Condition Based Prognostics of Passive Components —

A New Era for Nuclear Power Plant Life Management

S. Bakhtiaria, S. Mohantya, I. Prokofievb, R. Tregoningb

aArgonne National Laboratory (ANL), Argonne, Illinois, USA bU.S. Nuclear Regulatory Commission (NRC), Washington, DC, USA

Third International Conference on Nuclear Power Plant Life Management (PLiM) Salt Lake City, Utah, USA May 14 – 18, 2012

Work Sponsored by the US Nuclear Regulatory Commission

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Outline

Objective

Background – NDE and OLM

Scoping studies – Inspection

– Monitoring

Condition Based Prognostics of Passive Components – Scope of application to nuclear industry

– Attributes of an Effective OLM & OLP system

– Preliminary investigations at ANL

Concluding Remarks

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Perform scoping studies, in concert with the nuclear industry, to identify sensors and techniques that

– Fill critical inspection or monitoring needs

– Are closest to being both technically and commercially viable and the industry is likely to pursue

– Could be utilized in LWR and/or advanced reactor applications

Candidate sensors and instruments included those to – Monitor real-time materials degradation

– Characterize residual stress (RS)

– Inspect and monitor component fabrication

– Assess radionuclide and associated chemical species concentrations in ground water and soil (Compliance with 10CFR Part 20.1406)

– Monitor long-term performance of dry cask storage systems

– Monitor severe accident conditions

In-situ inspection and Real-Time monitoring of materials degradation – Existing and emerging NDE/NDI technologies

– OLM techniques including those for health monitoring and damage forecasting

– Focus of studies on inspection and monitoring of passive SSCs

Objective

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Nondestructive evaluation/inspection (NDE/NDI)

– Material characterization • Most accurate NDE methods can be selected for a given application • Alternative NDE methods and destructive examinations can be used for validation/verification • Efficiency is often not a major constraint

– ISI and PSI • Assess present condition of a component (integrity and fitness for service) • Predict performance for subsequent operating cycle (meet safety margins) • Limited number of practical/qualified techniques for any particular application • Inspection efficiency is an important factor

On-line monitoring (OLM)/Structural Health Monitoring (SHM)

– Monitor process parameters (temperature, pressure, level, flow, vibration, radiation, etc.)

– Conventional OLM tools operate primarily as fault-detection systems (sensor validation tools) • Instrument channel performance • Calibration assessments

– Emerging systems can provide improved diagnostics and prognostics capability • Not only detect but also predict the health of operating machinery (beyond analysis of process

parameters)

• Significant R&D is being conducted on advanced OLM systems • SSCs in existing plants may not be readily adapted to incorporate new technologies

– Health monitoring of passive components remains to be a challenge • Measurement of process parameters may not provide relevant and sufficient information • R&D is needed on new sensor technologies and monitoring techniques

Background

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Higher degree of inspection automation (hardware and software)

– More prevalent use of compact and efficient robotic and remotely

operated scanners, crawlers, and vehicles and better access in

confined spaces

– Advanced visualization and automated data analysis tools

– Model assisted inspections

Faster inspections through employment of linear and matrix array sensor configurations

Increased accuracy and quantification capability

– Improved signal-to-noise ratio (S/N) as a result of improved probe

design and on-board signal conditioning electronics

– Use of advanced and efficient algorithms for real-time analysis of

large amounts of data

Greater penetration depth and higher spatial resolution

More flexible and modular tools allowing incorporation of multiple sensors in the probe assembly

Compact systems (integrated inspection units for rapid deployment)

Rugged probes for operation in harsh environments (elevated

temperature, pressure, radiation, moisture, and corrosive media)

Inspection techniques that are less affected by the surface condition of components

Common Features of Modern NDE Equipment

A crawler robot for

inspection and repair (ZR-100® by Zetec, Inc.)

Integrated eddy current tester

and pusher-puller units (MIZ®-80iD by Zetec, Inc.)

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Advanced eddy current test (ECT) methods – High spatial resolution arrays

– Thin-film surface-conforming sensors

– Solid-state sensors including GMR and AMR probes

– Magneto-optic imagers (MOIs)

– Pulsed eddy current (PEC) technique

– Remote-field eddy current testing (RFECT)

– Superconducting quantum interference device (SQUID)

Ultrasonic Testing (UT) methods including – Time-of-flight diffraction (TOFD),

– Phased array UT (PA-UT),

– Laser UT (LUT),

– Electromagnetic Acoustic Transducer (EMAT)

– Guided-wave UT (GW-UT)

Some Emerging NDE technologies

Industrial MOI system for inspection

of aging aircrafts [Shih et al].

(Top) SwRI-developed RFEC for inspection

of 6-8 in.-diameter natural gas pipelines.

(Bottom) Explorer II robotic transport tool for

RFEC probe developed by Carnegie Mellon

University [www.swri.org/4org/d18/nde/eddycurrent.htm].

Guided wave system

that can perform wall

thinning inspection over

long lengths of piping

[Odakura et al].

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Infrared (IR) thermal imaging – Pulsed thermal imaging

– Thermal tomography

– Vibrothermography

Enhanced visual inspection using miniature cameras and advanced image processing techniques

Radiographic testing (RT) with portable instruments

RF and Microwave remote sensing methods

Typical NDE methods deployed using pipeline inspection gauges (pigs) for buried components

– ECT including PEC and RFECT

– Magnetic flux leakage (MFL)

– Ultrasonic testing including EMAT and GW-UT

– Visual/video testing

Emerging NDE methods for in-situ RS measurement – laser shearography

– UT based on critically refracted

longitudinal waves

Some Emerging NDE technologies (Contd.)

Prototype EmatScan

CD pig for inspection of

pipelines. Also shown

(top corner) is the SH-

wave probe in a spring

loaded suspension

[Salzburger, 2009].

ITSTM transient thermography

system [QUEST Integrated, Inc.].

Multiple data set MFL tool

(Enduro Pipeline Services,

Tulsa, Oklahoma).

Emerging OLM Technologies

Sensor technologies

– New sensor systems

• Distributed smart/intelligent sensors

• MEMS/NEMS based sensors

– Integrated sensor platforms with alternate sensing modalities

– Energy efficiency and harvesting

– Long term operation in harsh environments

Fiber optic transmission

Wireless sensor networks with improved protocols

Adaptability for continuous or periodic monitoring

More effective diagnostics and prognostics algorithms (e.g., hybrid systems)

Efficient data mining and management

Modern SHM systems developed mostly for non-nuclear applications may be adapted to monitoring of SSC in NPPs

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Wireless sensor networks (WSNs) – Integration of sensors with embedded processing capability and

wireless communication networking protocol – Any number of different sensing elements (acoustic,

electromagnetic, radiation, etc.) may be used for sensing – Several embedded operating systems have been developed for

different software platforms to render optimal exploitation the memory limits of WSNs (e.g., IEEE 802.15.4, ZigBee, TinyOS)

– Other features include • Self-healing (compensating for faulty sensors by routing the

information through other nodes) • Energy harvesting (use of ambient or process generated energy to

prolong the lifetime of battery powered nodes)

Sensor types – “Smart sensor” — a combination of a sensor, an analog interface

circuit, an ADC and a bus interface in one housing – “Intelligent sensor”— a smart sensor with one or several

intelligent functions such as self-testing, self-identification, self-validation, self-calibration, self-compensation, self-adaptation

A large number of WSNs are available

Rapidly expanding area of R&D

Demonstration of technology for some NPP applications is under investigation

Of particular interest for severe accident scenarios

Wireless Sensors for In-situ Monitoring

(a) PZT, (b) PVDF, and (c) MFC piezoelectric

sensors [Yun et al].

Some wireless smart sensor prototypes

[Cho et al.,2008].

Condition Based Prognostics of Passive Components NDE vs. OLM/Embedded NDE

Long term sustainability of SSCs requires implementation of regular ISI and repair strategies

– ISI is done by implementing appropriate NDE procedures for inspection of passive components (e.g., SG tubes, RPV, RCS piping, etc.)

– A variety of application-dependent NDE methods are employed for ISI of SSCs

OLM is the process of continuously interrogating a system or component for degradation

– Typical OLM system consists of networks of sensors which are attached to a SSC in conjunction with the associated electronics and signal processing software

– Principal advantage of OLM is that it is done in-situ and in a near-continuous or real time basis

– OLM/SHM techniques are sometimes referred to as embedded NDE techniques

Both conventional OLM and NDE tools can only infer the state of the structure at any given instant of time

– Merely identifying degradation through NDE or OLM does not necessarily imply that the part cannot survive the remainder of its design life

– Economical constraints are always factored in when making decisions about repair and replacement

A reliable predictive system is needed to estimate the remaining life of the structure

– Could be achieved using online prognostics (OLP) or a condition based life prediction system

– Can improve the economy and safety by continuously accessing the remaining useful life of SSC

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Scope of Application to Nuclear Industry

Approximately 20% of the total power in the US is generated by 104 LWRs

Structural integrity of critical NPP components is of vital importance

SCC and CF are among degradation mechanisms of primary concern

– Typically occur in connection with locations of high residual stress in SSCs

– Affected components include SG tubes, welds in the RPV penetration, nozzles and other primary piping welds

– Some examples of past field experience • 47 recordable PWSCC indications in the degraded CRDM penetration nozzles found at OCONEE NPP in 2001.

Cracks were either axial or below-the-weld circumferential crack in the CRDM nozzle.

• Axial TW and a small circumferential crack found in the first weld between the RPV nozzle and the RCS hot leg piping at V.C. Summer in 2000.

• Multiple SCC type flaws were found in such places as surge line to hot leg weld and pressurizer relief valve nozzle weld at Calvert Cliffs in 2006.

• Circumferential SCC/CF cracks found in the surge, relief, and safety nozzle-to-safe-end dissimilar metal butt welds at Wolf Creek in 2006.

• An axial TW SCC/CF related flaw was detected in decay heat removal drop line weld during weld overlay work on the drop line at Davis-Besse in 2008.

Cracking could start long before and not noticed during the scheduled ISIs (i.e., missing of indications below the detection limit of the NDE technique)

Unnoticed small cracks could in certain cases grow aggressively and lead to a failure event before the next scheduled outage

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Scope of Application to Nuclear Industry (Contd.)

OLM in conjunction with OLP can play an important role in minimizing failure events

– The OLM system estimates the current degradation or state of the structure using advanced signal processing algorithms and data management schemes

– The OLP system predicts the future damage (remaining life) using mechanistic and probabilistic based forecasting models

– Provides sufficient time to plan for repair/replacement

– OLM sensors can be permanently mounted at structural “hotspots” such as

• Dissimilar metal welds in RPV penetrations

• RCS dissimilar weld with RPV nozzle

• RCS pipe elbows

• SG nozzle welds

• RPV belt lines (radiation embrittlement related aging)

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Schematic of degradation time-line of any structural system

Attributes of an Effective OLM-OLP system OLM System

Sensor selection – Match OLM sensor to the component being monitored

– Active sensors • Use a deterministic fixed input signal that is transmitted using an

actuator/excitation source

• Signals are analyzed to determine the presence of damage, its type, and its severity

• Generally more suitable for passive SSCs such as RPV and stiff RCS piping

• Can be further divided based on the type of input signal

– Narrowband input signal is less complex to analyze

– Broadband input systems excite multiple smaller damage features and hence are more helpful for detecting small changes

• Examples include ultrasonic transducers and EM induction probes

– Passive sensors • Do not require any actuators (e.g., vibration, strain, and AE sensors)

• Infer the state of the structure using the environmental or operating conditions of SSC

• System response is inferred from the dynamic system input to the sensor

• Generally more suitable for active SSCs including systems with moving or rotating components such as pressurizer, coolant pumps, and valves

– Requirements on the size of the defect to be detected • Nearly always advantageous to select high-frequency sensors for detecting small flaws

• Lower frequencies are used to achieve longer sensing range

• Frequency selection is always a trade-off between sensitivity and resolution

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Permanently

bonded

piezoelectric

actuator

Permanently

bonded

sensor

Broadband

ultrasonic

wave

Surface/

subsurface

cracks

OLM system (Contd.)

Signal processing

– One of the principal challenges of the OLM system design • Extracting information associated with small damage features under field conditions

• Signal of interest often corrupted by noisy operating environment

– Feature Extraction (essentially a statistical pattern recognition approach)

• Extract damage sensitive features from the time-series sensor signals

• Use statistical signal features that are implicitly related to change in physical behaviour of the structure

– Degradation assessment • Detection can be done by statistical comparison of signal distribution between healthy and

damaged structure

• Classification can be done by using machine learning algorithms (e.g., based on the hidden Markov model) and support vector machines

• Quantification can be achieved by using supervised/unsupervised damage estimation techniques

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Diagram of no damage and

damage present hypothesis

OLM system (Contd.)

Information fusion and data reduction

– Individual sensor node may consist of identical or different types of sensors

– Extracting useful information from multiple sensor nodes and heterogeneous sensors

• Information fusion

• Cognitive decision science

• A number of popular tools used for SHM applications can be adapted for this purpose

– Handling of large quantities of data generated by OLM sensors

• Increases the cost of data transfer and storage

• Traditional framework for data compression is to first sample the entire signal, and then perform compression

• Use of modern data compression algorithms

– Always more efficient to compress the data at the source

– New data compression methods such as compressive sampling are viable solutions for directly collecting relevant information from sparse, high-dimensional measurements

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OLP System

Estimating the remaining life of components

– Depends primarily on

• Microstructure

• Loading environment (i.e., temperature, pressure, water chemistry, etc.)

• Initial damage condition

– Current approach to life prediction for NPP components

• Estimated using stress/strain life curves

– Damage growth rate obtained from simulated rector condition tests

– Models based on the design requirements and field conditions estimated by NDE from periodic ISI

• Highly empirical methods based on laboratory test data, which may not accurately simulate field conditions (differences in material heat and loading environment)

• lack of information about the initiation time could result in inaccurate prediction of the remaining useful life (i.e., initiation of cracking in between the scheduled ISIs)

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OLP System (Contd.)

Emerging OLM-OLP systems for passive components

– Integration of an OLM system with an accurate predictive model

• OLM based on active and passive sensor technologies

• Predictive models of crack propagation based on accurate mechanistic models

– Need to update early fracture mechanics-based crack growth models (developed mostly for non-nuclear applications)

– Damage growth models are deterministic in nature (i.e., predict a deterministic remaining life)

– Include micro structural material variability, loading and environmental variability, and other random factors into damage growth curves (e.g., Ford- Andresen model for SCC and CF)

– Develop an OLP probabilistic model by using

» First order statistics-based probabilistic fracture mechanics model

» Fracture mechanics models based on advanced nonlinear Bayesian statistics

– Hold promise to overcome some intrinsic limitations associated with periodic ISIs

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Schematic of integrated OLM-OLP system

Preliminary OLM Related Investigations at ANL

Performed limited number of small scale experiments for proof-of-concept demonstration

Detect initiation and monitor growth of SCC in thin-wall alloy SG tubes with flaws in the apex region of U-bend specimens

Employed active sensing method

– Wide-band ultrasonic transducers (< 1MHz)

– Small number of permanently bonded sensors

– Large number of high frequency excitation modes

Analysis of data based on a system identification approach

– Current state estimator at an individual sensor node

– Future condition based state forecasting at an individual sensor node

– Overall structural integrity assessment using Bayesian fault tree approach

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NI DAQ system

I/P & O/P signals

MATLAB based

online signal

processor

MATLAB based

online state

estimator

Signal conditioner

hardware for low

frequency noise

cancellation

Actuator

signal

Broadband

chirp input

PZT

Actuator

& Sensor

Output signal

Sensor

signal

Online

damage states

Preliminary OLM Related Investigations at ANL Experimental Results

A broadband active sensing technique was demonstrated for OLM of SCC

Preliminary tests on U-bend specimens showed good trend in online estimated states

Active monitoring clearly indicated crack initiation and growth states

Significantly better S/N and discrimination capability than AE monitoring that was done in parallel

Promising technology for real-time monitoring of passive NPP components

Well suited as elements of a sensor network for OLM of large structures

Further research and development is needed

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Online monitoring of SCC using broadband active sensing;

(top) circumferential crack developed in U-bend apex

region and (bottom) time-series damage state of the tube

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NDE Techniques – A number of advanced and newly developed inspection techniques are available for

improved quantification of damage and assessment of component integrity

– Various programs (federal and private) are being supported to evaluate their applicability to NPP life extension

– Confirmatory studies are needed to assess • Reliability and accuracy of techniques in detecting and characterizing early stage damage

• Ability of emerging automated systems to replace manual inspections (decision-making process)

• Effectiveness of performance demonstration and qualification procedures

OLM Techniques – Rapidly evolving area of R&D

– Advanced diagnostics and prognostics techniques are being developed

– A wide range of smart/intelligent WSNs are available • Primarily used in civil engineering, aerospace, automotive and transportation industries

• Adaptation of technology to NPP industry has not been fully investigated

– Reliable OLM methods for monitoring of degradation in passive components are needed • Beyond analysis of process parameters

• Promising active sensing techniques need to be further investigated

– Need to implement meaningful performance demonstration and qualification programs (different from qualification of ISI techniques)

Concluding Remarks