We promised you the big picture of the
PI System. And let's break it down, what does that look
like? So
the big picture of the PI
System. So before we zoom in on any one spot,
it really helps to see it all. So often
this is what our customers are starting with. They have
on the left, a bunch of machines or
sensors that are producing data. And then on the
right, you have a whole bunch of people that have to do tasks.
And those people are you, you can see the striking
resemblance.
So I
have
a question, how many people in the audience use more than two
sources of data with your PI System?
OK. How many
use more than three?
OK, How about more than five?
More than ten?
We have one over there.
Wow, OK. So a lot of our customers use a lot of different sources of
data with the PI System. And
did you have anything else you wanted to ask?
Alright, now this is the traditional strategy
we find customers had before they used the PI System.
They had a lot of sources of data and they had things they needed to do with it.
And in between there are a lot of systems that were
put in that were all necessary like an asset reliability
system, there is an Historian to store our data, there is a condition based maintenance system.
And we started bringing the data out of the source
and putting it to the places it needed to go. So
we'd take it and move it over to the Historian and then we'd export that. We'd move it over to the
asset reliability system. But then we'd need to
get a port down to the condition based maintenance system.
But that system needed to get some dedicated
special values from the manual logger. So
those got sent over. But then we needed to forward that around.
And then we needed to get it to the users. And we had all of
these custom built solutions linking all of them. And we
end up with this which is what one customer
I think out of Finland termed the spaghetti
strategy where things are just everywhere. Now the thing I point out about this
is for one, it's obvious
how much this can be friction in your gears and slow you down.
But also that this works.
Doesn't it? You have data and it gets to
where it needs to go and you get your business running.
But there are limits to it, there are limits to your freedom to
try new things. Or the speed at which you can do stuff.
Maybe you want to spend more of your time doing the job that you were
trained and educated to do and less on wrangling data.
Also to add to this,
if you were basically only getting the data that you
requested, you are getting the data to solve something very specific.
But you might be missing out on data that's actually going
to tell you something that you never bothered to request the data
for because the system was way too messy and way too complicated.
And what we see is when we have the
PI System, we are going to really make this simplified.
There is going to be a single layer that's going to be
responsible for collecting data. And it's going to be able to
collect the data from any data source that you have.
We have another layer that's going to be responsible
for basically storing and enhancing
that data. And then finally, we are going to be able to
deliver that data. Again, if you look at this,
these are the same data sources. This is the same
users, the same systems. But we are able to do
this in a simplified way
with three layers. And our product maps
onto those three layers. And we'll be going through that in a
moment. The other thing I'd point out, yes we deliver data to users directly using tools that we
built, but we also make it so
that systems that
we have applications that we don't do and won't do like asset
reliability or enterprise resource planning, we can provide data to
those in a clean fashion so that they can get all of their data
from one spot and not have to worry about
spaghetti'ing it around. So you have the big picture
here and you say that's great, there are three layers.
But what's really behind those three layers? None of
you have a piece of software on your machine
that is called these three layers. And we are actually going to be jumping in
to really basically break down what are those pieces.
What are the pieces of the PI System?
So you might've heard some terms
here like,
the PI Server. Maybe you've heard PI Tags
or PI Data Archive or PI Coresight or PI ProcessBook.
Or Asset Framework. Or maybe you've heard
RT Reports. You've heard a lot of words
and sometimes they are just jargon and you don't know what it means.
It's like they are speaking another language.
And so we'd love to demystify that for you.
So rather than just us getting up here giving you a
definition, this is where the demo comes in. Because rather than
us just telling you what are
these tools, how do they work, we are going to actually be showing you.
So
we promised that we
would help connect you to the
pieces that we showed and when you came in, you should've received a
hand out. And we're actually going to be matching up the things you
saw in the demo to that hand out. So it looks
like, you know
what sheet I'm talking about. It's the one we handed you earlier
and there are two sides to this sheet. Your
handy cheat sheet.
Yes, this.
If you are new to the PI System or if you are old to the PI System,
we encourage you to keep this around. So on one side we have a set of
common acronyms you might hear
and what they mean. I find acronyms sometimes
disqualify me sometimes. I'm like, I don't know what that means and I can't understand the rest of the conversation.
But there is also PI System
vocabulary. So these are some of the major products and
terms you might hear associated with OSIsoft
products and what they actually are. And then if you
flip it over on the back, there is a map. And this is the map
that we are going to walk through and connect it to the demo right now.
That's right. So the PI System
has three layers like we said before. But
what products are actually in each of those layers? So it
starts with, we have data we need to collect. We have
tasks we need to do with that data. The first layer
is the PI Interfaces, that's this orange layer. And in it are
any of the pieces of software we've written that
connect to other systems. There are a lot of these.
OPC is one that a lot of people are familiar with, it's a common standard.
We also specific
about writing interfaces that connect natively
to SCADA systems or other systems because we find the
performance of that connection is ideal.
So we have hundreds of interfaces and
they connect to just about anything that our customers
use a lot of. And afterwards,
we talked about the second layer, that's really about storing and
enhancing. So first of all, we have things like historical
data. We have customers that have over 30 years of data
stored in their PI System. And then we are sending the real-time data there as well.
And in the future, we'll also have
future data and all of this data storage, it's
occurring in something that we call the PI Data Archive.
And the PI Data Archive, that's basically the
machine side of the PI Server. That's where all the data
gets stored. It's very machine friendly. It works
with those interfaces really well and it has for years.
Next, there is a
few of the features that Camille and I have talked about.
There is Asset Analytics, this is where I want to make a
calculation that's running continuously
and maybe storing that analysis so I can look back on it
as if it was historical data. There is also the
asset structure, we were looking at that tree of Stuart's
process over and over again. And that can be something in different
client tools. And that can scale from very simple like I
showed, 5 things to very complex and big.
There is Event Frames,
this is the ability to.. I have this big thing of data and
searching through it would be like searching for a needle in a hay stack.
Or I can go put flags in it, so I can go back and find that
spot in my data really quickly later on, so that I don't need
to spend a lot of time searching for it. And when we are talking about
Event Frames, again we showed a particular example
that was for something that's repeatable like a shift.
But Event Frames are really great for down times,
for process excursions
too, and that way you can basically can really track the things that are important.
And you have that marker built into your data.
One other thing I'll put into that, I expect our
customers to come up with uses for Event Frames
for instance that we never thought it would be used for.
So it's not limited to those things but
that's definitely some use cases for it.
And we made a reference to PI Notifications. So you
can setup alerts that are based on specific
conditions on your data and then you can receive email
notifications from those alerts. And
this all takes part in the piece of software called
the Asset Framework Server. That's the human friendly side of the
equation. It makes all that data easily digestible
for all the humans like us. And this is the side of the PI Server
that has really evolved over the last five years.
So this is the part that all the new
exciting stuff is really built into. So those are the
first two layers of the PI System. The third layer of the PI System
is the PI Clients. This is the layer
that delivers the data to the people and the other machines that are going to use it.
One example is PI
Coresight, that's the product that goes over the web. It can now
show PI ProcessBook screens. You can bring up any
data that's in your PI Data Archive or in the Asset Framework.
It can be shown through PI Coresight.
And usually that's setup on your internal domain
so that as long as you are connected to your internal domain, you can access it.
And we also saw PI ProcessBook.
You can import PI ProcessBook displays into
PI Coresight like you saw that we were showing. And PI ProcessBook displays,
those are fully customizable.
Make those however your control system looks
like. Make those are as rich as possible.
And that's something that again a lot of people that are heavily
involved in the process, they are going to be looking at these every day.
And there is PI
DataLink. In short, what's PI DataLink?
I asked my friend Brian, what should be my pitch about PI DataLink?
And he was like, are you an engineer? Yes. Do you use data? Yes.
You'll want PI DataLink if you use spreadsheets as that's what it's for.
It moves data into spreadsheets and it does it very well.
There is also
PI WebParts, that's a plug-in into Microsoft Sharepoint
that allows you to put PI data onto Sharepoint. We
find different regions tend to use
PI WebParts more or less. So PI WebParts and it's used a lot.
That's not the end. We have
specific PI client tools that are designed to help you do
things but you can also start to use
applications whether they are something you design, a
partner designed, 3rd party applications are out there.
And some of the things -- where are we going, where are we going next?
This is kind of a snapshot of
a lot of the existing stuff that's out there. But you've heard different
references throughout the opening session about
what's coming or what are some of the new pieces.
So we want to take a minute and if you want to write on this, you can
write on this map. But when we talked about PI
Interfaces, you might've heard the term
PI Connectors.
Who has heard of the term PI Connectors?
OK, excellent.
Do you know what PI Connectors are? Or no I don't know what PI Connectors are?
OK that's pretty good.
So one, if you forget anything that we saw, it is still on the
sheet. We do have the definition. But the PI
Connectors are really the next generation of PI Interfaces. They
are going to be able to help you collect the
data but to do it in such a minimal amount of time.
Because the PI Connectors, they are going to be building the
PI Tags automatically for you. They are going to be building that
Asset Framework structure for you. So I think
we have four out now, and again we have hundreds of
interfaces but moving forward, there is going to be even more PI Connectors.
One big hurdle for a
lot of customers is building out the AF
database the way that they think it should be, or there is a way it should be.
PI Connectors build that for you based on what the source of data is.
OK, anything else you
want to cover on there? You've heard the term
Integrators, we also have that defined and you heard some
references to the BI Integrators, to the Esri ArcGS
Integrators and these are things that are going to be coming.
I love PI Integrators because I was talking to my friend
Marcy here yesterday and I asked her what were PI Integrators
and she said, "I don't know. It's basically data
wrangling. It's like before you had all these cats, these
data cats that were running around and you had
to wrangle them into your ultimate system." Well the Integrator does that
for you. It formats it. It makes it pretty so that your Big Data
analysis tool, Hadoop or whatever else it is,
can actually process it and give you useful
intelligence rather than just feeding it crud. Because if you feed it crud, you are going to get
crud out. The Integrators take care of giving good food
to those systems.