Streaming IoT Analytics with the PI Integrator for Azure ... · Big Data Store Data Lake, SQL, DW...

© Copyright 2017 OSIsoft, LLCUSERS CONFERENCE 2017 #OSIsoftUCosisoft@

Presented by

Streaming IoT Analytics

with the PI Integrator for

Azure, Cortana Intelligence

and Azure IoT

Prabal Acharyya

Landry Khounlavong


IoT Analytics Challenges

3


Transforming the Value of Data

1 | Data 2 | Information 3 | Intelligence

Real-time DecisionSupport

Business/Operation Intelligence


Intelligence Cognitive Services, Bot Framework. Cortana

Dashboards and Visualizations Power BI SSRS, DataZen, R R visualizations

Machine learning and Analytics Machine learning, Stream Analytics, HDInsight SSAS, SQL Server R Services Microsoft R

Big Data Store Data Lake, SQL, DW OLTP, DW, Hadoop, EDSs Hadoop, Teradata, Linux, Windows

Information Management Data Factory, Data Catalog, Event Hubs SSIS, DQS, MDS Spark SQL/ETL

Cortana Intelligence Suite Microsoft RSQL Server 2016

Solutions

Microsoft big data & advanced analytics

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


8


Data Scientists perform too much Data janitor-work

9


OSIsoft and Microsoft: Joint Value


Observed advance analytics goals

11

• Augment decision making with data

• Minimize work needed to begin analyzing

• Invest in out-of-the-box, reusable capabilities for

advanced analytics

• Prove advanced analytics can achieve a business

outcome

© Copyright 2017 OSIsoft, LLCUSERS CONFERENCE 2017 #OSIsoftUCosisoft@ 12

BGE003 FI-111 02T100AF_FLOW3 02:T103 AT401

ACEDemo.Unit1.Output

TI-178 B352_W778 0_CMP_SVLV_PCT

02F104 CD:F161

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

1-8.Net

Volume03LBA32CT0

01-2

DC.SJ.ITLoad.P

WR

TI-145FR2001

TI-178

GE04_OS

FT9001

FT9001FR5001

AF_NOISE

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

GE04_Status

02:F101.C

FIC-14402F100 fasttag

FI-1510_ENG_AUX_STS

AQUA2-SI005.PV GE02_Energy0_ENG_MODE_STSFI-151 02T103.QDC.Z1R

0_CMP_HDR

GE05_Energy C1:14AT5 AC03.Air FlowFeedBin.Cmt Boiler Cold Reheat Pressure

B737_FG117 DC.TimeLoad

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

DC.Rk07RDC.Srv06R GE04_Energy

TI-121 FT9001

FAC.OAK.Power-Kh-Val.PV

fic1001.C GE02_OT

02F102.1HR

AVGBGT001

PI-111facility_output

DM-05:BW.R

DC.SJ.C1.Z1.R1.Rk06.S2.O03.PWR

QI-111FinalProductBin.On

94:GRDIDX.ProdIDBoiler-209.Fuel

Gas Flow

GE01_A_DT

fic1001.C

02:F101.C

FR2001 TIC-121

Aso AT401 DC.Srv01R

TI-178

asset1_outputActive Meters

GE01_A_DT aso

AF_NOISE PI-115DM-05:BW.R

FI-101Volume

0_CLR_FINAL_OUT_B_TMP F506_E990339511775_Clear SkyGlobalHoriz GE01_DT

FI-111

% CO2GE05_ES T

GE01_CONAlarmTest.Input.

Float32.1

DC.NY.Actual.PWR.day.Tot

80-5.Net VolumeGE01_A_DT

45-2.Net Volume

Boiler-209.Fuel Gas

Flow DC.Srv01R

94:GRDIDX.Tr

trigger

AC09.Power

403511195_Wind

Speed

DC.C2Z1.Pwr.Rippl

e GE01_A_DT

AF_NOISE

1-16.Net VolumeCB1992_MS

0_CMP_FLOW_TOTAL

FT9001

fasttag

FeedBin.Cmt

DC.Zone1.Number

FT9001

AlarmTest.Input.Float32.1

FT9001

DC.Srv01RBoiler-

125.Fuel Gas

Volume

1-13.Net

VolumeB045_FG978

AT401

Anacortes Refinery.Alkylation.Asset

Problems B210_FG005.KPIExcursion

D-110.Tank

Pressure.P

V Boiler Feed

Pump #1

Challenge: making sense of the data

94:BW.R TI-101F723_E889

369512185-TempCompressor-439.Feed Rate

DC.CH.DCE FIC-172:210

FI-121 AF_FLOW3

0_ENG_MODE_STS

GE03_Q

DY-131:166 GE01_TD

Boiler-334.Feed Rate

GE04_OSAsset1.Problems QI-122 FI-151

DC.SJ.SiteRealTim

eITLoad.PR FT9001

Weather

Conditions

Relative Humidity: 34%

Current Temp: 85 F

High: 92

Low: 57 F

Wind: 8 mph/N

Crude Furnace

Draft Pressure: -0.5 WC

Stack Temp: 316 F

Oxygen: 2.5%

Outlet Temp: 840 F

Cold Oil Velocity: 6 ft/sec

Crude Desalter

Operating Pressure: 110 psi

Charge Rate: 14 gph

Mix Valve Pressure: 8 psi

Water Rate: 8%

fic1001.C FR5001

AQUA2-TI-201.PV

TIC-181

BGE003 FI-111 02T100

AF_FLOW3 02:T103

ACEDemo.Unit1.Output

TI-178 B352_W7780_CMP_SVLV_PCT

02F104 CD:F161

AQUA2-SI005.PV GE02_Energy

0_ENG_MODE_STSFI-151 02T103.Q

DC.Z1R 0_CMP_HDR_SUC_PRS

DM-05:BW.R

DC.SJ.C1.Z1.R1.R

k06.S2.O03.PWR

QI-111FinalProductBin.On

94:GRDIDX.ProdID Boiler-

209.Fuel GasFlow

FI-101 bf5e1d1d-

39c9-4b5b-b3d3-

c2ce05fa3a26DM-05:BW.R AT401

AT401

DailyTrigger FrqPrbCost_ER

GE01_DT 409510395_Wind Speed

QI-109 GE01_DT Cooling Fan


364511575-AC Power

DY-101 02:F101.C

0_CLR_FINAL_OUT_B_TMP F506_E990

339511775_Clear Sky Global

Horiz GE01_DT

80-13.Net

DY-131DC.SJ.PUE


GE01_DT

FI-101 bf5e1d1d-39c9-

4b5b-b3d3-c2ce05fa3a26DM-05:BW.R AT401

4-36.NetVolumeAC04.Air Flow

02T100 03LBB02CT001-2

DC.SJ.SiteRealTim

eITLoad.PR FT9001

02F102.1HRAVGBGT001

PI-111 facility_output

AQUA2-SI005.PV GE02_Energy

0_ENG_MODE_STS FI-151

02T103.Q DC.Z1R 0_CM P_ H D R _SU C _ PRS

403511195_Wind Speed

GE02_Energy

FI-101 bf5e1d1d-39c9- 4b5b-b3d3-c2ce05f

Solution: PI System


PI Data Archive

PI Asset Framework

PI Event Frames

PI System stores and organizes data

Element Attributes

DowntimeProcess Excursion Startup

ShiftBatchShutdown

Environmental Excursion Grade Change

Event Frame Attributes

13


Solution: PI Integrator for Microsoft Azure

03:00 03:10 03:20 03:30 03:40 03:50

Speed

Oil

Temp

Oil

Temp

Speed

Torque

03:00

03:10

03:20

03:30

03:40

03:50

Oil

Te

mp

60

60

60

120

60

Oil

Te

mp

50

50

50

50

45

Sp

ee

d

10.0

10.2

9.8

10.0

10.1

Sp

ee

d

9.8

10

10

9.9

9.9

To

rqu

e

25

24

25

25

25

Comm Failure

Different

start times

Additional

Measure

Uneven Spacing

Spike /

Out of Range

Challenge: cleaning & preparing sensor-based data


Challenge: implementing data science experiments

Solution: Cortana Intelligence Suite

Quick deployment

of resources

Off-the-shelf

data science tools


Picture / Image

RESULTSCHALLENGE SOLUTION

COMPANY and GOAL

Company

Logo

Leveraging the PI System and Cortana Intelligence

to Increase Process Efficiency

Deschutes Brewery is the 7th largest craft brewery in US, and wanted to maximize production with its existing infrastructure to fund construction of a 2nd

brewery in Roanoke, VA

Batch’s phase transition happens between manual density measurements occurring every 8-10 hours

Use data science to achieve

accurate predictive analytics

for determining a batch’s

density measurements

Ability to eliminate production time losses and increase production capacity

• PI System

• PI Integrator for Microsoft Azure

• SQL Data Warehouse

• Azure Machine Learning

• Azure Data Factory

• Accurate predictions of when a

batch’s phase transitions from

fermentation to free rise

• Impact: Losing up to 72 hours in

production time

16


SQL Data Warehouse

On Premises

Machine Learning

Power BI

INGESTPREPAREDATA SOURCES

ANALYZE PUBLISH CONSUME

Cortana

Web/LOB Dashboards

SQL Data Warehouse

PI System

On Cloud

Azure Data Factory (Orchestration)

Predictions as Future Data

PI Integrator for Microsoft Azure

An example of how to operationalize predictions

17


Event Hub

IoT Hub

SQL Database

SQL Data Warehouse

Data Lake Store

Bridging the PI System and Microsoft Azure

18

PI Integrator for

Microsoft Azure

Released

Nov 2016

Summer 2017


AnnouncingRed Carpet Incubation Program (RCIP)

a limited service offering

alongside Microsoft

that uses a process

to accelerate advanced analytics projects

for selected customers


Analytics Journey => Transforming the Value of Data

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.PWRQI-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

DailyTrigger

FrqPrbCost_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

1 | Data

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

2 | Information 3 | Intelligence

$

Total Production

Energy Efficiency

Downtime

Descriptive &Diagnostic Analytics

Predictive & Prescriptive Analytics

Basic Analytics


High Fidelity Historical….. and Future Data from Analytics

Time-series data• Temperature• Pressure• Flow

Process• Well drilling• Site

Asset details• Name• Model• Manufacturer

23

Need OT Context – “must” for Analytics & IoT

Operational Context

Element Attributes


DowntimeExcursion

Batch

Related Data

Event Attribute Value

Name Ex 20141215-0002

Start 15-Dec-2014 10:35:02

End 15-Dec-2014 10:47:26

Duration 12 min, 24 sec

Asset Boiler-352

Excursion Type High Violation

Fuel Gas

Flow.Avg37.12 k sft3/h

Fuel.Start 823.48 k sft3/ton

myPIKPI.Max 47.19 bbl/d

• The most powerful way to automatically bookmark PI time-series data

• More meaningful use for engineers and business users• Easier to find, analyze, and report on

7

Asking How & Why: PI Event Frames easily identifies events for deeper root cause analysis


Streaming and Batch – IoT Analytics

SQL Database

SQL Data Warehouse

Data Lake Store

PI Integrator for

Microsoft Azure

Released

Nov 2016

Event Hub

IoT Hub

Summer 2017

Streaming IoT Analytics with the PI Integrator for Azure ... · Big Data Store Data Lake, SQL, DW...

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