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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. Nuclear installations in Norway.

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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


Nuclear installations in norway

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, ...


Npp i c activities

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


Hrp members

HRP Members

  • 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

Signatory 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


Hrp activity sectors

I&C

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


Hammlab experimental facility

HAMMLAB Experimental Facility


Human performance human reliability

Home

Plant

Training

Exploratory Study

Field visits

First scenario

Last scenario

Human Performance/Human Reliability


Innovative human system interfaces

Innovative Human System Interfaces

Task based displays

Function oriented displays

Ecological displays

Innovative BWR displays


Design of large screen displays

Design of Large Screen Displays


Mixed reality for design planning training

Mixed Reality for Design, Planning & Training


Sw system dependability

SW system dependability


Computerised operation support

Computerised Operation Support

Knowledge Management

Simulator technology

Computerised Procedures

Work Processes

Advanced Alarm Systems

Function Allocation

Performance Monitoring

Core Monitoring and Simulation

Condition Monitoring

Prognostics

Virtual Sensing


Highlights

Highlights

  • Large-scale Signal Validation

  • Vision-based Diagnostics

  • Cable Monitoring

  • Mímir Framework & Toolbox

  • Prognostics

  • Recommendations to the IAEA TWG-NPPIC

  • HOLMUG 2009


Large scale signal validation

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


The multi group ensemble approach

20-60 signals

Single validation model

Validated signals

Hundreds of signals

Single validation model

Validated signals

The multi-group ensemble approach

?

?


The multi group ensemble approach1

20-60 signals

Single validation model

Validated signals

Multi-group ensemble approach

Group 1

Model 1

Group generation

Model aggregation

Hundreds

of signals

Group 2

Model 2

Validated signals

1

Group K

Model K

3

2

The multi-group ensemble approach

Hundreds of signals

Single validation model

Validated signals


The multi group ensemble approach issues

The multi-group ensemble approach: issues

  • Optimized (MOGA)

  • Randomized (RFSE)

1

Group generation

Ensemble model

2

  • Artificial Neural Networks (PEANO)

  • Principal Components Analysis (PCA)

Ensemble aggregation

  • Weighted average

  • Simple average

  • Trimmed mean, Median

3


Applications

Applications

1) 84 signals from Oskarshamn BWR

2) 215 signals from Loviisa PWR

3) 920 simulated signals Forsmark-3 BWR (HAMBO)


Loviisa 215 signals

Signal grouping: optimized; 150 groups; 8 – 147 signals

1

Validation model: PCA

2

Ensemble model aggregation: Weighted average

3

Loviisa – 215 Signals

Reconstruction of signal 205 (steam temp. in condenser SD51, °C): ensemble VS single model


Ongoing work

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


Reporting on i c status recommendations to the iaea on npp i c

Vision-Based Diagnostics

Mechanical Systems

Compressor

Heat exchanger

Electrical Systems

Internal breaker connection problem.

Hot fuse connection.


Reporting on i c status recommendations to the iaea on npp i c

The Vision Application

  • 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


Reporting on i c status recommendations to the iaea on npp i c

Tests at the Halden Reactor

Thermographic observation of valve heating up:


Reporting on i c status recommendations to the iaea on npp i c

Tests at the Halden Reactor cont.

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

Temperature

Time index


Reporting on i c status recommendations to the iaea on npp i c

Tests at the Halden Reactor

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

Temperature

Time index


Cable monitoring

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


Local degradation detection with lira

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


Comparison tests tecnatom

Comparison Tests (Tecnatom)

  • Compared Techniques

    • Line Resonance Analysis (LIRA)

    • Elongation at Break (EAB)

    • Time-Domain Reflectometry (TDR)

    • Insulation Resistance (IR)

LIRA

TDR


Lira localization accuracy

LIRA - Localization Accuracy

AVGERR= 0.23 % of cable length

STD = 0.08%


Global degradation assessment

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

Newcable

20 years

equivalent

40 years

equivalent

60 years

equivalent


Eab cbac correlation epr tecnatom

EAB/CBAC Correlation, EPR (TECNATOM)

new

20 years

Elongation At Break

40 years

60 years

CBAC

LIRA Global degradation measure


M mir

Intelligence

Pattern

Classification

Regression

Estimation

Hypothesis

Testing

Uncertainty

Estimation

RiskOptimisation

Pattern

Classification

Regression

Estimation

Hypothesis

Testing

Uncertainty

Estimation

RiskOptimization

Knowledge

Input

Selection

Data

Clustering

Statistical

Analysis

Modelling

Performance

Analysis

External Tools

(e.g. SAS)

Input

Selection

Data

Clustering

Statistical

Analysis

Modeling

Performance

Analysis

External Tools

(e.g. SAS)

Information

Data

Conditioning

Data

Filtering

Feature

Extraction

Data

Normalization

Data

Conditioning

Data

Filtering

Feature

Extraction

Data

Normalization

Data

Input

Data

Input

Data

Mímir

Data Validation,

Reconstruction, andCalibration Monitoring

Early Fault Detection

and Diagnostics

...

Lifetime and Performance Prediction

Pattern

Classification

Regression

Estimation

Hypothesis

Testing

Uncertainty

Estimation

RiskOptimization

Input

Selection

Data

Clustering

Statistical

Analysis

Modeling

Performance

Analysis

External Tools

(e.g. SAS)

Data

Conditioning

Data

Filtering

Feature

Extraction

Data

Normalization

. . .

Data

Input

TOOLBOX


Why m mir

Signal Grouping

Signal grouping for large scale applications through the use of Random Feature Selection Ensemble

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

Virtual Sensing

Empirical Ensemble-Based Virtual Sensing using regression models to estimate quantities not directly measured with physical instruments

Why Mímir


Why m mir1

Mìmir

Wavelet Filtering

Clustering

Genetic Algorithms

Filtering

Feature Selection

Regression Model

Classification

Normalisation

Neural Network

Ensembles

Why Mímir

Filtering

Ensembles

Clustering

Filtering

Filtering

Genetic Algorithms

Ensembles

Feature Selection

Normalisation

Ensembles

Normalisation

Normalisation

Classification

Neural Network

Regression Model

Neural Network

Regression Model


Reporting on i c status recommendations to the iaea on npp i c

Intelligence

Regression

Estimation

Regression

Estimation

Regression

Estimation

Knowledge

Information

Data

Filtering

Data

Filtering

Data

Filtering

Data

Mímir

Data Validation,

Reconstruction, andCalibration Monitoring

Data Validation,

Reconstruction, andCalibration Monitoring

Early Fault Detection

and Diagnostics

Lifetime and Performance Prediction

Pattern

Classification

Hypothesis

Testing

Uncertainty

Estimation

RiskOptimisation

Pattern

Classification

Hypothesis

Testing

Uncertainty

Estimation

RiskOptimisation

Regression

Estimation

Pattern

Classification

Hypothesis

Testing

Uncertainty

Estimation

RiskOptimisation

Input

Selection

Data

Clustering

Statistical

Analysis

Modelling

Performance

Analysis

External Tools

(e.g. SAS)

Data

Clustering

Input

Selection

Data

Clustering

Statistical

Analysis

Modelling

Performance

Analysis

External Tools

(e.g. SAS)

Input

Selection

Data

Clustering

Statistical

Analysis

Modelling

Performance

Analysis

External Tools

(e.g. SAS)

Data

Normalization

Data

Conditioning

Feature

Extraction

Data

Normalization

. . .

Data

Conditioning

Feature

Extraction

Data

Normalization

Data

Filtering

Data

Conditioning

Feature

Extraction

Data

Normalization

TOOLBOX

Data

Input

Data

Input

Data

Input

Data

Input


Industry standards

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


Iso 13374 and mimosa s osa cbm

ISO-13374 and Mimosa’s OSA-CBM

External

systems,

data archiving,

and block

configuration

Advisory Generation (AG)

Technical

displays and

information

presentation

Prognostics Assessment (PA)

Health Assessment (HA)

State Detection (SD)

Data Manipulation (DM)

Data Acquisition (DA)

Sensor / Transducer / Manual entry


M mir demonstrator

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)


Example case study signal validation of 14 signals from oskarshamn o 3

Example Case Study Signal Validation of 14 Signals from Oskarshamn O-3


Example case study signal validation of 14 signals from oskarshamn o 31

Example Case Study Signal Validation of 14 Signals from Oskarshamn O-3


Signal validation in a nutshell

Signal Validation in a Nutshell

PlantSignals

SignalValidation

Validated

Signals

Plant Signals

Validated

Signals

EstimateSignals

SignalHealth

Assessment

Residual

Calculation


Reporting on i c status recommendations to the iaea on npp i c

Intelligence

Knowledge

Information

Data

Mímir

...

Pattern

Classification

Regression

Estimation

Hypothesis

Testing

Uncertainty

Estimation

RiskOptimization

PCA

Estimation

Pattern

Classification

Uncertainty

Estimation

Hypothesis

Testing

RiskOptimisation

SPRT

Pattern

Classification

Regression

Estimation

Uncertainty

Estimation

RiskOptimization

Input

Selection

Data

Clustering

Statistical

Analysis

Modeling

Performance

Analysis

External Tools

(e.g. SAS)

Input

Selection

Data

Clustering

Statistical

Analysis

Performance

Analysis

External Tools

(e.g. SAS)

Modelling

Input

Selection

Data

Clustering

Statistical

Analysis

Performance

Analysis

External Tools

(e.g. SAS)

Modeling

Data

Conditioning

Data

Filtering

Feature

Extraction

Data

Normalization

. . .

STD

Normalization

Data

Conditioning

Data

Filtering

Feature

Extraction

Data

Conditioning

Data

Filtering

Feature

Extraction

Data

Normalization

Data

Input

Data

Input

Data

Input

TOOLBOX


Signal validation in m mir simple set up

Test_1

Test_2

Test_3

PCA

AANN

SPRT

σ-2σFixedBound

C#

Matlab

Fortran

Java

Signal Validation in Mímir - Simple Set-up

Advisory Generation (AG)

Prognostics Assessment (PA)

I/O Display

Health Assessment (HA)

DataDisplay

State Detection (SD)

TrendGraph

NormaliseSTD

DenormaliseSTD

Data Manipulation (DM)

I/O Data Feeder

Data Acquisition (DA)


Case tests

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)


Signal validation in m mir simple set up1

Test_1

Test_2

Test_3

C#

Matlab

Fortran

Java

Signal Validation in Mímir – Simple Set-up

SPRT

I/O Display

Health Assessment (HA)

DataDisplay

σ-2σFixedBound

TrendGraph

PCA

NormaliseSTD

DenormaliseSTD

Data Manipulation (DM)

AANN

I/O Data Feeder

Data Acquisition (DA)


Pca reconstruction of signal offset

PCA reconstruction of signal offset


Aann reconstruction of signal offset

AANN reconstruction of signal offset


Peano reconstruction of signal offset

PEANO reconstruction of signal offset


Sprt health assessment based on pca residual

SPRT health assessment based on PCA residual


Pca reconstruction of signal drift

PCA reconstruction of signal drift


Reporting on i c status recommendations to the iaea on npp i c

AANN reconstruction of signal drift


Peano reconstruction of signal drift

PEANO reconstruction of signal drift


Sprt health assessment based on pca residual1

SPRT health assessment based on PCA residual


A prognostics case study from oskarshamn o1

A Prognostics case study from Oskarshamn O1

Use of dP measures over heat exchanger filters


Okg case study use of differential pressure measures for cbm of heat exchanger filters

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)


Work orders

Pre-planned Based on expert judgement (condition)

Work orders


Differential pressure at first heat exchanger

Differential pressure at first heat exchanger


Flow measurement at first heat exchanger

Flow measurement at first heat exchanger


Feature based on bernoulli s principle

Feature based on Bernoulli`s principle

Incompressible flow equation:

  • Can`t be explained by p and v

  • trend of status of pumps ?

  • trend of generator effect (revision) ?

  • trend of sea water temperature ?

  • other ?


Recommendations to the iaea

Recommendations to the IAEA


Recommendations to the iaea 1

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?


Recommendations to the iaea 2

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


Recommendations to the iaea 3

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


Recommendations to the iaea 4

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


Recommendations to the iaea 5

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


Reporting on i c status recommendations to the iaea on npp i c

Invitation

6thHOLMUGmeeting

(HaldenOn-LineMonitoringUserGroup)

Loviisa, Finland, October 8th- 9th, 2009


Reporting on i c status recommendations to the iaea on npp i c

  • 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


Topics

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!


Meeting location

Meeting location

The meeting will be held at the Loviisa NPP site on the Southern coast of Finland


Meeting information

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-tourFinnish Sauna

http://www.ife.no/events/holmug2009


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