ba summit 2014 predictive maintenance: met big data het lek dichten
DESCRIPTION
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.TRANSCRIPT
Predictive and Business Intelligence Predictive Maintenance and Quality
© 2014 IBM Corporation
IBM Predictive Maintenance
and Quality
Wannes Rosius
September 18, 2014
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 = +
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
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
© 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
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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
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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
Why predictive maintenance?
• Prevent unnecessary maintenance
• Maximize effective asset lifetime
• Prevent failure and downtime
Put simply…
• Save costs
• Increase customer satisfaction
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
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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
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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
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Leak of March 12, 2013
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The challenge for the Proof of Value
14
Find an innovative way to
localize leaks using internal and
external data sources
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
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)
Machine learning approach
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ID t Location attr1 … attrN Outcome
variable
id pressure,
flow, etc. material tree Leak y/n
case
Split into train
and testset
Model
Step 1: Analyse outcome variable
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2 Geo data to table structure
3 Time series to table structure
1 Outcome variable
4 Modelling
Model
5 Results
Distance to leak as outcome variable
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Leak
Station 1 Station 2
Station 3
Distance to leak
Every station-leak combination is a record
Increase number of cases
Step 2: Geo data to table structure
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2 Geo data to table structure
3 Time series to table structure
1 Outcome variable
4 Modelling
Model
5 Results
buffer
bounding box
(xmin, ymin)
(xmax, ymax)
network
reference mask
grid
id
1 2 3 4 … 6 7
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Vector map(points)
shapes in scope
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
Step 3: Time series analysis
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2 Geo data to table structure
3 Time series to table structure
1 Outcome variable
4 Modelling
Model
5 Results
Time series: a repeated series of measurements
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Time
Temperature
Interesting event
Extract only interesting
events or patterns
Average of
last 24 h
…but what is an interesting pattern?
Find your day of birth in the decimals of pi…
25
Not all patterns or events are relevant
Water demand Netherlands-Mexico June 29, 2014
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Break
End of match
Additional drinking breaks
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
• …
Vitens: 200 original and derived signals
28
p
Station
F / p
F
G
T
… About 200 original and derived signals
Step 4: Modelling
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2 Geo data to table structure
3 Time series to table structure
1 Outcome variable
4 Modelling
Model
5 Results
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
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Regression
model
significant
predictors
Predicted
distance
Construct circles
around station
Heat map
Hot spots
indicate leaks
all possible
predictors
Feature
selection
Step 5: Results
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2 Geo data to table structure
3 Time series to table structure
1 Outcome variable
4 Modelling
Model
5 Results
An example
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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
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< 2.5 km
50% within 2.5 km Search area may exceed
30 x 30 km in manual
localisation
Hard- and software in our lab
IBM Netezza appliance, hosted by BP Solutions
Netezza and
SPSS Modeler
High performance
with database
pushback
• 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
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Making the case for predictive maintenance
Our commitment to you We approach every engagement with one
objective in mind: to help clients succeed
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