Cognitive Analytics and

Next-Gen Prognostics for

the Industrial Internet

Usman Shuja - @kshujaSumant Kawale - @sumantkawale

@SparkCognition

Focused on large growing markets

5

IoT Security Software Market in Billions ($) Machine Condition Monitoring Market in Billions ($)

10.5

8.47.1

2018

6.5

20192014

7.7

+8%

9.79.0

201720162015 2020

14.4 18.2 22.9 28.4 34.8 42.1 50.1

Estimate of number of connected devices in billions

SparkCognition is targeting a $10B

Internet of Things Security market

20182014 2015 2016 2017

+6%

2013

$240B is lost in the US due to bearing failures

$150B in waste across major industries that the Industrial Internet can eliminate

Predictive maintenance within the

Industrial Internet is a sizeable market

1.61.8

2.12.2 2.4 2.5

Source: IDC, Gartner and SV Biz Journal; 30% SW of the tech market. 16% of this Security, per current “Global SW : Sec SW” ratio;

Machine Condition Monitoring market IBIS and Global Strategic Business

SparkCognition’s Focus, Customers & Partners

3

Manufacturing Safety & Machine Failure

Energy

Utilities

Oil & Gas

Rail, automotive and

aerospaceTransportation

Devices, Servers, VMsCloud

Industrial Internet Segments

Devices, fitness appsHealthcare

Consumer

M2M (e.g. Smart House)

Retail

Smart CitiesPublic

Ou

r P

rim

ary

Fo

cu

s

Customers

Partners

Select Relationships

• 20+ customers

• 9 Fortune 500 Companies

o 3 Utilities

o 3 Oil & Gas

o 1 Aviation

o 2 Financial Services

Proven Use Case: Industrial Internet

4

Value Addition

• Sophisticated failure prediction extended forewarning

(from hours to days)

Insights

• Client - Top-5 Power company in the US

• Decisions on replacing expensive capital assets (Six

large boiler feed pumps)

Solution

• Asset-agnostic prediction to cost-effectively support a

large fleet of diverse assets

• Automated model building to augment and expand the

capabilities of human data scientists

• Automated model tuning over time to adapt to

changes in operating conditions and environment

• Automated anomaly identification in historic data

• Powerful analytics, visualization and alerting

4

Client Case Study

Business Problem and Current Limitations

Business Problem

Flowserve, world-leading supplier of industrial and environmental machinery such as pumps, valves

sought cognitive technologies for better failure detection, forewarning and maintenance insight for its

customers

Limitations

Current signature library based approaches, and threshold systems could only identify failures a few

hours prior to them occurring

Additional issues with the current approach

• Insufficient time to respond effectively

• Hard to maintain prognostic models

• Inability of prognostic models to adapt to unique conditions of each individual pump

6

Results

7

Application 1 Application 2

Objective

• Recognize known operating modes

• Detect anomalies with wide

variations

• Advanced warning of possible

problem earlier than what simple

threshold detection methods provide

Results

• >99% accuracy in identifying desired

operating modes

• Application meets the four criteria set

by Flowserve

• Potential failures predicted 5 to 6

days in advance

• Minimal false positives

Identify operating modes, provide advance warning of failures

Data CleansingDimension

Reduction

Feature Space

NavigationOptimization Prediction

• Aggregated data

from 3 files based

on common time

stamps

• Analyzed data

over time to study

trends and to

reduce

dimensions

• Created new

feature abstract

spaces using

Machine Learning

• Analyzed the

derivative

features to

identify trends

• Applied

optimization

methods to

reduce false

positives and

maximize time to

prediction

• Used a prediction

algorithm to

predict the next

failure once

higher order

features were

extracted and

optimized

In Summary: Cognitive approach enabled us to

predict failures early

Enabled Flowserve to predict failure ~6 days ahead of time, minimal false positives

8

Valuable Insights

9

Asset State

• Is there a problem?

• If so, what kind of problem?

• Is there a problem we’ve never seen before? (signature DB

approaches don’t work well here)

Fleet State• Is the entire system operating well?

• Is the entire fleet optimized?

Failure Prediction• When will the problem occur?

• What problem will likely occur next?

Forensics • What factors were most responsible for a failure?

• What factors were most responsible for a sub-optimal state?

Other Cognitive Analytics

Applications

Next Generation Analytics & Prognostics

• Accurate failure prediction and anomaly detection

• Automated model building, selection & management

• Insights through deeper-order analyses

• Flexible and scalable architecture

• In-context technical advisory with IBM Watson

Improve safety and reduce remediation cost through intelligent prognostics

Proven Use Case for Energy

11

Potential vectors for traditional security approaches

Many potential vectors of attack even in an air-gapped facility

Proven Use Case for Security

12

Improve efficiency and reduce failures

Goal Decisions

Improve well

efficiency by

reducing stuck

pipe

• Recommend design based on formation type, well trajectory such as

type & placement of stabilizer in BHA

• Predict requirements for hole clean outs/ wiper trips

• Optimize drilling & operating parameters such as mud weight, mud

type & maximum connection time

Improve well

efficiency by

improving ROP

(rate of

penetration)

• Recommend BHA design such as Bit type, Downhole motor type

based on formation

• Optimize drilling parameters such as Weight on Bit, torque, RPM

Reduce

failures

(downtime)

• Predict when downhole equipment (e.g. mod motor) might fail

1

2

3

Potential Use Cases for O&G

13

Contacts

Usman Shuja, VP Market Development

ushuja@sparkcognition.com

Sumant Kawale, Sr. Director Business Development

skawale@sparkcognition.com

www.sparkcognition.com

@sparkcognition

6034 W. Courtyard Drive, Suite 100

Austin TX 78730