How Industrial Big Data & Analytics Can Optimize Assets and Operations · 2018-08-13 · How...

powered by #IndustrialInternet

How Industrial Big Data & Analytics Can Optimize Assets and Operations

Build your digital industry

September 30, 2015


Presented By

Matthew Denesuk

GE Digital


Topics

① What is Industrial Data Science (iDS)?

② Key iDS applications & examples

③ How do you plan/do iDS

④ Digital Twin deep dive


Sensor and asset health data are streaming in from:

• Wind Turbines • Medical Equipment • Gas Turbines • Steam Turbines • Generators • Locomotives • Oil and Gas • Aircraft Engines

GE Assets Are Generating Huge Volumes of Data

300,000+

Complex

Assets

Monitored

World Wide

1 gas turbine blade with

sensors: 500GB data/day

Illustrative


What is Industrial Data Science?


Industrial Data Science & the Industrial Internet

PHYSICAL & ANALYTICAL

BRILLIANT MACHINES

INDUSTRIAL BIG DATA

PEOPLE & WORK

Industrial Data Science is the

“magic” of the Industrial Internet. • Platform capabilities • Consulting & service delivery • Integration with software, UX,

& data management


Event Equipment component failure

Ad click-thru Media purchase

Cost/value per event Huge tiny small

Freq of event tiny Huge Large

Diversity/# of “cause” variables Large small small

“Cleanliness” of data low Very high Very high

Quantity of data Widely varied Huge Large

Physics-based models Many, highly relevant ? ?

Legacy / process

integration Critical Minimal Minimal

… … … …

Therefore iDS & Industrial Internet Solutions must employ different:

1. ICT Infrastructures (data, connectivity, compute)

2. Types of people 3. Data Science techniques 4. Partnering approaches 5. Technology & business

strategies 6. Deployment approaches

Industrial Data Science is very different from traditional Data Science

powered by #IndustrialInternet 7

Physics/engineering-based models

• Need much less data

• Powerful, but difficult to maintain & scale

Empirical, heuristic rules & insights

• Easy to understand

• Capture knowledge of your experts

Data-driven techniques – machine learning, statistics, optimization, advanced visualization, …

• Usually not enough data

• Biased to parameter space of normal operation

• Easiest to maintain and scale – by far

The Three Basic Components of Industrial Data Science


Physics

Knowledge

Experience

Data (lots)

Compute (lots)

Do

ma

in

Model

Data Science

DEVELOP

Optimizing Assets: From Idea to Impact


Physics

Knowledge

Experience

Data (lots)

Compute (lots)

Do

ma

in

Model

Model

Data

(less, low-latency)

Compute

(less)

Workflow!!

Business

Results

Learning… .

Data Science

DEVELOP

Data Science

DEPLOY

Optimizing Assets: From Idea to Impact


Sensor Data

Another Industrial Example: use advanced physical models to create new features for machine-learning (ML) approaches

Predicted Values and Ds

Variety of Machine Learning Techniques

Outcome data

New ML features :

1. Deviations from expected physics

2. Inferred or hidden parameter estimates

Provide much richer, less noisy data, resulting in much stronger predictions and models.


“Analytics-based Maintenance” (ABM) is the future of equipment health modeling & performance management

1. A broader approach to the concept of “wear”

1. Hybrid approach for integrating & extending physics-based models

2. Adding explicit stochastic elements to “deterministic” models

3. Utilizing Massive-Scale Feature Augmentation to scale the integration of domain/physics with big data approaches

4. The optimized maintenance & management policies based on these things


Automated

synthesis of multi-factor wear models.

Out-of-the-box ability to configure and create new ABM

models in any domain.

ABM Framework: Rapid creation of hybrid physics & big data models.

Analytics-based Maintenance (ABM): rapid, scalable, lifing & performance analytics

Historical Data

Time Series Data

Operational

Cumulative Damage

Index

Physics & Usage

Component ABM Models

Preventative Maintenance

Dynamic Pricing Operational Optimization

Replacement Forecast Inefficiency Identification


Execution Strategies


Predix integration & deployment

Data-science driven solutions (UX/DS/SW)

Early warning… engines, generator, pump,

motors, heat exchanger etc.

Anomaly Detection

Decision Support,

Prescriptive Apps

Diagnostics, Forecasting

& Lifing

Multi Dimensional Time Series Raw Data

Historical inspection report, M&D, & PACs

(text, drawing)

Part condition (Inspection Output)

Expert knowledge

Predix-based Applications

Design models

Environmental Data Weather, atm.

chemistry

Configuration

Shop Analytics & Mgmt.…Scrap detection, forecasting (shop

cost, parts etc.), work scope)

Reasoning & Root cause

Decision Support…Disruption management,

operational recommendations

CBM… fleet segmentation,

inspection recommendation

Customer Solutions Inputs

Model As a Service… fast

model development at a scale

Tactical data science field investigations

“GET CONNECTED” “GET INSIGHTS” “GET OPTIMIZED”

Reliability Response

Machine & Equipment Health

Maintenance Optimization

Anomaly Detection:

Suite I

Case Compariso

n

Degradation Assessment

Machine Health Early

Warning (basic)

Multi-model comparison

& ranking

Machine Health &

RUL Prediction

Asset Data Ingestion

Time-Series Data

Ingestion

Event Data

Ingestion

Data Services

Machine Health Early

Warning (Adaptive)

Case Management

Maintenance Prioritization & Scheduling

iDS Analytics Catalog

Machine Operating

State Change ID

Analytics Based

Maintenance Management

Workscope Optimization

DS Pilots and proof points

Objective: Deliver practical Data Science solutions quickly and predictably


Maximize the value and performance of Assets

APM

Modules

Machine & Equipment

Health

Reliability

Response

Maintenance

Optimization

APM

Capabilities

CONNECTIVITY PREDICTIVE DIAGNOSTICS PERFORMANCE BENCHMARK

ANOMALY DETECTION NOTIFICATION MANAGEMENT ASSET MAINTENANCE STRATEGY/SCENARIOS

ASSET CONDITION MONITOR CASE MANAGEMENT FINANCIALLY OPTIMIZED

ASSET STRATEGY

DATA WORK BENCH RESPONSE MANAGEMENT WORK SCOPING & PRIORITIZATION

ANALYTICS WORK BENCH KNOWLEDGE MANAGEMENT INVENTORY OPRTIMIZATION

Shift Verifier

Trend Verifier

Temporal anomaly detector

Multi-sensor pattern detector

Regime detector

Asset Performance is

optimized with a wide array of algorithms

powered by #IndustrialInternet Data completeness, breadth, quality

Da

ta S

cie

nce

Co

mp

lexi

ty

Basic Reporting

Advanced Reporting

Anomaly Detection

Predictive analytics

Prescriptive analytics

Operational optimization

GE Confidential

The Asset Optimization Journey …


Digital Twin: Driving Customer Outcomes

Build your digital industry

Sep 30, 2015


Presented By

Mark Grabb

Achalesh Pandey

GE Global Research


Big Data Physics Analytics Digital Twin

+


Go backwards to understand what happened

Go forward to understand what to do

Can the Digital Twin Time Travel?


Customers Want Outcomes

Asset Performance Mgmt. Operations Optimization Business Optimization

Single Asset Group of Assets Complex Operations

Reduce operating cost

Increase output

Reduce unplanned down time

Maintenance optimization

Increased revenue

Cost reduction

Analytics Based Maintenance Plant Level Optimization Trip Optimizer, Movement Planner

Life Models: Per Asset, Per Part and Per Failure Mode

Heterogeneous & Big Data

Accurate Performance Models

Complex Interactions

Market Conditions

Interactions at scale


Digital Twin

1. The model is applied per asset

2. The model must be used to create

demonstrable business value.

3. The model must be adaptable.

4. The model must be used in a

continuous update capacity.

5. The model must be scalable.

Design & Test

Operation

Inspection &

Maintenance

Market

ESN #906260

Model #906260

≈

Necessary Attributes


A Scalable Framework for Industrial Analytics

Online Sensor data

Performance Models

Data Lake and

Predix Cloud

Inspection data, e.g., Borescope

Operational State data, e.g., Temp etc.

Asset Performance Management Operations Optimization

Business Optimization

Life Models

Asset Life data, e.g., cycles to failure

Field and Service Data and Actions

Data Layer Model Layer Application Layer

Engineering models that continuously increase insights in each asset to deliver specific business outcomes.


Customer Experience – Value Stream Co-Creation

Prioritize inspections/maintenance and

proactively manage and optimize fleet on

per engine basis

Saving tens of $MM in

unnecessary service overhauls

(per customer)

Digital Twin Business Outcomes Improved Availability

Single Component Distress Model

Cumulative Cycles ->

Cu

mu

lati

ve

Dis

tre

ss -

>


Magnetic

Sensors

Digital Twin for Crack Simulation

24x7 Continuous Monitoring

Compressor Blade Health Monitoring

Digital Twin Business Outcomes Improved Reliability

Challenges: • No direct measurement • Supersonic tip speeds • Operational variations & S/N ratio


Baseline

Business Outcome:

• Degradation monitoring for diagnostics and

forecasting for performance optimization

High fidelity Digital Twins Asset specific, adaptable,

scalable & continuous learning

LP compressor

issue diagnosed

Operating Time

Po

we

r Lo

ss

Digital Twin Business Outcomes Improved Performance & Reliability

Component Level Degradation Digital Twin System

Gas Turbine

Digital Twin

Adaptation

Machine Learning

Forecasting


Digital Twin: Using Artificial Intelligence Improved Model Fidelity

Automated Inspection Data Quality & Imputation

Automated anomaly detection & classification Deep Learning, physics knowledge & machine learning

to clean & impute the data (multiple strategies)

Deep Learning changes Inspection Paradigm

Data Preprocessing

Classification

Multi-modal data fusion

Measurement & characterization

Incoming image data Inspection insight Inspection Agent

Anomaly detection

Localization


Digital Twin Modeling Framework

Physical + Digital at Scale

Digital Twin Modeling


Key Enabling Technologies for Digital Twin

PREDIX PLATFORM 4

Innovation, Speed, and Scale

Inspection Capabilities

Automated Data Pre-processing

T 4 9

T 3

cycles

Inputs

Domain Data 1

Dynamic Performance Estimation

Cumulative Damage

Physical + Digital Engineering Models 2

Knowledge Extraction

Model Generation & Automation

Industrial Analytics 3

In-situ / on-wing inspection

Manual inspection to Automation

Quantitative inspection


Conclusion Digital Twin: What it is…

• Asset specific with continuous updates

• Deep physics models and advanced analytics

• Scalable & automated modeling framework

Digital Twin: What it does…

• Quantifies asset health in physical terms

• Provides continuously improving insights

• Predicts asset health, leveraging data & domain

• Enables a spectrum of customer outcomes

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materials do not constitute a representation, warranty or documentation regarding the product or service featured. Illustrations are provided for informational purposes, and your configuration

may differ.

This information does not constitute legal, financial, coding, or regulatory advice in connection with your use of the product or service. Please consult your professional advisors for any such

advice.

No part of this document may be distributed, reproduced or posted without the express written permission of General Electric Company.

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