Predictive maintenance is een van de big-datatoepassingen met enorme potentie. Voor Vitens, het grootste waterbedrijf van Nederland met meer dan 5,5 miljoen klanten, toonden CGI en IBM in een proof of value aan dat sneller en nauwkeuriger lekken lokaliseren in potentie miljoenen kan besparen. De primaire taak van Vitens is ervoor zorgen dat klanten te allen tijde kunnen beschikken over topkwaliteit drinkwater. Met een netwerk van meer dan 49.000 km relatief oude pijpleiding, is het kostenefficiënt onderhouden van het netwerk een voortdurende uitdaging. Veelal wordt gekozen voor preventief onderhoud waardoor pijpleiding vaak eerder wordt vervangen dan strikt nodig is. Desondanks treden er regelmatig lekken op met soms grote schade en bedreiging van de leveringszekerheid. Het lokaliseren van lekken gebeurt handmatig, wat veel tijd en geld kost omdat het zoekgebied vaak kan oplopen tot tientallen kilometers. Vitens vroeg CGI en IBM om met behulp van een big-datatoepassing een methode te ontwikkelen voor het lokaliseren van lekken. In een proof of value werd historische data geanalyseerd waarbij de helft van de geanalyseerde lekken tot op 2,5 km nauwkeurig kon worden gelokaliseerd. Door sneller lekken te lokaliseren of zelfs te voorspellen, kan Vitens niet alleen direct besparen op inzet van medewerkers voor lokalisatie en bezetting van het callcenter. Het maakt het ook mogelijk om de effectieve levensduur van pijpleidingen te verlengen of, bij minder kritische delen van het netwerk, zelfs te kiezen voor de maximale levensduur waarbij pas leiding pas wordt vervangen bij het daadwerkelijk optreden van lekken.

1. Predictive and Business Intelligence Predictive Maintenance and Quality
© 2014 IBM Corporation
IBM Predictive Maintenance and Quality
Wannes Rosius
September 18, 2014

2. Predictive Maintenance and Quality converges enterprise asset management and analytics capabilities
Analytical insights
Asset lifecycle manage- ment
Facilities operation
Staff planning
Supply chain processes
•Better maintenance windows to reduce operating expense
•More efficient assignment of labor resources
•Enhanced capital forecasting plans
•Enhanced spare parts inventory
•Automated analytical techniques, including anomaly detection for assets and sensors
•Improved reliability and uptime of assets
•Asset maintenance history
•Condition monitoring and historical meter readings
•Inventory and purchasing transactions
•Labor, craft, skills, certifications and calendars
•Safety and regulatory requirements
Enterprise asset management
Predictive Maintenance and Quality
Better outcomes
=
+

3. Analytics is a key enabler in maximizing asset productivity and process efficiency
Source: Aberdeen Group. Asset Management: The Changing Landscape of Predictive Maintenance. Mar 2014.
Figure 1: Best-in-Class companies leverage all technology enablers to enhance outcomes
“The number of companies that leverage predictive solutions has almost doubled from 17% in 2012 to 32% in 2013 and we expect it to reach 46% by end of this year. Many of these projects focus on better insights around physical assets which is a natural and critically important starting point into predictive for most companies.”
- Dr. Holger Kisker, VP & Research Director, Forrester Research, Jan. 2014

4. Predictive Maintenance and Quality provides several key features
Accelerated
Time to Value
Advanced Quality
Algorithms
Open Architecture
Big Data, Predictive Analytics, Business Intelligence Real-time Capabilities Quick and Accurate Decisioning
Maximo
Integration

5. © CGI Group Inc. 2014
Predictive maintenance Met big data het lek dichten
IBM Business Analytics Summit 2014
Bussum, September 18, 2014
Tim van Soest, Jan-Willem Lankhaar

6. 7
Costly/critical assets
Plan
Specs, risk, replacement cost,
downtime cost, resources
Maintain
Periodic maintenance
Respond reactively to failure
Costly/critical assets
Model-based planning
Specs, risk, replacement cost,
downtime cost, resources
asset history, usage, failure history, environmental factors, failure predictors, spare part and resource availability,
optimal downtime window
Maintain dynamically
Targeted maintenance
Proactive maintenance
Conventional maintenance
Predictive maintenance

7. 8
Costly/critical assets
Plan
Specs, risk, replacement cost,
downtime cost, resources
Maintain
Periodic maintenance
Respond reactively to failure
Costly/critical assets
Model-based planning
Specs, risk, replacement cost,
downtime cost, resources
asset history, usage, failure history, environmental factors, failure predictors, spare part and resource availability,
optimal downtime window
Maintain dynamically
Targeted maintenance
Proactive maintenance
Conventional maintenance
Predictive maintenance
Big data analytics
Volume
Variety
Velocity
Veracity

8. Why predictive maintenance?
•Prevent unnecessary maintenance
•Maximize effective asset lifetime
•Prevent failure and downtime
Put simply…
•Save costs
•Increase customer satisfaction
9

9. … but, it’s not plug-and-play
Key elements
•An analytical model for your specific situation
•The right data
Proof of Value
•Part of Data2Diamonds® proposition
•Answer specific question in CGI’s big data lab
•Use client’s own data
•Short period of time, low risk
10

10. 5.5 million customers
106 sourcing areas (3,000 ha)
96 production sites
49,000 km pipes
350 million m3 water
•Largest drinking Dutch water company
•Globally active in development projects
11

11. Leakages in water pipes
Leaks
… threaten delivery reliability
… lead to high call centre load
… cause much collateral damage
… cause water quality loss
•Detecting leaks is difficult
•It takes long to localize leaks
12

12. Leak of March 12, 2013
13

13. The challenge for the Proof of Value
14
Find an innovative way to localize leaks using internal and external data sources

14. 15
Project at a glance
•Combined analyses approach on variety of (streaming) data
•Heat maps for leakage localization
•Significant improvement in localization accuracy
Geospatial analysis
Time series analysis
Modelling
}
Heat map

15. Automatic localisation = machine learning
Model
1
Train model Predict outcome
Adjust model until predictions fit
actual outcome
Historical data (known outcome)
=
?
2
Test model
Candidate
model
Predicted outcome
Historical data (known outcome)
=
?
Other
data
3
Use model
Model
Predicted outcome
New data
(Unknown outcome)

16. Machine learning approach
17
ID
t
Location
attr1
…
attrN
Outcome
variable
id
pressure, flow, etc.
material
tree
Leak y/n
case
Split into train and testset
Model

17. Step 1: Analyse outcome variable
18
2 Geo data to table structure 3 Time series to table structure
1 Outcome variable
4 Modelling
Model
5 Results

18. Distance to leak as outcome variable
19
Leak
Station 1
Station 2
Station 3
Distance to leak
Every station-leak combination is a record
Increase number of cases

19. Step 2: Geo data to table structure
20
2 Geo data to table structure
3 Time series to table structure
1 Outcome variable
4 Modelling
Model
5 Results

20. buffer
bounding box
(xmin, ymin)
(xmax, ymax)
network
reference mask
grid
id
1
2
3
4
…
6
7
14
Vector map(points)
shapes in scope

21. Graticulated sources
NRM (pipe topology)
InfoWorks (hydraulic model)
KLIC (digging activities/works)
BAG (residential objects)
TOP10 (landscape elements)
SAP (failures)
OSIsoft PI (pressure, flow, conductivity, temperature)
22

22. Step 3: Time series analysis
23
2 Geo data to table structure
3 Time series to table structure
1 Outcome variable
4 Modelling
Model
5 Results

23. Time series: a repeated series of measurements
24
Time
Temperature
Interesting event
Extract only interesting
events or patterns
Average of last 24 h

24. …but what is an interesting pattern?
Find your day of birth in the decimals of pi…
25

25. Not all patterns or events are relevant Water demand Netherlands-Mexico June 29, 2014
26
Break
End of match
Additional drinking breaks

26. Lots of techniques for time series analysis
•Descriptive (average, standard deviation etc.)
•Trend analysis
•Combine signals (using domain knowledge)
•Spectral analysis
•Modelling/prediction
•Filtering
•Wavelet analysis
•…

27. Vitens: 200 original and derived signals
28
p
Station
F / p
F
G
T
…
About 200 original and derived signals

28. Step 4: Modelling
29
2 Geo data to table structure
3 Time series to table structure
1 Outcome variable
4 Modelling
Model
5 Results

29. Predicting the distance to a leak
•Using the input variables, predict the distance to a leak from a station
•Every station-leak combination is a case
•Construct circles around the stations
30
Regression
model
significant
predictors
Predicted
distance
Construct circles around station
Heat map
Hot spots indicate leaks
all possible
predictors
Feature selection

30. Step 5: Results
31
2 Geo data to table structure
3 Time series to table structure 1 Outcome variable
4 Modelling
Model
5 Results

31. An example
32

32. 0
2
4
6
8
10
12
0-500
500-1000
1000-2500
2500-5000
> 5000
Number of leaks
Distance to hot spot (m)
Results
33
< 2.5 km
50% within 2.5 km
Search area may exceed 30 x 30 km in manual localisation

33. Hard- and software in our lab
IBM Netezza appliance, hosted by BP Solutions
Netezza and
SPSS Modeler
High performance with database pushback

34. •Increased delivery reliability
•Higher customer satisfaction
•Less fluid quality loss
•Reduced call centre load
•Reduced staffing deployment
•Less collateral damage
•Localise leaks rapidly
•Support operator decisions
Faster leak detection and localisation
means faster bypassing or repairing leaks
35
Making the case for predictive maintenance

35. Our commitment to you We approach every engagement with one objective in mind: to help clients succeed
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