Machine Learning & Cognitive Fingerprinting Delivers Next Generation Analytics to Improve Safety, Performance and Reliability of Assets

STUART GILLEN, BUSINESS DEVELOPMENT DIRECTOR, @THINGSEXPO 2016

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:

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

Agenda

Why Cognitive Analytics

Machine Learning Basics

Improving Asset Health through Predictive Models Example

NLP Augmenting the “Cognitive Approach”

Questions

Why should you consider Cognitive Analytics?

“

”

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/

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

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:

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

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

Machine Learning Basics

How do you label these?

Unsupervised Learning

Unsupervised Learning

SM

MD

LG

Supervised Learning

WH

GR

BL

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

Examples

“

”

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/

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

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

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 67 35 20

40

60

Days of Warning

500

1000

67 Days

35 Days

Results

Output Options

Overall Fleet Health Detailed Asset View

Natural Language Processing Empowering Safety

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 evidence Red 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

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

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 Mining Knowledge Representation

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

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

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

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.

www.sparkcognition.com

4030 W. Braker Lane, Suite 200

Austin TX 78730

Stuart Gillen

Director, Business Development

sgillen@sparkcognition.com