Real-time Decision Support

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Edward Palmer
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Real-time Decision Support Business/Operation Intelligence Boiler-209.Fuel Gas Flow DC.

Feed Rate DM-05:BW.R DC.SJ.C1.Z1.R1.Rk06.S2.O03.PWR QI-111 FinalProductBin.On 94:GRDIDX.

EV 3LBA32CT001-2 DC.SJ.PUE TI-102 DC.Zero DY-108 DC.SJ.C1.Z3.R3.PDU1.

Net Volume CB1992_MS 0_CMP_FLOW_TOTAL GE02_Energy Anacortes Refinery.Alkylation.

SiteRealTimeITLoad.PWR FT9001 FI-101 bf5e1d1d-39c9-4b5b-b3d3-c2ce05fa3a26 DM-05:BW.

Tank Pressure.PV Boiler Feed Pump #1 AlarmTest.Input.Float32.

1HRAVG BGT001 GE05_Energy C1:14AT5 AC03.Air Flow FeedBin.

PV GE04_DT QI-121 GE03_V_WIN DC.Rk07R DC.Srv06R GE04_Energy TI-121 FT9001 FAC.OAK.Power-Kh-Val.

c GE02_OT GE01_DT DC.C2Z1.Pwr.Ripple PI-111 facility_output 1-8.Net Volume Coal Motor Load 02F100.

3 Real-time Decision Support Business/Operation Intelligence Boiler-209.Fuel Gas Flow DC.Srv01R 94:GRDIDX.Tr igger AC09.Power GE01_DT _Wind Speed QI-109 GE01_DT Cooling Fan-711.Feed Rate 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 FR Net Volume Coal Motor Load 02F100.TOT.EV 3LBA32CT001-2 DC.SJ.PUE TI-102 DC.Zero DY-108 DC.SJ.C1.Z3.R3.PDU1.PF GE01_A_DT _Wind Speed DC.Srv01R Boiler-125.Fuel Gas Volume 1-16.Net Volume CB1992_MS 0_CMP_FLOW_TOTAL GE02_Energy Anacortes Refinery.Alkylation.Asset Problems B210_FG005.KPIExcursion AlarmTest.Input.Float32.1 AQUA2-TI-201.PV DC.SJ.SiteRealTimeITLoad.PWR FT9001 FI-101 bf5e1d1d-39c9-4b5b-b3d3-c2ce05fa3a26 DM-05:BW.R AT401 0_CLR_FINAL_OUT_B_TMP F506_E _Clear Sky Global Horiz GE01_DT D-110.Tank Pressure.PV Boiler Feed Pump #1 AlarmTest.Input.Float32.1 FIC F100 fasttag GE01_CON FI-151 0_ENG_AUX_STS 02F102.1HRAVG BGT001 GE05_Energy C1:14AT5 AC03.Air Flow FeedBin.Cmt Boiler Cold Reheat Pressure B737_FG117 DC.TimeLoad D-110.Tank Pressure.PV GE04_DT QI-121 GE03_V_WIN DC.Rk07R DC.Srv06R GE04_Energy TI-121 FT9001 FAC.OAK.Power-Kh-Val.PV FeedBin.Cmt DC.Zone1.Number DailyTrigger FrqPrbCost_ER DY-131 DC.SJ.PUE fic1001.c GE02_OT GE01_DT DC.C2Z1.Pwr.Ripple PI-111 facility_output 1-8.Net Volume Coal Motor Load 02F100.TOT.EV 03LBA32CT001-2 DC.SJ.ITLoad.PWR TI-145 FR2001 TI-178 FT9001 FR5001 GE04_OS FT9001 AF_NOISE GE01_A_DT FI-111 FT9001 Weather Conditions Relative Humidity: 34% Current Temp: 85 F High: 92 F Low: 57 F Wind: 8 mph/n 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 FT9001 fasttag AT401 Energy Efficiency Downtime Total Production $

Testing & Validation Operationalization Models Visualizations

5 Preparation Modeling Ingest Transform Cleanse Denormalize Profile Explore Visualize Feature & Algorithm Selection Model Testing & Validation Operationalization Models Visualizations Deploy Apps, Services & Visualizations Act: Score, Visualize Measure

6 Advanced Analytics Journey Basic Analytics Descriptive & Diagnostic Analytics Predictive & Prescriptive Analytics Prescriptive Analytics Today s Basic Analytics Point-by-point querying Descriptive Analytics What happened? Provides info about past problems fleetwide Diagnostic Analytics Why/When did it happen? Provides insight and visibility into what can be improved where Predictive Analytics 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 Analytics Maturity

7 Intelligent analytics across realms Embed R, Python or Azure ML on-premises or cloud Extensibility R Integration R? New R scripts Analytic Library T-SQL Interface Relational Data Built-in to SQL Server Data Scientist Interact directly with data Data Developer/DBA Manage data and analytics together Microsoft Azure Machine Learning Marketplace

8 REGIONAL SEMINARS

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

9 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.

10 Azure Cortana Intelligence Suite Data Sources Information Management Big Data Stores Machine Learning and Analytics Intelligence People Data Factory Data Lake Store Machine Learning Cognitive Services Data Catalog SQL Data Warehouse Data Lake Analytics Bot Framework Web Apps Event Hubs HDInsight (Hadoop and Spark) Cortana Apps Mobile Stream Analytics Bots Dashboards & Visualizations Sensors and devices Power BI Automated Systems Data Intelligence Action

11 Predictive Maintenance on Engine Failures On Premise with PI Integrator and SQL Server 2016 Enterprise and R Server DATA SOURCES PREPARE INGEST ANALYZE PUBLISH CONSUME PI Infrastructure SQL Server 2016 Enterprise Power BI R Server Predictions as Future Data (to PI 2015) On Premise

Intelligence DATA SOURCES PREPARE INGEST

12 Predictive Maintenance on Engine Failures Microsoft Azure: Cortana Intelligence DATA SOURCES PREPARE INGEST ANALYZE PUBLISH CONSUME SQL Server 2016 Enterprise R Services Cortana Azure SQL Data Warehouse Machine Learning Azure SQL Data Warehouse Power BI Predictions as Future Data (to PI 2015) Web/LOB Dashboards On Premise

13 Asset Framework (AF) data from PI System Engine 1 Engine

14 PI Integrator for Cortana Intelligence

16 Publish PI AF data to Cortana Intelligence Stores PI Integrator allows you to push Analytics Ready data directly to Cortana Intelligence

17 Azure Machine Learning Model

18 Understanding our Data Failure Points of Engines Strong Correlation among sensors

19 Using Principal Component Analysis PC1 shows a strong Variance Value of PC1 has strong correlation on RUL

20 Scoring the Predictive Model Prediction versus actual remaining life Using PC1 as our predictor, the model appears to be more concentrated and accurate as remaining life approaches to zero

The Digital Transformation Opportunity Dominic John (Ph.D.), VP of Marketing and Technical Communication. Copyright 2017 OSIsoft, LLC 1

The Digital Transformation Opportunity Dominic John (Ph.D.), VP of Marketing and Technical Communication You can t manage what you can t see Without data it s just an opinion You don t know what you don