pattern discovery technologies inc. 554 parkside drive...

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. This material 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 Sheremeto

President & CEO

Pattern Discovery Technologies Inc.

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 Based

Maintenance

Condition Manager

Asset Health Monitoring

Advanced Analytics

Big Data

Internet of Things

PdM


Economics for Predicting Failure is Compelling

X

Failure Occurs

Pro

ba

bili

ty o

f F

ailu

re

Low

High

High Low

Do

llars

$ 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

Ultrasonic Vibration

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 it happen?

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


Tim Sowell Blog – March, 2014



Advanced Analytics for Predictive Maintenance

Historical Data Model

Rules Action

• Control Data • Operational Logs • Maintenance Records • Diagnostic Information • Production Data (ERP) • Smart Sensors • Procurement • EAM • CMMS • Inventory • Energy • Environmental

INSIGHTS

CONTEXT

Equipment Hierarchy “Normal” Operation

Failure History Pattern Hub™

• Data Mining • Correlations • Statistical Analysis • Patterns • Patterns That

Matter™

PREDICTIONS

• Associations • Weight of

Evidence • Transparent

Rules • Probability

Discover*e™


Predictive Failure Modeling

A B C D E F

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=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

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


Pattern Discovery Technologies Inc.

• 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

Management For

+


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 proprietary algorithms 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 the

hidden relationships within data flows


INSIGHT DELIVERY

INTELLIGENT ETL

CONTEXTUAL ELEMENTS

DISCOVER*E ANALYTICS

DATA SOURCES

iNSI

GH

T D

ATA

A

CTI

ON

PR

OD

UC

TIO

N

INTE

LLIG

ENC

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

EnvironmentalInsight EnergyInsight AssetInsight ProcessInsight

Material Flow Data

Model

Signal Processing

Event Detection

Natural Language

Processing

Equipment Hierarchy

Relationships

Feature Selection/

WOCS


Waterloo Calgary Beijing Singapore

Questions and Contact Information

Paul Sheremeto President and CEO

[email protected] 519-888-1001 x249

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

mailto:[email protected]

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