GE01_DT 409510395_Wind Speed QI-109 GE01_DT Cooling Fan-711.Feed Rate

1-8.Net VolumeCoal Motor Load

02F100.TOT.EV

03LBA32CT001-2

DC.SJ.ITLoad.PWR

TI-145 FR2001

TI-178 GE04_OS

FT9001 FT9001

FR5001 AF_NOISE

DC.SJ.PUE TI-102 DC.Zero DY-108 DC.SJ.C1.Z3.R3.PDU1.PF GE01_A_DT

FIC-144 02F100 fasttag

FI-151 0_ENG_AUX_STS

GE05_Energy C1:14AT5AC03.Air Flow FeedBin.Cmt

Boiler Cold Reheat Pressure

B737_FG117 DC.TimeLoad

D-110.Tank Pressure.PVGE04_DT QI-121 GE03_V_WIN

DC.Rk07R DC.Srv06R

GE04_Energy TI-121 FT9001

FAC.OAK.Power-Kh-Val.PV

DY-131DC.SJ.PUE

fic1001.C

GE02_OT

GE01_DT

02F102.1HRAVG BGT001 PI-111 facility_output

DM-05:BW.R DC.SJ.C1.Z1.R1.Rk06.S2.O03.PWR QI-111 FinalProductBin.On

94:GRDIDX.ProdID Boiler-209.Fuel Gas Flow fic1001.C FR50011-

8.Net Volume Coal Motor Load 02F100.TOT.EV 3LBA32CT001-2

FI-101 bf5e1d1d-39c9-4b5b-b3d3-c2ce05fa3a26 DM-05:BW.R AT401

0_CLR_FINAL_OUT_B_TMP F506_E990 339511775_Clear Sky Global Horiz GE01_DT

FI-111

GE01_CON AlarmTest.Input.Float32.1

D-110.Tank Pressure.PV Boiler Feed Pump #1

Boiler-209.Fuel Gas Flow DC.Srv01R 94:GRDIDX.Tr igger AC09.Power

403511195_Wind Speed

DC.C2Z1.Pwr.Ripple GE01_A_DT

1-16.Net Volume CB1992_MS 0_CMP_FLOW_TOTAL GE02_Energy

FeedBin.Cmt

DC.Zone1.Number

DailyTriggerFrqPrbCost_ER

AlarmTest.Input.Float32.1 AQUA2-TI-201.PV DC.SJ.SiteRealTimeITLoad.PWRFT9001

FT9001

DC.Srv01R Boiler-125.Fuel Gas Volume

Anacortes Refinery.Alkylation.Asset Problems B210_FG005.KPIExcursion

FT9001

fasttag

AT401

Crude Furnace

Draft Pressure: -0.5 WC

Stack Temp: 316 °F

Oxygen: 2.5%

Firebox Temp: 860 °F

Outlet Temp: 840 °F

Cold Oil Velocity: 6 ft/sec

Weather Conditions

Relative Humidity: 34%

Current Temp: 85 °F

High: 92 °F

Low: 57 °F

Wind: 8 mph/N

$

Total Production

Energy Efficiency

Downtime

Real-time Decision

Support

Business/Operation

Intelligence

Act:

Score,

Visualize

Deploy Apps,

Services &

Visualizations

Measure

Preparation Modeling

Feature &

Algorithm

Selection

Model

Testing &

Validation

Operationalization

Models

Visualizations

Ingest

Profile

Explore

Visualize

Transform

Cleanse

Denormalize

Advanced Analytics Journey

Today’s Basic Analytics

DescriptiveAnalytics

• Point-by-point querying

DiagnosticAnalytics

PredictiveAnalytics

PrescriptiveAnalytics

Analytics Maturity

• What happened? • Provides info about

past problems fleet-wide

• Why/When did it happen?

• Provides insight and visibility into what can be improved where

• What will happen? • Provides predictions

(foresight) that lower maintenance costs, optimize efficiency and productivity

• How can we make it happen?

• Provides recommendations for the best course of action to achieve desired outcomes; based on predictive analytics

Descriptive &

Diagnostic Analytics

Predictive &

Prescriptive AnalyticsBasic Analytics

Data Scientist

Interact directly with data

Built-in to SQL Server

Data Developer/DBAManage data and

analytics together

Relational Data

Analytic Library

T-SQL Interface

Extensibility

?R

R Integration

010010

100100

010101

Microsoft Azure

Machine Learning Marketplace

New R scripts

010010

100100

010101

010010

100100

010101

010010

100100

010101

010010

100100

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Intelligent analytics across realmsEmbed R, Python or Azure ML on-premises or cloud

REGIONAL SEMINARS 2015 8

Scenario

We have 100 engines sending various

sensor data like rpm, burner fuel/air

ratio, pressure at fan inlet and 20

other measurements with

configuration settings for each

engines. The average life span of an

engine is about 206 cycles but it

varies widely from 140 to 360 cycles.

We want to predict the failure of

these engine ahead of time.

Azure Cortana Intelligence Suite

Action

People

Automated Systems

Apps

Web

Mobile

Bots

Intelligence

Dashboards &

Visualizations

Cortana

Bot

Framework

Cognitive

Services

Power BI

Information

Management

Event Hubs

Data Catalog

Data Factory

Machine Learning

and Analytics

HDInsight

(Hadoop and

Spark)

Stream Analytics

Intelligence

Data Lake

Analytics

Machine

Learning

Big Data Stores

SQL Data

Warehouse

Data Lake Store

Data Sources

Apps

Sensors and devices

Data

Predictions as Future Data (to PI 2015)

INGESTPREPAREDATA SOURCES

On Premise

Predictive Maintenance on Engine FailuresOn Premise with PI Integrator and SQL Server 2016 Enterprise and R Server

Power BI

ANALYZE PUBLISH CONSUME

SQL Server 2016

Enterprise

R Server

PI Infrastructure

Predictions as Future Data (to PI 2015)

Azure SQL Data Warehouse

INGESTPREPAREDATA SOURCES

On Premise

Predictive Maintenance on Engine FailuresMicrosoft Azure: Cortana Intelligence

Machine Learning

Power BI

ANALYZE PUBLISH CONSUME

Cortana

Web/LOB Dashboards

Azure SQL Data Warehouse

SQL Server 2016 Enterprise

R Services

Asset Framework (AF) data from PI System

13

Engine 1 Engine 100

PI Integrator for Cortana Intelligence

Publish PI AF data to Cortana Intelligence Stores

PI Integrator allows you to push “Analytics Ready” data directly to Cortana Intelligence

Azure Machine Learning Model

Understanding our Data

Failure Points of Engines Strong Correlation among sensors

Using Principal Component Analysis

PC1 shows a strong Variance Value of PC1 has strong correlation on RUL

Scoring the Predictive Model

Prediction versus actual remaining life – Using PC1 as our predictor, the model appearsto be more concentrated and accurate as remaining life approaches to zero