Machine Learning’s Impact on Utilities

Speakers

Stuart Gillen

SparkCognition

Director, Business Development

Jon Arnold

ZPRYME

Principal

Bernie Cook

WSC, Inc.

Executive Assistant

Large Power Utility - Smart Technology Application

Bernie Cook

Retired Duke Energy Director – Maintenance & Diagnostics

Currently Executive Consultant – WSCInc.

• Low-cost Sensor Technologies and New

Measurement Capabilities

• Data Analytics, Integration and Visualization

• Advanced Controls and Automation

• Monitoring and Diagnostics

• Digital Worker Technologies

Real Time Information

Distributed and

Adaptive Intelligence

Action and Response

Large Power Utility – investment in Smart Technologies

2010 -Catastrophic Equipment Failure Executive Directive – Leverage new

technologies to improve reliability

GAPSLack of online Sensors on

critical equipment

CBM Programs – manual 80%

collection & 20% Analytics

M&D Centers limited to available

instrumentation

Need more Auto Diagnostics

Smart Technology Design

Rem

ote

Mo

nit

ori

ng • M&D

Network

• Cyber Security

• New Sensors for Reliability

• Operations Zero Events

• Enhance M&D Center

• Wireless Sensors

Dia

gn

ost

ics

&P

rog

no

stic

s • Asset Fault Signature

• Big Data Analytics

• Rule Based Diagnostics

• Dynamic RUL

Dat

aV

isu

aliz

atio

n • Data Integration

• Data Hubs

• Dashboards

• Equipment Health Visuals

• Digital Worker

Data --------------- Information-------------Insight ---------Actionable Intelligence

6

Smart, Connected Plant AssetsAssets Sensors Data Acquisition-

M&D Network

Monitoring & Diagnostics Integration &

Visualization

10,000+ 33,000+ 2,400+ Nodes

Turbine Critical Equipment

Steam Turbine

Combustion Turbine

Generator

Boiler

Balance of Plant

Motors, Pumps, Gearboxes, Fans

Transformers

Iso-Phase Bus Ducts

Electrical Buses

Phase I = Base Installation

Temperature

Accelerometers / Vibration

Turbine Vibration Monitoring (VDMS –

Vibration Diagnostics Monitoring System)

Proximity

Oil Analysis

Phase II = Advanced Sensors

Cameras

Thermal Cameras

Infrared Sensors (IR)

Electro Magnetic Signature Analysis

(EMSA)

Motor Current Signature Analysis

Sensors (MCSA)

CT (foreign object & leak detection)

Phase III =

Advanced Sensors II

New Sensors for Major Component

Zero Event Operations

Focuses on reducing operational risk;

event free index

NI CompactRIO

(reconfigurable embedded control and

acquisition system)

NI cRIO-9068 / NI cRIO-9074

NI cRIO-9024 Turbine

NI InsightCM™ Enterprise

NI InsightCM™ Data Explorer

NI InsightCM™ Serve

APR Models

Efficiency Monitoring

& Thermal Modeling

ADVANCED ANALYTICS

Industrial Internet of Things – Interconnectivity DirectionBusiness Intelligence

SmartGen Sensor Technologies

H2 and NH3

Leak Detection

Oil Levels

Oil Dielectrics

Particle Counts

NI cRIO Monitoring Node SENSORS

Turbine Monitoring

Systems

Balance of Plant Systems

Embedded Turbine

Monitoring System

Vibration Sensors

3rd Party Systems

Sensors

Vibration

Temperature

Pressure

Flow

Position/Displacement

Oil Quality

Ultrasound

Infrared Thermography

Power/Current

Leak Detection

Dissolved Gas Analysis

Electromagnetic

Interference

Partial Discharge

Optics

Acoustic

Optics

Accelerometers,

Proximity Probes

Utility Example of ROI – Technology Application

FINDS

An investigated notification

identified an equipment issue

that requires corrective action

384 COST AVOIDANCE

Based on the difference

between probability and

impact of failure with and

without M&D center

interaction

$31.50M

198 $18.26

134 $4.39M

52 $8.85M

Equipment Finds

2013

2014

2015

One Utility Example:

By adding new

technologies…..

From 2013 to 2015

4x increase in

Equipment Problem

Detection

$10M+ annual increase

in avoided cost.

Increasing Challenges drives more Innovative Solutions

Power Generation Challenges Energy Efficiency lower load demands

Renewables affecting dispatching

Run plants with less staff & $$ Fourth Industrial Revolution

Age of Innovation

Battery Storage

Wind

Solar

Power

Generation has to

embrace innovative

new process and

technology solutions to

survive

People

Enhanced Processes

New Technologies

Smart Plant Connected Assets combines sensors, microprocessors, data acquisition, data storage and software

with critical hardware across the fleet such as steam turbines, combustion turbines, generators, transformers, and

large balance-of-plant equipment. The smart critical assets are also connected to each other via wired and wireless

technology. The resulting “smart, connected plant assets” have intelligence and connectivity that enable an entirely

new set of functions and capabilities:

PLANT ASSETS

Physical components e.g. combustion turbines,

steam turbines, generators, turbines, pumps,

motors etc.

SMART PLANT ASSETS

Sensors, microprocessors, data acquisition,

data storage, controls, software, embedded

operating system, enhanced user interface etc.

SMART, CONNECTED PLANT ASSETS

Ports, antennae, protocols enabling wired /

wireless connections with plant asset: one-to-

one, one-to-many, many-to-many.

Source: What is “Internet of Things” (IoT)? www.educatingplanet.com

Integration of People + Processes + Assets + Data = Better Decision Making

Interact Compute Connect

Smart Plant Connected Assets

Data → Information → Insight → Actionable Intelligence

11

Next Innovation Wave:

Advanced Analytics

Descriptive

Analytics

Diagnostic

Analytics

Predictive

Analytics

Prescriptive

Analytics

What happened?

Why did it happen?

DifficultyV

alu

e

Source: Gartner

What is likely

to happen?

What should I do

about it?

We are Here

Data in Context: Analytics for Equipment & Component Condition

Monitoring & Trending

Pattern Recognition & Identification

Early Identification &

Fault/Source Determination

Big Data Analytics

Fault Signature

Recognition

Dynamic RUL

Current State

Data --- Information ---- Insight ------ Actionable Intelligence

13

Apply new Prescriptive Analytics -

On Line Vibration Analytics

Predictive Auto Diagnosis

Failure Signature Recognition - Prognostics

PI

HistorianDCS

Existing & New Sensor Data

M&D Center

Models

Asset Health

Information

M&D

IT

Data Integration

Big Data Analytics

Digital Worker

Dynamic field interaction

Equipment Health, Dwgs,

Work orders, etc…

E n g i n e e r i n

g A n a l y s i s

E n g i n e e r i n

g A n a l y s i s

Monitoring

ToolsSMEsSMEs

DCSDCS

Walkdown

ALARMSALARMS

Walkdown

Troubleshooting an Issue: How This Could Change

TODAY TOMORROW

Monitoring

Tools

Prescriptive

Analytics

In Summary – How to get started??

Power

Generation has to

embrace innovative

new process and

technology solutions

to survive

People

Enhanced Processes

New Technologies

• Power Generation challenges are real

• Utilities need to embrace new technologies

and processes to survive

• Starts with Leadership Sponsorship & Buy-in

• WSC provides a Workshop & Assessment

• Educate Leadership

• Identify & Prioritize Issues

• Develop an Implementation Plan

• New Processes

• New Technology Application

• Fossil // Hydro // Nuclear

TM

Machine Learning’s Impact on Utilities

How would you write code to tell the difference between a banana, an apple, and a grape?

Bananas are Yellow Apples are Red Grapes are Green

How does Machine Learning tell the difference between a banana, an apple, and a grape?

• Feed in measurable characteristics to an algorithm• Height• Width• Height/Width• Color• Color variation• Shape

• Let the algorithm define the relationships between the measurable characteristics and the fruit they embody

SparkCognition Cognitive Analytics – Beyond Machine Learning

Natural language processing

• Enables recall of answers, in context• Analysis of human readable text for clues,

insights and evidence

Deep Learning and Reasoning algorithms

• Improves accuracy• Learns complex patterns• Scales efficiently: High speed, large data

implementations• Make decisions in the absence of training

data

Automated Model Building and Infinite Learning

• Watches data and derives rules• Incorporates human feedback to

strengthen or dismiss conclusions• Automatically learns from feedback and

greater volumes of data• More data = more accuracy, capability &

insight.

Powerful Visualization with Evidential Insights

• Provides transparency and evidence about what the cognitive system is learning and proposing

• Presents data elegantly – Analyst friendly interface, easy feedback

• Elevates evidence / reasoning for machine decisions

Powerful advancements in state of the art

Machine Learning can make sense of data from your enterprise

Year

s

Exp

on

enti

al D

ata

Gro

wth • Data explosion across all

departments (i.e. Lots of data!)• Complex relationships exist

between data• Silo’ed data sets limit visibility

into enterprise risks• Limited ability to add people to

analyze• Ability to codify knowledge

retention

Ideal Environment for Machine Learning

Asset Health Architecture

Detailed Evidence• Provide evidence behind the

insights• Provide tools for expert analysis

System Optimization• Optimize not at local but at a

global level• Plug insights into platforms such

as BI, Inventory mgmt., PLM etc.

Actionable Insights• Extend asset life• Avoid downtime• In-field, real-time

recommendations• Cyber Security Threats

Data Collection

Output

Analytics Platform

Assets

Data Lake

Applications in Utilities

Plants:• Predictive Maintenance Of Equipment• Plant Safety• Subject Matter Expert’s “Codification”

Transmission & Distribution:• Smart Metering & Grid Reliability• Outage Management • Energy Disaggregation

Energy Trading & Risk Management• Load Forecasting• Commodity Price Volatility Predicting

Cybersecurity• Critical Infrastructure Protection• Internal User Threat Detection• Security Operator’s Augmentation

TM

Use Cases

Challenge of analyzing transient events

A Day in the life of a Turbine

• Transients are when systems are stressed (think aircraft landings and takeoffs)

• Graph shows one variable (Speed)…and what is state of the art today, actually thousands of variables are being collected.

• Transients can last for different time periods (startups can be 20 min to 40 min long)

• Steady state can be analyzed today, with some difficulty, no tools to analyze transient events

Machine Learning compared to traditional analytics approachTraditional Machine Learning

Time As a Variable

Single point event prediction

Good at steady state, non time varying

events

Can’t handle transient events, where

time is a variable rather than an index

Complete time series treated as single event

Very good at comparing both steady state &

transient events (Startup, coast-down,

thermal vector, cooldown etc.)

User Skill Required

Knowing the relevant data is key to

building a model

Model effectiveness depends on the skill

of the user

Relevant data and key features are identified

automatically by the tool

The information is inherent to the process

Information can be captured on day one

Diagnostic Data

Identification

Fault diagnostic library must be defined

explicitly

Key features calculated and added to the

model for diagnosis

Fault diagnostic data is inherent to the event

1st, 2nd, & 3rd order features automatically

created

User input integrated into system to “learn”

from SME experience

Data Cleansing &

Model Training

Model training is done by Manually

excluding bad points from a data set

Model identifies normal (median) behavior

and 3 standard deviations automatically

Transient ChallengeThis is a coastdown This is a Startup

Axis is Speed vs Time

The picture changes as more features applied

As we add more variables the picture changes

• Now add thousands of features/turbine

• Identify new events coming in real-time

• No modeling required from SMEs to gain insights

• Model keeps learning SMEs can “nudge” model over time.

Initial Screen

• Classify Gas Turbine transient events via supervised learning (i.e. we know the “labels”)

• Automatically classifies multiple operating states

• Ability to utilize and capture SME expertise, then LEARNS

• Top down vs bottoms up approach

• Utilizes National Instruments cRIO and OSI PI historian data for vibration and speed

Plant 1 Plant 1

Plant 1 Plant 1

Plant 1 Plant 1

Thermal Event

Bad Bearing

Advanced Visualizations

TM

Natural Language Processing (NLP)

Empowering the end-user to improve business operationsSparkCognition and the client developed an IBM Watson

powered “Advisory” application for maintenance

Application enables Directors of maintenance and

technicians to:

• Conduct machine to human dialog to troubleshoot with high

accuracy

• Speedy identification to map the right fault codes and

troubleshooting tips using Natural Language Processing (NLP)

queries

• Optimize work flow and deliver relevant documentation for a

faster turnaround of planes

Lowered the cost of maintenance and improved asset availability for operators by up to 10%

SparkCognition Differentiators

• At our core we are a Machine Learning company. This isn’t the case for many organizations just hiring data scientists.

• We have developed cutting edge, Patented, Cognitive Analytics IP. Patented algorithms are rare.

• Our products rely on data driven, automation of model creation. Takes the burden off the customer.

• We provide operators solutions on day ONE (SparkPredict, SparkSecure), not just analysis tools.

• Reasoning utilizing NLP drives toward prescriptive maintenance

• Top Down approach vs. Bottom Up

• Outcomes Learn, and Adapt based on SME input (i.e. training)

• We have addressed multiple use cases with multiple techniques to provide value to nearly every customer in the last two years.