av-06 advanced analytics for predictive maintenance

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NOTICE: Proprietary and Confidential

This material is proprietary to Pattern Discovery Technologies Inc. It contains trade secrets and confidential information which is sole property of Pattern Discovery Technologies. Thismaterial shall not be used, reproduced, copied, disclosed, transmitted, in whole or in part, without the express written consent of Pattern Discovery Technologies Inc.

© 2013 Pattern Discovery Technologies Inc. All rights reserved.

Pattern Discovery Technologies Inc.

554 Parkside Drive,

Waterloo, ON

Canada N2L 5Z4

+1 (519) 888 1001 telephone

+1 (519) 884 8600 facsimile

www.patterndiscovery.com

Advanced Analytics for Predictive Maintenance

Oct. 1, 2014

Paul SheremetoPresident & CEO




1© 2013 Pattern Discovery Technologies Inc. All rights reserved.

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Agenda

Definitions

Trends – Drivers for Predictive Maintenance

The Internet of Things

Industrial Analytics / Predictive Modeling

AssetInsight ™

About Pattern Discovery Technologies




Definitions/Concepts

Predictive

Maintenance

Condition

BasedMaintenance

Condition

Manager

Asset Health

Monitoring

Advanced

AnalyticsBig

Data

Internet of

Things

PdM




Economics for Predicting Failure is Compelling

X

Failure Occurs

P r o b a b i l i t y o f F a i l u r e

Low

High

HighLow

D o l l a r s

$ x 1

$ x5

$ x10

Potential for Failure

Is Introduced

Uptime Magazine Dec10/Jan11

Page 21 – Figure 7

www.uptimemagazine.com

Plenty of time for

planning and

scheduling

Little or NO time for

planning and

scheduling

Time

UltrasonicVibration

Oil Analysis

Can Hear It

Can Smell It

Can See It

Hope you are

not too close!

The equipment will tell us if it is having problems before final failure…if we are listening!

Reactive repair and maintenance work

is 7x more expensive than planned

work

Awareness

< Risk< Capital Cost> Profitability




Trends – Drivers for Predictive Maintenance

Aging Workforce

Aging Infrastructure

Wireless Networks

Cloud Computing

Smart Devices

Mobile Computing

Internet of Things – M2M

Bottom Line:

We need to!

And we can!!!



Forecast for Industrial Analytics/Predictive Maintenance

ABI Research forecasts that revenues from maintenance analytics will total $9.1 billion this

year. Following a CAGR of 22%, the market’s size will reach $24.7 billion in 2019, driven

largely by adoption of predictive analytics and M2M connectivity. While the more

advanced forms of maintenance, predictive and prescriptive, still account for just 23% of

this year’s market, at the end of the forecasting period they will collectively represent 60%

of all revenues.

Senior analyst Aapo Markkanen comments, “Today, predictive maintenance is one of the

commercially readiest forms of M2M and IoT analytics, possibly second only to usage-

based insurance. It helps asset-intensive organizations transform their maintenance

operations and eliminate waste, reducing costly downtime. Infrastructure, vehicles, and

industrial equipment can all benefit from it.”

ABI Research, March 28, 2014



Industrial Analytics

Why/How did ithappen?

-----------------

Slice and Dice,

Hypothesis

Testing

What should we

do now?

-----------------

What-if Analysis,

Simulation

What’s the best

we can do?

-----------------

Data Mining,

Optimization

What will

Happen?

-----------------

Extrapolation,

Prediction

What Happened?

-----------------

Reports

What’s

Happening

-----------------

Dashboards,

KPI’s

Known

Info

New

Insights

Past Present Future

Intelligence

Solutions

Industrial

Analytics

Tim Sowell Blog – March, 2014



Industrial Analytics

Advanced Analytics for Predictive Maintenance

Historical

Data ModelRules Action

• Control Data

• Operational Logs

• Maintenance Records

• Diagnostic Information

• Production Data (ERP)

• Smart Sensors

• Procurement

• EAM

• CMMS

• Inventory

•

Energy• Environmental

I

N

S

I

G

H

T

S

C

O

N

T

E

X

T

Equipment Hierarchy

“Normal” Operation

Failure History

Pattern Hub™

• Data Mining

• Correlations

• Statistical Analysis

• Patterns

• Patterns That

Matter™

P

RE

D

I

C

T

I

O

N

S

• Associations

• Weight of

Evidence

• Transparent

Rules

• Probability

Discover*e™



Predictive Failure Modeling

ABCDEF

Historian (OSIsoft PI System) - Same Failure Over Past 5 Years

Pattern Discovery Event Detection Software

Suggested Failure Pattern



Semi-Supervised Learning

Surface events that may be of interest



AssetInsight ™

Consider Operations, Maintenance and Business Processes to improve equipment reliability and maintenance



AssetInsight ™ for Underground Mining Equipment



Predict the severity and location of Stress Corrosion Cracking (SCC)

in a pipeline to minimize environmental risk and guide maintenance

and repair activities.

Several factors combine to influence SCC

Environmental conditions (soil type, drainage,

temperature, exposure, etc.)

Stress loading due to pressures, temperatures and

flows (operational variables)

Material properties (pipe material, coating,manufacturer, inclusions, welds, etc.)

Prior maintenance and repair

AssetInsight - Failure Modeling for Pipeline Integrity Risk

Assessment Case study #1

IF wall thickness between (6.35, 7.14) AND soil type is tilled waterways AND topographic

pattern is leveled,

THEN severity = 3

IF soil code is 4 AND topographic pattern is inclined,

THEN severity = 2

Output – Predictive Models with Associated Rules for Interrogation and Interpretation

http://www.google.ca/url?sa=i&rct=j&q=stress+corrosion+cracking+pipeline&source=images&cd=&cad=rja&docid=JONUtUK9WChrwM&tbnid=EedufAt5Kq1fTM:&ved=0CAUQjRw&url=http://www.ngoilgasmena.com/article/Cracking-the-corrosion-code/&ei=HGwVUeDCBoPO0QGPoIGQDw&bvm=bv.42080656,d.dmQ&psig=AFQjCNHFbALFk7fsFCHLqH2E6NQRmWJFfg&ust=1360444691091463

http://www.google.ca/url?sa=i&rct=j&q=pipelines+spills+in+usa&source=images&cd=&cad=rja&docid=hC0MlJ7wRh1RVM&tbnid=PW2uLd5tWo8WoM:&ved=0CAUQjRw&url=http://www.popscreen.com/p/MTI5MzQ2OTQ2/Trans-Alaska-Oil-Pipeline-Alaska-USA-Photographic-Print-by-Waltham-&ei=YiwVUazBJuu30gHHsIHYBA&bvm=bv.42080656,d.dmQ&psig=AFQjCNFooyEbFo3bsE5K66vNqiZKB--2yg&ust=1360428411438371




• Spun out of the University of Waterloo (PAMI lab) in 1997 – Ontario,

Canada

• Core competency in data mining and predictive analytics

• Patented software suite – Discover*e

• Developers of Production Intelligence – an analytic framework to manage

and analyze data in complex industrial processes and equipment

• Primary focus on:

• ProcessInsight – oil sands and upstream processing

• AssetInsight - Equipment Reliability and Maintenance

• CompressionInsight – Compression evaluation for data historians (OSIsoft PI System)

• Partnership agreements with Schneider Electric, OSIsoft, Maerospace Ltd.,

Dean Wallace Consulting, Wireless Sensor Networks, Draeger Safety, Isaac

Instruments, Meir Soft Tissue Solutions,

• Joint Venture partnership in Beijing, China

Advanced

Analytics

Asset Performance

ManagementFor

+




Discover*e from Pattern Discovery

1. High order association discovery*

2. Pattern synthesis and data grouping*3. Pattern pruning and modeling

4. High order temporal pattern discovery

and analysis

5. Unique interactive visualization methods

1. Hyperbolic data visualizer

2. Hyperbolic tree visualizer

3. Data matrix

4. RouteMap™ association

visualizer

6. Intelligent Data Design

7. Natural Language Processing

Patented and proprietaryalgorithms on exploratory data

mining and predictive analytics as

the result of years of research…

… and a wide range of advanced modeling techniques

• ARMA

• CART

• CIR++

• Compression Nets

• Decision Trees

• Discrete Time Survival Analysis

• D-Optimality

• Ensemble Model

• Gaussian Mixture Model

• Genetic Algorithm

•

Gradient Boosted Trees• Hierarchical Clustering

• Kalman Filter

• K-Means

• KNN

• Stochastic modeling

• Multiple Linear Regression

• Logistic Regression

• Monte Carlo Simulation

• Multinomial Logistic Regression

• Neural Networks

• MDS

• Bayesian Networks

• SOM/Kohenen Nets

• Optimization: LP; IP; NLP

• Poisson Mixture Model

•

Projection on Latent Structures• Restricted Boltzmann Machine

• Sensitivity Trees

• Spectral Graph Theory

• SVD, A-SVD, SVD++

• SVM

PDT uses a basket of Machine Learning techniques to discover thehidden relationships within data flows




INSIGHT DELIVERY

INTELLIGENT

ETL

CONTEXTUAL

ELEMENTS

DISCOVER*E

ANALYTICS

DATA SOURCES

i N S I G H T

D A T A

A C T I O N

P R O D U C T I O N

I N T E L L I G E

N C E

Production Intelligence Platform

Production intelligence suite will emerge into a common platform that delivers management, performance and

operation insights to the manufacturing industry in a number of vertical applications

Internal/External Structured Data Internal/External Unstructured Data

Extract Profile Cleanse Link Merge Bundle Load

Association

Discovery Clustering Classification Visualization

Induction/

Segmentation

EnvironmentalInsightEnergyInsightAssetInsightProcessInsight

Material

Flow Data

Model

Signal

Processing

Event

Detection

Natural

Language

Processing

Equipment

Hierarchy

Relationships

Feature

Selection/

WOCS



© 2013 P tt Di T h l i I All i ht d

Waterloo Calgary Beijing Singapore

Questions and Contact Information

Paul Sheremeto

President and CEO

[email protected] 519-888-1001 x249

mailto:[email protected]

mailto:[email protected]

http://www.google.ca/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&docid=-Hy4PQYy9mexFM&tbnid=qA2p4o7COXm3VM:&ved=0CAcQjRw&url=http://realtrends.com/blog/the-power-of-question-based-selling&ei=McwhVMvvLISRNtSqgogH&bvm=bv.75775273,d.eXY&psig=AFQjCNH-Kj_qoxAhEIBFatMEP4RKhpnUkw&ust=1411587297489651

av-06 advanced analytics for predictive maintenance

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