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

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… 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

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Find an innovative way to

localize leaks using internal and

external data sources

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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)

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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…

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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

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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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