Source: http://sparkcognition.com Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines.

1. Machine Learning & Cognitive Fingerprinting Delivers Next
Generation Analytics to Improve Safety, Performance and
Reliability of Assets
STUART GILLEN, BUSINESS DEVELOPMENT DIRECTOR, @THINGSEXPO 2016

2. SparkCognition is deploying a Cognitive, data-driven Analytics
platform for the reliability, safety and security of the Industrial Internet
Company Overview
Launched:
April 2014; CEO: Amir Husain – Serial entrepreneur with
40+ awarded/pending patents, advisor to IBM on Cognitive
Computing/Watson
Advisors:
• Prof. Bruce Porter: Chair UT Austin Computer Science, AI Faculty
• Bob Stearns: Fmr. CTO Compaq & MD Sternhill Partners
• Dr. Tom Bradicich: VP Server R&D, HP & former IBM Fellow
• Michael Humphrey: Former VP of BD for Broadcast.com, Yahoo!
• Mike Frost: Founder of Techworks, SiteControls/SureGrid
Investors:

3. We’re an Austin-based Cognitive Analytics
Company
Partners
Top Global
Exchange
F250 Energy
Multi Billion
Energy
F250 Energy F250 Energy
Multi Billion
Energy
Multi Billion
Energy
F250 Fin.
Service
Government
F500 Retail F1000 Exchange
Select Customers
E-learning
F100 Aviation

4. Agenda
 Why Cognitive Analytics
 Machine Learning Basics
 Improving Asset Health through Predictive Models Example
 NLP Augmenting the “Cognitive Approach”
 Questions

5. Why should you consider Cognitive
Analytics?

6. “
”
Global Data Power estimates the
maintenance expenditure on wind
turbines vital to productivity is
expected to rise from $9.25B in 2014
to $17B in 2020
http://www.edie.net/news/6/Win-turbine-maintenance-costs-to-nearly-doubl/

7. Asset prognostics can’t scale to the Internet of Things
Amount of Data being collected is increasing exponentially
 Value from data is still tied to expert
resources that can analyze the data
 Physical Domain approaches are hard to
replicate across different assets and
operating conditions
 “Big Data” challenges can get in the way
 Combining data which might be relevant
but stored in different places / formats
adds to complexity
Data Intelligence Gap
Amount of Data
# of Data Scientists

8. NLP: Natural Language Processing
Processes
Information
Draws Conclusions Codifies Instincts &
Experience into Learning
Enables
machines to
penetrate the
complexity of
data to identify
associations
Presents
powerful
techniques to
handle
unstructured
data
Continuously
learns not only
from previous
insights, but also
for new data
entering the
system
Provides NLP
support to
enable human to
machine and
machine to
machine
communication
Does not require
rules, instead
relies on
hypothesis
generation built
on analyzed data
Cognitive Analytics inspired by the way the human brain operates:

9. Why Machine Learning and Cognitive Analytics?
External Factors
Can incorporate external factors
Scalability
Automated model building capability does not
require manual model building of every
asset/component
In-context Remediation
Advisor that understands natural
language to help technical teams
Security
Out-of-band, symptom-sensitive
approach beyond IT security
Adaptability
Adapts to new and changing
conditions automatically
Higher Accuracy
Automated feature enrichment and
extraction that can deliver better
insights and higher accuracy

10. With New Machine Learning Technologies The
Future Is Now
Leveraging new machine learning technologies provides
opportunity for step-change in approach for equipment
performance and reliability
Traditional maintenance & reliability programs have
done a great job advancing reliability, but at many sites
our long time customers are asking what’s next?
12-24 MTBF
• Reactive
• Minimal and
disparate system
• Victim mentality
18-36 MTBF
• Some planning and
scheduling
• Formal and informal
systems
• Dabbles in
Predictive
Maintenance (PdM)
• Lack of documented
asset management
strategies
36-60 + MTBF
• Documented
Strategic Body of
Knowledge (BoK)
based approach
• Leverages
Predictive
Maintenance
• Disciplined
behavior
80 + MTBF
• Enterprise
approach
• Exploits
technology
• Flexible market
capture
120 + MTBF
• Full and robust
life cycle
management
• Culture driven

11. Machine Learning Basics

12. How do you label these?
Unsupervised Learning

13. Unsupervised Learning
SM
MD
LG

14. Supervised Learning
WH
GR
BL

15. Unsupervised vs. Supervised Learning
Unsupervised Supervised
Index Date Time
Asset
ID Value
2 5-Apr-10 7:01 750 89
93 22-Mar-13 8:19 904 79
27 20-Oct-14 8:26 545 74
5 10-Jul-12 7:38 552 86
68 15-Sep-11 8:13 942 74
29 1-Jun-11 8:44 900 72
91 20-Jul-11 7:14 587 50
54 12-Jul-10 7:36 765 95
20 5-Sep-14 8:25 813 39
44 30-Jun-11 7:07 983 71
100 5-Oct-12 7:35 802 34
66 12-Mar-10 7:39 726 47
45 6-May-11 7:30 973 98
84 10-Dec-12 7:17 504 68
43 9-Jul-14 8:07 567 74
Action Taken Component
Repair Blade
Unknown Blade
Repair Gearbox
Replaced Gearbox
Replaced Gearbox
NTF Generator
Good Generator
NTF Blade
Repair Generator
NTF Gearbox
NTF Blade
Repair Gearbox
Unknown Gearbox
Repair Blade
Repair Gearbox

16. Examples

17. “
”
It is estimated that in 2011, nearly $40
billion worth of wind equipment in the
U.S. will be out of warranty, thrusting
the financial risk on the owner to
provide cost-effective operation and
maintenance.
http://www.renewableenergyfocus.com/view/26582/wind-getting-o-m-under-control/

18. About
 Develops, Owns, and Operates Power
Generation and Energy Storage Units
in US and Europe
 North America’s largest independent
wind power generation company
 Currently operating over 4GW of wind
Headquarters
Regional Office
Wind Project
Natural Gas
Solar Project
Storage

19. Gearbox Monitoring Application Trial
 Desired Results
 Predict gearbox failures with 30-60 day advanced notice
 Zero or minimal false positives
 “Dummy Light” output
 Data Provided
 4 years of historical data from site of ~100 turbines
 27 data variables at 10 minute resolution, no vibration variables collected
 Major component failure logs

20. Generated Prediction Signatures for all Catastrophic Gearbox
Failures
Risk Index for Gearbox Health
• Impending failure (red alert)
prediction for catastrophic failure >
1 month
• Advanced degradation warning
(amber warning) for failures is > 2
months
• We had zero false positives, that is
no alerts were raised which did not
have a failure follow
• We had zero false negatives, that is
no failures were missed
6
7 3520
40
60
Days of Warning
500
1000
67 Days
35 Days
Results

21. Output Options
Overall Fleet Health Detailed Asset View

22. Natural Language Processing Empowering
Safety

23. Answering complex questions requires more than
keyword evidence
This evidence suggests
“Gary” is the answer
BUT the system must
learn that keyword
matching may be weak
relative to other types of
evidence
Legend
Keyword “Hit”
Reference Text
Answer
Weak evidenceRed Text
Question: Supporting Evidence:
explorer
India
In May
1898
India
In May
celebrated
anniversary
in Portugal
In May, Gary arrived in India after
he celebrated his anniversary in
Portugal
400th
anniversary
celebrated
Gary
In May 1898 Portugal celebrated the
400th anniversary of this explorer’s
arrival in India
arrived in
Portugal
arrival in

24. Watson leverages multiple algorithms to perform
deeper analysis
Para-
phrases
Stronger evidence can be
much harder to find and
score…
Search far and wide
Explore many hypotheses
Find judge evidence
Many inference algorithms
On the 27th of May 1498, Vasco da
Gama landed in Kappad Beach
400th anniversary
Portugal
May 1898
celebrated
In May 1898 Portugal celebrated the 400th
anniversary of this explorer’s arrival in
India.
Legend
Temporal Reasoning
Reference Text
Answer
Statistical Paraphrasing
GeoSpatial Reasoning
Question: Supporting Evidence:
27th May 1498
Vasco da
Gama
landed in
arrival in
explorer
India Kappad Beach
Date
Match
Geo-KB

25. Training
Injury Description
SIMS – Injury Records
Unstructured Text to Semantic Features
Semantic Features to Knowledge Representation for
A specific Type of Injury
Safety
INPUT
injury
knee
lifting
Advanced Text MiningKnowledge Representation

26. Predicting | Advanced Auto-Fill Classifier Near Miss > Type of Future Injury
INPUT
NEW INPUT
Uncategorized
Near Miss Description
Knowledge Representation
Predict / Classify
Unknown Categories
Injuries
Near Misses
(1) Classification of Near Misses can be used to help prevent injuries by:
• Identifying and focusing on the most common injury types and
activities associated with those injury types
• Assess locations prone to these types of injuries
• Estimate most probable time of day occurrence of injuries
Model of various types of observed injuries
Change from being reactive to acting on most likely future injuries

27. Predicting | Case Study: Knee Injury
Found
50%
More Knee
Injuries
Change from being reactive (63) to acting on most likely future injuries (95)
669
Observed Injuries
6961
Near Misses

28. NLP can “understand” documents such as maintenance and
injury reports
ID: XXXX
Time11/04/2012 13:03
Confidence: 99%
Building Owner: XXXXXXXXX
Actions: Had a meeting with the tech and
talked about what happened
Description: While technician was driving to
site on services rod to site the technician
heard a thump when he looked in the
passenger side mirror he saw that a deer
had ran in to the side of the truck. There
were no injuries to the technician. there was
damage to the passenger side door.
ID: XXXX
Time21/05/2013 22:15
Confidence: 97%
Building Owner: XXXXXXXXXXX
Actions: NA
Description: While traveling SW on XXXXXXX Rd ,
an animal (believed to be a dog) ran out in the
road ahead of me , causing me to swerve to the
right , damaging the right front rim and right
front lower bumper on the curb of the road. No
other injuries occurred.
Question entered real time
NLP engine provides
immediate list answering the
question asked, details can
seen by clicking on list
entries

29. Cognitive Search of free natural language text
 Smarter search to include different and holistic terms
 Keyword search: Lower body injuries in free form results in 534 incidents
 Semantic search: Lower body injuries in free form results in 1027 incidents (leg, foot,
toe etc. injuries and non-injuries)
 Cognitive Search: Lower body injuries result in 347, which is more accurate
 Remove incorrect references to body parts: “foot” as a measurement, “toe of a board”
 Focus on references to body parts in the context of injuries
 Example Search: “Find incidents involving employees driving into animals”
 Picked the incident with the following text without any mention of driving or animals
 Coming to work in the dark and icy conditions , a deer ran in front of my vehicle. Was difficult
to stop without sliding off the road.

30. www.sparkcognition.com
4030 W. Braker Lane, Suite 200
Austin TX 78730
Stuart Gillen
Director, Business Development
sgillen@sparkcognition.com