Hello welcome to the course PI System: From Data Exploration to Notification.
I'm Curt Hertler and I along with Kim Penamora will be your instructor. This course targets
PI users wanted to learn more about how the features of the PI System can work together
to successfully address an operating problem from actual process data. The exercises will
walk you horizontally through PI's most advanced features to implement a solution;
it will take you through the processes of data engineering process modeling, operationalization,
socialization and notification; all this using only PI System tools. Our
objective is to begin your transformation from being a PI user to becoming a PI power user.
There are several learning objectives for this course: first to provide an improved
understanding of multiple PI System tools; all within one course. This will enable you
to address process problems more effectively; the more tools that you know more ways that
you know how to work with PI system the more valuable you'll be to your operation. Next
to gain an appreciation of the importance of interacting with the PI System. Asset
analytics provides ways of configuring analytics and also testing them and evaluating them and so
in this course we'll be doing quite a bit of that and one of the hands-on exercises we'll
be configuring an analytic several different times for improvement so we'll configure it,
we'll test it and we'll reevaluate it and then we'll change it and test it repeat that cycle
three different times; so it's kind of an iterative approach but sometimes you don't
get it the way you want it the first time and you have to try it again and AF analytics provides a
really nice way and a lot of tools to test those things out to make that fairly easy for you.
Then taking an idea for improvement we're going to discover how PI's features can work
together to positively impact an actual operating problem. So throughout the
entire course we're going to start with a set of data for a particular process;
we're going to have an idea of how we can make that process work better and we're going to
start from the very beginning and work it all the way through in solving this problem to show
how various steps and various tools of the PI infrastructure can be used to solve that
problem. And then finally as I said before we want to enable you or help you become a PI power user.
You've probably seen this slide before; it summarizes the overall
PISystem infrastructure from collecting the data, storing and enhancing it and then
delivering it to people and applications. In this course we'll be covering asset analytics,
we'll be configuring several analysis to add values that we can find useful for our
analysis. The process we've chosen operates under different cycles so we'll be using event frames to
prepare the data and frame it up within the time ranges that exist during those cycles.
We'll be using PI notifications to socialize our solution and that means sending emails to
plant operators and other personnel to make them aware of what we found in the data in real time.
We'll use the PI integrator for business analytics to put data into Power BI where we'll do some
multi-dimensional analysis and then finally throughout the lab we'll be using PI Vision
but we'll also use PI Datalink to bring data into Excel to build a predictive model. So you can see
that we're going to be covering quite a few of the PI System products and features and it should
give you a good overview of how they work and how they can work together to solve a problem.
The learning approach for this course is based on taking an idea for process improvement
and implementing it entirely within the PI System. We'll take you through this process
in five lessons. Starting off with gathering domain knowledge; this is a prerequisite to
everything we're doing in the course, we're going to show you how the process works,
it's a fairly simple process so you should be able to understand it but we're gonna
use PI Vision to teach you about it. First we're going to give you a process overview
so you understand how the process works and then we're also going to use PI Vision to
show you what things are going to end up looking like after we implement our idea.
The next step will be exploration and data engineering and here what we'll do is we'll
define the process operating states. Now in this case there are no digital state values
to go on so we don't know when things are on or off or when the cycle starts,
we're gonna have to figure that out based on process conditions and then we'll also be adding
some descriptive operating features so that we have more information to base our analysis on.
The contextualization and data preparation lesson will be used to set up an event frame templates
and in these templates we'll be positioning or capturing the data about the event frame that's
going to be useful in our analysis and then we'll have to generate those event frames themselves.
The data analysis and qualification step really is about extracting this data in
the right context of the event frames using the PI integrator for business analytics.
We'll be doing some multi-dimensional analysis on this data using Power BI.
Then we'll extract this data into Excel using PI Datalink. Here we'll develop a predictive
model and then the final lesson will be about putting everything into action. So
we'll operationalize our predictive analytic model using asset analytics and we'll create
a PI notification for proactive guidance to let the operator know when some action is acquired.
And finally there's some ground rules to the course: the use case follows one that was
related to us by a customer so we'll be solving a real industrial problem. The data was taken
from two molecular sieve dryers used to remove trace amounts of water from a hydrocarbon stream
in an oil refinery. The data is for a whole year 2019, it's evenly spaced at six minute intervals.
The analytical methodology is basic we're not intending this to be a data science course we
will be doing a predictive model inside of Excel but these statistical techniques are pretty
rudimentary, we're not really intending this to be something that gets off into data science although
we have other classes that go far deeper into this subject but in this class we really want
to focus on the PI infrastructure and the things you can do in preparing and operationalizing data.
The course VM contains all the examples completed and each section builds on the previous one. So
when you build out your hands-on work we ask that you prefix things or names of things with
the word my or your name or something that will distinguish it from what we already
have in the VM and this way as you move forward and you build up on top of what's already there,
you'll make sure you have something that works. If you want to change the name and you know make it
so that it works completely with your work that's fine but we just wanted to make sure everybody had
a working example to go from so that as they go through the class they'll get successful results.
And the final thing is that we've already created all the
PI points so when you build out your AF Analytics and do your backfilling there
are already tags created to receive the results of those calculations.
Okay that's about it for the introduction now let's get
started with the next section on gathering domain knowledge.