-risk pumps go through
for prediction or remaining use of life
and we surface this all the way to our
application uh let's start up again
our application here where we also have
alerts set up for remaining use of Life
less than 100 hours for our more
critical events
so on top of this now I can bring in a
scheduling model similar to my machine
learning models I can go and bring in
any type of Library I I require to set
up the scheduling optimization problem
I can go through with real data I can
store some real data look at some of my
maybe I've gone and created a few manual
scheduling
sheets I bring all that in I can analyze
it I can bring it into this and compare
it with this algorithm see if I get any
get a performance boost
and uh so so this is the process of
going through developing this or tools
model
uh it's just a constraint solver
and at the end I get some results out
for okay if I need to travel to my six
most critical pumps this would be my
um optimal route and it gives me a
distance metric for for this so in in a
similar fashion as what I've brought in
my machine learning models I can now
bring in that model through through a
python agent as an example that's very
as aforementioned there's a few
different options you can choose I might
bring that in here
and then from my list of pumps
I can tie that in and then from there on
out I can take that schedule and present
it boil it up here so I've got an
automated schedule that reacts to live
data so every time I get a new live data
point in and I might set it up to wait
for every every hour before running uh
my scheduling model
but I've got an hourly updated live
scheduling that I can access by just
opening my laptop
so the last Point here on the end
into in use cases the management so
now that I've
gone through and I've developed my
machine learning models and the benefit
the beauty of of using these notebooks
and and getting your data in with XM the
data streams is that I can iterate
quickly on these models I can get
results quickly I've got graphical
interface files I don't have to do
mappings to to extensive mappings to get
a point solution uh visible on a page
and usable
um I can iterate quickly on these on
these models and then I step into my
refinement process and this performance
process can can lead to very complex
models so that may be in my optimization
model or my two machine learning models
and this is where management comes in to
to look at some of the uh sorry about
slides kicking up
to look at some
some management options like a model
registry so some benefits getting in
some metadata of the models the lineage
versions I can put models into
production uh I can I can iterate on
these models add annotations and all
this type this type of um
extra information about the model that's
not just a set of a file with weighted
values in there so if I go back to my
data Stream So an option of of this is
ml flow
we've got a right a wide range and
there's a set of options you can choose
so the example there would be that
I have my model repository sitting here
in place of where I've execute my model
here I can select my model so I might
have version 10 running and I push my
live data through and I get it all
surfaced up to here and I've got a team
working hard in the in the basement and
they're working on the next the greatest
best model with some brand new data and
they've upgraded this to version three I
can click into this save it to version
three and it automatically comes into
this through this data stream
so this is the end-to-end solution for
this specific example where we've
defined our problem
we've identified what kind of models are
required what kind of actions would the
subject matter expert
how they would interact with this
application
we've looked at our data this has come
from our data streams so we've got live
data we've stored it's in some storage
and we've then looked at the
characteristics of this data we've
transformed it into into a different
space the PCA space and visualized it in
a 2d way so we can identify those
clusters
we've then stepped into our development
so that's looking at developing our two
machine learning models looking at their
results the metrics that come from them
and getting an idea of whether they're
suitable for the task and it it will be
dependent on your task if you have
something that's more at risk something
with the that it may have a lot of false
positives may not be well made good so
we do all these kind of decisions here
at this stage there and then we step
into our deployment of how do we get
this into the streams where do we want
this to run what kind of security do we
need do we
do we need this at the edge is it highly
computational those types of questions
then we look at
taking our resulting models getting into
a management system and continually
iterating back on so once we've got to
end of one stage we've got some good
results we might have a bit of feedback
from our subject matter expert that will
tell us this is not quite how this works
this model gives us too many false
positives
Etc
we go through we redefine our problem
um well we enhance our problem we bring
in some new data might need to bring in
some more
different variables that will account
for some variants in our in our
resulting data
um go through the development we might
use Auto ml we might actually now hyper
parameters the data might change so
there's a there's a set of different uh
there there's a lot of variance through
this process and that's why it makes it
such a benefit to have a fast iterative
process through these Excel Pro
notebooks and
and we stepped through all the way
again back to deploying these models
getting them through building up our
application
uh creating maybe potentially some drill
Downs where we get to the pump level and
we might look at the the pump data
visualizing that directly at the pump
from this um
interface and we can even step this
further back into looking at maybe I
have for each state I've got a set of
pumps and I want to look at the set of
pumps for those States
and I can allocate a person to to look
at a unique page that's focused purely
on in territory for example
okay thanks very much Sean um we've just
got a few questions that have popped in
during the session
um so the first one is why and when
would you use the python agent rather
than Jupiter notebook
yes so the
um
so the python agent would be if you have
a pre-existing solution or you have a
very simple type of
a script that needs to be needs to run
or maybe a library that just takes in
data uh your notebooks is is a lot
around investigating uh visualizing uh
all on a common common platform that
that everyone on your team has the
um the same set of libraries you can
share the notebooks so this is a
collaborative type
process here
um so that yeah that would be the reason
why
where so so the results from from this
notebook investigation it ends up being
a python script that you bring into this
to this agent here
awesome and just one other one I've got
time for one more question does it
support decoupling of development
platform versus deployment platform to
be different providers for example if
deployment is on AWS slash Azure while
development is on X and pro platform
yes yes so they um so it would
um it depends on
um what the setup is but that's uh
if sorry can I get that the last part of
that question against you yeah so
um can you just support decoupling of
development platform versus deployment
platform so can it be on different
providers for example deployment is on
AWS and development is on XM Pro
yes that's right yes yes so you you can
go into you can go and deploy uh sorry
develop your models uh anywhere and uh
you through our extensible library you
can also build agents to even interact
with those um
deployment points depending on if if
they can actually serve models for
example
um but that's uh that's 100 viable to to
go and create and develop somewhere else
and bring it into our data streams um
separately
excellent um okay well with that's all
the time we have for questions today um
thanks very much Sean and thanks
everyone for joining us today uh if
you're looking for more information you
can contact Sean or our team directly
and we'll send out the recording of this
this session shortly
um join us next month as we share how to
accelerate your digital twin use cases
with our blueprint accelerators and
patterns repository and you can register
um via these links in the chat box
um here and we look forward to seeing
you all next month thanks very much
everyone
thank you everyone
foreign
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