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. 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. Predictive Futures
Cognitive Analytics

2. Todays Speakers
Stuart Gillen
Director,
Business Development
Maggie Pakula
Manager,
Performance Analysis
Engineering
John Hensley
Manager,
Industry Data & Analysis

3. “
”
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/

4. “
”
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/

5. Cost of Gearbox Failures
u Romax Study estimated cost of
planetary bearing failures
>350k[1]
u In 2014 Siemens wrote down
€223M to replace bearings in
fleet <2 yrs. old[2].
u Controlling wind turbines with
data-driven software could,
models show, increase energy
production by at least 10% and
gains of 14-16% are possible[3]
u The average gearbox failure rate
over 10 years is estimated at
5%[4].
1 0.75 0.5 0.25 0 2 4 6 8
Electrical Systems
Electronic Control
Sensors
Hydraulic Systems
Yaw System
Rotor Brake
Mechanical
Brake
Rotor Hub
Gearbox
Generator
Supporting Structure/Housing
Drive Train

6. Use Case

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

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

9. Generated Prediction Signatures for all Catastrophic Gearbox Failures
Risk Index for Gearbox Health
Trial Outcomes
• Impending failure (red alert)
prediction for catastrophic
failure > 1 month
• Advanced degradation warning
(amber alert) 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
For 100 Turbines
67
352
0
4
0
6
0Days of Warning
50
0
1000
67 Days
35 Days

10. Output Options
Overall Fleet Health Detailed Asset View
OR
Connect output to
existing systems
GMS
SCADA
Customer User Interface

11. Some secondary observations
u Other failures (likely blade), have very short failure signatures
u Failure prediction made in days compared to longer signatures for
gearbox failures
u Seasonality of Gearbox failures
u Catastrophic gearbox failures show high correlation to seasons
u Most failures in second half of the year: Q1 – 1, Q2 – 1, Q3 – 9, Q4 –
7
u Other failures relatively independent of seasons
u Catastrophic failures distributed more uniformly: Q1 – 3, Q2 – 4, Q3
– 5, Q4 – 5

12. Next Steps
u Expand to 5 sites
u Pending results of expanded sites, committed to enterprise wide
roll-out
u Explore predictive models for other major components

13. Using Machine Learning and Cognitive
Fingerprinting™ to Drive Results

14. Category Key Features
Business Intelligence (BI)
• Centralizedanalysis
• Uniform data collection
• Average visualizations
Rules Based Modeling
• Fixed rules must account for all types of transactions in all
types of conditions; lead to rule proliferation and
management challenges
• May be good measures for some simple situations, but
average (or even sub-par) measures for others
Statistical Analysis
• Identifies deviations from “normal”
• More a platform for model building and data scientists
than an alert generating solution
• Not automated to account for changing conditions
Physics Based Modeling
• Asset-type specific
• Model building Is a very hands-on process involving
laboratory experiments
• Domain experts apply these physical models universally to
assets
Common Approaches

15. 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 using multiple data sets
which might not always appear connected or relevant
Benefits of Cognitive Analytics
NLP: Natural Language Processing
Cognitive Analytics is inspired by the way
the human brain operates:
Processes
Information
Draws
Conclusions
Codifies Instincts &
Experience into Learning

16. Basics of Machine Learning

17. How do you label these?
Unsupervised Learning

18. Unsupervised Learning
SM
MD
LG

19. Supervised Learning
WH
GR
BL

20. 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

21. The SparkCognition Methodology-
Cognitive Fingerprinting™

22. Our Algorithms
Artemis
• Proprietary regularization toolfor
feature selection
• Automated class balancing
• Automated model selection
• Automated checks on overfitting
• Turn-key solutions with health
index for industrial use cases
Iris
• Proprietary clustering algorithm
• Optimal clustering of data leading
to state generation
• Semantic indexing of states
• Classification from indexed states
• Turn-key solutions with health
index for industrial use cases
Pythia
• Proprietary regularization tool
for feature selection
• Genetic algorithms for
optimizing neural networks

23. Cognitive Algorithms-SparkArtemis™
Overall Vibration
MAX Temp
Min Temp
Tensile Force
Shear Strength
First Order Features Second Order Features
Wavelets
Enveloping
Joint Time Frequency
Double Integration
Created Feature n
Third Order Features
Crest Factor
Integration
Running Average
Cauchy Stress Tensor
Created Feature 1

24. Cognitive Algorithms-SparkPythia™
Artemis
Artemis FeaturesTake Artemis features
Captures the state of and
evolution to failure/event
including subtle influencers
Start Neural net genetic comp
Predict Based on a Function
Significantly advanced compared
to existing algorithms
Feature Selection
Automatically find significant data
Adaptive & Self-learning
Identify multiple top performers
Define Relationships

25. Cognitive Algorithms-SparkPythia™

27. Cognitive Algorithms-SparkIris™
Which is Better?
…
Model 1 Model 1 Model 1 Model 1 Model n

28. Cognitive Algorithms-SparkIris™
Answer: Neither
Model 1
Model 2
Model 3

29. Objectives
Monitor Critical Assets during
startups and coast-downs
Predict Remaining
Useful Life
Analyze failures, alert on
impending failures, optimize design
Client
Asset
Big Utility
Turbine Generator
Big Utility
Wind Turbine
On-shore driller
Electrical Submersible Pump
• Data collection from multiple
assets
• Detects failures, graduating to
predictions
• Self-learning system with access
to in-context advisory powered by
IBM Watson
• RUL (Remaining Useful Life)
prediction and anomaly detection
• Automated model building,
selection & management
• Insights through deeper-order
analyses
• Failure identification and
classification
• Automated failure alerting
• Critical variable identification
• Design and process optimization
to reduce specific failures
Solution
Feature
Business
Impact
• Estimated increase in productivity
of 25% –30%
• 50X ROI
• Estimated savings of ~40% in
O&M budgets
• ~$2MM per year for 100 MW
power generation plant (wind),
40X ROI
• 3X increase in life of ESP through
proper monitoring and design
• Savings of up to $150,000 per
asset per year, 50X ROI
Other Energy Sector Applications

30. Use Case -Improve Safety and
Reduce Remediation Cost
Through Intelligent Prognostics
u SparkCognition has developed an IBM Watson
“Advisory” application for Asset Maintenance
u SparkCognition’s powered by IBM Watson will allow
Directors of Maintenance and technicians to:
§ Conduct machine to human dialogue to troubleshoot
fault codes
§ Predict impending failures and faults
§ Identify the right fault codes and troubleshooting tips
using natural language queries
§ Find solutions to problems and advise technicians
§ Optimize work flow and deliver relevant
documentation for a faster turnaround

31. Machine Learning & Cognitive Analytics
can deliver several benefits
External Factors
Can incorporate external factors (e.g.
environmental issues such as birds &
bats)
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

32. Questions