Presented at the IEEE Rock Stars of Big Data event in San Jose, CA, October 2015

1. Big Data, Physics, and the
Industrial Internet
How Modeling Analytics are Making the World Work Better.
Matt Denesuk
Chief Data Science Officer
GE Software
October 2014
Imagination at work.
Contact: matthew.denesuk@ge.com
© General Electric Company, 2014. All Rights Reserved.

2. What’s this all about?
Industries that are all about
data IT see outsized
productivity performance
gains
• Telecom, financial srvcs,…
2
Making industrials all about data
IT will transform how the world
works
• Power, water, aviation, rail, mining, oil
gas, manufacturing, …
And Big Data + Physics is the enabler

3. What happened when 1B people
became connected?
Entertainment
digitized
[ ]
© General Electric Company, 2014. All Rights Reserved.
Social
marketing
emerged
Communications
mobilized
IT architecture
virtualized
Retail ad
transformed
Consumer
Internet
[ ]
[ ]
[ ]
[ ]

4. Now what happens when 50B Machines
become connected?
Industrial
Internet
Brilliant
Power
Logistics
Optimization
Brilliant
Factory
Factory
Optimization
Smart
Grid
Hospital
Optimization
Real-time
Network
Planning
Intelligent
Medical
Devices
Connected
Machines
Brilliant
Hospital
Brilliant
Rail Yard
Shipment
Visibility
Employees increase OT is virtualized Analytics become predictive productivity
Machines are self healing automated Monitoring and maintenance is mobilized [ [ © General Electric Company, 2014. All Rights Reserved.

5. Cornerstone of IoT Transformation is
Software-Defined Machines (SDM’s)
CONSUMER
COMMERCIAL INDUSTRIAL
• Easily connect machines to Internet
• Embed apps and analytics into machines and cloud, making them intelligent and self-aware
• Change and update capabilities of machines and devices without changing hardware
• Deliver intelligence to users providing continuously better outcomes
• Extend Industrial Internet platform via API and ecosystem

6. Example: Wind Farm in Analytics Age
(40 TB/yr/
500 wm
farm)

7. The Value to Customers is Huge
Efficiency and cost savings, new customer services, risk
avoidance – 1% improvements cuts $276B in waste across
industries
Industry Segment Type of savings
$30B
7 GESoftware.com | @GESoftware |
#IndustrialInternet
Aviation
Power
Healthcare
Rail
Oil and Gas
Estimated value
over 15 years
$66B
$63B
$27B
$90B
Commercial
Gas-fired
generation
System-wide
Freight
1% fuel savings
Exploration and
development
1% fuel savings
1% reduction in
system inefficiency
1% reduction in
system inefficiency
1% reduction in
capital expenditures
Note: Illustrative examples based on potential one percent savings applied across specific global industry sectors. Source: GE estimates

8. 4 Big Data
Forces shaping
the Industrial Internet
8 GESoftware.com | @GESoftware |
#IndustrialInternet
Internet
1 of things
Intelligent,
SW-defined
machines
2 Big Data
Analytics
3 Physics +
A living network
of machines, data,
and people
Increasing system
intelligence through
embedded software
Employing deep
physics engineering
models to leap-frog
what’s possible with
data-driven
techniques
Transforming massive
amounts of data into
intelligence,
generating data-driven
insights, and
enhancing asset
performance

9. Reference Architecture
Platform for the Industrial Internet must bridge OT IT
Single Record
of Asset
Business Process Management
Industrial Big Data Management
Event Processing
PaaS
SaaS
Industrial
Data Lake
Analytics
Modeling
Integration with ERP / CRM
Device mgmt. M2M, M2H,
M2C
Insight to Action
• Maintenance
• SW Upgrades
• Machine Control
Mobility and Collaboration
Cyber-Security Operational Reliability
Any
Machine
Any
Device

10. What do we need from Data Science?
10

11. 11
Two ways of seeing a data set* (and the world)
Computer Scientist: “get the knowledge locked in the data”
The data set is record of everything that happened, e.g.,
• All customer transactions last month
• All friendship links between members of social networking site
Goal is to find interesting patterns, rules, and/or
associations.
Physical Scientist – “get the knowledge”
(*See D. Lambert, or R. Mahoney, e.g.)
• The data set is an partial, and often very noisy
reflection of some underlying phenomenon, e.g.,
– Emission spectra from stars
– Battery voltage varying with current, time, and temperature
• Goal is better understanding or ability to predict
aspects of that phenomenon, often through a
mathematical model
For certain kinds of problems, immense power in the
combination

12. Example: Statistical Translation
• Employ language experts to codify
rules, exceptions, vocabulary
mappings, etc.
• Apply transformation to user’s query.
• Gather and classify lots of translated
docs (websites, UN, books, …)
• Identify match patterns
• Map to user’s translation query.
Regular Science
approach
Statistical (data-driven)
approach
Use of language is infinitely
complex, but you can teach a
computer all the rules and
content.
People say the same kind of
things over and over. And
somebody has already
translated it.
• Costly, hard to scale
• Can translate nearly any statement
(but accuracy variable)
• In theory, could be better than
human.
• Incrementally low cost, highly
scalable.
• Limited in scope to digitized docs
that have been translated before
• Limited by skill of human translators
Will flop with innovative
use of language (new
poetry, …)
Too expensive and
difficult to deploy
comprehensively

13. 13
Three basic components of Industrial Data
Science
Physics/engineering-based models
• Need much less data
• Powerful, but difficult to maintain and scale
Empirical, heuristic rules insights
• Straightforward to understand
• Captures accumulated knowledge of your experts
Data-driven techniques – machine learning,
statistics, optimization, advanced visualization, …
• Often not enough data in the industrial domain
• Bias: limited to regions of parameter space traversed
in normal operation
• But easiest to maintain and scale

14. 14
© 2014 General Electric Company - All rights reserved
Some Patterns

15. 15
Industrial Example: improving rule based systems
Many equipment operators have a system something like this, with rules
derived based on experience and intuition.
Rule sets
implemented in
Analytics Engine
Produce alerts
Low-latency
operational
data
Alerts

16. 16
Industrial Example: improving rule based systems
Rule sets
implemented in
Analytics Engine
Produce alerts
Low-latency
operational
data
Pattern, sequence,
association mining, etc.
Outcome
data
Combine ML plus rule-based
alerts with outcome data to
produce better alerts
More
actionable
alerts

17. 17
Industrial Example: improving rule based systems
Rule sets
implemented in
Analytics Engine
Low-latency
operational
data
Outcome
data
Recommendation
engine
Use ML and outcome data to refine
and extend rule base, providing yet
further actionability, resulting in
substantial improvements in
operational outcomes.
Tune parameters of
existing rules, and
create new rules.
Actionable
Recommendations

18. 18
Another Industrial Example: use advanced physical
models to create new features for ML approaches
Sensor Data
Predicted Values
and Δs
Variety of Machine
Learning
Techniques
Outcome
data
Using as ML features the:
1. Deviations from
expected physics,
2. Inferred or hidden
parameter estimates
provides much richer and
effectively less noisy
data, resulting in much
stronger predictions and
models.

19. Fleet/operation-wide optimization levels.
Trade-offs to optimize business
performance
19
Climbing up the value chain toward Condition-based
Performance Management and Business Optimization.
Need:
• Earlier detection
• Root cause
• Scaling to more
equipment Types
instances
19
Fix it when it breaks
Prescriptive recommendations (multi-channel)
Predictive Maintenance (“future”)
Condition-based Maintenance (“now”)
Model-driven
Work-driven
Time-driven
New levers for
optimization across the
operation or business
“Equipment heath
is not a given, but
a variable”

20. 20
Capability / Impact Ramp
Sophisticated, optimized
management of business
Complexity
Science Predictive
analytics
Rules
Data Anomaly
augmentation
Detection
Advanced
Basic
Reporting
Reporting
Data completeness, breadth, quality Operational
optimization
Prescriptive
analytics
Alerts
Highly-actionable
management
info
High-value
guidance
operations

21. Broad range of deep Data Science capabilities
needed
Optimizes the design
operations of complex
business and physical
systems, extracting more
value at lower risk
Innovates new ways of
performing reliability
analysis, statistical
modeling of large data,
biomarker discovery and
financial risk management
Focuses on developing
algorithms and systems for
real time video analysis
Research in algorithms and
software systems that analyze
understand images to produce
actionable insights
Develop scalable and cross-disciplinary
machine learning
predictive capabilities to
derive actionable insights from
big data
Modeling complex system and
noise processes to detect subtle
deviations and estimate critical
system parameters
Industrial
Data
Science
Employing deep physical and
engineering understanding of
equipment and processes to
generate normative models.
Sensor
Signal
Analytics
Knowledge
Discovery
Delivering data and
knowledge-driven decision
support via semantic
technologies and big data
systems research
Applied
Statistics
Physics
expert-based
Modeling
Machine
Learning
Computer
Vision
Image
Analytics
Optimization
Management
Science
21

22. 22
“Industrial Data Science”
① Outcome-oriented application of mathematical physics-based
analysis models to real-world problems in industrial operations.
② Tools processes needed to do that continually at scale.
Improve the performance of industrial operations, e.g.,
• Higher equipment uptime, utilization,
• Lower maintenance/shop costs, longer component life
• Fleet level optimization trade-offs
• Business optimization (linking to financial customer data)
• Service / contract management
Combination of :
• Physical expert modeling experience depth
• Installed base of industrial equipment and data.
• Big Data, Machine Learning, and statistical capabilities
Industrial
Data
Science
What
is it?
Why do
we do it
What’s
needed