Speakers. Juan Ontiveros University of Texas-Austin, Utility & Energy Management. Hojoon Seo HanAra Software

Transcription

1 Speakers Juan Ontiveros University of Texas-Austin, Utility & Energy Management Hojoon Seo HanAra Software

2 Overview 2 Predictive Analytics UT-Austin and Predictive Analytics UT Case Study Demo Q & A

3 3 Predictive Analytics Analyzing data to notify you of anomalies in real time

4 Need a Good Historian 4 1. Central gathering of data 2. Central cog to make data available to applications 3. Essential in root cause analysis of upsets 4. Historical memory of past issues 5. Perform complex analysis

5 What Makes a Good Historian? 5 Fast Reliable Historian Easy-to- Use Accurate Intuitive

6 Why Predictive Analytics? 6 Cost Savings Operational Stability Improvements Increased performance over routine/time-based maintenance Power of Big Data

7 Future of Maintenance-Asset Management 7 Reduce nonproductive time/downtime Break-Down Preventative Predictive Proactive Increase labor efficiency Increase plant safety Reduce equipment cost

8 8 UT-Austin and Predictive Analytics Goal Uninterrupted and Cost-Effective Utilities

9 89% of Failures are Random 9 Source: Smith, Anthony, Hinchcliffe, Glenn (2004). RCM Gateway to World Class Maintenance, 59.

10 Why this Solution? 10 Point P: Potential Failure Early Warning with Predictive Analytics PF Curve Performance Normal Operation Predictive Preventive Reactive Lead Time to Functional Failure Point F: Functional Failure

11 Use of Data 11 Historical data knows: All patterns How the plant operates How the plant should be operating in different conditions Predictive Analytics: Analyzes historical data Creates patterns Alerts you of differences

12 Predictive Monitoring Solution 12 Process Variable Predictive Data Modeling Plant DCS/PLC/SCADA Expected Values + - Residuals YOU Early Warning Tracing a Cause of Anomalies Equipment Health Index SuccessTree Entire Plant Monitoring Plant Heath Index

13 Predictive Data Modeling 13 Learn Historical Data Learn Learn Model Group Sampling Tag A: COND PP STATOR WINDING TEMP U Tag B: COND PP STATOR WINDING TEMP V Tag C: COND PP STATOR WINDING TEMP W Tag D: COND PP MOTOR DE BRG TEMP Tag E: COND PP MOTOR NDE BRG TEMP

14 Patterns and the Seasons 14 Summer Season Winter Season Tag A: COND PP STATOR WINDING TEMP U Tag B: COND PP STATOR WINDING TEMP V Tag C: COND PP STATOR WINDING TEMP W Tag D: COND PP MOTOR DE BRG TEMP Tag E: COND PP MOTOR NDE BRG TEMP

15 Empirical-Based Modeling Example 15 Tag A: CONDENSOR PUMP STATOR WINDING TEMP U Tag B: CONDENSOR PUMP STATOR WINDING TEMP V Tag C: CONDENSOR PUMP STATOR WINDING TEMP W Tag D: CONDENSOR PUMP MOTOR DE BEARING TEMP Tag E: CONDENSOR PUMP MOTOR NDE BEARING TEMP

16 Empirical-Based Modeling 16 Normal Actual Value Tag A Tag B Tag C Tag D Tag E Sample 1 Sample 2 Trained Sample Data Tag A Tag B Tag C Tag D Tag E Sensor Abnormal Sample 3 Sample Equipment Abnormal Sample 5 Sample 6 Sample Normal Data Abnormal Sensor Data Equipment Abnormal Data Tag A Tag B Tag C Tag D Tag E Tag A Tag B Tag C Tag D Tag E Tag A Tag B Tag C Tag D Tag E Act Value Act Value Act Value Exp Value (Sample 5) Exp Value (Sample 4) Exp Value Residual Residual Residual Index Index Index

17 17 UT Case Studies Compressor Inlet Temperature Control Issue and Boiler FD Fan Issue

18 18 Case 1: Compressor Inlet Temperature Control Issue Unstable and Changing Pressure Speed and Acoustic Vibration Fluctuations Stopped/Tripped CTG 10 Control Logic Change Stable T2 Control Time :46:38 Tag Name CTG10-HPCompressor- Discharge_Acoustic_Dynamic_Pr_PX36A_ PT8090A CTG10-HPCompressor- Discharge_Acoustic_Dynamic_Pr_PX36A_ PT8090B Act Value Exp Value Res Value Operating Value EU Range Min Max Low High Unit PSID PSID

19 19 Early Warnings through Predictive Analytics

20 Expected versus Actual 20 CTG10 Tripped

21 Results 21 Before After Better Operation Less Stress Better Performance

22 Case 2: Boiler FD Fan Issue 22 Varying West FD Fan torque Fan Swap Boiler Shutdown Differential Pressure Transmitter Variance Continued Calibration for Accurate Readings Time Tag Name Description Act Value Exp Value Operating Value (KSCFH) EU Range (KSCFH) Min Max Low High 12/10/2015 7:26am B3-FDF-Fan West_Torque FD Fan west Torque

23 23 Main Index Trend for Boiler 3 FD Fan

24 24 Early Warnings with Predictive Analytics 1 2

25 Results 25 Detect Hidden Failures in Advance Prevent Failure Improve Operation and Process

26 26 Quick Look

27 27 Achieving Operation Excellence Reduced unexpected equipment failure, unplanned maintenance activities, and outage time

28 28 Next Steps 1. Operationalize into the Plants Create a dedicated position using a trusted Supervisor Level person 2. Standardize on the Prism Historian throughout 3. Implement an APP to be able to trend data remotely

29 29 Any questions? Juan Ontiveros University of Texas-Austin, Utility & Energy Management Hojoon Seo HanAra Software