Leveraging the Power of Machine Learning at GE

Girish Modgil

Leveraging the Power of Machine Learning at GE 2© 2016-2017 General Electric Company. All rights reserved.

GE Power

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Topics for Discussion

• General Electric – A Digital Industrial Company

• Predix

• Data Science Platform (Why?-What?-How?)

• Machine Learning at GE: A Business Example

GE: A DIGITAL INDUSTRIAL COMPANY300,000+ people operating in 175 countries

Revenue 2015

~$21B ~$8B ~$6B $16B $25B $6B $18B $9B

POWER ENERGY CONNECTIONS

RENEW. OIL & GAS AVIATION TRANS. HEALTHCARE

APPL. & LIGHT

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Digital Industrial for Operational Excellence

Asset-centric industries have the most to gain from the next wave of disruptive digital innovation

Systems of Record

Systems of Assets

Systems of Engagement

Optimized Digital

Business

Financials,HR, Inventory, Purchasing,

Supply Chain, etc.

Smart connected products, asset

performance mgmt, 0perations 0ptimization,

etc.

Real-time collaboration, Social Media, Mobile, etc.

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GE’s Digital Industrial Journey

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Transforming Industrial Operations

Asset Performance Management

Operations Optimization Digital Twin/Digital Thread

Maximize performance and asset availability

Increase system efficiency across operations

Optimize lifecycle of design, manufacturing, service,

& repair cycles

CreatingNew Value

Improved operational performance and efficiency

1

New customer services and business models2

Continuous innovation and faster time to market3

Predix: Machine to Cloud Platform as-a-Service

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Industrial Analytics

Physics-basedAdvanced Data Science Applied Engineering

DataContinuous, accessible

StatisticsIdentify trends and anomalies

PhysicsApply asset and domain

expertise

Industrial Outcomes

One platform for OT and IT teams to collaborate and

innovate+ + =

Operator A

Operator B CruiseTakeoff

Tem

pera

ture

Threshold

Clim bPart Temperature

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Open Data Science Platform

Need for a modern analytics platform (’Why?’):

• Global Teams: U.S. , India, Switzerland

• Real-Time Collaboration

• Simplify Administration

• Common ML development platform

• Consistent Methods

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Open Data Science Platform

One of our Analytics Platforms (‘What?’):

• 50 users

• HWX cluster: AEN compute node + Cluster Head Node

• Hue (Edge Node), Hive (HWX Cluster)

• Anaconda, R, Spark, Hadoop

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Open Data Science Platform

Implementation (‘How?’):

• Digital Industrial Strategy

• Tightly integrated teams

• Software Centers of Excellence (CoE)

• Free exchange of ideas across GE businesses

• “Everyone will know how to Code” – Jeff Immelt, Aug 2016

• Predix/GE Store/Digital Twin

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Machine Learning: Resource Forecasting

• Outage Field Engineer (TFA)• Predict number of field resources needed by region and specialty

• Life Extension Service (LES)• Predict number of hours needed by region and skill

• Gas/Steam/Generator On-site Repair (OSR)• Predict number of hours needed by region and skill

Objective:Ø Identify factors driving the resource requirement Ø Build data science models to predict resource needs

• Outage Field Engineer (TFA)• Predict number of field resources needed by region and specialty

• Life Extension Service (LES)• Predict number of hours needed by region and skill

• Gas/Steam/Generator On-site Repair (OSR)• Predict number of hours needed by region and skill

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Machine Learning: Resource Forecasting

• Built linear regression/time series models based on historical timesheets, seasonality, and outage counts; forecast based on future outage schedule

• Previous SME models over estimated whereas data science models better match the actuals

• Manual• SME

Old

• Automated• Data Driven

New

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Machine Learning: Resource Forecasting

• Cloud based analytic

• Results published to big data platform

• Minimal requirement on users (upload, run, post-process)

• Operationalized and in-use

• Shared across GE businesses via GE Store

Tools

5 minutes end-to-end processing time