NUREG/CR-6638 

ANL-99/9

Advanced NDE for 

Steam Generator Tubing

Argonne National Laboratory 

U.S. Nuclear Regulatory Commission 

Office of Nuclear Regulatory Research 

Washington, DC 20555-0001
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DISCILAIMER

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NUREG/CR-6638 

ANL-99/9

Advanced NDE for 

Steam Generator Tubing 

Manuscript Completed: October 1999 

Date Published: January 2000

Prepared by 

S. Bakhtiari, D. S. Kupperman 

Argonne National Laboratory 

9700 South Cass Avenue 

Argonne, IL 60439 

J. Muscara, NRC Project Manager 

Prepared for 

Division of Engineering Technology 

Office of Nuclear Regulatory Research 

U.S. Nuclear Regulatory Commission 

Washington, DC 20555-0001 

NRC Job Code W6487
NUREG/CR-6638 has been reproduced 

from the best available copy.
Advanced NDE for Steam Generator Tubing

S. Bakhtiari and D. S. Kupperman 

Abstract 

This report provides an assessment of various advanced nondestructive evaluation (NDE) 

techniques for inservice inspection of steam generator tubing. Selected results are presented on 

the application of data processing and visualization techniques; data analysis, including depth 

sizing and the development of correlations between eddy current signals and tube failure 

pressure by multivariate regression and neural networks; inspection of sleeved tubes; 

applications of ultrasonic NDE; and related work on emerging NDE technology.

NUREG/CR-6638 iii
Contents 

A bstract ........................................................................................................................ m 

Executive Summary .................................................................. . . ........................ xi 

Acknowledgments ........................................................................................................... xiii 

Acronyms and Abbreviations ........................................................................................ xv 

1 Introduction ..................................................................... ............................... 1.. 

2 Data Processing and Display ............................................. 1 

2.1 Computer-Ailded EC Data Conversion and Calibration Routines ...................... 1 

2.1. 1 Conversion of Raw EC Inspection Results ........................... 2 

2.1.2 Automated Calibration and Display Algorithms ................................... 2 

2.2 Visualization of RPC Data .............................................................................. 4 

2.3 Application of Digital Filters ........................................................................... 9 

3 Data Analysis ....................................................................................................... 13 

3.1 Depth Sizing ................................................................................................ 13 

3.1.1 Improvement of Back-Propagation Algorithm for Flaw Sizing .............. 13 

3.1.2 Multivariate Regression Analysis ..................................................... 18 

3.2 Correlation of Failure Pressure with Bobbin Probe Measurements ................ 24 

4 Advanced Ultrasonic NDE of Steam Generator Tubing .......................................... 37 

4.1 Guided Ultrasonic Waves ............................................................................. 37 

4.2 Acoustic Microscopy ..................................................................................... 41 

4.3 Laser-Cut Slots for Calibration and Modeling .............................................. 44 

4.4 Bore-Side Ultrasonic Examination of Tubes with ElectrosleeveTM Repairs ......... 45 

5 Emerging Technology ............................................................................................. 50 

References ....................................................................................................................... 53 

v NUREG/CR-6638
Figures 

2.1. MATLAB-based GUI tools have been implemented to allow direct conversion and 

segmentation of raw EC inspection results acquired with conventional commercial 

instruments for independent analysis ........................................................................... 3 

2.2. Typical display of data calibration GUI tool and strip chart display of raw EC inspection 

results by data calibration function of ECTool ........................................................... 5 

2.3. Typical strip chart and lissajous pattern display of calibrated EC inspection results by 

data calibration function of ECTool GUI ..................................................................... 6 

2.4. Outputs of data visualization tool showing mesh plot, image display, and frequency 

slice display of ASME standard tube ......................................................................... 7 

2.5. Output of data visualization tool showing image of tube standard with 6.35-mm (0.25

in.)-long axial and circumferential ID and OD notches .......................... 8 

2.6. Output of data visualization tool showing image- of mock-up tube specimens with 

longitudinal IDSCC indications at roll transition region ............................................ 8 

2.7. Output of data visualization tool showing image of mock-up tube specimens with 

circumferential ODSCC indications at roll transition region ...................................... 9 

2.8. Transfer function of bandpass FIR filter composed of 50th-order low-pass and 100th

order high-pass fiters ............................................................................................. 11 

2.9. Power spectral density of 400 kHz (a) differential and (b) absolute bobbin coil signal for 

TSP indication ............................................................................................................ 11 

2.10. Normalized horizontal and vertical components of (a) original and (b) filtered differential 

bobbin coil signal for TSP indication at 400 kHz ...................................................... 12 

2.11. Normalized horizontal and vertical components of (a) original and (b) filtered absolute 

bobbin coil signal for TSP indication at 400 kHz ...................................................... 12 

3.1. Normalized horizontal and vertical components of EC inspection data for tube standard 

that contained axial notches .................................................................................. 21 

3.2. Normalized horizontal and vertical components of EC inspection data for the tube 

standard that contained circumferential grooves ..................................................... 21 

3.3. Normalized traces of horizontal and vertical components of EC inspection data for 

representative specimen with longitudinal ODSCC degradation .............................. 22 

3.4. Normalized traces of horizontal and vertical components of EC inspection data for 

representative specimen with circumferential ODSCC degradation .......................... 22 

3.5. Normalized traces of horizontal and vertical components of EC inspection data for 

representative specimen with IGA degradation ....................................................... 23 

3.6. Maximum measured vs. predicted depth in percent of wall thickness for all available 

specimens when training set consisted of all calibration standard tubes with simulated 

axial and circumferential notches .......................................................................... 23 

3.7. Maximum measured vs. predicted depth in percent of wall thickness for all available 

specimens when training set consisted of all calibration standard tubes with axial and 

circumferential indications, plus two specimens with shallow OD indications ............. 24 

3.8. Distribution of pulled-tube and model-boiler indications as a function of failure 

pressure used for multivariate analysis ................................................................... 27

NUREG/CR-6638 vi
3.9. Regression- and neural-network-predicted vs. measured failure pressure when all 

model boiler and pulled tube indications were included for training ........................... .30 

3.10. Regression- and neural-network-predicted vs. measured failure pressure when =50% 

of model-boiler and pulled-tube indications were randomly selected for training ......... 31 

3.11. Regression- and neural-network-predicted vs. measured failure pressure for model

boiler and pulled-tube indications, with LOOT training and prediction method .......... 32 

3.12. Residual vs. regression-predicted failure pressure when all model-boiler and 

pulled-tube specimens were included for training ................................................... 33 

3.13. Change in predicted residual failure pressure shown in Fig. 3.12 for model-boiler and 

pulled-tube specimens when following a LOOT training and prediction scheme was 

followed. ............................................................ 33 

3.14. Cumulative distribution of residual failure pressure, based on an assumed normal 

distribution function, with training and prediction based on all indications, and 

LO O T . ....................................................................................................................... 34 

3.15. Regression-predicted vs. measured failure pressure for all model-boiler and pulled

tube indications, using the APC model ................................................................... 35 

3.16. Residual vs. regression-predicted failure pressure for all model-boiler and 

pulled-tube specimens, using the APC model ........................................................ 36 

3.17. Cumulative distribution of APC model failure pressure, based on an assumed normal 

distribution function .............................................................................................. 36 

4.1. Specially fabricated Lamb wave probe with a 6-mm piezoelectric crystal on a wedge, 

operating in a pulse echo mode .............................................................................. 37 

4.2. Ultrasonic Lamb wave echo from a circumferential ODSCC in an Alloy 600, 22.2-mm 

(7/8 in.)-diameter tube with a laboratory-grown crack in the roll transition ............... 38 

4.3. Dependence of ultrasonic Lamb wave amplitude on crack depth, evaluated with 

numerous tubes and 40-ps round trip transit time .................................................... 39 

4.4. Dependence of Lamb wave amplitude on crack depth, evaluated with numerous 

tubes............................................................... 39 

4.5. Ultrasonic Lamb wave image of laboratory-grown circumferential ODSCC in an Alloy 

600 SG tube .............................................................................................................. 40 

4.6. Schematic representation of Sonoscan Inc. acoustic microscope .............................. 41 

4.7. Ultrasonic shadow of a 60% TW laser-cut slot produced by an angled 50-MHz shear 

wave launched from inside a water-filled tube ........................................................ 42 

4.8. Depth estimated by acoustic microscopy vs. actual depth of two EDM notches in two 

Alloy 600 22.2-mm (7/8-in.)-diameter tubes ......................................... ............... 43 

4.9. Shadow of crack from tube with ODSCC ................................................................ 43 

4.10. Photomicrograph of axial OD laser-cut slot, 75% TW, in Alloy 600, 22.2-mm 

(0.875-in.)-diam eter tube . ...................................................................................... 44 

4.11. Ultrasonic and EC signals from a I-mm-deep OD circumferential notch in parent metal 

of Electrosleeved tube ............................................................................................ 46 

4.12. Ultrasonic and EC signals from 0.5-mm-deep OD circumferential notch in parent metal 

of Electrosleeved tube ............................................................................................ 47

vii NUREG/CR-6638
4.13. Ultrasonic and EC signals from a 1-mm deep OD axial notch in parent metal of 

Electrosleeved tube . ............................................................................................... 48 

4.14. Ultrasonic and EC signals from a 0.5-mm-deep OD axial notch in parent metal of 

Electrosleeved tube . ............................................................................................... 49

NUREG/CR-6638 viii
Tables 

3.1. Summary of tube samples used for neural-network studies ..................................... 14 

3.2. Depths predicted by trained neural-network with training samples of various 

threshold values .................................................................................................... 16 

3.3. Depths predicted by trained neural-network with training samples of various 

threshold values ........................................................................................................ 16 

3.4. Depths predicted by trained neural-network with two types of nonlinear transfer 

functions for first layer and two threshold values of 0 for selecting each training 

specim en ................................................................................................................... 17 

3.5. Frequency of unsuccessful training of neural-network with random initialization of 

weight vectors and two types of first-layer neuron transfer functions ....................... 17 

3.6. Depths predicted by trained neural-network with two types of nonlinear transfer 

functions for first layer and two threshold values of 0 for selection of training 

sam ples . ................................................................................................................... 18 

3.7. Composition of pulled-tube specimens from ARC data base incorporated for failure 

pressure analysis .................................................................................................... 26 

3.8. Destructive examination and failure pressure measurements for 22.2-mm (0.875 in.)-. 

diameter tubes from ARC model-boiler database that contained ODSCC degradation 

at TSP intersections ................................................................................................. 26

ix NUREG/CR-6638
Executive Summary 

As part of a program on steam generator (SG) tube integrity sponsored by the U.S. Nuclear 

Regulatory Commission (USNRC) at Argonne National Laboratory, the reliability of 

nondestructive evaluation (NDE) methods for the detection and characterization of defects in SG 

tubing is being evaluated. In addition to work on the effectiveness of current NDE techniques, 

the potential impact of advanced and emerging inspection technology on the effectiveness of 

inspections is being assessed. This report provides an assessment of various advanced NDE 

techniques for the inservice inspection of SG tubing. 

Multicoil rotating and array probe inspections generate enormous amounts of data. 

Conversion and manipulation codes have been developed to permit more efficient and flexible 

analysis of this data. Signal processing and data analysis algorithms have been developed that 

use the PC-based software MATLAB, which is a high-level scripting language that provides an 

efficient environment for the development of the algorithms as well as convenient graphical user 

interfaces (GUIs) and graphical displays of the results. A series of algorithms has been 

implemented under the MATLAB environment to carry out the conversion, normalization, and 

reformatting of eddy current (EC) readings for subsequent analysis. An interactive MATLAB 

program calls the data retrieval and calibration routines through a main GUI control window. 

A flaw imaging and display software is described that attempts to simplify interpretation of 

data acquired by rotating and array probes through improved visualization and automated 

analysis methodologies. The software provides two views of the data: a two-dimensional plan 

view and a four-dimensional modified isometric view that uses color to represent relative phase 

information, unlike the conventional isometric plot, which presents only amplitude information. 

In its present form, the software estimates the depth of a flaw by using an algorithm similar to 

that used in conventional analysis. However, instead of estimating depth at isolated points that 

are selected by the analyst, it does so over the whole region around the flaw and presents the 

results in terms of a color map. This approach greatly improves "pattern recognition" and 

makes it much easier to see the morphology of the cracking and seems to reduce the 

dependence on the analyst's judgment. The 4-D modified isometric view has the advantage that 

it is relatively familiar to analysts accustomed to working with a conventional isometric plot, and 

also simultaneously provides relative phase information for every point in the region that 

surrounds the flaw. 

Results are presented on the use of back-propagation neural-network structures to predict 

flaw depth. Our studies showed that, because accuracy is more critical for deep defects, the 

criteria for selecting training data could be weighted to include more samples with deeper 

defects and achieve an accuracy comparable to that obtained by using all of the available 

defects, with significantly shorter times required for training. Multivariate regression algorithms 

were evaluated as alternatives to neural-network structures for the development of correlations 

between EC data and flaw size. 

Conventional repair and plugging criteria for SG tube integrity are based on estimated 

degradation depth. Tubes with EC indications that correspond to degradation greater than a 

specified limit (typically >40% of nominal tube wall thickness) are either repaired or plugged. 

For indications of outer-diameter stress corrosion cracking (ODSCC) at tube support plate (TSP)

)d NUREG/CR-6638
locations, an alternate plugging criterion (APC) has been developed based on direct correlation of 

bobbin coil voltage amplitude (rather than estimated defect depth) to failure pressure and leak 

rate. The data on pulled and model boiler tube specimens that were originally used by the 

Westinghouse Owners Group to develop an APC for indications of ODSCC at TSP locations in 

Westinghouse plants with drilled tube support plates have been reanalyzed by multivariate 

regression analysis and neural network models. The global prediction bands of multivariate 

models are somewhat tighter than those achieved by a linear least-squares fit to the peak 

bobbin voltage. The linear models predict the failure pressures nearly as well as the nonlinear 

neural-network models. 

The use of ultrasonic guided (Lamb) waves to improve detection of SG tubing flaws and to 

identify attributes that may be useful for characterizing defects in field-degraded tubes is also 

discussed. Because ultrasonic test (UT) methods involve shorter wavelengths, they inherently 

provide finer spatial resolution than EC methods; therefore, cracks that can be identified as 

segmented by ultrasonic interrogation, may only be resolvable as a single crack by EC 

examination. Studies have shown that the amplitude of Lamb wave echoes from flaws are not 

very sensitive to depth and hence provide little depth sizing information. 

In collaboration with Sonoscan Inc. (Bensenville, IL), high-frequency ultrasonic imaging has 

been explored as a possible method to help characterize defects in SG tubes under laboratory 

conditions. Excellent results were obtained from laser-cut slots and from electro-discharge

machined (EDM) notches. However, SCCs, which are typically very tight, do not appear to 

produce shadows that are as clear as those produced by notches or fatigue cracks. Because the 

tip is the tightest portion of the crack, crack depths would be expected to be underestimated. 

The difficulties in estimating depths of SCCs by acoustic microscopy suggest that significant 

difficulties could also be encountered with procedures that attempt to use crack-tip echoes to 

estimate depth. Cracks that cannot be shadowed also will not scatter significant ultrasonic 

energy to reveal the location of the crack tip, resulting in underestimation of depth.

NUREG/CR-6638 xii
Acknowledgments 

The authors acknowledge the contributions of D. R. Perkins and C. W. Vulyak to the 

experimental work described in this report. The authors thank M. Brook (ABB AMDATA), S. 

Canumalla (Sonoscan Inc.), C. V. Dodd, X. Kong (Northern Illinois University), and R. Kurtz 

(PNNL) for their contributions, and D. R. Diercks and W. J. Shack for their reviews and 

comments. This work is sponsored by the Office of Nuclear Regulatory Research, U.S. Nuclear 

Regulatory Commission (NRC), under FIN W6487; The NRC Project Manager is Dr. J. Muscara, 

who provided useful guidance in the performance of this work.

xiii NUREG/CR-6638
Acronyms and Abbreviations

ABB ASEA Brown-Boveri 

ANL Argonne National Laboratory 

APC alternate plugging criterion 

ARC alternate repair criteria 

ASME American Society of Mechanical Engineers 

ASTM American Society for Testing and Materials 

CSF conjugate spectrum filters 

EC eddy current 

EDM electro-discharge-machined 

EPRI Electric Power Research Institute 

FBH flat-bottom hole 

FIR finite-impulse response 

FTI Framatome Technologies, Inc. 

GUI graphical user interface 

HP Hewlett Packard 

ID inner diameter 

IDSCC inner-diameter stress corrosion crack/cracking 

IGA intergranular attack 

IIR infinite-duration impulse response 

IPC interim plugging criterion 

ISI inservice inspection 

LOOT leave-one-out training 

MLR multiple linear regression 

NDE nondestructive evaluation 

NRC U.S. Nuclear Regulatory Commission 

OD outer diameter 

ODSCC outer-diameter stress corrosion crack/cracking 

PC (IBM) personal computer 

PCR principal component regression 

PNNL Pacific Northwest National Laboratory 

RFEC remote-field eddy current 

RPC rotating pancake coil 

SCC stress corrosion crack/cracking 

SG steam generator 

S/N signal-to-noise ratio 

TOF time of flight 

TSP tube support plate 

TW throughwall 

UT ultrasonic test/testing

xv NUREG/CR-6638
1 Introduction 

As part of a program on steam generator (SG) tube integrity sponsored by the U.S. Nuclear 

Regulatory Commission (USNRC) at Argonne National Laboratory (ANL), the reliability of 

nondestructive evaluation (NDE) methods for the detection and characterization of defects in SG 

tubing is being evaluated. In addition to work on the effectiveness of current NDE techniques, 

the potential impact of advanced and emerging inspection technology on the effectiveness of 

inspections is being assessed. In this report, an assessment of studies on advanced NDE 

techniques for the inservice inspection (ISI) of SG tubing. Results are presented on the 

application of data processing and visualization techniques; data analysis, including depth 

sizing and the development of correlations between eddy current (EC) signals and tube failure 

pressure by multivariate regression and neural networks; inspection of sleeved tubes; 

applications of ultrasonic NDE; and related work on emerging NDE technology. 

2 Data Processing and Display 

Multicoil rotating and array probe inspections generate enormous amounts of data. 

Conversion and manipulation codes have been developed to permit more efficient and flexible 

analysis of these data. The EC inspection system currently in use at ANL is a Zetec MIZ-30 EC 

inspection instrument under computer control by Eddynet95 software that runs on a HP 

workstation under the HP-UX operating system. 

The NDE data acquired by this system is in a proprietary format that makes it difficult to 

process by algorithms other than those provided in the Eddynet95 environment. To facilitate 

development of improved algorithms for the evaluation of EC inspection results, codes have been 

developed to convert the raw inspection data to a standard ASCII fie format. I 

The data analysis and signal processing algorithms have been developed by using the PC

based software MATLAB, a high-level scripting language that provides an efficient environment 

for developing algorithms, as well as convenient graphical user interfaces (GUIs) and graphical 

displays of the results. 

2.1 Computer-Aided EC Data Conversion and Calibration Routines 

A series of algorithms has been implemented under the MATLAB environment to carry out 

the conversion, normalization, and reformatting of EC readings for subsequent analysis. An 

interactive MATLAB program, ECToo!, calls the data retrieval and calibration routines through a 

single GUI control window. This software currently retrieves and processes inspection data 

acquired with either the Zetec MIZ-18 or MIZ-30 EC inspection instruments. With appropriate 

modifications, the software could also be used to retrieve inspection results from other standard 

commercial instruments. 

The main control window contains virtual instrument controls such as pull-down menus, 

editable text, and push buttons that activate various functions for manipulation of the NDE 

results. Data files in standard Eddynet format are initially read by these codes. The data 

conversion routine extracts the essential header information (currently assumed to be from

1 NUREG/CR-6638
MIZ- 18 or MIZ-30 instruments), such as the number of channels and their associated 

frequencies, from the original Eddynet-formatted files. The decoded header information 

contains the frequency and channel configuration that is subsequently used to sort out raw EC 

readings. The reformatted data matrix, along with the header and coil configuration 

information, is then stored in a user-defined data file. Normalization values of all the available 

channels are calculated by using the inspection results from a calibration standard tube. These 

values consist of amplitude scaling factors, phase angle rotations, and null values that are 

automatically calculated based on user-defined approximate locations of known indications on 

the tube. Although the necessary information for standard normalization procedures is defined 

as a set of default values in the program, the code also allows calculation of these values in an 

arbitrary fashion. Finally, raw EC readings are calibrated by another code that is also activated 

from the main menu and applies the previously calculated normalization factors to each new 

raw data file. To avoid incorrect application of normalization factors, the codes initially check 

the header information for consistency between the original calibration standard tube and new 

data files that are subsequently loaded. 

2.1.1 Conversion of Raw EC Inspection Results 

Eddynet95 provides inspection data in MIZ-18 and MIZ-30 formats. The header sizes for 

MIZ-18 and MIZ-30 are currently preset to contain a fixed number of bytes. Following the 

header is the data stream with the sequence 

horizontal Channel 1 (2 byte signed integer) 

vertical Channel 1 (2 byte signed integer) 

horizontal Channel N (2 byte signed integer) 

vertical Channel N (2 byte signed integer) 

Because the file header format is proprietary, the header has to date been only partially 

decoded. Nonetheless, the necessary information can be extracted to carry out the data 

conversion. This information reveals whether the data are in the MIZ- 18 or MIZ-30 formats and 

the numbers of channels in the data file. Once these two pieces of information are determined, 

the conversion routine can retrieve and convert EC data files to a format suitable for any future 

analysis and evaluation. Figure 2.1 shows an intermediate display of the tools under conversion 

and segmentation functions that are activated from the main menu. After the conversion of raw 

data to a standard format, the code allows selection and possible modification of channel 

configuration information. 

2.1.2 Automated Calibration and Display Algorithms 

Calibration of raw EC data plays an important role in any data analysis procedure. 

Conventional phase angle calibration procedures for normalizing EC data are carried out 

manually and routinely involve visual alignment of the impedance-plane signal trajectory (i.e., 

lissajous plot) with respect to some reference indication. This implies that the extent of 

background fluctuations and signal-to-noise (S/N) ratio, as well as analyst judgment, could 

affect the calibration process and consequently lead to varying estimates of a desired parameter.

NUREG/CR-6638 2
Fig. 2.1. MATLAB-based GUT tools have been implemented to allow direct conversion and 

segmentation of raw EC inspection results acquired with conventional commercial 

instruments for independent analysis. 

Automatic calibration of multifrequency EC inspection results could provide more efficient 

data analysis and more important render consistent calibrations by reducing variability in 

analyst interpretation in the presence of noise (i.e., background fluctuations and artifacts). 

Algorithms for automatic calibration have been developed and incorporated into ECTool that 

display the acquired data in strip chart, lissajous, image, and isometric formats. Sample 

outputs of these programs are presented next. 

Calibration of Bobbin and Rotating Coil Data. Signals from artifacts on a calibration 

standard tube are used to normalize the amplitude and phase of raw EC data. Amplitude 

scaling is performed with respect to peak signal voltage from a known indication on a calibration 

standard. Rotation of the phase angle at each frequency is calculated by using the lift-off signal 

and a second outer-diameter (OD) indication such as a simulated tube support plate (TSP) to 

determine the proper quadrants of the impedance-plane trajectory. When the lift-off signal 

cannot be detected reliably, incorporation of either a shallow inside-diameter (ID) indication or 

simulated lift-off artifact can improve the calibration results. Unlike conventional phase angle 

adjustments that are performed manually by visual alignment of the lissajous plot, the code 

uses a linear least-squares fit to multiple lift-off signals to estimate the phase angle. This

SNUREG/CR-6638

An 1-111M Mai

-. LAN -form

I

I I ill 'i . ...

3
should improve the calibration accuracy, because the phase is typically not single-valued 

because it depends on such parameters as the condition of the tube inner-surface, sampling 

frequency, and probe alignment. 

Figure 2.2a displays raw EC readings that were collected with the 2.92-mm (0.1 15-in.)

diameter unshielded primary pancake coil of a three-coil rotating probe on a groove standard. 

The code subsequently asks the user to interactively provide information on the approximate 

positions of a lift-off indication, a sound portion of the tube, and an amplitude scaling artifact; it 

calculates the phase angle rotation, null value, and scaling factors for uniform normalization of 

all of the data files that belong to the same calibration group. The calibrated RPC data are 

shown in Fig. 2.3a. Figure 2.3b shows typical output for a single channel when applied to 

bobbin coil readings on an ASME calibration standard. In this case, the- differential signal 

amplitude was scaled with respect to the 20% OD flat-bottom holes (FBHs) to 4 V. All channels 

in the data set were calibrated in near-real-time. For more precise adjustment of bobbin phase 

angle, a thin layer of epoxy material was affixed on the inner surface of the calibration tube near 

one end to simulate the probe lift-off signal. The phase angle was then adjusted so the lift-off 

signal would be horizontal. 

2.2 Visualization of RPC Data 

A MATLAB program has been developed to display rotating probe c-scan data. It provides 

two views of the EC inspection results: two-dimensional (2-D) plan view (i.e., image format) and 

a four-dimensional (4-D) modified isometric view, which uses color mapping to display the 

additional relative phase information, unlike the conventional isometric plot, which provides 

only the amplitude information for a signal component. In its present form, it estimates the 

depth of a flaw by using an algorithm similar to that used in conventional analysis; however, 

instead of estimating depth at isolated points that are selected by the analyst, it does so over the 

whole region around the flaw and presents the results in terms of a color map. This approach 

improves the pattern recognition and makes it much easier to see the morphology of the 

cracking and seems to reduce the dependence on analyst judgment. 

The program computes axial (in. or mm) and circumferential (in degrees) scaling of the data 

in accordance with a user-defined distance from two known markings on the tube. Helically 

scanned rotating probe data -are automatically mapped to a rectangular grid by using the 

position information available from the trigger channel. In-phase and quadrature signal 

components at each frequency are subsequently processed and stored in a complex-number 

format for further manipulation. The 4-D modified isometric view has the advantage that it is 

relatively familiar to analysts accustomed to working with a conventional isometric plot, and 

simultaneously provides relative phase information for every point in the region that surrounds 

the flaw. For the image display, the depth of each indication is proportional to the intensity of 

each pixel. In the future, it is expected that the underlying algorithm could be improved by 

using techniques like multivariate regression analysis or neural-network analysis. Frequency 

and spatial-domain image enhancement and restoration algorithms are also being evaluated to 

improve plan view presentation of the results. 

Figures 2.4a-c display typical outputs of the program. The data shown pertain to the 

2.92-mm primary pancake coil of a three-coil RPC used to inspect 22.2-mm (0.875-in.)-diameter 

tubing. The image shown in Fig. 2.4b clearly displays the positions and shapes of all OD and ID

NUREG/CR-6638 4
indications on the calibration standard tube. Figure 2.4(c), from top to bottom, displays the 

normalized data from the highest to the lowest available frequency. With the depth of 

penetration of eddy currents being proportional to the operating frequency, these image slices, 

in effect, represent radial cross sections of the tube. Screening of the c-scan inspection results 

in this manner would aid the determination of ID/OD aspects of an indication. Figure 2.5 is a 

c-scan image of another tube standard with axial and circumferential ID and OD electro

discharge-machined (EDM) notches. Once again, the shape and orientation of each indication 

closely matches the corresponding artifact on the tube. Finally, Figs. 2.6 and 2.7 show a series 

of image displays for circumferential and axial indications from representative laboratory-grown 

specimens from the ANL tube bundle mock-up. Despite the fact that all indications were in the 

roll transition region of the tube, in all cases, the shape and extent of indications are clearly 

resolvable.

Um,•a, twnof , , .... r. Dt I IJ P043 

(a)

(b)

Fig. 2.2. Typical display of(a) data calibration GUI tool and (b) strip chart display of 

raw EC inspection results by data calibration function of ECTool. 

Measurements were made on groove standard with 2.92-mm (0.11 5-in.)

diameter primary pancake coil of a three-coil rotating probe.

5 NUREG/CR-6638
x Lj Da. xAhnPqFE

(a) (b)

l~o~a CA,*. A 1

(a) (b) 

Fig. 2.3. Typical (a) strip chart and (b) lissajous pattern display of calibrated EC 

inspection results by data calibration function of ECTool GUI. Measurements 

were made on (top) groove standard with 2.92-mm (0.11 5-in.)-diameter primary 

pancake coil of a three-coil rotating probe and (bottom) ASME standard with 

18.4-mm (0. 725-in.) -diameter bobbin probe.

NUREG/CR-6638

I J Data EaWalwn MrA13i

11ý_j Data CaWati. AP ci I

6
*4J 

~.1 

(a) 

(b) 

(c) 

Fig. 2.4. Outputs of data visualization tool showing (a) mesh plot, 

(b) image display, (c) frequency slice display of ASME 

standard tube. Indications from left to right consist of 

100%, 80%, 6096, 40%, four 2096 OD FBHs, simulated 

TSP, 10% OD and 20% ID circumferential notches. 

7 NUREG/CR-6638
A0 

30 

40 

50 

20 40 $0 80 100 120 140 160 180 200 220 

AC 

C 20 40 60 80 !00 120

Fig. 2.5. Output of data visualization tool showing image of tube 

standard with 6.35-mm (0.25-in.)-long axial and 

circumferential ID and OD notches. 

20 

.•40 

60 

80 50 100 150 200 250 300 Ax 

0 20 40 60 80 100 120 

(a) 

60 

60 100 150 200 250 300 

Ax 

0 20 40 60 80 ¶00 120 

(b) 

Fig. 2.6. Output of data visualization tool showing image of mock-up 

tube specimens with longitudinal IDSCC indications at roll 

transition region. Indications consist of (a) four through-wall 

cracking regions and (b) multiple axial cracks with =80% 

maximum depth.

NUREG/CR-6638 8
10 

53

2N 40 00

(a)

S40 

50 1x0 150 200 203 

Ax 

03 2 40 60 6 10C2 :1

(b) 

Fig. 2.7. Output of data visualization tool showing image of mock-up tube 

specimens with circumferential ODSCC indications at roll transition 

region. Indications consist of (a) two adjacent parallel cracks with 

maximum depth of -90% OD, and (b) a single crack of =60% OD.

2.3 Application of Digital Filters 

Analog and digital filters are often integral parts of any electronic measurement device. 

They can be implemented either in the hardware or software and help discriminate between real 

signals and noise. Frequency domain bandpass finite-impulse response (FIR) digital filters are 

nonrecursive filters that offer certain advantages and limitations over recursive infinite-duration 

impulse response (IIR) filters. The primary advantages are their linear phase, stability, finite

duration transients, and efficiency of implementation. FIR filters are always stable, but require 

longer sequences to meet the same requirements as their IIR counterparts. This latter property, 

a direct consequence of their simpler transfer function, often translates into more computation 

time for nonrecursive filters. As with all frequency-domain filters, when conditions are not ideal, 

the increase in S/N ratio gained by the filtering process is always accompanied by various 

degrees of perturbation of amplitude and phase of the original signal.

9 NUREG/CR-6638
The time-domain difference equation for a linear filter with input sequence x(k) and output 

sequence y(n) could be written in a general form as 

M N 

y(n)= Ib(k)x(n- k) + 7-a(k)y(n- k), (2.1) 

k=O k=1 

in which the coefficients a and b define the ifiter response. The transfer function of the IIR filter 

in z domain, which is the z-transform of the time-domain difference Eq. 2.1, can be described by 

the following equation 

M7, b(k)z Y(z) = k=0 (2.2) H(z) = N-k' 

X(z) ao + ,a(k)z

k=1 

where ao= 1 for the case of a FIR filter, and the summation in the denominator of Eq. 2.2 

vanishes. The difference between the two types of filters can be seen by examining the filter 

frequency response, which is a rational function in z' for an IIR filter and represents a discrete 

Fourier transform for a FIR filter. Regardless of filter type, unless the data sequence in the 

Fourier domain is composed of sinusoidal components of the desired signal and noise that are 

well separated, the amplitude and phase of the filtered signal will always experience 

perturbation with respect to those of the original signal. This is of particular concern in the 

case of EC signals in which the frequency content of the entire data sequence is often closely 

spaced and thus requires very sharp (high-order) filters to separate real signals from 

background variations. 

The use of FIR filters to improve the S/N ratio of bobbin probe data was examined. Figure 

2.8 shows a typical transfer function for the FIR filters of interest. The sampling frequency for 

the raw EC data was taken to be 400 Hz (twice the Nyquist frequency). The transfer function 

was constructed by using 50th-order lowpass and 100th-order highpass filters that are 

subsequently applied to the signal. Although a single bandpass filter could be implemented to 

meet similar cut-off requirements, such a filter would be less efficient because of its symmetric 

transfer function. High-pass filters for EC signal conditioning often require a sharper rising 

edge and consequently a larger number of coefficients, because much of the useful information 

content of the Fourier domain spectrum is at the low-frequency end. 

Figures 2.9a and b show the power spectral densities that were used to determine the filter 

pass band for the differential and absolute bobbin coil signals for a TSP indication. Figures 2.10 

and 2.11 show a set of original and filtered data segments at f = 400 kHz. In both cases, the 

original differential and absolute signals contain large background fluctuations. The S/N ratio 

of the filtered data segment in all cases has improved substantially when compared with the 

original data. On the other hand, the filtered horizontal and vertical components do not exhibit 

the identical phase relationship of the original data. Preliminary assessment of the results 

suggests that frequency domain filters are generally more suitable for improving detection 

probability. Phase angle perturbations that are a result of filter application can often lead to 

more complex impedance-plane trajectories, which could complicate conventional data analysis 

procedures that rely on phase angle information.

NUREG/CR-6638 10
0.8

CL a. 

S0.6 

S0.4

0.2

0 0.1 0.2 0.3 0.4 0.5 0.6 

Normalized frequency

Fig. 2.8. Transfer function of bandpass FIR filter composed of 

50th-order low-pass (solid line) and 1 00th-order high

pass (dashed line)_filters (Nyquist frequency = 1).

(a) (b) 

Fig. 2.9. Power spectral density (Nyquist frequency = 1) of 400 kHz (a) differential and 

(b) absolute bobbin coil signal for TSP indication.

NUREG/CR-6638

I -

0.7 0.8 0.9

-20 

S-.30 

C•

Jl ...........

1

11
100 150 200 250 

(a)

0.6 

!0.4 

zo 0.2 , 

0 50 100 150 200 250 SC 

1* 

0.68 

Z 02r

300 0 50 100 150 200 250 3

Mb)

Fig. 2.10. Normalized horizontal and vertical components of (a) original and (b) filtered 

differential bobbin coil signal for TSP indication at 400 kHz. Horizontal axis 

shows data points along the tube axis.

CO.8 0.6 

0.4 

m0.2

0 50 100 150 200 250 3(

0. 

O0, I: 

o0.

"0 " 100 150 200 250 

(a)

> 0o 

*Z 0.41

300

0 50 100 150 200 250 3C

o50 100 150 200 250 300

(b)

Fig. 2.11. Normalized horizontal and vertical components of (a) original and (b) filtered 

absolute bobbin coil signal for TSP indication at 400 kHz. Horizontal axis 

shows data points along the tube axis.

NUREG/CR-6638

1.

go0.8 

0.6 

o0.4 

ZO0.2

.; >

.5 *I / 

-. I '.-.. Is I,' I .5. 

II I

I•1 T50I•

~0.6l l 

'1j 00.4 

02. z

Q6 

.2[ t 

4- i , * 

2- ~*

II 

.V f

I I *'. S. - -.

I

I I . ... I I

O0 

O0 10 so

12
3 Data Analysis 

Linear and nonlinear multivariate analysis techniques have been used to develop models 

that correlate EC NDE results with flaw size and tube failure pressure. The robustness and 

range of applicability of such correlation models is limited by the range of degradation 

morphologies and inspection artifacts in the corresponding data base. It is expected that these 

correlations will be revised and updated as additional data are developed in the course of the 

program or become available from other sources. 

3.1 Depth Sizing 

Multivariate models for analysis of RPC data to estimate flaw depth have been developed. 

Both neural-network and multiparameter linear regression models have been considered. 

3.1.1 Improvement of Back-Propagation Algorithm for Flaw Sizing 

The use of a standard back-propagation neural-network structure with momentum to train 

a feed-forward network to predict flaw depth has been studied. This work is an extension of 

work reported in Ref. 1. The data base used to train the network contained two EDM standards 

and 19 tubes with laboratory-grown defects. These tubes, fabricated from Alloy 600, with tube 

OD of 22.2 mm (0.875 in.) and a wall thickness of 1.27 mm (0.05 in.), all containing outer 

surface defects, were obtained from the Pacific Northwest National Laboratory (PNNL). Thirteen 

of the tubes contained axial ODSCC, four tubes contained circumferential ODSCC, and two 

tubes exhibited IGA degradation. The EDM notch standards contained both circumferential 

grooves and axial notches. Nominal notch depths were 20, 40, 60, 80, and 100% throughwall 

(TW) for both standards. The specimens with cracks have been examined by metallography to 

determine the "true" crack depth. 2 Table 3.1 lists the specimens in the data set. The data were 

recorded at 65, 130, 260, and 520 kHz, every 0.75mm (-0.03 in.) along the tube axis by using a 

rotating probe with a P90 pancake coil. Readings were made every 22.5' around the 

circumference, simulating a 16-coil array configuration. 

Because the optimization function for neural networks is often highly nonlinear, training 

can converge to a local rather than the desired global minimum. To overcome this obstacle, 

momentum was introduced to allow the network to escape shallow minima. This procedure 

often does not provide the most efficient computation time, although shorter training times are 

always desirable. One way to achieve shorter training time, keeping the convergence criteria 

intact (tolerance on the solution accuracy), is to use a variable learning rate that attempts to 

keep the learning step size as large as possible while keeping learning stable. However, effective 

control of the size of the learning step can be difficult. One promising alternative is the 

Levenberg-Marquardt optimization method. This algorithm is more sophisticated than the 

simple gradient descent used in backpropagation. Data analyses were performed on a test set 

that was described in an earlier report. 1 Depth prediction results suggest that the performance 

of the neural-network that was trained according to the Levenberg-Marquardt method is 

comparable to the performance of one trained with the backpropagation method. However, 

when compared with backpropagation, only a fraction of the training time is required to satisfy 

similar convergence criteria.

13 NUREG/CR-6638
Table 3.1 Summary of tube samples used for neural

network studies. 

Tube Name Defect Typea Usable Pointsb

vbs003d.dat 

vbm001.dat 

vbm002.dat 

vbm003.dat 

vbm004.dat 

vbm005.dat 

vbm006.dat 

vbmO07.dat 

vbm008.dat 

vbm009.dat 

vbm010.dat 

vbm0 11.dat 

vbm012.dat 

5-01 

5-14 

4-11 

w-23-03 

w-23-09 

w-40-07 

2-05 

b- 10-02 

b-10-10 

b-30-02 

b-45-06 

b-49-05 

b-49-09 

b-55-08 

b-59-10 

b-62-09 

b-63-07 

e-13-06 

1-14-06 

TOTAL

Standard 

Standard 

Standard 

Standard 

Standard 

Standard 

Standard 

Standard 

Standard 

Standard 

Standard 

Standard 

Standard 

IGA 

IGA 

CODSCC 

CODSCC 

CODSCC 

CODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC 

LODSCC

625 

575 

575 

575 

575 

575 

575 

575 

575 

575 

575 

575 

575 

2573 

2130 

685 

2875 

1215 

2796 

302 

2045 

1433 

990 

875 

798 

102 

3057 

2557 

1380 

83 

2646 

3257 

39,290

a CODSCC: circumferential ODSCC; LODSCC: longitudinal 

ODSCC 

b -Usable Points" indicates that a corresponding 

metallographic depth was available for the EC 

measurement.

NUREG/CR-6638 14
Selection of Training Sample. Because it is important to estimate larger defect depths 

with better accuracy, the criterion for selecting training data was weighted to ensure that the 

training population emphasized the nonlinear mapping between EC measurement and defect 

depth. The weighting method can be described as follows: Let D be the defect depth (normalized 

by the tube wall thickness) of a training sample candidate, and let 0 be a threshold value in the 

range [0, 1] and U a random variable with uniform distribution in the range [0, 1]. Then the 

training sample will be included in the training population (i.e., higher weight for greater defect 

depth) ifD >9 x U. For example, selecting 0 = 0.6 allows all available samples with a defect 

depth of 60% or greater to be selected, while a sample with defect depth of 20% will have a 33% 

chance of being selected as a member of the training population. 

Table 3.2 summarizes the performance of the neural-network that was based on training 

populations with various threshold values. The candidate training sample set comprised all of 

the EC measurement and defect depth pairs in the test set except the five indications that were 

to be predicted. A threshold value of 0.0 is equivalent to using the entire training sample set for 

training. The sizing accuracy of the predicted tubes fluctuates with the threshold value (i.e., 

composition of the training population). The results in Table 3.2 suggest that prediction 

accuracy is optimized when the threshold value is =0.5. The accuracy obtained with a threshold 

value of 0.5 is comparable to that obtained with a value of 0.0, and the training can be 

accomplished in a much shorter time because fewer indications are actually used for training. 

Another potential advantage of using a threshold value > 0.0 is that undesired variations in EC 

measurements caused by artifacts, such as TSP, may be reduced because only a portion of the 

defect-free data will be included. The results in Table 3.2 were obtained with 12 hidden-layer 

nodes. Table 3.3 shows results that were obtained when the network contained 10 hidden-layer 

nodes and all other conditions were the same as those in Table 3.2. In this case again a 

threshold value of =0.5 seems to give the best results. 

Effects of Nonlinear Transfer Function. The structure of a neural-network can 

significantly influence its performance. In this study, the effect of the type of neuron transfer 

function on the performance of neural-network algorithm discussed earlier was evaluated. 

Specifically, two most commonly used transfer functions for mapping neuron input to its output 

were examined. These transfer functions can be expressed as 

(1-e-x) 

tan sig(x) = (+x 3.1) 

(1+ ex) 

and 

log sig(x) = 1 (3.2) 

The function tansig maps the input range (-**,**) to output range (- 1,1), whereas logsig maps 

the same input range to (0,1). Because the desired output in this study is the degradation 

depth in percentage of the tube wall thickness, Eq. 3.2 seems more suitable for the output layer 

neurons. The effect of using either Eqs. 3.1 or 3.2 at the first layer was examined by comparing 

the accuracy of the defect depth prediction. Table 3.4 summarizes the results of this analysis

15 NUREG/CR-6638
for a network 

training data.

with 12 hidden nodes. The parameter 0 again controls the selection of the

Table 3.2 Depthsa predicted by trained neural-network with training 

samples of various threshold values. The network contains 12 

hidden layer nodes; the training set contained 17 specimens. 

Tube Defect Predicted depth with 0 Actual 

Name lypeb 0.0 0.3 0.5 0.8 1.0 Depthc 

4-11 CODSCC 100 31 99 60 61 100 

B-10-10 LODSCC 46 51 41 43 43 43 

B-63-07 LODSCC 70 59 65 57 62 69 

E- 13-06 LODSCC 15 26 21 23 17 0 

vbm005 standard 75 73 73 76 73 75 

a Values shown are maximum depths as percentage of tube wall thickness. 

b CODSCC: circumferential ODSCC; LODSCC: longitudinal ODSCC. 

c Obtained by metallography. 

Table 3.3 Depthsa predicted by trained neural-network with training 

samples of various threshold values. The network contains 10 

hidden layer nodes. 

Tube Defect Predicted depth with 8= Actual 

Name Typeb 0.0 0.3 0.5 0.8 1.0 Depthc 

4-11 CODSCC 31 56 93 46 52 100 

B-10-10 LODSCC 45 48 46 48 42 43 

B-63-07 LODSCC 54 67 63 66 69 69 

E- 13-06 LODSCC 18 32 27 26 30 0 

vbm005 standard 73 91 74 73 74 75 

a Values shown are maximum depths as percentage of tube wall thickness. 

b CODSCC: circumferential ODSCC; LODSCC: longitudinal ODSCC. 

c Obtained by metallography.

The results suggest that both forms of the neuron transfer function yield comparable defect 

depth predictions. However, when the logsig transfer function is used, a network with randomly 

initialized weights sometimes cannot be trained successfully, i.e., the initial weight vector 

cannot be updated to escape a local minimum of the optimization criterion. This phenomenon 

was never observed when the tansig transfer function was used at the first layer. Table 3.5 

summarizes the frequencies at which the network fails to be trained properly with these two 

transfer functions at the first layer. A failed network is expected to produce a zero depth 

estimate for all indications. 

Number of Hidden-Layer Neurons. The optimal number of hidden-layer neurons depends 

on the complexity of the relationships between input-output pairs presented to the neuralNUREG/CR-6638 16
network. A neural-network with a smaller number of hidden-layer nodes may not exhibit 

sufficient input-output complexity to deduce a useful relationship between measured NDE 

parameters and defect depth. On the other hand, an arbitrarily chosen large number of hidden

layer neurons could introduce additional nonlinearity to the structure. Such additional 

nonlinearity makes the optimization function of the network more complex and thus increases 

the probability for the training process to be trapped in local minima. 

Tables 3.2 and 3.3 and Tables 3.4 and 3.6 can be used to compare predictions for networks 

with 12 hidden-layer neurons with predictions from networks with 10 hidden-layer neurons. 

Reducing the number of hidden-layer neurons from 12 to 10 does not seem to affect the 

performance of the trained network. For similar convergence criteria, because the network with 

a smaller number of hidden-layer neurons could be trained more efficiently, these results 

suggest that 10 hidden-layer neurons are preferred over 12. It should be noted, however, that 

the optimal size of hidden-layer neurons could also depend on the measurement technique and 

probe characteristics. 

Table 3.4. Depthsa predicted by trained neural-network with two 

types of nonlinear transfer functions for first layer and two 

threshold values of 0 for selecting each training specimen. 

Network contains 12 hidden-layer nodes. 

Predicted Depth 

Tube 0 = 0.0 0 = 0.5 Actual 

Name logsig tansig logsig tansig Depthb 

4-11 93 75 99 100 100 

B-10-10 52 39 49 60 46

B-63-07 58 71 62 67 69 

E- 13-06 22 27 30 44 0 

vbm005 97 73 87 77 75 

a Values shown are maximum depths as percentage of tube wall thickness. 

b Obtained by metallography. 

Table 3.5. Frequency of unsuccessful training of neural

network with random initialization of weight 

vectors and two types offirst-layer neuron transfer 

functions. Total training conducted for each hidden 

node condition is 50.

Hidden 

nodes 4 6 8 10 12 

logsig(.) 0.08 0.04 0.04 0.08 0.08 

tansig(.) 0.0 0.0 0.0 0.0 0.0

17 NUREG/CR-6638
Table 3.6. Depthsa predicted by trained neural-network with two 

types of nonlinear transfer functions for first layer and two 

threshold values of 0 for selection of training samples. 

Network contained 10 hidden-layer nodes. 

Predicted Depth 

Tube 0 = 0.0 0 = 0.5 Actual 

Name logsig tansig logsig tansig Depthb 

4-11 97 94 64 97 100 

B-10-10 45 52 43 97 46 

B-63-07 64 60 65 70 69 

E-13-06 24 32 33 55 0 

vbm005 90 73 74 84 75 

a Values shown are maximum depths as percentage of tube wall thickness. 

b Obtained by metallography. 

3.1.2 Multivariate Regression Analysis 

Three distinct multivariate regression algorithms have been evaluated as alternatives to 

neural-network structures for the development of correlations between EC data and flaw size 

and tube failure pressure. The three approaches consisted of multiple linear regression (MLR), 

principal component regression (PCR), and partial least squares (PLS) techniques. To date, PLS 

has been shown to be the most versatile of the three techniques for EC data analysis. 

Regression Algorithms. Multivariate analysis techniques are often utilized to explore 

unknown trends in large quantities of data. The two basic stages in such analyses are generally 

referred to as calibration and prediction. In calibration stage, a data matrix is constructed from 

the probe response at various time or frequency slots for a given set of calibration samples. The 

calibration phase produces a model that relates the probe response to known values of the 

parameters to be estimated. In the prediction stage, the matrix of coefficients and weights that 

describes the system response (input-output relationships) is used to estimate the parameters of 

interest in new measurement test sets. Of the three multivariate techniques, MLR, an extension 

of the ordinary least squares algorithm, is used most frequently. Having the response matrix [XI 

and the parameter matrix [Y], MLR develops a linear combination of variables in [X] that 

minimizes the errors in reproducing [11. This is accomplished by constructing a relationship 

between [X] and [11 such that 

[Y] = [X][(p] + [E], (3.3) 

where [P] is the matrix of regression coefficients and [4 is the matrix of errors associated with 

the MLR model. The matrix [f] is found by linear regression to minimize the difference between 

the true and estimated elements of the parameter matrix in a least squares sense as 

NM ^ 2 NM 2 

]= I11 -=I {3.4 n=l m=l n=lmr=l

NUREG/CR-6638 18
The MLR approach is often a good choice when dealing with linear systems that are 

described as exhibiting linear response, low noise, no interference, and no colinearities. 

However, when the EC inspection data discussed in Sec. 3.1.1 were analyzed by MLR, the 

correlations that were developed initially were not always satisfactory because of external 

artifacts, background fluctuations, and colinearities in instrument response at various 

frequencies. MLR attempts to use all of the available information in the independent variable 

matrix for model construction. Both relevant (signal) and irrelevant (noise) information will be 

weighted equally by this method when input-output relations are constructed. To overcome this 

limitation, the two factor-based techniques (PCR and PLS) were subsequently examined. The 

factors are linear combinations of the original variables. Factor-based models attempt to use a 

minimal set of factors that best describe the true relationship between the independent and 

dependent variable matrix. Because factor-based models do not attempt to directly invert the 

covariance matrix, potential colinearities in data could be accommodated without causing 

singularities. 

PLS was chosen to construct a multivariate regression model for the EC data. For the PLS 

algorithm, a general model would consist of a regression between the scores of the relationships 

for the dependent variable [Y] and independent variable [X]. The outer relationships can be 

written in matrix form as 

N 

[X] = [T][P]+ [E] = Vp +[E] (3.5) 

n=1 

N 

[Y]=[U][Q]+[F]= Iunq +IFl, (3.6) 

n=1 

with the inner relation defined by 

f = bntn, 

(3.7) 

where 171, [U], [PI, and [Q] are the eigenvectors and eigenvalue matrices and [B] is the vector that 

contains the inner relationships. The principal components are simultaneously calculated for 

both blocks. The intention is to minimize J[FI[ (i.e., Norm{i[f}), and hence to construct a useful 

relationship between IX] and [11. 

Preliminary results are shown next for implementation of a PLS algorithm to estimate 

degradation depth for the tube data set described in Sec. 3.1.1. Once again, the NDE 

parameters consisted of horizontal and vertical components of the measured signal at four 

frequencies. A MATLAB program was written to carry out the multivariate analysis. Unlike the 

nonlinear analyses described in earlier sections, calibration and prediction with this approach 

can be carried out in real time. In the initial calibration stage, the code calculates the weight 

and coefficient matrices from a user-defined set of normalized data files. Estimated depths are 

then sequentially calculated for each test specimen and profiles are displayed as a function of 

position along the tube. Because the available data set analyzed in this study contains only OD 

indications, a single-output depth profile was estimated for each tube specimen. Estimated 

depth profiles can be displayed simultaneously by the code for both OD and ID indications.

19 NUREG/CR-6638
Figures 3.1-3.5 display representative EC traces of the available standard and defect

containing tube specimens. Figures 3.1 and 3.2 show the normalized horizontal and vertical 

components of data for the standard tubes with axial and circumferential defects, respectively. 

Several measurements of the standard with circumferential grooves were available in which a 

simulated OD ferrite support ring was placed at various locations on the tube. Figures 3.3-3.5 

show signal components at the highest frequency for representative tubes that contained 

longitudinal ODSCC, circumferential ODSCC, and IGA degradations, respectively. The 

inspection data from all of the specimens in this set showed a variable but generally high level of 

noise. The noise level is somewhat atypical; its source is not known, but could be either the 

manufacturing process or the treatment of the tubes when defects were created. 

Various calibration and prediction scenarios were examined. Results are presented here for 

the case in which the calibration/training is based primarily on the tube standards. For the 

first test case, all of the data on the two calibration standard tubes with axial and 

circumferential defects were used in the training stage. For the second test case, two specimens 

with shallow OD indications were added to the training data set to improve noise suppression. 

Figure 3.6 shows the plot of maximum measured vs. estimated depth when the training set 

consisted of all the available tube standard data files. Figure 3.7 shows the results when two 

specimens with shallow OD indications were added to the training set to help suppress the 

excessive noise in these tubes. Comparison of Figs. 3.6 and 3.7 indicates that the addition of 

the OD indications helped eliminate artifacts and improved the overall estimation of the depths 

for the entire range of available degradation morphologies. Results of the second test also 

indicate less scattering of estimated maximum depths and better correlation of measured and 

predicted depths. Training exclusively on circumferential and axial notches appears to lead to 

underestimation of shallower OD indications. In both cases, the multivariate model was able to 

effectively suppress background variations and indications from simulated tube support and 

ferrite rings. 

The choice of the EC probe configuration strongly affects the results of the analysis. As 

stated earlier, the P90 coil that was utilized to inspect tube specimens was selected to try to 

provide an optimal trade-off between circumferential coverage (when used in 16-coil array 

configuration) and sensitivity. Examination of all signal traces in the data set generally 

indicates a lack of probe resolution and insensitivity (i.e., low S/N ratio) to shallow OD 

degradations (e.g., Figs. 3.3 and 3.5). Inasmuch as EC indications are proportional to the 

degree that the paths of induced currents are perturbed by the defect, small isolated 

indications, which seem to be characteristic of at least some of the specimens, would be 

expected to produce low S/N ratios which, in turn, could lead to underestimation of degradation 

depth. Sensitivity and resolution could be improved by using smaller coils. The penalty for the 

higher resolution achieved with the smaller rotating probes is reduced inspection speed and 

additional inspection data. Multivariate analysis for sizing with smaller rotating probes is 

currently under study.

NUREG/CR-6638 20
E 

-10 

0 

z -15 

0 100 200 300 400 500 600 700 

position 

0. 10 

8 0 

Z 

-5

0 100 200 300 400 500 600 700 

position 

Fig. 3.1. Normalized horizontal and vertical components 

of EC inspection data for tube standard that 

contained axial notches. 

"_.5 

-10 

-15 

E 

-20 

-50 10O0 200 300 400 500 600 

6I 

4

oE2 

0 100 200 300 400 500 600 

position

Fig. 3.2. Normalized horizontal and vertical components 

of EC inspection data for the tube standard 

that contained circumferential grooves.

21 NUREG/CR-6638
'a 0.5 

E 

0 

Z -0.5 

0 500 1000 1500 position 

E <0 

z -0.5 

-1 

0 500 1000 1500 

position 

Fig. 3.3. Normalized traces of horizontal and vertical 

components of EC inspection data for representative 

specimen with longitudinal ODSCC degradation. 

Z -6 

0 100 200 300 400 50 600 700 

position 

is1 • I j 

-8 0 100 200 300 400 500 600 700 

position 15 

-50 10O0 200 Soo 400 Soo 600 700 

position 

Fig. 3.4. Normalized traces of horizontal and vertical 

components of EC inspection data for representative 

specimen with circumferential ODSCC degradation.

NUREG/CR-6638 22
"•.1.5 

E 

g0.5 

z 

0 

-0.5 

0.5 

-0.5 

Z -1 

-1.5

position

2500

Fig. 3.5. Normalized traces of horizontal and vertical 

components of EC inspection data for 

representative specimen with IGA degradation.

90 

80 

70 

E60 

so 

40 

30 

20

10

Fig. 3.6.

0 10 20 30 40 50 60 70 80 90 100 

Measured depth 

Maximum measured vs. predicted depth in percent of 

wall thickness for all available specimens when 

training set consisted of all calibration standard tubes 

with simulated axial and circumferential notches. 

23 NUREG/CR-6638

......................................... ......-. 

S. ......... .......... • ......... ............ .......... .......... k .......... ..... .....% ........... :......... 

S. . ... . .. . ................... .......... ) .......... ......... i......... .................... 

S................... : .......... ......... X *.......... ............................... ........... . . . 

31 : : 

...... .. ........... 

. ......................... • ...... : .......... ' .......... ........................................ S.. 

*......... ....... • . ..... . .. .. . .. . . ...... ...... ......... 

.. . .. .I .. . . . . . . .. . . . . . . .........

4n Art. .

A• ,
1 UV 

9 0 . . ..................................................................... : .......... ......... • ........ 

8 0 . ......... : ......... ' ............................... :..................... !....................• ........ 

7 0 ......... ........................................... ......... .......... !........... ......... ; ......... 

S6 0 . .......... . . .. ..................... .................... ......... .................... ......... 

2 oo• 5 . .......... ......... .. . . .................. ......... ............................... ......... 

el 4 0 . ........ ,.... ..... . . ........... . . . . .-.. ......... )1 ........ • ......... 

3 0 . ........ ......... !: ......... i......... .......... .......... ......... .......... ........... .: ........ 

2 0 . ........ i ........ :. ........ .. ........ )K ....... .......... ............................... .:........ 

1 .0 ..:.. .. .. . . .. . .. . .. . ... .............. . .. . . . . ....... ............ i ........ 

10 20 30 40 90 60 70 80 90 100 

Measured depth 

Fig. 3. 7. Maximum measured vs. predicted depth in percent 

of wall thickness for all available specimens when 

training set consisted of all calibration standard 

tubes with axial and circumferential indications, 

plus two specimens with shallow OD indications 

(used for noise suppression). 

3.2 Correlation of Failure Pressure with Bobbin Probe Measurements 

Conventional repair and plugging criteria for SG tube integrity are based on estimated 

degradation depth. Tubes with EC indications that correspond to degradation greater than a 

specified limit (typically >40% of nominal tube wall thickness) are either repaired (by sleeving) or 

taken out of service (e.g., plugged). For some types of degradation, e.g., those characterized by 

short deep crack segments separated by ligaments, depth measurements of an individual crack 

may not be the best measure of structural integrity. For ODSCC indications at TSP 

intersections, a voltage-based alternate plugging criterion (&PC) has been developed. The 

criterion is based on direct correlation of bobbin coil voltage amplitude, rather than estimated 

defect depth, to failure pressure and leak rate. NRC guidance for the application and 

development of such criteria are provided in Generic Letter 95-05. The correlations between 

voltage and failure pressure and leak rate have been developed empirically from inspection data 

and tests on service-degraded pulled-tube specimens and laboratory-grown cracks. 

An objective of the SG tube integrity programs at ANL is to assess ex-isting models that 

correlate NDE results to parameters that are related to tube integrity. As part of this work, the 

data on pulled and model boiler tube specimens that were originally used by the Westinghouse 

Owners Group to develop an APC for ODSCC indications at TSP locations in Westinghouse 

plants that use drilled tube support plates have been reanalyzed by using multivariate 

regression analysis. Recent work in this area at ANL has focused on examination of

NUREG/CR-6638 24
multivariate regression analysis and neural networks to develop empirical models to relate 

bobbin probe data with tube failure pressures. 

Our initial studies on alternate correlation models focused on independent analyses of 

pulled and laboratory (model boiler) tube test sets. Both MLR and neural-network models were 

successful in producing reasonable correlations for the individual data sets. Signal features, 

such as maximum and integrated amplitude, were considered as model parameters. The signal 

features ultimately selected were the integrated amplitudes at multiple original and mixed 

frequencies. Preliminary results of this study on pulled-tube data suggested that an optimal 

(i.e., minimum number of indications for an acceptable correlation) calibration/training set 

should include representative tubes that describe the range of variation in inspection data that 

is included from each SG unit. As expected, prediction confidence intervals were narrower for 

the model boiler test set. Subsequent attempts to develop correlation models with a combined 

set of pulled and model boiler specimens by using similar EC test parameters failed to produce 

acceptable prediction confidence intervals. Eventually, an independent normalization procedure 

was devised for this analysis. Hypothetical test case calibration and prediction scenarios, which 

utilized a PLS algorithm described earlier in this report, were then implemented to check the 

stability of the model predictions. It should be noted that the robustness of any correlations 

developed in this study might be limited by the range of crack morphologies and inspection 

artifacts included in the available database. 

Multiple Linear Regression and Neural-Network Model; ARC Data Base. Current 

alternate plugging and repair criteria (APC) for ODSCC degradation at TSP intersections for 

22.2-mm (0.875-in.)-diameter tubing are based on bobbin voltage amplitude (e.g., signal 

amplitude from 400/100 kHz mixed frequency channel). Unlike conventional procedures that 

relate degradation size to tube integrity, the APC is based on direct correlation of tube structural 

integrity (i.e., failure pressure and leakage) with bobbin voltage amplitude. 

The use of multivariate regression analysis procedures and neural-network models to 

develop the relationships between bobbin probe signals for ODSCC degradation in the TSP area 

and tube failure pressure have been studied. Forty-one indications from the ARC database were 

used for the analysis. The indications were from 22 model-boiler and 19 pulled-tubes. All of 

the examined tubing was 22.2 mm (0.875 in.) in diameter, with a nominal wall thickness of 

1.27 mm (0.05 in.). The pulled-tube indications were from the first three TSPs on the hot leg 

side. The pulled-tube specimens are from three plants. Table 3.7 lists the number of specimens 

from each SG unit that were incorporated for analysis. Table 3.8 lists the indications from the 

ARC model-boiler tube database that were included in the analysis. Before a specimen was 

included in the analysis, the following information about it was required: an ASME calibration 

standard data file, a measured failure pressure, and data at a consistent set of EC inspection 

test frequencies (400, 200, 100 absolute and differential channels). Although procedures to 

correlate EC readings at different test frequencies for similar-type probes have been proposed, to 

avoid an additional source of uncertainty, such procedures were not used. 

Figure 3.8 is a histogram plot of the distribution of measured failure pressure for pulled

and model-boiler-tube indications. As stated earlier, the pulled- and model-boiler tube 

populations cluster at high and low failure pressures, respectively, with relatively few data 

points at intermediate pressures. Furthermore, the NDE measurements are affected by such

25 NUREG/CR-6638
Table 3.7. Composition of pulled-tube specimens 

from ARC data base incorporated for 

failure pressure analysis. 

Plant SG Unit Indication no. 

A 1 1-4 

A 2 5-8 

B 1 9-11 

C 1 12-19

Table 3.8. Destructive examination and failure pressure measurements for 

22.2-mm (0.875 in.)-diameter tubes from ARC model-boiler 

database that contained ODSCC degradation at TSP intersections. 

Destructive Examinationa Adjusted 

Specimen Maximum Lengthb Pressurec 

No. Depth (%) (in.) (psi) 

528-2 100 0.67 (0.5) 4,668 

532-1 100 0.7 (0.52) 4,010 

532-2 100 0.75 (0.58) 4,087 

535-1 100 0.28 (0.11) 8,265 

555-3 100 0.75 (0.42) 4,209 

533-4 100 0.34 (0.14) 6,964 

536-1 90 0.4 7,959 

542-4 N/A N/Ad >4,867 

543-1 N/A N/A >2,633 

543-2 N/A N/A >3,153 

543-4 98 0.52 5,459 

547-1 N/A N/A 5,561 

576-2 100 0.3 (0.22) 6,628 

576-4 100 0.6 (0.43) 5,602 

557-1 90 0.18 >7500 

557-2 100 0.52 (0.44) >4,974 

557-4 N/A N/A 11,633 

558-1 100 0.4 (0.32) 5,969 

568-1 100 0.33 (0.25) 6390 

568-2 N/A N/A >4,635 

571-1 100 0.44 (0.35) >4,950 

574-4 100 0.39 (0.33) 5,506 

a Measurements were carried out by Westinghouse Electric Corporation. 

b Total crack network length, with throughwall degradation length given in 

parentheses. 

c Normalized to 150 ksi flow stress (sum of yield and ultimate stress). 

d N/A: Data not available.

NUREG/CR-6638 26
I I I I I

"0 

-5 iu fnH 

0 ..... 01 H 

10 20 30 40 50 60 70 80 

Measured Pressure (MPa) 

€.o_ 4 . .... ;.................................. ,' '. ............. i.............. ............ i............ . . . 

-=• .! .......i ...., ..v .,, .i .::,.. ! ........... Eo -.. . ............ :........ i .... . .I - :.... .... ............ :............... . . 

.. .. ... ............ ........ . . 1 .. ........ .... .. .. ... .n --

0 

10 20 30 40 50 60 70 80 

Measured Pressure (MPa) 

Fig. 3.8. Distribution of pulled-tube (top) and model-boiler 

(bottom) indications as affunction of failure pressure 

used for multivariate analysis. 

factors as variations among calibration standards, probe wear, and dissimilarity of tube/artifact 

geometry (i.e., NDE carried out with or without TSPs in place, presence or absence of deposits, 

etc.) as well as actual differences in defect size and morphology. 

To reduce the influence of such factors on NDE data, the EC readings were normalized and 

frequency-mixed. The normalization procedure consisted of automatic phase angle adjustment 

for each channel and amplitude scaling in reference to four 20% FBHs on an ASME calibration 

standard tube. For 400/100, 400/200, and 200/100 kHz, where the upper frequency was 

taken as the primary channel, linear two-frequency least-squares mixes were used to 

reduce/suppress the TSP signal. A three-frequency 400/200/100 mix was also examined, and, 

based on the regression analysis results, provided a small improvement over the 400/100 mix. 

The mixing algorithm was carried out for all data files regardless of whether or not the file 

contained a TSP indication. The resampled data points for the mixed signals were chosen to 

cover the entire support plate region. 

All manipulations of NDE data, such as searching each file for proper channel 

configuration, selection of data segments that contain indications at the TSP region, 

normalization, resampling, and construction of mixed-frequency channels, were implemented by 

a series of computer codes implemented at ANL as described in Section 2. 

The integrated area under each TSP indication was determined by the equation

27 NUREG/CR-6638
SAD = N 2(IH + VJ) (3.8) f n=-N12 

where S represents the integrated signal magnitude at frequency f for each absolute A and 

differential D channel, and H and V are the horizontal and vertical signal components at each 

frequency. The integrated region was bounded by N/2 points on either side of the TSP center (n 

= 0), with the sampling rate of each data segment being adjusted in accordance with the 

simulated TSP ring from the corresponding ASME tube standard. 

Although the integrated TSP signal of Eq. 3.8 could be correlated reasonably well with the 

tube failure pressure when the model-boiler and pulled-tube data sets were considered 

separately, the correlation when both types of specimens were included in a single database was 

poor. Consequently, additional transformations of the signals were explored. The selected 

transformation involved mean-centering and energy-scaling of each data segment 

independently. Mean-centering is performed to reduce the effect of signal offsets; energy

scaling, to render equal weight for each data segment. As in the case of Eq. 3.8, the integrated 

area under the transformed signals was determined by 

^A,D N 2 f x In+*•n) (3.9) 

Sý -n=-N1 (IA.+239 

where the transformed ft and 1ý components are defined as 

where p represents the mean and ý is the signal energy. For equal-length mean-centered 

signal component 4x is proportional to the standard deviation ax of each trace. It should be 

noted that transformation in accordance with Eq. 3.10 will retain the shape of the original 

impedance plane trajectory (i.e., a lissajous trace). 

Training and prediction schemes were subsequently employed to evaluate the ability of 

each correlation model to capture all of the useful trends in the data. These are designated as, 

All: Information from all (both model-boiler and pulled-tube) indications are used to 

establish the predictor. 

LOOT: Leave-one-out technique (LOOT), i.e., a sampling-with-replacement, where the 

information from all but one tube is used to establish the predictor. The predictor 

is then used to predict the failure pressure of the one sample not used for training. 

The procedure is repeated until a prediction result is obtained for all indications 

available in the database. 

Mixed: Information from half of the randomly selected samples is used to establish the 

predictor, which is then used to estimate the failure pressures of the remaining 

half. 

Initially, all of the available original- and mixed-channel signals (14 components) were 

incorporated for multivariate analysis to determine the best fit to the data as well as to identify

NUREG/CR-6638 28
the minimum number of independent variables that best describe all of the variations in the 

data. 

Figures 3.9a and b display the multivariate regression and the neural-network fits, 

respectively, when all samples were incorporated for the model-building stage. Also shown are 

the upper and lower bounds of the 95% confidence interval, calculated through a first order 

least-squares fit to the measured-vs. -predicted pressure. As expected, the neural-network can 

better capture all of the variations in the data. However, it should be noted that this test case 

does not represent the prediction capability of the model but rather the ability of the model to 

construct a fit to all of the data. Figure 3.10 displays the prediction results of the two models 

when -50% of the indications from each tube category were included in the training stage. For 

this test case, comparable confidence intervals were achieved by both methods. Finally, 

Fig. 3.11 displays the prediction results of the two models, based on the LOOT training and 

prediction scheme. Once more, the confidence intervals for both methods of fitting show 

comparable prediction accuracy. 

Figure 3.12 is a plot of residual vs. regression-predicted failure pressure when all model

boiler and pulled-tube specimens were included in the training. The distribution of residual 

pressure does not display any distinct pattern, a finding that is consistent with the assumption 

that a normal distribution of residual pressures is implicit in the calculation of the confidence 

interval. Figure 3.13 shows the changes in residual failure pressures when the LOOT training 

and prediction procedure was performed. For multivariate models, the variation of residual 

pressure obtained with the LOOT procedure is an indicator of model stability. For a stable 

model, the predictions should be relatively independent of individual data points in the training 

set, i.e., the residual pressures in Fig. 3.12 should be small when compared with the predicted 

failure pressures. In the present case, the change in the residuals is -10%. For this type of 

data, such a change is reasonably small. 

Figure 3.14a shows a plot of the cumulative distribution of the residual pressure, assuming 

that it is are normally distributed when the training set included all of the available indications. 

Figure 3.14b shows similar results when a LOOT training/prediction procedure was 

implemented. The standard deviation of the distribution when the LOOT procedure was 

followed is =50% greater than when all of the data were used to fit the model. It is possible that 

the LOOT training scheme provides the most conservative estimate of the accuracy of the 

predictors. 

A study was also carried out to indirectly assess the prediction accuracy of the multivariate 

regression model by comparing the results presented above with those obtained by using a 

linear regression model. The data set used in this work consisted of renormalized EC inspection 

results that were used to develop the voltage-based APC model. One data point from the model 

boiler set was not used for this analysis because the voltage reading for that indication (i.e., 

marked as not reliable in the ARC data base) was not available. The renormalization was 

performed by referencing all measurements to a single ASME tube standard. A linear least

squares fit to the logarithm (log1 o) of the measured voltage-vs.-failure pressure was implemented 

in accordance with the APC-recommended model. Although a direct analogy between the APC 

linear regression and multivariate models cannot readily be described, comparison of the 

predicted residual pressures suggests tighter global prediction for the multivariate model.

29 NUREGICR-6638
. ... .. . .. . .. . .. ., * . . .. .. . .. . ... ... .. .... . . . . . 

* ": E X*.. 

... .... .... .... S... ... .... ... ...... ,. ............................ " . ........... ..... . . . . . . 

........ . ,........... .. .. ... % ...... 

. .. ......... . . . .... ..... .. ....: ........ , .: -x ....... .. -. ... . . .- ( :. . . .> " . . 

... ... " .... . . : ( : . . 

I % .. . I ... . . .. . . . ... ... ... 

.. . .. .. ".: ... S .... " " "XX . S............. .... :....: ..... ,i........... •.•........ ............ ............... 

Xx . 

S .......... ... 0................................................. 

..... ... ......... ............ 

..... .... .... .... ....

10 20 30 40 50 60 70 80 

Measured Pressure (MPa) 

(a)

10 20 30 40 50 60 

Measured Pressure (MPa)

70 80

(b) 

Fig. 3.9. (a) Regression- and (b) neural-network-predicted 

vs. measured failure pressure when all model

boiler (x) and pulled-tube (*) indications were 

included for training. Also shown are the upper 

and lower bounds of the 95% confidence interval, 

based on a linear least-squares fit to the data. 

NUREG/CR-6638 30

80 

70 

- 60 0. 

S50 

CL 

40 

CL30 

20 

10

80 

70 

• 60 0. 

50 

a. *2 40 

0-30 

20 

10

* . . . . .** , N , iS... . ........... ............ .......................... . ......... .. 4 ........ X, ....... . . 

X ... . .... ........... ................... 

.. . . . . . . . .. . . . . . . . . . . .•". . . . . ::. . . . .............. :. . ............ 

.. ....... " x ... .ii .. ..... 

: ~ ~ X .'X:,P 

........... : .:....... . .: .......................... ! ........... ............ ... ........... . . . S.. 

S. i . .. .. ...". -:............ :........... ;............ !... ........... ............ ............ . . .

I
80 

70 

,Z60 

(L so 

• 50 

a 40 

.L 30 

20 

10 

80 

70 

S60 

a

CD N 50 

" 40 

CL 0 

20 

10

10 20 30 40 50 60 70 80 

Measured Pressure (MPa) 

(b) 

Fig. 3.10. (a) Regression- and (b) neural-network-predicted vs. 

measured failure pressure when -50% of model

boiler and pulled-tube indications were randomly 

selected for training. Also shown are the upper and 

lower bounds of the 95% confidence interval, based 

on a linear least-squares fit to the data.

NUREG/CR-6638

i I IiiI j 

............ ........... ~ ~~~~ ~ ...... ... .... ....... i'..... •.... 

* . . . . - +,~ 

*. . . .. . . .

+ +4+ 

. . . . S. . .. . . .. .•... . . . . . . . . . .: . . . . . .... . . . . . . . . . . .. .: : 

"".. ...... ... ........ ....... 

* + 

"' ~..... .... ..... .. .... " ............ + .. + .. ..: " " : ........ .> . .' 

....... . .. . . .... ... .... ... , ............ ..... . .. . . 

*+..i +! .. + 

. .......... ... ... . I................... ..... ...... ............ .......... 

S..... i ~............ i ...... . . .i ........... : ............ : ........... :............ i. ........ .....

10 20 30 40 50 60 70 80 

Measured Pressure (MPa) 

(a)

.. . . . . .. . I . . . .. . .. . .. . .. . . 

L ..... : ......................... : ........................ !........... .......... .i•:... ..... . ..... 

S... .... i ... . ........... ....... . ..<-.•.-.'' . . .. . 

+ . 

+'4 

+ 

.. .i. . . .. . .. . ...... ... •... .:...... .• . ..:............ ,, ......... :........ .. ,:....... 

S. ............................. : .'. ......... .......... .:",,". - , ,. ...... ........... . . . 

.. . ........... ............ ; ........ : . .. I . ., ..... . ...... . .. . ....... ..

H i .....

31
10 20 30 40 50 60 70 80 

Measured Pressure (MPa) 

(a)

10 20 30 40 50 60 70 80 

Measured Pressure (MPa) 

(b) 

Fig. 3.11. (a) Regression- and (b) neural-network-predicted 

vs. measured failure pressure for model-boiler (x) 

and pulled-tube (*) indications, with LOOT training 

and prediction method. Also shown are the upper 

and lower bounds of the 95% confidence interval 

based on a linear least-squares fit to the data.

NUREG/CR-6638

......... ............... ...................... : .... /: ..... ........... . :. .............

S.... :.. - • !.." .9K' .. . . ! _ . 

...... ..... .. .... .• .. . .. ...-... ._ .... ....... . . . . 

x 

S.. i .. .... .. ... ... :": ' "• .. .i ! ...-x ... .: ...... . .. ... 

--

xx -x 

* x ' -" 

* S.. x ...... ....... ..:. . •... .... . . .. .. . . ... . .;... ...:..... .. ... . ... ....... .... ... .. .. .... .. . 

X 

.. .. .................. .. .....'.... : ............ •............. ............ .............. . . 

S.. . ........... .,. .-: ........ : ........... ............. ............ !............ ::............ . . .

80 

70 

60 

= 50 Cd, 

S4o 

= so 

IL 30 

20 

10

80 

70 

S'60 

CL 

50 

0 

14 

o-0 

20 

10

x 

.............. ............ ............ *............ ............ ;.......... !.-.-........ !..... S. 

. ........................ ......................... !....... ..... ............ ..... ...... ,'! " S.. 

-:xx -" • 

K) 

... .. .. .. ....... --.. "K .... . •.."... ... ... 

... ........... .i ............. ... ........ :.......... ..• ....... .. .. :......... . .. .......... .. . 

,-'"•X ~ ') . X• :- • -" ... . .... . ........... . . .............. 

...--.............. ... i : •........ . . .... ........ ............ .. . 

. .. . . . . . ........... .. . . . . .. . . . ... . . . . .. . . ..... .. . . . 

....... . :" ..:. "...... •....× .... x........ :..... :. ............ ............ ............. . .

32
25 

20~~~~~~~~~~~~ . . . . . . . ............. .:.......... . . . . . . .. ~~~~~~ . . . . . . . . . . . .. . . . . . . . . 

2 0 .. .... .. 

1 5 ... . ... ... ... ... ... ... ............ .. .......... ". . . . . .". . . . . .. ' . . . . . . . . . 

15 ............. . . . . . . . . . . . . . . .:: ........ x ' ............ ""× ........... + . . . . . . . 

10 ................. ....... • ........... . ....... 

Sx 

0 X X K E .. . . . ........... .. .. .. : ........... .• .-..... --•... . .. . . . . . . . . . 

nK 5 U) ,. ........ ........... "o.. .. • " x I. ......... :X . . . . . . . . .. . ....... . . . . . . . . . . 

m i.. " 0 ... .. .. ..: .. .. ... . . .. .. .. .X : . . . .......... 7X 

-5 .. .. x : .. .. .. ... .. .. . . .. . . . . .............. . . 

: :x -10 .... +.... ........ : .. .. .. ... .. ... .. .. .. .. . 

-1 . .5 .*6 . . . . .. .......... .................................... .. .......... i. . ....... . . . 

-2 .. . . .. . ... . ........... ......... .. . ... ....... I ........... ............ .... .. . . . . . -20 

I25 10 20 30 40 50 60 70 80 

Regression Pressure (MPa) 

Fig. 3.12. Residual vs. regression-predicted failure pressure 

when all model-boiler (x) and pulled-tube (*) 

specimens were included for training. 

25 , , 

20 .............................. ............... ............ a .o1 ..... a.......... ............ .............. ............ +.......... .............. ! ............ i . 

1 i 0 ........ ................. . . . . . . . .. .. ......... I ........... ............ .. ...... . . . . . 

I o .. ... .. ... .............. : .......... -. * ......... 7 ........... ...... .......... . . . . . 

0 xl x x x 

0. ..... ...... ' . 5.... X.. W M i 

x )K 

t -5 E ................ ......... ............................. . ... ...... ...... ....... •x 6...... : ....... ... : X .... K .... .. E ..... ................. 

10 X 

-1 5.. .......................... ...................... ... . .......... ...... ... .............. . . 

15 

-2 0 . .................. ' ........... ........... ............ .......... ............ "............. . . 

-20 25 I 1 I 10 

10 20 30 40 60 70 80 

Regression Pressure (MPa) 

Fig. 3.13. Change in predicted residual failure pressure 

,shown in Fig. 3.12for model-boiler (x) and pulled

tube (*) specimens when a LOOT training and 

prediction scheme was followed. 

33 NUREG/CR-6638
-20I -1 -1 I5 0 I5

5 -20 -15 -10 -5 0 5 

Residual Pressure (MPa) 

(a)

0: 

:0 

0 

.. o ... ... ... ...... 

... ........... : ......... :.......... ,.......... •............ .................... :................... 

.. ~~......... ......... ............ ... ... . ........... I ................... 0 0 

0.......... ! . . . . ................................. ........ S................................... 

. .. . . . . . . ........................... 0 0 . . . . . .............................. ........ 

0 

00: 

* o : . . ..: .. . . . .. :. . . . . . . . . .. . . . . . . . . 

-25 -20 -15 -1 -5 0

- 5 -20 -15 -10 -5 0 5 

Residual Pressure (MPa) 

(b)

10 15 20 2

10 15 20 25

Fig. 3.14. Cumulative distribution of residual failure 

pressure, based on an assumed normal 

distribution function, with training and prediction 

based on (a) all indications, and, (b) LOOT.

NUREG/CR-6638

1fM

90 

80 

70 

60 

50 

40 

30 

20 

10

0 cc 

.0 

0. 

0 

E 

U 

C0 

1.L

00 

. . ........... : ......... ............ ; ................ :.... . .: .......... .................. S.. 0 

0 

............................ .......... ......... i ........ .......... ................... 

0 

S.. ..... ..... .. .. . .......... ......... i. . . . ...................... .................. 

S........................................... . l ......... ....... i .......... I ................ 

. ..... ................. ..................... 

* . 0 

0 c0: 

S. . . . . . ............... :. ... i 0 . .......... ..................... ......... Z ........ 

-0 

. ....... ......... ......... ; ." ....... ".......... ".......... "...................... ......... ........

-2

90 

80 

70 

60 

5o 

40 

30 

20 

10

B 

e 

0. 

0-

34

lfMI

n n i I [ I r I

5
The failure pressures predicted by the APC linear regression model vs. measured failure 

pressures are shown in Fig. 3.15. Also shown is the 95% confidence interval for the first order 

least-squares fit of the measured-vs.-predicted failure pressures. A linear correlation coefficient 

R of .0.88 was calculated for this fit. Figure 3.16 is a plot of the residual failure pressure vs. 

regression-predicted pressures. Once again, the distribution of the residual pressures does not 

display any distinct pattern, a finding that is consistent with the assumption that a normal 

distribution of residuals is implicit in the calculation of the confidence interval. Assuming that 

the residual failure pressure is normally distributed, the comulative distribution of the residual 

pressure is shown in Fig. 3.17.

10 20 30 40 50 

Measured Pressure (MPa)

60 70 80

Fig. 3.15. Regression-predicted vs. measured failure 

pressure for all model-boiler (x) and pulled-tube (*) 

indications, using the APC model. Also shown 

are the upper and lower bounds of the 95% 

confidence interval based on a linear least

squares fit to the data.

NUREG/CR-6638

80 

70 

"•60 

a. 

' 50 

-04 

2Q 10 

20 

10

S. ........... .... ............ ........... i. ............... ............ ...... 

.. ................... .. . i..... ....... ....... > 

.. . . ..... .- ....... .. •. ................. ,,..... !..... 

: :, 

x 

S. . ... ...... ....... ". 

. ...... .... . .......................... ...........

I ......

35
0. 

nzo 

20 

15 

10 

5 

0 

"-5 

-10 

-15 

-20 

-25

10 20 30 40 50 60 70 

Regression Pressure (MPa) 

Fig. 3.16. Residual vs. regression-predicted failure pressure 

for all model-boiler (x) and pulled-tube (,) 

specimens, using the APC model

inn.X

2 

I> cc 

B 

E 

2

90 

80 

70 

60 

50 

40 

30 

20 

10

80

-25 -20 -15 -10 -5 0 5 10 15 20 25 

Residual Pressure (MPa) 

Fig. 3.17. Cumulative distribution of APC model residual 

failure pressure, based on an assumed normal 

distribution function. 

NUREG/CR-6638 36

.i................ ......... S.. ..... .. ... ..... .... .... .. ! ....... ... .i. ... ...... .... .... .. •... 

.. ... .. .. ... )K::• .. . I . . . . . .. .!. . . . . . . . . ............... . . .'...... . . . . . ........... 

S...................... × .:. .. . . . . ........................... ............ ....... !..... 

S. .i. . .............. .. . .............. .. . . . .; . . . < . ......... .* ........... . . 

X X X 

: X X 

i.............. S.... ........... • ......................... . -- ,...... ............. ............. . . 

.. . .. . . .. . .. . . .. . .. . . .. . . . . .. . . . . . .. . .. . . . . . . . .

xx . . ..... ... ... : .... .............. .. ;.... ... ... !.... ..... .:. x ... ... S.. ... ... ... .. .... ...... .. ... .. 

.. . . . .. . . .. . .. . . . . . . . . . . . . .. . . . . . . . . 

X: 

S....................:. .......... • ................... ! ........... . .. . . ............................... 

.. .............. ......... ................ . ......

N I -',I I I t I I ( I

I
4 Advanced Ultrasonic NDE of Steam Generator Tubing

4.1 Guided Ultrasonic Waves 

Guided waves (Lamb waves) were explored as a possible method to help characterize defects 

in SG tubes and to help identify attributes that may be useful for characterizing defects during 

field inspections. One significant advantage of Lamb waves is their insensitivity to the presence 

of roll transitions. This insensitivity makes it easier to detect cracks at the transition of rolled 

tubes. In addition, Lamb waves will be less sensitive to the presence of artifacts such as 

support plates and deposits and can be used to detect flaws at a distance. 

The Lamb wave inspections were carried out from the inside of the tube, with the tube filled 

with water and the contoured plastic wedge of the probe in contact with the tube ID surface. A 

specially fabricated Lamb wave probe with a 6-mm piezoelectric crystal on a wedge (Fig. 4.1), 

operating in a pulse echo mode, was used. Figure 4.2 shows the echo from a circumferential 

ODSCC in an Alloy 600, 22.2-mm (7/8-in.)-diameter tube with a laboratory-grown crack in the 

transition region of a tube that had been mechanically rolled into a heavy metal collar to 

simulate the geometry at the top of the tube sheet. Ultrasonic diffraction and EC evaluation of 

the crack suggest that its maximum depth is 80% throughwall. With the probe on the inside of 

the tube and 75 mm from the crack, the Lamb wave echo is clearly evident. The crack is -1400 

in circumferential extent. With the probe on the side of the tube opposite from the crack, only a 

relatively weak signal from the roll transition is evident. 

In general, the attenuation of Lamb waves is relatively low attenuation and echoes from 

defects in the tube wall can, in principle, be obtained for long distances between probe and 

defect. In our tests, the amplitude of the echo varied only moderately as the probe was moved 

along the axis, thus, by moving the probe a measured distance and noting the change in transit 

time of the echo, the Lamb wave velocity can be measured. A value of 4800 m/s was measured 

for an ultrasonic frequency of 1.2 MHz. Rose et al.3 measured a velocity of 4790 m/s and 

theoretically derived a L(0,2) mode group velocity of 4670 m/s at 1.2 MHz and a wall thickness 

of 1.25 mm. Rose et al. also noted that the frequency of 1.2 MHz was especially effective for 

water-filled tubes, a finding that is consistent with the present results. 

Transducer 

Crystal 

Mode Converted 

Lamb Wave 

Fig. 4.1. Specially fabricated Lamb wave probe with 6-mm piezoelectric crystal 

on a wedge, operating in pulse echo mode.

37 NUREG/CR-6638
Fig. 4.2. Ultrasonic Lamb wave echo (top half of figure) from a circumferential 

ODSCC in an Alloy 600, 22.2-mm (7/8-in.)-diameter tube with a 

laboratory-grown crack in the roll transition. UT and EC evaluation 

suggest a maximum depth of 80% throughwal. 

Attenuation measurements were made on Alloy 600, 22.2-mm (7/8-in.)-diameter tubing 

filled with water. For a transducer-to-end-of-tube distance of 10-50 cm, the echo amplitude of 

the signal at 1.2 MHz decreased by =12 dB and then remained approximately constant (with 

S/N ratio of =5: 1) up to a maximum distance of 250 cm. 

The variation of the amplitude of the reflected wave with crack depth was also studied. 

Figure 4.3 shows the results for circumferential OD EDM notches, laser cut slots, fatigue 

cracks, and a circumferetial ODSCC for a 40-gs acoustic path (=75-mm wedge-to-crack 

distance). The depth of the circumferetial ODSCC is estimated from other ultrasonic and EC 

measurements. All of the reflectors are wider than the transducer crystal. Depth estimates 

determined by other means, such as crack tip diffraction, are generally accurate for notches and 

fatigue cracks. I Figure 4.4 shows measurements for a 60-ýis round trip time for the echo. No 

relationship between signal amplitude and depth is evident for the 60-ps path, although a slight 

monotonically increasing trend is seen for the shorter 40-gs acoustic path. 

Although these results show that the amplitudes of the Lamb wave echoes are not very 

sensitive to depth, the amplitude gives some estimate of the depth when the probe is close to the 

defect. Better estimates of depth might be obtained by comparing, simultaneously, the echo 

amplitude of two Lamb wave modes; however, a unique and specialized probe would be required 

to make that kind of measurement. 

Lamb waves may be most useful in the detection of circumferential cracks in the roll 

transition and in the determination of the length of such cracks. They can be used to image the 

circumferential extent and segmentation of such circumferential cracks. Because they can 

provide detectable signals over relatively long distances, Lamb wave probes could be used to

NUREG/CR-6638 38
100

00 5) 

o', 

.Jw 

4

E0

80 

60 

40 

20

0

0 20 40 60 80 100 

Depth (%TW) 

Fig. 4.3. Dependence of ultrasonic Lamb wave amplitude on 

crack depth, evaluated with numerous tubes and 

40-1ps round trip transit time.

0 20 40 

Depth

60 

(%TW)

80 100

Fig. 4.4. Dependence of Lamb wave amplitude on crack 

depth, evaluated with numerous tubes. 

Measurements are reported for Lamb wave probe 

with 60-ps round trip times for the echoes.

NUREG/CR-6638

100

.0 0) 

5) SJ

80 

60 

40 

20

* EDM Notches I I I

* ODSCC 60ps Path 

* Fatigue Crack 

0 Laser Slots 

A

0

39
examine the tube sheet without having to insert the probe very far. Time-of-flight 

measurements of flaw echoes could be used to determine the axial position of a defect. 

Although axial cracks are not detectable with the probes that have been used for the current 

studies unless circumferentially oriented branches are present, it is possible, in principle, to 

make a Lamb wave probe that could detect axial cracks. 

Using a Lamb wave probe, M. Brook at ABB-AMDATA examined SG tube samples with 

circumferential SCC. Calibrations for these examinations were carried out with a standard that 

contained circumferential EDM notches with throughwall depths of 20, 40, and 60% and arc 

lengths of 20, 40, and 600. The signal from a 20% TW, 600 arc notch was set to 80% full screen 

height. The probe was moved axially 1.3 mm (0.05 in.) for every 3600 scan. A-, B-, and C-scans 

were generated. The scan mode covered a range of 140 mm (5.5 in.). The probe was operated at 

a frequency of -5 MHz. A PC-based Intraspect ultrasonic system was used to collect the data. 

As indicated previously, not enough information is present in the Lamb wave echo signal to 

determine the depth, but general estimates can be made by measuring the echo amplitude. The 

length of the defect is accurately estimated by observing the drop in signal amplitude as the 

defect is scanned. 

The image obtained from a laboratory-grown circumferential ODSCC in the roll transition of 

a 22.2-mm (7/8-in.)-diameter Alloy 600 tube section is shown in Fig. 4.5. The horizontal axis 

represents the full circumference of the tube. The segmentation and relative axial positions of 

the segments are clearly seen. There appear to be six crack segments, with five at the same 

axial location and one offset axially with an arc length of 50'. The total arc length indicated by 

UT is 1300. Destructive analysis (by PNNL) showed a crack with a total arc length of 160', with 

a segment showing an arc length of 600 axially offset from the larger segment by =1.3 mm 

(0.05 in.). Although destructive examination of the lower part of the crack shown in Fig. 4.5 

does not reveal well-defined segments, it does show widely varying depths for this portion of the 

crack. The segments indicated ultrasonically are presumably the result of low-amplitude 

signals from the shallow parts of the continuous crack. 

Fig. 4.5. Ultrasonic Lamb wave image (from 

ABB-AMDATA) of laboratory-grown 

circumferential ODSCC in an Alloy 

600 SG tube.

NUREG/CR-6638 40
4.2 Acoustic Microscopy

In collaboration with Sonoscan Inc. (Bensenville, IL), high-frequency ultrasonic imaging has 

been explored as a possible method to help characterize defects in SG tubes. Although intended 

primarily for laboratory investigation, acoustic microscopy could be applied in the field in 

special cases. 

In this approach, ultrasonic waves with frequencies of =50 MHz are used to generate a 

crack shadow. The measurement of depth is determined directly from the extent of the 

ultrasonic shadow on the OD of the tube. The part of the beam that misses the crack passes 

through the tube wall and is scanned and imaged by a 50-Am spot-size receiving transducer 

oriented to receive the ultrasonic wave as it passes through the tube wall. All coupling is 

accomplished with water. Figure 4.6 shows a schematic representation of the acoustic 

microscope. 

I Ultrasonic transducer.___, 

(scanned focused receiver) k' ' 

Shadow-_, / 

/ __Crack 

S- - Incident beam at angle 

Fig. 4.6. Schematic representation of the Sonoscan Inc. acoustic microscope. 

50-MHz ultrasonic waves, launched from inside the tube, were 

received by a focused transducer at the tube OD. A special fixture is 

used to hold the transducer at the correct angle of incidence to launch 

a 50-MHz, 450 longitudinal beam from the ID. 

The shadow of a 60% 1W laser cut slot was examined. A special fixture was used to hold 

the transducer at the correct angle of incidence to launch a 50-MHz, 450 longitudinal beam from 

the ID. Figure 4.7 shows the acoustic shadow of a laser-cut slot. The beginning and end of the 

shadow are clearly evident. Because through-transmission waves are used, this configuration 

could not be used under field conditions. A pulse echo mode configuration with transmitter and 

receiver on the ID would have to be used for field work. 

Several other tubes were also examined. Excellent results were obtained from laser-cut 

slots and from EDM notches. The estimated-vs. -actual depth for two notches, using 50-MHz 

ultrasonic waves, is shown in Fig. 4.8. A throughwall fatigue crack in an Inconel 600 SG tube 

was also examined. This crack, which initiated on the OD, could be profiled over a depth range 

of 100% TW to =40% TW. However, SCCs, which are typically very tight, do not appear to 

produce shadows as clear as those produced by notches or fatigue cracks. Because the tip is

41 NUREG/CR-6638
the tightest portion of the crack, crack depths would be expected to be underestimated. An 

example from a tube with an ODSCC is presented in Fig. 4.9. From the shadow in this figure, 

the estimated depth is =10% TW. Based on estimates from +Point data, which have been shown 

to be reasonably accurate for cracks of this type by destructive evaluation, the depth is more 

likely to be =25% TW. The difficulties in estimating depths of SCCs by acoustic microscopy 

suggest that significant difficulties could also be encountered with procedures that attempt to 

use crack-tip echoes to estimate depth (from time-of-flight differences between tip echo and 

bottom-of-crack echo). Cracks that cannot be shadowed also will not scatter significant 

ultrasonic energy to reveal the location of the crack tip and thus lead to underestimates of the 

depth. 

Fg. 4.7. Ultrasonic shadow of a 60% TW laser-cut slot 

produced by an angled 50-MHz shear wave launched 

from inside a water-filled tube, which is horizontal in 

the photograph. Tube axis is represented by the 

white line. A highly focused (50-yam spot size) water

coupled receiving transducer was used to scan the 

outer surface of the tube to create the shadow from 

the through-transmitted ultrasonic waves. Black 

band is the shadow of the laser-cut slot. Figure 

below the shadow shows amplitude as a function of 

position in the shadow along the white line. (Image 

courtesy of Sonoscan Inc.)

NUREG/CR-6638 42
U 0 20 40 60 80 100 

Actual Notch Depth (%TW) 

Mg. 4.8. Depth estimated by acoustic microscopy vs. actual depth 

of two EDM notches in two Alloy 600 22.2-mm (7/8-irL)

diameter tubes.

T

Width of ultrasonic shadow is 

used to estimate crack depth

Fig. 4.9. Shadow of crack from tube with ODSCC. From 

this shadow, estimated depth is -.10% TW. 

Depth is estimated from extent of shadow on 

OD of the tube.

NUREG/CR-6638

100 

80 

60 

40

20

z 

IM 

E 

*6• 

a. 

a 

"0 

0 

zg 

4) 

U

I

-> <o43
4.3 Laser-Cut Slots for Calibration and Modeling

Laser-cut slots in 22.2-mm (7/8-in.)-diameter Alloy 600 tubes for calibration and model 

validation have been prepared by Oxford Lasers, Ltd. Figure 4.10 is a photomicrograph of a 

representative axial OD laser-cut slot (Oxford Laser Ltd.), 75% TW (0.95 mm), in an Alloy 600, 

22.2-mm (7/8-in.)-diameter tube. Sectioning of this slot revealed occasional bridging across the 

faces, as expected. The opening at the OD was =50 pgm. 

Although laser-cut slots are somewhat more representative of field cracks than an EDM 

notch because they are tighter, they may not be a good choice for a calibration standard 

because the possibility of bridging across the slot faces may make it difficult to obtain 

reproducible results for EC data calibration. The potential difficulty can be illustrated by 

comparing the EC responses to 40 and 80% throughwall circumferential OD laser-cut slots that 

are 25.4 mm (1 in.) long. 

Eddy current signal amplitudes from 40 and 80% 7W EDM notches that were measured 

with a +PointTM probe differ by about a factor of three. The corresponding amplitude from the 

80% TW laser-cut slot was only -30% higher than that from the 40% TW slot. Phase angle 

analysis with a +PointM probe at 200 and 400 kHz, based on calibration on circumferential OD 

EDM notches, gave estimated depths of 50-60% for the 80% TW laser-cut slot and -20% for the 

40% TW laser-cut slot. 

Laser-cut slots may be useful for assessing depth estimation techniques because their 

maximum depths are known with reasonably high confidence. Laser-cut slots could also be 

used for modeling to validate predicted EC response to features such as ligaments or bridging 

across crack faces. 

Fig. 4.10. Photomicrograph of axial OD laser-cut slot (Oxford Laser Ltd.), 

75% TW (0.95 umm), in Alloy 600, 22.2-mm (0.875-in.1-diameter 

tube. Sectioning of slot revealed occasional bridging across the 

faces, as expected from this process. Opening at OD is =50 pm.

NUREG/CR-6638 44
4.4 Bore-Side Ultrasonic Examination of Tubes with ElectrosleeveTM Repairs 

The ElectrosleeveTM is a method of repairing SG tubes that uses a sleeve of ultrafine-grained 

nickel electrochemically deposited on the inner surface of a degraded tube to form a structural 

repair. The thickness of the ElectrosleeveTM is =1-mm, comparable to the 1.3-mm wall thickness 

of the parent tube. The use of EC and ultrasonic techniques for NDE of tubes with 

ElectrosleevesT' has been carried out at ANL. 

The ultrasonic techniques for detecting circumferential and axial cracks that were 

investigated involved the use of probes that were sized for the opening and curvature of a 22.2

mm (7/8-in.)-diameter tube with an ElectrosleeveTM repair. One circumferential probe, operating 

at 5 MHz, launches 450 shear waves in a pulse echo mode from the tube ID with either water or 

glycerol as couplant. A second probe launches 70' shear waves. Both probes can detect a 

13-mm-long, 0.5-mm-deep (40% TW) OD circumferential EDM notch in the parent tube. 

A third compound probe launches 45 and 70' waves that travel around the tube to detect 

axially oriented flaws. This compound probe can detect a 0.5-mm-deep (40% TW) OD axial EDM 

notch in the parent tube. The signals from the 450 beams are generally stronger than those 

from 700 beam, as expected. However, the 70° beam may be more effective in detecting planar 

flaws that do not intersect either the ID or OD of the tube and thus do not exhibit a corner trap 

reflector. The use of 5 MHz, rather than higher frequencies, helps minimize signal distortion 

due to the greater surface roughness of the ElectrosleeveTM. Although these ultrasonic 

techniques were successful in detecting notches, echoes from SCCs are expected to be 

considerably weaker than echoes from notches because of the tightness of the cracks. EDM 

notches may help to establish the limiting case for detection (i.e., SCCs would have to be larger 

than the smallest detectable EDM notch). Additional tests are planned on tubes with 

laboratory-grown SCCs. 

Eddy current traces obtained with a +PointTm probe at 100 and 300 kHz for the EDM notch 

samples are shown in Figs. 4.11-4.14, along with the corresponding ultrasonic echo patterns. 

Because the high conductivity of nickel would reduce the penetration depth of the EC probe, it 

is somewhat surprising that at 300 kHz, EC signals from notches were detectable. However, the 

conductivity of the ElectrosleeveTM material is lower than that of conventional nickel because of 

the much smaller grain size and the phosphorus additions. To ensure that the apparent notch 

signal was not an experimental artifact, a test was performed to verify that a copper foil (20 x 5 x 

1 mm) placed on the outside of a flaw-free tube with an ElectrosleeveM could be detected at 300 

kHz. Figure 4.11 shows the results for a 1-mm-deep (80% TW) OD circumferential notch in the 

parent tube. Both the EC and ultrasonic (70° shear) signals are strong and clear. Figure 4.12 

shows the results for a 0.5-mm-deep (40% TW) OD circumferential notch in the parent tube. 

Both the EC and the ultrasonic (701 shear) flaw signals are clear. Figure 4.13 shows the results 

for a 1-mm-deep (80% TW) OD axial notch in the parent tube. Both the EC and the ultrasonic 

(700 shear) flaw signals are clear, although the ultrasonic signal exhibits a better S/N ratio. 

Figure 4.14 shows the results for a 0.5-mm-deep (40% TW) OD axial notch in the parent tube. 

Both the EC and the ultrasonic (700 shear) flaw signals are detectable, but again, the ultrasonic 

signal shows a significantly better S/N ratio. Signal-to-noise ratios for axial notches are even 

better for 450 than for 70° shear waves.

45 NUREG/CR-6638
It is more difficult to detect defects that originate on the ID of the parent tube that are not 

100% TW, because nonthroughwall cracks do not intersect the OD of the parent tube to form a 

strong ultrasonic reflector (corner trap). Higher angle beam ultrasonic NDE, including use of 

surface waves, should be explored for detection of such cracks. The possibility that variations 

in ElectrosleeveTm acoustic properties that could distort the ultrasonic beam and thus lead to 

missed defects or false calls should also be evaluated.

Electrowlee~v Echo Froma: 

.:Parent. Tube 00 C ,I•M Notch 80%TW 

S+MHz 7fl*8

300 Diff V

'a

100 Diff V , - .. 9 S -an 86 r- %

E 

w

EC Response from a 80%TW 

Circ. OD Parent Tube EDM 

Notch in an Electrosleeved 

Tube using a + Point Coil 

at 100 and 300 kHz. ii

Fig. 4.11. Ultrasonic (top) and EC (bottom) signals from a 1-mm

deep (80% TW) OD circumferential notch in parent metal 

of Electrosleeved tube. Ultrasonic (70' shear) and EC 

flaw signals are both strong and clear.

NUREG/CR-6638

I4 &"AIc

46

M=

14 IS

I

I

. .

I 

-- I i r I ......... ...

I

...... ..... .
* Electraaleave'Echio Froz,: . 

Parent Tu1be 00 Cir6EMNotch .. 

40%. zw 5 'Miz 70-S

EC Response from a 40%T'IV 

Circ. OD Parent Tube EDM 

Notch in an Electrosleeved 

Tube using a + Point Coil 

at 100 and 300 kHz

Fig. 4.12. Ultrasonic (top) and EC (bottom) signals from a 0.5-mm

deep (40% TW) OD circumferential notch in parent metal 

of Electrosleeved tube. Ultrasonic (70' shear) and EC 

flaw signals are both clear.

47 NUREGICR-6638
'p~rl~t ub ,e 6 Axist, EDM ,Notc6 

.pA T W .... ....... 

S ~z70* 

I.MY

300DI 100 Diff V 10.15 v/d & 178 rot 252 

EC Response from a 80%TW Axial OD 

Parent Tube EDM Notch in an 

ElectroslecvedTube using a 

+Point Coil at 100 and 300 kliz. 

/ 

" ............. ............. 

Sb -," rtxR v• 74•.2

Fig. 4.13. Ultrasonic (top) and I 

deep (80% TW) ODc 

Electrosleeved tube. 

signals are both clear.

7C (bottom) signals from a 1 -mm

ixial notch in parent metal of an 

Ultrasonic (70' shear) and EC flaw

NUREG/CR-6638 48
'Par*i• Tube OD A•4Ia EDIWotch

300 Dtff V 100 Difif v 4. sdw85 rat 90 

EC Response from a 40%TW Axial OD 

Parent Tube EDM Notch in an 

Electrosleeved Tube using a 

-Point Coil at 100 and 300 kHz. 

Siii 

.t..b.ff . . ... ...... I V.. 

dat bu fe ....... -: 2 ,X ... .. ... . . ... .. ..

Fig. 4.14. Ultrasonic (top) and EC (bottom) signals from a 0.5-mm

deep (40% TW) OD axial notch in the parent metal of 

Electrosleeved tube. Ultrasonic (700 shear) and EC-flaw 

signals are both detectable but ultrasonic signal is 

better.

49 NUREG/CR-6638
5 Emerging Technology 

The use of fiber optic and satellite links makes possible faster EC data analysis. Raw EC 

data files can be transferred from an optical disc at a data acquisition location to the data 

analysis location via a fiber optic or satellite link, thus eliminating a delay of -4 h that would 

otherwise be experienced because of the need to physically transfer the optical disc to an off-site 

location for analysis. To use digital satellite communication, a transportable earth satellite 

station can be installed at a plant site, while a permanent earth station can be established at 

the location where the analysis will take place. Cost savings and faster inspections can be 

realized with this communication option.4 

Remote-field EC (RFEC) inspection employs low-frequency alternate current to inspect 

tubes from the inside. RFEC is a throughwall inspection technique wherein a solenoid coil 

generates the EC, while detectors are axially displaced from the exciter by two or more tube 

diameters and may be oriented to measure any component of the flux density. The tube acts as 

a waveguide; the pickup coil senses mainly the indirect electromagnetic coupling. 

In contrast to conventional EC inspection systems, in RFEC inspection, the field on the 

outside of the tube wall is much greater than the field on the inside. As in conventional EC 

analysis, defects cause changes in the amplitude and phase of the signal. Impedance plane 

diagrams are used for signal analysis. In general, the angle of the defect trace on the monitor is 

used to characterize an anomaly, but amplitude is also used. Unlike the conventional 

impedance plane, the RFEC voltage plane is not restricted to the first quadrant. Points on the 

RFEC plot represent amplitude and phase of through-transmission signals. RFEC signals cause 

almost no phase change and are well separated from lift-off, but separation of various types of 

defects is relatively difficult. The technique might be useful for characterizing outer-surface 

defects. 5 

Eddy current analysis is different from radiographic or ultrasonic techniques, where the 

reconstruction of defects is possible because the physical phenomena are governed by the wave 

equation. The time-reversal aspect of the wave equation permits the use of procedures such as 

holography and tomography to reconstruct defects. It is possible that defects can be 

reconstructed from EC data, but the reconstruction depends on the diffusion equation and one 

must rely on indirect algorithmic approaches to solve the inverse problem. 6 The "solution" to 

the inverse problem for EC techniques could be the use of pattern recognition algorithms to 

interpret the signals. The variations in shape of the trajectories of the signal in the impedance 

plane are used in pattern recognition schemes. 

In addition to the ongoing work in this program, others have investigated the application of 

a three-dimensional (3-D) electromagnetic field analysis to predict EC response to defects. 7 

Finite-element and boundary-element methods were applied to the EC problem. Experimental 

and calculated probe impedance trajectories for 100 and 400 kHz are presented for four 50% 

external slits with a width of 0.25 mm (0.010 in.), located 90' apart. The agreement between 

calculated and experimental (differential bobbin coil) data is satisfactory. Because the computer 

time required to perform 3-D calculations is very long, simplified 2-D analyses have been 

examined. Two- and 3-D analyses of EC signals at 100 and 400 kHz frequencies from 50 and 

100% TW cracks in SG tubes were compared with experimental measurements. The results are 

qualitatively similar, but, quantitatively, the 3-D analysis appears significantly better (2-D

NUREG/CR-6638 50
results were -1/2 of the experimental value). The problem with the 2-D analysis may be due to 

the lack of information about the volume of the crack. 

Advances in digital signal processing may dramatically improve the ability to extract 

information from EC inspections. It may be possible to use sophisticated digital filters to solve 

problems that are now approached by multifrequency techniques, such as the elimination of 

signals from TSPs. Stepinski and Maszi8 address the problem of detection and classification of 

material defects during an EC inspection. They consider algorithms for detecting and 

characterizing flaws and propose a new type of filter for classifying EC data. The new 2-D 

conjugate spectrum filters (CSFs) are sensitive to both phase angle and shape of the EC 

response. They transform all of the EC patterns into straight lines and thereby simplify the 

decision-making process. If digitized EC data that was sampled at a frequency synchronized 

with the inspection speed is available, CSFs could be used for SG tubing. 

Rose et al.3 describe a method for inspecting SG tubing with guided ultrasonic waves over 

long distances. The advantage of this concept is the absence of a moving probe and 100% 

coverage of the tube. Launching of guided waves with sufficient energy to inspect a long length 

of tubing is very difficult and sensitive to ultrasonic frequency, but theoretical considerations 

have significantly helped to optimize probe design. The ultrasonic wave is injected at the top of 

the tubing and travels the length of the tube with little attenuation. Echoes that return to the 

transmitting probe indicate defects in the tube. Defects that ;re only 10% of the total cross

sectional area have been detected in tubes nearly 17 m (55 ft) long. Tests have shown that 

guided waves can travel at least 49 m (160 ft). Although ultrasonic guided waves may be useful 

for inspection of SG tubing, satisfactory characterization of a detected flaw may not be possible 

for a fixed-probe configuration, because of the distortion of the ultrasonic wave as it propagates 

in the tube wall. The use of guided waves as part of a scanner, as described in this report, may 

be more effective for detecting circumferential cracks. 

More recent developments were presented at the 17th EPRI Steam Generator NDE 

Workshop. 9 Some preliminary work on EC tomographic imaging was presented by French 

researchers, but the development is still in its infancy (Benoist et. al., CEA/CEREM and Savin, 

Framatome Technologies, Inc. [FTI]). The continuing effort requires development of 

sophisticated computer algorithms. Tomographic techniques are not expected to be used in real 

time or in-line but rather for more detailed off-line evaluation of flaws suggested by other EC 

procedures. 

The results on EC array probes presented in Ref. 9 show that their reliability and sensitivity 

must be improved. The work to date suggests that arrays may be useful as an initial screening 

tool, but supplementary high-resolution rotating coil inspections will still be needed for 

verification. The difficulties associated with handling and analyzing data from multi-channel 

arrays may outweigh the potential improvements in detection capability over conventional 

bobbin inspections. Hardware requirements are more stringent, partly because special 

cables/wiring are needed and because the electronics are complex. It is also difficult to convert 

data from numerous coils (up to 32) into a conventional isometric image of the EC response, 

because of coil-to-coil variations in signal response and the difficulty in calibrating the array. 

Longer manual data analysis time offsets the time gained by faster scanning. If array probes are 

to replace bobbin coils as the initial screening tool, automated data analysis algorithms must be 

incorporated into the system.

51 NUREG/CR-6638
A comparison of the performance of rotating (+Pointm) and bobbin coil probes with that of 

array probes, including the Cecco-5, the MICA, and two Zetec arrays (a surface-riding array 

probe, and a nonsurface-riding probe) was presented (Raschiatore, Zetec). The MICA probe did 

not show significant improvement in reliability or detection over previous versions; in many 

cases, it exhibited the lowest sensitivity of the probes that were examined and was the least 

reliable during field tests. The sensitivity of the Cecco array probe to small defects was low. 

Results were also presented at the workshop 9 on the Ghent rotating probe (Watts, 

Westinghouse NSD). The added complexity of this surface-riding transmit/receive rotating 

probe did not seem to lead to improved performance compared to the more widely used pancake 

and +Pointm probes. Although no comparative studies were presented, new and more complex 

rotating probes, such as P4-5 (i.e., Levy and Betsch, Intercontrolle), did not appear to show 

significant improvement over conventional rotating probes. 

Five papers9 addressed ultrasonic inspection of SG tubing. The number of presentations 

on UT methods at this meeting indicates a growing interest in the use of this technique to 

supplement or even replace EC testing. However, slow inspection speed, often significantly 

slower than that of rotating EC probes, and criticality of alignment still limit the use of UT as a 

supplementary NDE technique for SG tubing. 

An evaluation of a 64-element annular ultrasonic phased array designed at Framatome was 

presented (Key, FTI and Quinn, Westinghouse). The array seems to perform better than 

mechanically rotated UT probes. Improvements in durability and redesign for compatibility with 

pusher-pullers are needed. A second paper on a phased ultrasonic array applied to inspection 

of the helical tube bundle of the French Super Phoenix reactor indicated that this system 

apparently worked well and allowed the inspection at the Super Phoenix reactor to be carried 

out at an acceptable inspection rate. 

A paper on the depth sizing of circumferential SCC at the top of the tube sheet was 

presented (Krutzen, Nuson Inspection Services).9 Four ultrasonic techniques were considered: 

echodynamic, time of flight diffraction, crack-tip echo, and guided wave. The crack tip echo 

technique was the most effective for sizing. However, no specific examples were presented. An 

increase in the use of ultrasonic techniques in ISIs is expected. Ultrasonic techniques can 

provide good estimates of the length and depth of cracks if a crack tip echo can be detected. 

However, the detection of a crack-tip echo from a SCC can be extremely difficult and unreliable, 

and obtaining depth estimates from crack tip echoes will require significant signal processing to 

improve S/N ratio.

NUREG/CR-6638 52
References 

1. D. R. Diercks, S. Bakhtiari, K. E. Kasza, D. S. Kupperman, S. Majumdar, J. Y. Park, and 

W. J. Shack, "Steam Generator Tube Integrity Program," Annual Report, Aug. 1995 - Sept. 

1996, NUREG/CR-6511, ANL-97/3, Vol. 2, 1997. 

2. C. V. Dodd, "Data Analysis for Steam Generator Tube Samples," Oak Ridge National 

Laboratory, NUREG/CR-6455, ORNL/TM- 13206, 1996. 

3. J. L. Rose, K. M. Rajana, and F. T. Carr, "Ultrasonic Guided Wave Inspection Concepts for 

Steam Generator Tubing," Mater. EvaL, pp. 307-311, Feb. 1994. 

4. H. Houserman, R. Warlick, and R. Vollmer, "Using Satellite Technology to Improve SG Eddy 

Current Inspections," NucL Eng. Int., Vol. 37(456), pp. 37-38, July 1992. 

5. D. L. Atherton, D. D. Mackintosh, S. P. Sullivan, J. M. S. Dubois, and T. R. Schmidt, 

"Remote-Field Eddy Current Signal Representation," Mater. Eval, pp. 782-789, July 1993. 

6. S. J. Norton and J. R. Bowler, "Theory of Eddy Current Inversion," J. AppL Phys., Vol. 73(2), 

Jan. 15, 1993. 

7. K. Satake, M. Tanaka, N. Shimizu, Y. Araki, and & Morimoto, "Three-Dimensional Analysis 

on Eddy Current Testing for S/G Tubes," IEEE Trans. Magn., Vol. 28(2), pp. 1466-1468, 

March 1992. 

8. T. Stepinski and N. Maszi, "Conjugate Spectrum Filters for Eddy Current Signal 

Processing," Mater. EvaL, pp. 839-844, July 1993. 

9. Proceedings of the 17' Annual EPRI Steam Generator NDE Workshop, EPRI Report TR

111547, Sept. 1998.

53 NUREG/CR-6638
NRC FORM 335 U.S. NUCLEAR REGULATORY COMMISSION 1. REPORT NUMBER 

(2-89) (Assigned by NRC. Add Vol., Supp., Rev,, 

NRCM 1102. and Addendum Numbers, ifany.) 301,3= BIBLIOGRAPHIC DATA SHEET NUREG/CR-6638 

(See #inxctons on the verse) AT-99/9 

2. TITLE AND SUBTITLE 

Advanced NDE for Steam Generator Tubing 

3. DATE REPORT PUBLISHED 

MONTH YEAR 

January 2000 

4. FIN OR GRANT NUMBER 

W6487 

5. AUTHOR(S) 6. TYPE OF REPORT 

S. Bakhtiari and D. S. Kupperman Technical; Topical 

7. PERIOD COVERED (Inclusive Dates) 

8. PERFORMING ORGANIZATION - NAME AND ADDRESS (If NRC, provide Division, Office or Region, U.S. Nuclear Regulatory Commission, and mailing address; if contractor, provide 

name and mailing address.) 

Argonne National Laboratory 

9700 South Cass Avenue 

Argonne, IL 60439 

9. SPONSORING ORGANIZATION - NAME AND ADDRESS .(If NRC, type 'Same as above': if contractor, provide NRC Division, Office or Region, U.S. Nuclear Regulatory Commission, 

and mailing address.) 

Division of Engineering 

Office of Nuclear Regulatory Research 

U. S. Nuclear Regulatory Commission 

Washington, DC 20555 

10. SUPPLEMENTARY NOTES 

J. Muscara, NRC Project Manager 

11. ABSTRACT (200 words or less) 

This report provides an assessment of various advanced nondestructive evaluation (NDE) techniques for inservice inspection of steam generator tubing. Selected results are presented on the application of data processing and visualization techniques; data analysis, including depth sizing and the 

development of correlations between eddy current signals and tube failure pressure by multivariate 

regression and neural networks; inspection of sleeved tubes; applications of ultrasonic NDE; and 

related work on emerging NDE technology.

12. KEY WORDS/DESCRIPTORS (List words or phrases that will assist researchers in locating this report) 13. AVAILABILITY STATEMENT 

Steam Generator Unlimited 

Tubes 14. SECURITY CLASSIFICATION 

Stress Corrosion Cracking (This Page) 

Eddy Current Testing Unclassified 

Nondestructive Evaluation (rhis Report) 

In-service Inspection Unclassified 

Ultrasonic Inspection 15. NUMBER OF PAGES 

Multivariate Analysis 

Alloy 600 16. PRICE

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