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What is a Good SmartSignal Model? Presented by Joe Milton Engineering Manager Reliant. Outline. Reliant generating fleet and OSIsoft PI infrastructure Virtual M&D concept Human factors What is a good model? KPIs and other metrics. Generating Fleet Location and Fuel Type.

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Presentation Transcript
slide1

What is a Good SmartSignal Model?

Presented by

Joe Milton

Engineering Manager

Reliant

outline
Outline

Reliant generating fleet and OSIsoft PI infrastructure

Virtual M&D concept

Human factors

What is a good model?

KPIs and other metrics

common plant historian osisoft pi
Common Plant Historian -- OSIsoft PI

29 PI Servers

203 interfaces to various systems

1000+ process displays & reports

350K tags (real-time data points)

slide5

OSIsoft PI took care of history and current value but not what the value should be….

We selected SmartSignal to leverage our existing OSI PI infrastructure and predict…

smartsignal predictive maintenance tool
SmartSignal Predictive Maintenance Tool

SS takes data from our PI infrastructure (like vibration, temperature, pressure, etc.) and inputs it into a software model. This model shows how a a typical piece of equipment is supposed to operate over various loads and ambient temperatures.

If the data goes outside of the predictive model, an alarm is sent to the station for further investigation.

Basically, we are making the computer do the work of scanning all of the sensors and instruments in the plant for changes in known equipment behavior.

defining watchlist graphing terms actual estimate residual alert incident
Defining WatchList Graphing Terms: Actual, Estimate, Residual, Alert, Incident

Actual (blue) from OSI PI

Estimate (green) generated by SSC models

Residual = Actual - Est

Alerts (red X) Statistically significant residual value

Incident (diamond) multiple samples or sensors showing alerts; drives the WatchList

typical results additional terms
Typical Results & Additional Terms

VSG (green diamond) Virtual Signal Generation was enabled

Missing Value (blue X) Signal gave NaN value

smart signal site watchlist
Smart Signal – Site WatchList
  • Models
  • Can see incidents by clicking the arrow button.
smartsignal site model detail
SmartSignal – Site Model Detail

Site that you are on.

  • Incident List – This is a list of incidents on the models.
  • Chart View –The Blue line is the actual PI data and the Green line is the model data.
smartsignal reliant scope
SmartSignal-Reliant Scope

67 coal & natural gas power units across U.S.

Total 13,450 MW power

Rotating & non-rotating balance-of-plant assets monitored

411 assets, 1174 models

Turn 30K+ sensors to exception-based monitoring

Goals include

Early warning of equipment faults & process problems

Maximize availability

Minimize forced outages

Improvement of unit heat rates

reliant s virtual m d center
Reliant’s “Virtual” M&D Center

Staff of three working 5*8 at their own cubes:

Review WatchList

Email plants when equipment issues are detected

Tune

Build new models

Teach WatchList classes

Document issues and model changes.

Plant champions

Look at the WatchList

Use log

Deal with the easy ones

typical plant response
Typical Plant Response

The Plant Champion will commonly use Pi Processbook or Datalink to confirm the item and issue a work order

example of full cycle of a catch
Example of Full Cycle of a Catch

From:

Sent: Thursday, November 30, 2006 1:34 PM

This is a pretty significant movement on FD Fan Motor outboard bearing (about 17 deg above expected currently).

The oil levels are all good and the filters have been changed. The filters were dirty and the temps are dropping on the motor after the change out.

truth table logic
Truth Table (Logic)

SS Catch

SS Catch

False Catch

True Catch

SS no Catch

SS no Catch

No Machine issue

Machine issue

what is a good model so far
What is a Good Model So Far?

Modeling elements

It has catches (detects changes)

It has no misses (or few)

It uses as-found data

Human elements

It has engaged users

metrics
Metrics

True Catches

Look at the tracking log-equipment/instrumentation catches

Missed Catches

Long term – Use Power GADS to review major events

Was the system modeled? If not, should it be? Build it.

If modeled, review and improve the model.

No Catches & No Machine Problems

Could be a sign of a good program

Could mean just wait

False Catch

Track issues

Use log statistics and target problematic models

catches
Catches

The table shows true catches and false catches

*2006 catches not a full year

false catch
False Catch

SmartSignal alerts indicate a change from the model data set (State matrix)-

A repair could cause a change

A new season

An operational mode change

An abnormal configuration

Or bad modeling data

model with false alerts
Model with False Alerts

Detailed review of this model

data set found that it had two

modes. A and B with the same

Inlet temperature and A down

stream of B

model with false alerts1
Model with False Alerts

Detailed review of this model

data set found that it had two

spikes that were included in the

model.

human factors
Human Factors

How can acceptance be measured?

Is the user engaged?

What are good KPIs?

How can it be made “fair” for peaking and base load unit?

levels of smartsignal utilization
Levels of SmartSignal Utilization

basic level is where the WatchList Team (WLT) serves as an M&D center. Plant responds to WLT email questions about their units and WatchList items.

the engaged user logs into the WatchList at least once a week (on average).

The committed user logs in several times a week (on average). They also add information to the log and clear alerts from the WatchList.

The power user exceeds the committed user level by pressing model issues and providing a dialogue about known plant repairs and changes.

kpi issues
KPI Issues

Terms like basic, power user work well for a full-year but they did not track seasonal units well

We moved to a new metric that is much closer to the desired outcome– If you are running- login

ss login ratio
SS Login Ratio

“Average number of service hours between logins”

log entries
Log Entries
  • 2007
  • 50% of plants had no log entries
  • 78% of log entries were at one plant “C”
  • 2008
  • Only 10% have no log entries for 2008
  • Log entries more evenly spread across fleet
  • 119% increase in total log entries
lessons
Lessons

The knowledge of how the plant should run comes from how it has run (PI data)

Keeping the messenger alive is part of the task (instruments)

Eliminate variables (do not add them)

Model to detect changes

Exception-based

Statistics-based technologies are not common in plant environments.

Tools are not complete solutions – People still have to act