AV-06 Advanced Analytics for Predictive Maintenance
Transcript of 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
Pattern Discovery Technologies Inc.
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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
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Definitions/Concepts
Predictive
Maintenance
Condition
BasedMaintenance
Condition
Manager
Asset Health
Monitoring
Advanced
AnalyticsBig
Data
Internet of
Things
PdM
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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
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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!!!
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The Internet of Things
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The Internet of Things
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The Internet of Things
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The Internet of Things
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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
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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
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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™
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Predictive Failure Modeling
ABCDEF
Historian (OSIsoft PI System) - Same Failure Over Past 5 Years
Pattern Discovery Event Detection Software
Suggested Failure Pattern
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Semi-Supervised Learning
Surface events that may be of interest
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AssetInsight ™
Consider Operations, Maintenance and Business Processes to improve equipment reliability and maintenance
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AssetInsight ™ for Underground Mining Equipment
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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
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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
ManagementFor
+
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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
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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
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© 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