Now that we've made improvements to some of the analytics we can repeat the directed
activity from lesson two and see the improved results. In order to see a difference we will
need to restart the analytic service to clear the performance statistics and then
let it run for a period of time. Most of our analytics in the course run every few minutes,
so a 10 minute run period is enough to see some data but if you have
time letting the system run for an hour will result in more interesting results.
To reset the analytic service takes the following steps: first launch the services application,
then find the PI analysis service, right click and select restart to restart the service. This
will reset the performance statistics inside the analytic scheduler and allow the calculations to
rerun with the improvements we have made throughout the lab. Now I'm going to let
my system run for an hour and in an hour we'll come back and analyze the performance changes.
Νow that we've let our virtual machine run for a period of time, we can analyze the performance
of our analytics with the new changes we've made. Τhe first step to this is to launch PI
System Explorer and go to the management tab, then we'll right-click in the lower right and
say view analysis service statistics, we'll click Save and we're going to call this analysis service
statistics underscore after and save. And we'll go back into Windows PowerShell or relaunch it if you
closed it. First step is to import our file for manipulation, so we'll copy the first line and our
PowerShell and Excel content to copy dot text file from the c-class folder. We'll add the underscore
after to our dot txt file. Τhe next step is to restore the relevant performance information
into the variable eval summary, so we'll copy the next line in our text file. Paste that into
PowerShell and execute it, then we're going to export the relevant performance information to
a new CSV file so we'll copy the line in our text document, paste it into PowerShell and
we'll change the file name from AnPerfStat CSV to AnPerfStatAfter dot CSV then press return.
Now we should have a file called AnPerfStatAfter dot CSV in our C class folder.
This time we want to use our previous Excel file and just add some new tabs, so from our c-class
folder will open up AnPerfStats dot xlsx and if the activation wizard pops up click cancel
again and we'll click the plus to add a new tab. I'll call this AnPerfStatsAfter and in this case
we're going to import the file. We'll go to data from text. Select our c-class folder and select a
AnPerfStatsAfter dot CSV and click import we'll choose delimited and click next. We'll uncheck
tab and check comma, next and then finish. Then click OK to import into the worksheet.
So here you should see our new performance information
from our CSV file that we created in PowerShell.
Next we'll modify our data in Excel like we did before in lesson two so the first step
is to add a template column, so we'll insert a new column to the right of the display name
column. We call this template and we can copy the data for that out of our text file again. Paste
it into the first cell and then fill down. Next we'll calculate the average triggering
interval so to the right of execution count we'll create an average trigger column and
again we can copy this from our text document and fill down. Then we create a weighting
factor column to the right of that and again we can copy from our text file and fill down.
Next we'll create a new sheet or a new tab inside our workbook. I'll call this pivot table after.
Next on our new sheet from the insert ribbon we'll select pivot table. We'll select our new
data for the data range. Then as before we'll add our fields to the pivot table. So first we'll drag
template to rows, next we'll drag AvgElapsed to values. We're going to change this to an average
using value field settings. We're going to drag max elapsed to values and we'll change this to a
maximum. We're going to drag average lag down change this to an average. We'll change drag
max lag down and change it to a max. We'll drag average trigger to values and we'll change it
to an average and finally our weighting factor. We'll change that to an average. Now we should
have a pivot table like before. Now we can sum it by our weighting factor, so we'll select the drop
down and say more sort options. We'll select descending by average of weighting factor.
And finally we want to add conditional formatting just like before. So we'll select the waiting
factor column and from the home ribbon select conditional formatting and data bars and select
the gradient fill that you prefer. So now we should have our two pivot table displays and this
one after we've made our changes you can see the improvements that we made. The lifetime production
analytic which is not one of the ones we worked on is much more expensive than it was before,
but our high wind speed calculations which was one of the worst before is down to a
point nine eight weighting factor as opposed to a four point one weighting factor before.
Our lost power 30-day average was at three point seven seven weighting factor before
and now it's down to point one four weighting factor. So because these weighting factors are
somewhat relative it's not the best indication of exactly how much our performance improvements
worked on a particular analytic, but it is nice to see that where we focused our efforts is no
longer the worst thing in our pivot table. The high wind speed calculations is always
going to be an expensive calculation but it's dramatically less expensive than it
was before and that's what we're going for. So if I was going to look at this
this particular pivot table with a customer after we'd made these changes I would then
think we needed to go back and spend some time working on our lifetime production.
Now you should be able to apply this analysis to your own asset analytics implementation my
hope is that this will be a useful tool for users of the PI System when trying
to improve their PI System performance. If you do try this method on your own PI
System and are willing to share your results or at least feedback please
contact a representative of OSIsoft delivery services that you have worked with before,
your OSIsoft Account Manager or send me an email. We would love to see user results using this tool.
Thanks for watching this training and I hope it proves useful in helping
to improve the performance of your asset analytics system.