Reporting on I&C Status & Recommendations to the IAEA on NPP I&C
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Transcript of Reporting on I&C Status & Recommendations to the IAEA on NPP I&C
Reporting on I&C Status&
Recommendations to the IAEA on NPP I&C
IAEA TWG-NPPIC meeting, Vienna, May 20-22 2009
Dr. Davide Roverso
Manager COSS
OECD Halden Reactor Project
Institute for energy technology (IFE)
NORWAY
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Nuclear installations in Norway
• The Institute for energy technology, , operates two research reactors, the only nuclear installations in Norway
• Halden Boiling Water Reactor (HBWR)• 20 MW, used for research on fuel and materials• High burn-up, water chemistry, stress corrosion cracking, ...
• JEEP II Reactor – Kjeller• 2 MW, used for basic physics research,• Neutron source for Neutron Activation Analysis (NAA)• Nanomaterials, silisium doping, ...
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NPP I&C Activities • Most NPP I&C activities at IFE are conducted as part of
the OECD Halden Reactor Project (HRP)• International co-operative effort affiliated to OECD NEA in Paris• Project established in 1958 (50 years’ celebrated in 2008)• Jointly funded by its Members:
• 18 countries • > 100 nuclear organisations world wide
• Hosted and run by IFE, Norway• Participant types
• Utilities, Vendors, Licensing Authorities and R&D centres
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HRP MembersSignatory members:• Norway – IFE
• Institutt for energiteknikk• Belgium - SCK/CEN
• Belgian Nuclear Research Centre• Denmark - Risø DTU
• Risø National Laboratory • Finland - Finnish Ministry of Trade and
Industry• Operator VTT
• France - EDF• Electricité de France
• Germany - GRS• Gesellschaft für Anlagen- und Reaktorsicherheit• BMFT, Utilities (VGB), Siemens (AREVA)
• Japan - JAEA• Japan Atomic Energy Agency
• Korea - KAERI• Korean Atomic Energy Research Institute
• Spain - CIEMAT• Spanish Centro de Investigaciones Energéticas,
Medioambientales y Tecnológicas• Sweden – SSM
• SSM (SKI), Swedish Radiation Safety Authority• Utilities, Westinghouse Atom
• Switzerland – HSK• Swiss Federal Nuclear Safety Inspectorate
• UK - Nexia Solutions (BNFL)• USA - USNRC
• United States Nuclear Regulatory Commission
and as Associated members:• Czech Rep. - NRI
• Czech Nuclear Res.Institute• France - IRSN
• French Institut de Radioprotection et de Sûreté Nucléaire
• Hungary - KFKI • Atomic Energy Res. Inst.
• Kazakhstan – Ulba Metallurgical Plant• Russia - “TVEL” Company
• Russian Research Centre “Kurchatov”• Slovakia - VUJE
• Nuclear Power Plant Research Institute• USA
• Westinghouse, EPRI and GE• Japan
• CRIEPI, Mitsubishi and 11 utilities
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HRP Activity Sectors
• Nuclear Safety and Reliability• Operation of Halden BWR• Fuel and Materials technology research• 140 employees
• Safety MTO – Man Technology and Organization• Human performance and reliability• Control room technology• Virtual Reality (VR) technology• Operator Support Systems• Software Systems Dependability• 85 employees
I&C
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HAMMLAB Experimental Facility
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Human Performance/Human Reliability
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Task 1 - Response Time Isolation of Leakage
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C o lle ctive -E ffica cy Es tim a te s Acro s s th e Tw e lve Sce n a rio R u n sR1 ; L S M e a n s
Cu rre n t e ffe ct: F(1 1 , 1 8 7 )=4 ,5 4 0 6 , p = ,0 0 0 0 0
E ffe cti ve h yp o th e si s d e co m p o si ti o n
V e rti ca l b a rs d e n o te 0 ,9 5 co n fi d e n ce i n te rva l s
RUN-1RUN-2
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Co
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Expert Judgement o f T eamwork Q ua lity Across the T we lve Scenario RunsR 1 ; L S Me a n s
C u rre n t e ffe ct: F(1 1 , 5 5 )=2 ,8 2 9 8 , p =,0 0 5 3 3
E ffe ctive h yp o th e s is d e co m p o s itio n
Ve rtica l b a rs d e n o te 0 ,9 5 co n fid e n ce in te rva ls
1 -R U N2 -R U N
3 -R U N4 -R U N
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9 -R U N1 0 -R U N
1 1 -R U N1 2 -R U N
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First scenario Last scenario
Exploratory StudyHome
Plant Training
Field visits
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Innovative Human System Interfaces
Task based displays Function oriented displays Ecological displays
Innovative BWR displays
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Design of Large Screen Displays
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Mixed Reality for Design, Planning & Training
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SW system dependability
System A System CSystem B
Requirements
Design
Implementation
Development life cycle
Similar characteristics of different development phases of different systems
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Computerised Operation Support
Performance Monitoring
Computerised Procedures
Advanced Alarm Systems
Condition Monitoring
Knowledge Management
Work Processes
Simulator technology
Function Allocation
Core Monitoring and Simulation
Prognostics
Virtual Sensing
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Highlights
• Large-scale Signal Validation• Vision-based Diagnostics• Cable Monitoring• Mímir Framework & Toolbox• Prognostics
• Recommendations to the IAEA TWG-NPPIC• HOLMUG 2009
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Large-scale Signal Validation
• Increase the applicability of signal validation and diagnostic tools
• Method needed for supporting monitoring of a large number of signals
• Signal grouping + Ensemble of models• Each model handles a small group of signals
Mario Hoffmann, Giulio Gola
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The multi-group ensemble approach
Hundreds of
signals
Single validation
model
Validated signals
20-60 signals
Single validation
model
Validated signals
? ?
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The multi-group ensemble approach
Hundreds of signals
Single validation
model
Validated signals
20-60 signals
Single validation
model
Validated signals
Hundredsof signals
Multi-group ensemble approach
Group generation
Model 1
Model aggregation
Validated signals
Model 2
Model K
Group 1
Group 2
Group K12
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The multi-group ensemble approach: issues
1 Group generation
2 Ensemble model
3 Ensemble aggregation
• Optimized (MOGA) • Randomized (RFSE)
• Artificial Neural Networks (PEANO) • Principal Components Analysis (PCA)
• Weighted average • Simple average• Trimmed mean, Median
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Applications
1) 84 signals from Oskarshamn BWR
2) 215 signals from Loviisa PWR
3) 920 simulated signals Forsmark-3 BWR (HAMBO)
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Loviisa – 215 Signals1Signal grouping: optimized; 150 groups; 8 – 147 signals
2Validation model: PCA
3Ensemble model aggregation: Weighted average
Reconstruction of signal 205 (steam temp. in condenser SD51, °C): ensemble VS single model
Ensemble
Single model
MAE 2.84 22.03
Max AE
16.77 46.41
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Ongoing work
• Verification of the proposed procedure on 802 measured signals from Oskarshamn BWR
• Implementation of a randomized-wrapper grouping technique
• Implementation of the final grouping scheme in the PEANO signal validation system • Within 2009
Giulio Gola
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Vision-Based Diagnostics
Compressor Heat exchanger
Mechanical Systems
Electrical Systems
Internal breaker connection problem.
Hot fuse connection.
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• Converts gray-scale images (with linear color palette and upper/lower temperature bounds) into temperature images
• Automatic monitoring and analysis of visual/thermographic images/segments and detection of anomalies compared to previous snapshots
• Image augmentation to visualize when crossing pre-determined thresholds
• Upon anomaly detection, initiates image sequence recording
• Image sequence playback
The Vision Application
25°C 36°C
gray
ir-1
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ir-4
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gray ir-1 ir-2
ir-3 ir-4 ir-525°C 36°C
gray
ir-1
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gray ir-1 ir-2
ir-3 ir-4 ir-5
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Tests at the Halden ReactorThermographic observation of valve heating up:
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Tests at the Halden Reactor cont.
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Index 42 - Upper average intensity limit reached for segment 'Valve_02'
Index 54 - Upper relative fraction limit reached for segment 'Valve_02'
Index 57 - Upper average intensity limit reached for image
Time index
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Tests at the Halden Reactor
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Index 29 - Upper relative fraction limit reached for segment 'SteamBoxThermo'
Index 30 - Upper threshold fraction limit reached for image
Index 33 - Upper average intensity limit reached for segment 'SteamBoxThermo'
Index 35 - Upper average intensity limit reached for image
Time index
Tem
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Cable Monitoring
• LIRA (LIne Resonance Analysis) as a Cable Analyzer
• Local degradation detection and localization• Thermal degradation• Mechanical damage• Gamma irradiation damage
• Global degradation assessment and residual life estimation• Thermal degradation• Gamma irradiation degradation• Harsh environment
Paolo Fantoni
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Local degradation detection with LIRA
• Based on discontinuities of the characteristic impedance caused by mechanical or thermal degradation
• Sensitive to very small electric properties change (5pF/m for 0.3m in the picture)
• Localization error average less than 0.3% of total length
Hotspot at 50m ΔP
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Comparison Tests (Tecnatom) • Compared Techniques
• Line Resonance Analysis (LIRA)• Elongation at Break (EAB)• Time-Domain Reflectometry (TDR)• Insulation Resistance (IR)
TDR LIRA
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AVGERR= 0.23 % of cable lengthSTD = 0.08%
LIRA - Localization Accuracy
Localization Error (% cable length)
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Global degradation assessment• 3 EPR samples, 20 m long,
were aged at 140 °C for 10, 20 and 30 days, producing a thermal degradation equivalent to 20, 40 and 60 years
• Developed and tested two measures
• CBAC: Central Band Attenuation for Capacitance
• CBAL: Central Band Attenuation for Inductance
EPR Global Ageing
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TEREF TEG1 TEG2 TEG3
CB
AC
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Newcable
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EAB/CBAC Correlation, EPR (TECNATOM)EAB/CBAC correlation
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LIRA Global degradation measure
Elon
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Info
rmat
ion
Dat
aK
now
ledg
eIn
telli
genc
e
Data Validation,Reconstruction, and
Calibration Monitoring
Early Fault Detection
and Diagnostics
Lifetime and Performance
Prediction...
. . .DataConditioning
DataFiltering
Feature Extraction
DataNormalization
Input Selection
Data Clustering
Statistical Analysis
Modeling
Pattern Classification
RegressionEstimation
UncertaintyEstimation
DataInput
HypothesisTesting
Performance Analysis
RiskOptimization
TOOLBOX
External Tools(e.g. SAS)
DataConditioning
DataConditioning
DataFiltering
DataFiltering
Feature Extraction
Feature Extraction
DataNormalization
DataNormalization
Input Selection
Input Selection
Data Clustering
Data Clustering
Statistical AnalysisStatistical Analysis
ModellingModeling
Pattern Classification
Pattern Classification
RegressionEstimationRegressionEstimation
UncertaintyEstimationUncertaintyEstimation
HypothesisTesting
HypothesisTesting
Performance Analysis
Performance Analysis
RiskOptimisation
RiskOptimization
External Tools(e.g. SAS)
External Tools(e.g. SAS)
DataInputDataInput
Mímir
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Why Mímir
aladdin
Performs early fault detection and diagnosis through the dynamic recognition of observable changes in measurement signals
aladdin
Performs early fault detection and diagnosis through the dynamic recognition of observable changes in measurement signals
PEANO
A system for Signal Validation and On-line Calibration Monitoring based on auto-associative empirical models
PEANO
A system for Signal Validation and On-line Calibration Monitoring based on auto-associative empirical models
Virtual Sensing
Empirical Ensemble-Based Virtual Sensing using regression models to estimate quantities not directly measured with physical instruments
Virtual Sensing
Empirical Ensemble-Based Virtual Sensing using regression models to estimate quantities not directly measured with physical instruments
Signal Grouping
Signal grouping for large scale applications through the use of Random Feature Selection Ensemble
Signal Grouping
Signal grouping for large scale applications through the use of Random Feature Selection Ensemble
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Why Mímir
Filtering
Feature Selection
Clustering
Normalisation
Neural Network
Classification
Regression Model
Filtering
Neural Network
Filtering
Normalisation Ensembles
Regression Model
Normalisation
Genetic AlgorithmsEnsembles
Ensembles
Mìmir
Wavelet Filtering
Feature Selection
Clustering
Filtering
Normalisation Neural Network
ClassificationRegression Model
Ensembles
…
……
…
… …
Genetic Algorithms
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Early Fault Detection
and Diagnostics
Data Validation,Reconstruction, and
Calibration Monitoring
Info
rmat
ion
Dat
aK
now
ledg
eIn
telli
genc
e
Data Validation,Reconstruction, and
Calibration Monitoring
Lifetime and Performance
Prediction…
. . .DataConditioning
Feature Extraction
Input Selection
Data Clustering
Statistical Analysis
Modelling
Pattern Classification
UncertaintyEstimation
HypothesisTesting
Performance Analysis
RiskOptimisation
DataConditioning
Feature Extraction
Input Selection
Statistical Analysis
Modelling
Pattern Classification
UncertaintyEstimation
HypothesisTesting
Performance Analysis
RiskOptimisation
DataConditioning
Feature Extraction
Input Selection
Statistical Analysis
Modelling
Pattern Classification
UncertaintyEstimation
HypothesisTesting
Performance Analysis
RiskOptimisation
TOOLBOX
External Tools(e.g. SAS)
External Tools(e.g. SAS)
External Tools(e.g. SAS)
Data Clustering
Data Clustering
Data Clustering
RegressionEstimationRegressionEstimationRegressionEstimationRegressionEstimation
DataFiltering
DataFiltering
DataFiltering
DataFiltering
DataNormalization
DataNormalization
DataNormalization
DataNormalization
DataInputDataInputDataInputDataInput
Mímir
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Industry Standards
• ISO-13374• Condition monitoring and diagnostics
of machines – Data processing, communication and presentation
• MIMOSA OSA-CBM• Open System Architecture for
Condition-based Maintenance (OSA-CBM)
• Implementation of ISO-13374• A standard architecture for moving
information in a condition-based maintenance system
• Mìmir • Is being designed to be compliant
with ISO-13374 and the MIMOSA OSA-CBM specification
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ISO-13374 and Mimosa’s OSA-CBM
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostics Assessment (PA)
Advisory Generation (AG)External systems,
data archiving,and block
configuration
Technicaldisplays andinformation
presentation
Sensor / Transducer / Manual entry
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Mímir Demonstrator
• Version 1• Based on Java Plug-in Framework
• October 2008
• Version 2• Based on OSA-CBM Modular Implementation Framework
• Penn State University, Applied Research Lab (ARL)• U.S. Army Logistics Innovation Agency (USALIA)
Add new modules by simply dropping in “zip” files in the plugins folderAdd new modules by simply dropping in “zip” files in the plugins folder
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Example Case Study Signal Validation of 14 Signals from Oskarshamn O-3
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Example Case Study Signal Validation of 14 Signals from Oskarshamn O-3
Nr.
Label Tag Sub-system Measurement Unit
Range
Min Max
1 7 260KW316 Fuel comp. Calc. Flow kg/s 0 200
2 9 260KW951 Fuel comp. Calc. Power MW 0 4000
3 11 312KA502 Feed Water Lines Temperature °C 0 250
4 13 312KC502 Feed Water Lines Temperature °C 0 250
5 34 422KA111 Steam Reheating Pressure MPa 0 1
6 43 423KB501 Steam Extraction Temperature °C 0 300
7 45 423KB503 Steam Extraction Temperature °C 0 300
8 50 441KB509 Main Cooling Water Temperature °C 0 60
9 55 462KA109 Condensate Pressure MPa 0 4
10 58 462KA503 Condensate Temperature °C 0 60
11 73 463KA503 Turbine plant feedwater Temperature °C 0 200
12 76 463KB501 Turbine plant feedwater Temperature °C 0 300
13 77 463KB503 Turbine plant feedwater Temperature °C 0 200
14 79 463KB507 Turbine plant feedwater Temperature °C 0 300
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Signal Validation in a Nutshell
PlantSignals
SignalValidation
ValidatedSignals
Plant Signals
EstimateSignals
ValidatedSignals
ResidualCalculation
SignalHealth
Assessment
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...
Info
rmat
ion
Dat
aK
now
ledg
eIn
telli
genc
e
. . .DataConditioning
DataFiltering
Feature Extraction
DataNormalization
Input Selection
Data Clustering
Statistical Analysis
Modeling
Pattern Classification
RegressionEstimation
UncertaintyEstimation
DataInput
HypothesisTesting
Performance Analysis
RiskOptimization
TOOLBOX
External Tools(e.g. SAS)
DataConditioning
DataConditioning
DataFiltering
DataFiltering
Feature Extraction
Feature Extraction
STDNormalization
DataNormalization
Input Selection
Input Selection
Data Clustering
Data Clustering
Statistical AnalysisStatistical Analysis
ModellingModeling
Pattern Classification
Pattern Classification
PCAEstimationRegressionEstimation
UncertaintyEstimationUncertaintyEstimation
SPRTHypothesisTesting
Performance Analysis
Performance Analysis
RiskOptimisation
RiskOptimization
External Tools(e.g. SAS)
External Tools(e.g. SAS)
DataInputDataInput
Mímir
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Test_1 Test_2 Test_3
I/O Display
NormaliseSTD
Signal Validation in Mímir - Simple Set-up
Data Acquisition (DA)
Data Manipulation (DM)
State Detection (SD)
Health Assessment (HA)
Prognostics Assessment (PA)
Advisory Generation (AG)
SPRT
σ-2σ FixedBound
PCA
AANN
I/O Data Feeder
DenormaliseSTD
C# Matlab
Fortran Java
DataDisplay
TrendGraph
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Case Tests
• Test 1 – Signal Offset• PCA Reconstruction• AANN Reconstruction• PEANO Reconstruction• σ-2σ Fixed Bounds Signal Health Assessment (on PCA
residual)• SPRT Signal Health Assessment (on PCA residual)
• Test 2 - Signal drift• PCA Reconstruction• AANN Reconstruction• PEANO Reconstruction• σ-2σ Fixed Bounds Signal Health Assessment (on PCA
residual)• SPRT Signal Health Assessment (on PCA residual)
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Test_1 Test_2 Test_3
NormaliseSTD
Signal Validation in Mímir – Simple Set-up
Data Acquisition (DA)
Data Manipulation (DM)
Health Assessment (HA)SPRT
σ-2σ FixedBound
PCA
AANN
I/O Data Feeder
DenormaliseSTD
C# Matlab
Fortran Java
I/O Display
DataDisplay
TrendGraph
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0 5000 10000 15000-1000
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0 5000 10000 15000-1000
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AANN reconstruction
0 5000 10000 15000-1500
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AANN reconstruction of signal offset
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AANN reconstruction
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PEANO reconstruction of signal offset
0 5000 10000 15000-1000
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Input to PEANOPEANO reconstruction
0 5000 10000 15000-1500
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0 5000 10000 15000-1000
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Input to PEANOPEANO reconstruction
0 5000 10000 15000-1500
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0 5000 10000 15000-1000
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Input to PCAPCA reconstruction
0 5000 10000 15000
Faulty
Warning
Healthy
Thermal Power (MWt) - Offset 20%
SPRT health assessment based on PCA residual
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0 5000 10000 15000100
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Real value
Input to PCAPCA reconstruction
0 5000 10000 15000-20
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PCA reconstruction of signal drift
0 5000 10000 15000100
150
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250Feed Water Line Temperature (C) - Drift 10%
Real value
Input to PCAPCA reconstruction
0 5000 10000 15000-20
-10
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0 5000 10000 15000100
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Real value
Input to AANNAANN reconstruction
0 5000 10000 15000-150
-100
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AANN reconstruction of signal drift
0 5000 10000 15000100
150
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250Feed Water Line Temperature (C) - Drift 10%
Real value
Input to AANNAANN reconstruction
0 5000 10000 15000-150
-100
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PEANO reconstruction of signal drift
0 5000 10000 15000100
150
200
250Feedwater Line Temperature (C) - Drift 10%
Real value
Input to PEANO
PEANO reconstruction
0 5000 10000 15000-30
-20
-10
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0 5000 10000 15000100
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Real value
Input to PEANO
PEANO reconstruction
0 5000 10000 15000-30
-20
-10
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0 5000 10000 15000100
150
200
250Feed Water Line Temperature (C) - Drift 10%
Real value
Input to PCAPCA reconstruction
0 5000 10000 15000
Faulty
Warning
Healthy
Feed Water Line Temperature (C) - Drift 10%
SPRT health assessment based on PCA residual
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A Prognostics case study from Oskarshamn O1 Use of dP measures over heat exchanger filters
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OKG Case Study: Use of differential pressure measures for CBM of heat exchanger filters•Maintenance orders
•Differential pressure measurements
•Flow measurements
•(Pump status, generator effect)
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Work orders
Pre-planned Based on expert judgement (condition)
All Heat Exchangers
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Differential pressure at first heat exchanger
721E1 Pressure
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K209 (1) AO FU Diff.Pressure
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Flow measurement at first heat exchanger
721E1 Flow
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K341 AO FU
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Feature based on Bernoulli`s principle
Incompressible flow equation: .2
2
constp
gzv
Diff P/Sqare Flow
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Diff/sqare flow AO FU
Can`t be explained by p and v
• trend of status of pumps ?
• trend of generator effect (revision) ?
• trend of sea water temperature ?
• other ?
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Recommendations to the IAEA
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Recommendations to the IAEA (1)
• Standards• Identification and analysis of existing standards for
condition-monitoring, diagnostics and prognostics• ISO-13374• MIMOSA OSA-CBM• …
• Can these be applied as-is to the nuclear industry or does the nuclear industry need new specific standards?
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Recommendations to the IAEA (2)
• “Aging” of digital systems• Digital I&C and SW systems have comparatively
very short life spans due to rapid technological advances
• Systems need to have technology modernisation and replacement as a fundamental design requirement in order to age gracefully
• Possible approaches could include:• Identification of several levels of abstraction in the system design and
architecture so that lower levels close to the implementation can be more easily modernised swapping obsolete components with modern ones without affecting the overall system
• Investigate automatic code generation from platform-independent specifications
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Recommendations to the IAEA (3)
• Uncertainty Management• Highly automated I&C and SW systems will rely on
real time data and additional information originating from other systems (e.g. condition monitoring and diagnostic systems)
• Most sources of information will have associated a certain degree of uncertainty that will have to be appropriately assessed and taken into account in further information processing
• Mechanisms for defining and treating uncertain information will be necessary
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Recommendations to the IAEA (4)
• Advances in Human System Interfaces• New I&C and SW systems deployed in new
settings will require new HSI solutions• New work practices, higher automation, modular plant designs
• Context-aware, multi-abstraction, multi-user HSIs• Emerging technologies could enable new work
practices• Augmented Reality and hand-held technologies enable portable
access to technology and advanced guidance• New interaction and collaboration technologies for distributed
decision-making• User interfaces dynamically integrating data from multiple sources• Integrated Operations
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Recommendations to the IAEA (5)• Interrelation between Technological, Human and
Organisational Factors• Factors related to structure, including established roles and
responsibilities (within a specific role), established task description procedures, established training procedures (often activity-oriented), and established supervision and management strategies
• Factors related to culture, including the manners of using artefacts to produce cultural contents, Artefacts can be of tangible nature, ones such as manuals, computers, etc., or of intangible nature, such as language, ethical values, senses of realism, etc.
• Factors related to process, that are directly the result of using cultural contents to produce cultural expressions – the manifestation of the contents. Examples here are established or “accepted” ways of communication, experienced patterns of conflicts, and experienced ways of handling changes
Atoosa P-J Thunem
Invitation
6th HOLMUG meeting(Halden On-Line Monitoring User Group)
Loviisa, Finland, October 8th- 9th, 2009
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• Objectives• Information dissemination and User Feedback
on on-line monitoring:• Methods• Available systems• Regulatory aspects• Feedback from utilities, research institutes, universities, and vendors
• Previous Meetings• 2003 in Halden, • 2004 at the EHPG in Sandefjord• 2004 IAEA technical meeting on ”Increasing instrument
calibration interval through on-line monitoring technologies”, in Halden
• 2006 at Oskarshamn (OKG), Sweden• 2007 at Olkiluoto (TVO), Finland• 2008 at Ringhals (Vattenfall), Sweden
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Topics• Calibration monitoring and signal validation
• Tools (e.g. PEANO) and methods• Experiences
• Equipment condition monitoring• Tools (e.g. aladdin, TEMPO, LIRA) and methods• Experiences
• Core Surveillance• Tools (e.g. SCORPIO, VNEM) and methods• Experiences
• Regulatory aspects• Requirements and experiences
You are welcome and encouraged to contribute!
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Meeting location
The meeting will be held at the Loviisa NPP site on the Southern coast of Finland
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Meeting information
• Expression of interest: June 30th, 2009• Contribution deadline: September 18th, 2009• Registration deadline: September 23rd, 2009
• Accommodation: Sannäs Manor
www.sannaskartano.fi• Visits: Loviisa plant site-tour
Finnish Sauna
http://www.ife.no/events/holmug2009
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