On-Line Sensor Monitoring Techniques How accurate are they? What is the payback?

Transcription

1 On-Line Sensor Monitoring Techniques How accurate are they? What is the payback? by Dr. J. Wesley Hines Nuclear Engineering Department Maintenance and Reliability Center The University of Tennessee Knoxville, Tennessee

2 Presentation Outline On-Line Monitoring System Objectives Background Techniques Comparisons Case Studies TVA Payback

3 On-Line Sensor Monitoring Objective: to monitor process sensor calibrations on-line. Need: small instrument drifts are difficult to detect. Costly in terms of quality and efficiency. Can cause incorrect control actions. Current defense: periodic manual calibrations. Costly in terms of labor, radiation exposure, and down time. Can cause damage or failures. May be unnecessary.

4 Additional On-Line Monitoring Benefits Fault detection. Component malfunction. Prevents control system masking. Early Warning.» Catastrophic failures» Gradual deterioration Operating Condition Changes Improper operations Cyclical changes. Reduces Downtime Optimizes Maintenance

5 Industrial Application Areas Process: Power Generation: Nuclear, coal, gas. Chemical Aviation Rail

6 Background Original Need: The Nuclear Power industry requires all safety critical sensors be calibrated each fuel cycle (18 months). There are ~25 sets of 4 redundant sensors in a NPP. The Office of Nuclear Reactor Regulation Application issued a safety evaluation report (SER) for: "On-Line Monitoring of Instrument Channel Performance". n-redundant sensors will be manually calibrated every n-fuel cycles. Redundant sensors are first checked in-group and then across groups for common mode failure.

7 UT Industrial Projects History Experimental Breeder Reactor II, Florida Power Corporation Nuclear Plant, Dow Corning Plant, High Flux Isotope Reactor, Idaho National Engineering and Environmental Lab, TVA Kingston Fossil Power Plant, Electric Power Research Institute Oak Ridge Y-12 Plant SmartSignal Inc present. Nuclear Regulatory Commission 2004-present.

8 Basic Theory Use information in correlated sensors to estimate true values. s i : Measured sensor values s i : Best estimates of sensor values r i : Sensor residuals = s i -s i Measured Signal Values Signal Predictions x Prediction Residuals r Sensor Status s x x 1 x 2 : x n Prediction Model Comparison Module Decision Logic

9 Index PSIG PSIG Early Example Result Neural Network Reactor output pressure prediction R225: Reactor Outlet Pressure - B 2150 Sensor AANN Difference Between Sensor Signal And AANN Estimate SPRT Fault Hypothesis time in 2 minute intervals (7/16/93 to 7/20/93)

10 Nuclear On-Line Monitoring Implementation Project Project objectives: Demonstrate on-line monitoring technology in operating nuclear plants (currently 7) for a variety of systems and applications» Harris Sizewell B Limerick TMI Salem VC Summer Sequoyah Verify that on-line monitoring is capable of identifying instrument drift or failure under a variety of conditions Apply on-line monitoring as a performance and condition monitoring tool

11 Modeling Techniques MSET developed by Argonne National Lab Licensed by SmartSignal Inc. Similar method by Expert Microsystems Implemented at several US Nuclear Power Plants. PEANO developed by Halden Reactor Project Neural Network based NLPLS developed by University of Tennessee Implemented at TVA fossil plant Also implemented at Halden

12 Model Goals Empirical modeling strategies should produce accurate results,» No Free Lunch Theorem states no method is best on all data sets. produce repeatable and robust results, have an analytical method to estimate the uncertainty of the predictions, be easily trained and easily retrained for new or expanded operating conditions.

13 Uncertainty Analysis The NRC safety evaluation [2000] requires an analysis of the uncertainty in the empirical estimates. Argonne National Laboratory has developed Monte Carlo based simulations to estimate MSET uncertainty producing average results for a particular model trained with a particular data set. UT developed analytical techniques to estimate prediction intervals for all of the major techniques

14 Ease of Training and Retraining It is virtually impossible for the original training data to cover the entire range of operation. The operating conditions may change over time. MSET is a non-parametric techniques and are thus not trained. New data vectors can simply be added to the prototype data matrix. ANNs require comparatively long training times. NLPLS can be trained much faster. A PEANO system using NLPLS performed with accuracy equal to an AANN and was trained in minutes versus days [Fantoni 2002].

15 Case Study Nuclear Power Plant Feedwater Flow Measurement Venturi meter known to drift due to corrosion products Costly consequences ($3 million /year) Model trained with 24 parameters highly correlated with feedwater flow. Training data from first 600 points (2 weeks) Drift calculated at 6 months 2.18% drift calculated

16 Case Study

17 TVA Fossil Plant Installation System monitors the condition of plant process sensors and indicates when sensors are producing faulted measurements. System also sensitive to equipment and process faults.

18 Conceptual System Interaction Design Plant S e n s o r s D C S Error Correction ISCV System Process Model - + Best Estimates Residuals SPRT Decision Performance Monitoring Workstation Fault Detection and Isolation System Other Systems Maintenance Scheduling

19 Monitoring Module

20 Prediction Results Description Unit MAE Mean (MAE/Mean)*100 Score >.7 >.5 1 First Stage Pressure A PSIG First Stage Pressure B PSIG Measured Steam Flow - Raw KLB/HR FW Heater 1 Extraction Pressure PSIG FW Heater 3 Extraction Pressure PSIG FW Heater 4 Extraction Pressure PSIG FW Heater 5 Ext Press - Abs PSIA FW Heater 8 Water In Temp DegF X X 9 FW Heater 8 Water Out Temp DegF FW Heater 5 Drain Temperature DegF FW Heater 6 Drain Temperature DegF FW Heater 6 Extraction Temp A DegF FW Heater 7 Water Out Temp DegF FW Heater 7 Extraction Temp A DegF FW Heater 7 Drain Temperature DegF FW Heater 8 Drain Temperature DegF FW Heater 8 Extraction Temp A DegF X X 18 FW Heater 6 Water Out Temp DegF FW Heater 5 Water Out Temp DegF Feedwater Flow #1 - Raw KLB/HR Feedwater Flow #2 - Raw KLB/HR Selected Drum Pressure PSIG FW Heater 3 Water In Temp DegF X X 24 FW Heater 2 Water In Temp DegF FW Heater 1 Water In Temp DegF

21 OLM Implementation Issues Data Acquisition and Quality Interpolation Errors Data Quality Issues Model Development Results Interpretation Lost or missing data. Single or multiple outliers in one sensor or several. Stuck data in which the data value does not update. Random data values. Unreasonable data values. Loss of significant digits.

22 Interpolation Errors Power (percent) Apr 6-Apr 7-Apr 8-Apr 9-Apr 10-Apr 11-Apr Time

23 Data Quality Issues

24 Model Trained With Bad Data Model With Bad Data Removed Model From Bad Training Data

25 Model Development Before Repair Maximum values in training data Transients exceed training data limits Flow (percent) After Repair 72 Mar May Jul Sep Nov Date Equipment Repair 1F7594A 1F7597A 1F7600A Transients Cyclical Operations

26 Steam System Model Example 4 Loops

27 Drift Example Steam Generator Level Sensor (blue) drifts low while MSET estimates (red) remain unchanged

28 Drift Example Steam Generator Level Residual Drift alarms when residuals (difference between observations and estimates) about 0.2%

29 Noise Example Data Acquisition Card Replacement Before Input Module Change After Input Module Change

30 Observed Drift Events in On-Line Monitoring Models Plant Number of Channels Included in Models Number of Unacceptable Drift Occurrences # # # # Total: With less that 1% of instruments requiring calibration, the reduction in required maintenance will be sizable.

31 Nuclear Cost Benefit Analysis Costs software licensing, equipment, model development, training, and maintenance. license amendment for safety critical variables. Benefits direct benefits from reduced manual calibrations» the cost of a manual calibration is approximately $910. indirect benefits include performance enhancements and equipment monitoring»just one avoided incident would pay for the system costs many times over.

32 OLM Installation Payback Analysis

33 Summary EPRI has organized the research and implementation of instrumentation On-Line Monitoring for nuclear plants. Pilot testing at seven nuclear plants. New research focuses on equipment monitoring. Special regularization techniques are needed for the collinear data to optimize uncertainty. Uncertainty analysis is necessary. Data collection problems abound. Payback for calibration reduction alone is not strong. Payback when considering equipment condition monitoring is strong.