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ERRATA

April 30, 2003

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NUREG/CR-6746

Advanced Nondestructive Evaluation for Steam Generator Tubing

Division of Engineering Technology

Office of Nuclear Regulatory Research

U.S. Nuclear Regulatory Commission

Washington, DC 20555-0001

September 2001

This publication supersedes and replaces NUREG/CR-6746

due to corrected equations.

Publishing Services Branch

Office of the Chief Information Officer
NUREG/CR-6746

ANL-01/21

Advanced Nondestructive

Evaluation 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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NUREG/CR-6746

ANL-01/21

Advanced Nondestructive

Evaluation for Steam

Generator Tubing

Manuscript Completed:August 2001

Date Published: September 2001

Prepared by

S. Bakhtiari, J.Y. Park, D.S. Kuppernan, S. Majumdar, W.J. Shack

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
Advanced NDE for Steam Generator Tubing

S. Bakhtiari, J. Y. Park, D. S. Kupperman, S. Majumdar, W. J. Shack

Abstract

This report describes research activities carried out at Argonne National Laboratory (ANL)

as part of the Steam Generator (SG) Tube Integrity Program sponsored by the U.S. Nuclear

Regulatory Commission (USNRC). As a follow-up to a preceding publication on the assessment

of advanced nondestructive evaluation (NDE) techniques for inservice inspection of steam

generator tubing. This report provides an overview of more recent studies associated primarily

with signal processing and data analysis techniques applied to eddy current NDE. Results of

efforts made to date on implementation of various algorithms that pertain to computer-aided

analysis of eddy current rotating probe data are discussed. As a benchmark for NDE accuracy,

production and destructive characterization of specimens with laboratory-induced cracking are

described. The effect of uncertainties in NDE estimates of flaw size on the prediction of tube

structural integrity is addressed.

iii
Contents

Abstract . ............................................................................ iii

E xec tive S u ni ar. ...................................................... .. ............... . ..... ............... xv

Ex ecutive Summary . . .......................................................................... xv

Acknowledgments .............. ;;;:xvi

Acronyms and Abbreviations . . . . . xvii

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

2 Computer-Aided Analysis of Eddy Current Inspection Data ...................................... . 1

2.1 Data Processing Structure ................................................................... 2

2.2 Conversion and Calibration Routines ............................................ . 2

2.2.1 Data Conversion ................................................................... 4

2.2.2 Automated Data Calibration ................................................................... 4

2.3 Signal Processing, Data Analysis, and Display Routines ...................... . 7

2.3.1 Application of Digital Filters ................................................................... 8

2.3.2 Signal Conditioning ................................................................... 9

2.3.3 Signal Enhancement and Restoration ....................................................... 11

2.4 Multiparameter Data Analysis ................................................................... 28

2.4.1 Least Squares Regression and Factor-Based Techniques ......................... 29

2.4.2 Multifrequency Mixing Techniques .......................................................... 30

2.4.3 2-D Suppression of Unwanted Signals . .32

2.4.4 Multivariate Analysis for Estimation of NDE Proffle . . 35

2.5 Rule-Based Flaw Identification . .................................................................. 35

2.6 Data Quality Issue .............. ..................................................... 36

3 Data Analysis Results ........................................................................ 38

3.1 Comparison of Original and Processed NDE Profiles ............................................ 38

3.2 Analyses of Laboratory-Grown SCC Specimens . ................................................... 38

3.3 Assessment of Sizing Accuracy in Presence of Artifacts . ....................................... 64

4 Benchmarking of the Multiparameter Algorithm . . ......................................................... 71

4.1 Stress Corrosion Cracks Used in the Benchmark Tests ...................... . 71

4.2 Comparison of Crack Geometries Determined by Fractography and

Multiparameter Algorithm . ........................................................................ 71
4.3 Comparison of Failure Pressures Predicted by Using NDE and Fractography Data 73

5 Concluding Remarks .......................................... 84

References .86

Appendix: Characterization of Corrosion and Stress Corrosion Cracks in Steam Generator

Tube Specimens .87

vi
Figures

1. Schematic diagram showing basic structure of computer-aided

data analysis algorithm .................... ........................................................ 3

2. Display of GUIs for (a) main ECTool menu with data conversion and segmentation

submenus and (b) strip chart and lissajous display of segmented raw data ................... 5

3. Display of GUIs for (a) main ECTool menu with data calibration submenus, (b) raw

data and trigger channel, and (c) calibrated data in strip chart and lissajous display

format ........................................................................... 6

4. Series of MATLAB-based Graphical User'Interface (GUI) tools for automated analysis of

EC inspection results acquired with standard commercial instruments ........ ................ 8

5. (a) Typical amplitude response of RPC to 100% 'W, 6.35-mm (0.25-in.)-long axial EDM

notch at 300 kHz and (b) normalized Gaussian and Lorentzian impulse responses

having approximately same parameters......................................................................... 15

6. Spatial domain 2-D kernels for Gaussian and Lorentzian impulse responses ................. 15

7. Flaw geometries in set of 24 laser-cut specimens ....................... ................................... 16

8. (a) Original and (b)-(c) restored signals with 2-D Lorentzian kernels .. ........................ 18

9. (a) Original and (b) restored signal for rype-2 laser-cut specimen with nominal OD

flaw depth of 80% '1W ................ 19

10. (a) Original and (b) restored signal fbr Type-3 laser-cut specimen with nominal OD flaw

depth of 40% TW ............................................................................ 20

11. (a) Original and (b)-(c) restored signals with 2-D Lorentzian kernels for Type-4 laser-cut

specimen with nominal OD flaw depth of 80% TW ................... ..................................... 21

12. (a) Original and b) restored signal for Type-4 laser-cut specimen with nominal OD

flaw depth of 40%IW ................ - 22

13. (a) Original and (b) restored signal for Type-S laser-cut specimen \with nominal OD

flaw depth of 80%'IW ............................................................................ 23

14. (a) Original and (b) restored signal for Type-8 laser-cut specimen with nominal OD flaw

depth of 40% W ........................................................................... 24

15. (a) Original and (b)-(c) restored signals with 2-D Lorentzian kernels for Type-9 laser-cut

specimen with nominal,OD flaw depth of 80%I TW .......................................................... 25

vii
16. Data analysis results for specimen with laboratory-grown longitudinal ODSCC showing

(a) terrain plot of relative OD depth profile for cracked zone and (b) restored profile by

inverse filtering. Maximum crack depth is estimated to be >80% IW ............................ 26

17. Data analysis results for specimen with laboratory-grown longitudinal ODSCC showing

(a) terrain plot of relative OD depth proffle for cracked zone and (b) restored profile by

inverse filtering. Maximum crack depth is estimated to be =80% IW ............................ 26

18. Data analysis results for specimen with laboratory-grown skewed ODSCC showing (a)

terrain plot of relative OD depth proffle for cracked zone and (b) restored profile by

inverse fltering. Maximum crack depth is estimated to be =80% TW ............................ 27

19. Data analysis results for specimen with laboratory-grown circumferential ODSCC

showing (a) terrain plot of relative OD depth profile for cracked zone and (b) restored

proffle by inverse filtering. Maximum crack depth is estimated to be =60%IW ............. 27

20. Data analysis results for specimen with laboratory-grown circumferential ODSCC

showing (a) terrain plot of relative OD depth profile for cracked zone and (b) restored

profile by inverse fltering. Maximum crack depth is estimated to be =80%'IW ............. 28

21. Data analysis results for specimen with laboratory-grown longitudinal IDSCC at

dented TSP intersection showing (a) terrain plot of relative ID depth profile for cracked

zone and (b) restored profile by inverse filtering. Maximum crack depth Is estimated to

be <40%lW ........................................................................ 29

22. Demonstration of 2-D signal suppression on RPC traces from a 40% 'IW axial OD

notch ........................................................................ 34

23. Demonstration of 2-D signal suppression on RPC traces from 40% 'IW axial OD and

40% 'IW circumferential ID notch at arbitrarily selected locations within a 2700 tube

support ring ........................................................................ 34

24. (a) Conventional isometric plot (Eddynet) of +Pointm probe amplitude response and

(b) multiparameter sizing proffle of the same specimen with laboratory-grown

circumferential ODSCC .................. ...................................................... 40

25. (a) Conventional Isometric plot (Eddynetf) of +Pointm probe amplitude response and

(b) multiparameter sizing profiles of same specimen with laboratory-grown

circumferential ODSCC ........ ................................................................ 41

26. (a) Conventional isometric plot (Eddynetfm) of +Pointfm probe amplitude response and

(b) multiparameter sizing profiles of same specimen with laboratory-grown axial

ODSCC ........................................................................ 42

27. (a) Conventional isometric plot (Eddynetf) of +Pointl probe amplitude response and

(b) multiparameter sizing profiles of same specimen with laboratory-grown ODSCC ...... 43
28. (a) Conventional isometric plot (Eddynetf m ) of +Point probe amplitude response and

(b) multiparameter sizing profiles for two specimens with laboratory-grown SCC

indications at roll-transition region .......................................................................... 44

29. Representative display of data analysis results for specimen with laboratory-grown

circumferential ODSCC. Results indicate =3600 circumferential cracks, with

maximum depth of =80% SW .............................................................. ............... 45

30. Representative display of data analysis results for specimen with laboratory-grown

shallow circumferential IDSCC. Results indicate =3600 circumferential cracking with

maximum depth of >20%¶TW ............................................................................. 46

31. Representative display of data analysis results for specimen with laboratory-grown

circumferential ODSCC. Results indicate =3600 staggered circumferential cracking

with maximum depth of >80%'IW .................. 47..........; 

32. Representative display of data analysis results for specimen with laboratory-grown

axial IDSCC. Results indicate dominant single axial crack with maximum depth of

>80%'IW ........ . .;..................... 48

33. Representative display of data analysis results for specimen with laboratory-grown

axial ODSCC. Results indicate >15-mm (0.6-in.)-long axial crack with maximum depth

of =90%.1W ....-............... 49

34. Representative display of data analysis results for specimen with laboratory-grown

circumferential ODSCC. Results indicate =3600 circumferential cracks with maximum

depth >90%IW ........... . 50

35. Representative display of data analysis results for specimen with laboratory-grown

axial ODSCC. Results show two indications with maximum depths >80% IW .51

36. Representative display of data analysis results for specimen with laboratory-grownaxial ODSCC. Results indicate two closely spaced parallel (axially offset) cracks with

maximum depths reaching 100% I .. 52

37. Representative display of data analysis results for specimen with laboratory-grown

axial ODSCC. Results indicate multiple axial cracks with maximum depths

>80% .W...... -- 53

38. Representative display of data analysis -results for specimen with laboratory-grown

axial ODSCC. Results indicate multiple axial cracks with maximum depths

>80%'IW ...... ; ; .- .................... 54. 

39. Representative display of data analysis results for specimen with laboratory-grown

ODSCC. Results indicate single crack with maximum depth >90% 1W. 55

ix
40. Representative display of data analysis results for specimen with laboratory-grown

ODSCC. Results indicate multiple cracks around circumference, with maximum depth

of dominant flaw reaching 100% TW ......................................................................... 56

41. Representative display of data analysis results for specimen with laboratory-grown

ODSCC. Results indicate two circumferentially offset cracks, with maximum depth

reaching =100% TW .............................................................................. 57

42. Representative display of data analysis results for specimen with laboratory-grown

ODSCC. Results indicate segmented crack with maximum depth of =100% TW ............ 58

43. Representative display of data analysis results for specimen with laboratory-grown

ODSCC. Results indicate single axial crack with maximum depth >90% IW ........ ........ 59

44. Representative display of data analysis results for specimen with laboratory-grown

.ODSCC. Results indicate multiple cracks around circumference, with maximum depth

>60%VTW .............................................................................. 60

45. Representative display of data analysis results for specimen with laboratory-grown

ODSCC. Results indicate multiple cracks around circumference, with maximum depth

of dominant flaws reaching =100% TW ......................................................................... 61

46. Representative display of data analysis results for specimen with laboratory-grown

ODSCC. Results indicate multiple cracks at several axial locations around

circumference, with maximum depth >60% TW ............................. ............................... 62

47. Representative display of data analysis results for specimen with laboratory-grown

IDSCC. Results indicate multiple cracks, with dominant crack depth <35%IW ........... 63

48. Data analysis results for specimen with laboratory-grown ODSCC with TSP collar.

Estimated maximum depth (>90% W from plain tube) varies by <10% 'W for all

simulated artifacts ......................................................................... 65

49. Data analysis results for specimen with laboratory-grown ODSCC with TSP collar.

Estimated maximum depth (<50% TW from plain tube) varies by <10% TW for all

simulated artifacts ......................................................................... 66

50. Data analysis results for specimen with laboratory-grown ODSCC with sludge collar.

Estimated maximum depth (>90% 'W from plain tube) varies by <10% 'W for all

simulated artifacts ......................................................................... 67

51. Data analysis results for specimen with laboratory-grown ODSCC with sludge collar.

Estimated maximum depth (40% 'IW from plain tube) varies by <10% nW for all

simulated artifacts ........................................................................ 68

52. Data analysis results for specimen with laboratory-grown ODSCC with magnetite

collar. Estimated maximum depth (<50% 'W from plain tube) varies by <10% TW for

all simulated artifacts ........................................................................ 69

x
53. Data analysis results for specimen with laboratory-grown IDSCC with magnetite collar.

Estimated maximum depth (<40% 'IW from plain tube) varies by <10% IW for all

simulated artifacts ........................................................................... 70

54. Comparison of NDE and fractography profiles for specimen AGL 224 with

circumferential IDSCC (crack #1) ........................................................................... 73

55. Comparison of NDE and fractography profiles for specimen AGL 224 with

circumferential IDSCC (crack #2) ........................................................................... 74

56. Comparison of NDE and fractography profiles for specimen AGL 288 axial IDSCC ......... 74

57. Comparison of NDE and fractography profiles for specimen AGL 394 with

circumferential ODSCC .................... ....................................................... 74

58. Comparison of NDE and fractography profles for specimen AGL 533 with axial

ODSCC ........................................................................... 75

59. Comparison of NDE and fractography profles for specimen AGL 535 with axial

ODSCC ................. 75

60. Comparison of NDE and fractography profiles for specimen AGL 536 with axial

ODSCC .......................................................................... 75

61. Comparison of NDE and fractography profiles for specimen AGL 503 with axial

ODSCC .... 76

62. Comparison of NDE and fractography profiles for specimen AGL 516 with axial

ODSCC .... 76

63. Comparison of NDE and fractography profiles for specimen AGL 517 with axial

ODSCC .......................................................................... 76

64. Comparison of NDE and fractography profiles for specimen AGL 824 with axial

ODSCC ........................................................................... 77

65. Comparison of NDE and fractography profiles for specimen AGL 826 with

circumferential ODSCC ................. : 77

66. Comparison of NDE and fractography profiles for specimen AGL 835 with axial

ODSCC .... - ; 77

67. Comparison of NDE and fractography profiles for specimen AGL 838 with

circumferential ODSCC .................... ....................................................... 78

68. Comparison of NDE and fractography profiles for specimen AGL 854 with axial

ODSCC .... I I..................................... 78
69. Comparison of NDE and fractography profiles for specimen AGL 855 with axial

ODSCC ........................................................................... 78

70. Comparison of NDE and fractography profiles for specimen AGL 861 with axial

ODSCC ........................................................................... 79

71. Comparison of NDE and fractography proffles for specimen AGL 874 with axial

ODSCC ............................................................................ 79

72. Comparison of NDE and fractography profiles for specimen AGL 876 with axial

ODSCC ........................................................................... 79

73. Comparison of NDE and fractography profiles for specimen AGL 883 with axial

ODSCC ........................................................................... 80

74. Comparison of NDE and fractography profiles for specimen AGL 893 with

circumferential ODSCC ..................... ...................................................... 80

75. Comparison of NDE and fractography profiles for specimen AGL 816 with axial

IDSCC ........................................................................... 80

76. Comparison of NDE and fractography proffies for specimen AGL 816 with axial

IDSCC (2) ........................................................................... 81

77. Comparison of maximum depth determined by multiparameter algorithm with that

determined by fractography ........................................................................... 81

78. Comparison of depths determined by multiparameter algorithm with those by

fractography, and regression fit and estimated 95% bounds for observed depth as a

function multiparameter depth estimate ....................................................................... 81

79. Comparison of depths determined by multiparameter algorithm with those by

fractography ........................................................................... 82

80. Comparison of predicted (a) ligament rupture pressures and (b) unstable burst

pressures of specimens with SCC using crack depth proffles from fractography and

advanced NDE technique ........................................................................... 82

Al. Inscribed identification of tube specimen ...................................................................... 87

A2. Dye penetrant examination of tube specimen SGL 865 ................................................. 88

A3. Wire markers attached to tube specimen ...................................................................... 89

A4. EC NDE evaluation of tube specimen ........................................................................... 90

A5. Tube specimen SGL 413 with OD axial SCC after pressure-leak test ............................. 91

xli
A6. Cross-sectional optical metallography ......................................................................... 92

A7. Scanning electron microscopy for fractography of tube specimen SGL 413 .................... 93

A8. Scanning electron microscopy of OD SCC ...................................................................... 93

A9. Size and shape of OD axdal SCC in tube specimen AGL 536 determined by EC NDE and

Fractography ......................................................................... 94

,dfi
Tables

1. List of all available laser-cut tube specimens and their nominal flaw dimensions .......... 17

2 Comparison of RMSE for depth estimates by multiparameter algorithm as function of

the fractographic crack depth ..................................................................... 83

3 Comparison of RMSE for depth estimates by multiparameter algorithm (MP) and by

regression fit as a function of predicted crack depth ........................ ............................. 83

xiv
Executive Summary

- - This report is an overview of recent studies associated primarily with signal processing and

data analysis techniques in application to eddy current nondestructive evaluation (NDE) for

inservice inspection (SI) of SG tubing. The work was carried out at Argonne National Laboratory

(ANL) as part of the Steam Generator (SG) Tube Integrity Program sponsored by the U.S. Nuclear

Regulatory Commission (USNRC). Results of efforts made to date on implementation of various

algorithms that pertain to computer-aided analysis of eddy current rotating probe data are

discussed. Also described are production and destructive characterization of specimens with

laboratory-induced cracking used to benchmark NDE accuracy. The effect of uncertainties in

NDE estimates of flaw size on the prediction of tube structural integrity is also addressed.

An overview is presented of the basic structure of a data analysis scheme that was

implemented at ANL to explore the applicability of such algorithms for more accurate and

efficient processing of eddy current rotating probe data. Some key objectives of this work have

been to characterize flaws in the ANL tube bundle mock-up in order to minimize the expense of

tedious destructive examinations, and to further assist In parallel studies under this program

on prediction of tube structural integrity from NDE estimates of flaw profiles. NDE assessments

so far have been limited to analyses of data from rotating pancake coils, however, many of the

fundamental processes described here are applicable to other probe geometries and coil

configurations.

Implementation of data conversion and calibration routines at ANL for off-line manipulation

of eddy current data Is initially addressed. Subsequently, a general description of signal

processing and data analysis schemes adapted for manipulation of C-scan recordings with highresolution probes is presented. The issue of data quality and its implications on reliability and

uniformity of NDE results is also discussed. Selected test cases from analyses of various

batches of machined and laboratory-produced specimens with chemically induced cracking are

provided to illustrate the NDE results. Comparisons of estimated flaw profile by NDE with true

state by fractography are subsequently presented, following a brief description of destructive

examination procedure. Finally, results of limited statistical analysis to determine the

signiflcance of NDE uncertainties on predicting the structural integrity of tubing are shown.

Reference is provided to a report with a detailed description of analytical formulations used here

to estimate the failure pressure from NDE and destructive proffles.

xv
Acknowledgments

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

experimental work described in this report. The authors further thank D. R. Diercks for his

reviews and comments. This work is sponsored by the Office of Nuclear Regulatory Research,

U.S. Nuclear Regulatory Commission (NRC), under Job Code W6487; The NRC Project Manager

Is Dr. J. Muscara, who provided useful guidance in the performance of this work.

xvi
Acronyms and Abbreviations

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

EC eddy current

EDM electro-discharge-machined

EPRI Electric Power Research Institute

FBH flat-bottom hole

FIR finite-impulse response

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

MLR multiple linear regression

MP Multiparameter

NDE nondestructive evaluation

NRC U.S. Nuclear Regulatory Commission

OD outer diameter

ODSCC outer-diameter stress corrosion crack/cracking

PC (Windows) personal computer

PCR principal component regression

RPC rotating pancake coil

SCC stress corrosion crack/cracking

SG _steam generator

S/N signal-to-noise ratio

SVD Singular value decomposition

TSP tube support plate

TW throughwall

xvii 
1 Introduction

-A description of research activities carried out at Argonne National Laboratory ANL) as part

of the Steam Generator (SG) Tube Integrity Program sponsored by the U.S. Nuclear Regulatory

Commission (USNRC) is provided in this report. It Is a follow-up report to a preceding

publication on the assessment of advanced nondestructive'evaluation (NDE) techniques for

inservice inspection (IS) of SG tubing. An overview of more recent studies associated primarily

with signal processing and data analysis techniques in application to eddy current NDE of SG

tubing is presented. In particular, results of efforts made to date on implementation of various

algorithms that pertain to computer-aided analysis of eddy current (EC) rotating probe data are

-discussed. Production and destructive characterization of specimens with laboratory-induced

cracking, used to benchmark NDE accuracy, are also described. Finally, the effect of

uncertainties in NDE estimates of flaw size on the prediction of tube structural integrity is

briefly addressed.

In the following sections, the basic structure of a computer-aided data analysis algorithm

that was implemented at ANL as a means for more accurate and efficient processing of EC

rotating probe data Is described. Implementation of data conversion and calibration routines for

off-line manipulation of EC data is initially addressed. A general overview of signal processing

and data analysis schemes adapted for manipulation of C-scan recordings with high-resolution

rotating probes is presented. The issue of data quality and its implications on reliability and

uniformity of NDE results is also discussed. Selected test cases from analyses of various

batches of laboratory-produced specimens with chemically induced cracks are provided to

illustrate the NDE results. Comparisons of estimated flaw proffle by NDE with true state by

fractography are, subsequently presented, following a brief description of destructive

examination procedure. Finally, results of statistical analysis to determine the significance of

NDE uncertainties on predicting the structural integrity of tubing are presented. Reference to

reports with a detailed description of analytical formulations used here to estimate the failure

pressure from NDE and destructive proffies are provided.

2 Computer-Aided Analysis of Eddy Current Inspection Data

Manual analysis of multiple-frequency EC'data is'a tedious and challenging process. No

qualified technique, manual or automated, currently exists'that could provide reliable

estimation of flaw size over a wide range of SG'tubing damage. Conventional data analysis

methods become rather subjective whendealing 'with complex forms of degradation such as

stress corrosion cracking (SCC). Signal distortion by interference from iriternal/external

artifacts in the vicinity of a flaw further complicates discrimination of flaw signals from noise. In

this context, noise is referred to any random or coherent'signal that is not induced by a flaw. -In

comparison to high-speed bobbin coil inspections, high-resolution multicoil rotating and array

probes generate enormous amounts of data over comparable scanning lengths. Rotating probe

ISI of SG tubing is thus generally restricted to areas that are historically predisposed to known

damage mechanisms and sections of particular interest that are flagged by the initial bobbin coil

examinations. More extensive application of such probes for improving NDE reliability rests in

part on automating various stages of the data screening process. Computer-aided data analysis

1
is a viable means to overcome many of the challenges associated with reliable processing of data

acquired with high-resolution probes.

An overview of the basic structure of a data analysis scheme that was implemented to

explore the applicability of such algorithms for the processing of eddy current rotating probe

data is presented next. Some key objectives of this work have been to characterize flaws in the

ANL tube bundle mock-up in order to minimize the expense of tedious destructive examinations

and to further assist in parallel studies under this program on prediction of tube structural

integrity from NDE estimates of flaw profile. NDE assessments so far have been limited to

analyses of data from rotating pancake coils: however, many of the fundamental processes

described here are applicable to other probe geometries and coil configurations. The following

section includes a brief description of the structure and fundamental operations adapted for

computer-aided analysis of EC rotating probe data.

2.1 Data Processing Structure

Computer-aided data analysis here refers to a series of routines assembled to carry out

various stages of processing of the raw EC inspection data. Figure 1 depicts the basic

architecture of the algorithm implemented to allow for off-line analysis of data acquired with a

commercial EC testing instrument. Blocks associated with the processes mentioned in this

report are numbered. Output of the acquisition block shown in Fig. 1 is the digitized recordings

of a multiple-frequency inspection system. The EC testing instrumentation and software

currently in use at ANL are a Miz-30TM (Zetec, Inc.) remote data acquisition unit that is

controlled under the HP-UX-based EddynetTM (Zetec. Inc.) environment. To facilitate

development of algorithms for evaluation of EC inspection results, codes have been developed to

convert the raw ispection data to a standard file format. All data analysis and signal

processing algorithms have been implmented by using the PC-based software MATLAB, which is

a high-level scripting language that provides an efflcient environment for developing codes,

together with convenient graphical user interfaces (GUls) and graphical displays of the results.

The block diagram shown in Fig. 1 can be divided into three basic components: data

conversion, calibration,-and analysis stage. A description of each block is provided in the

following sections. In the conversion stage (block 1) digitized recordings of inspection data that

sequentially represent the in-phase and quadrature signal components are converted to a

readable format for off-line manipulations. In the subsequent stage, multiple-frequency raw EC

data, shown as block 2. are calibrated for all the recorded frequency channels. Finally, in the

data analysis stage, represented by blocks 3-6, calibrated data are processed to ultimately

produce NDE profiles that represent sizing estimates along a selected test section of a tube.

2.2 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, ECTool, 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 convert inspection results from other standard

commercial instruments.

2
Calibration'

Amplitude scaling

:Phase alignment

, L

Conversion

Industry format

to

Standard format

. 7Acquisition77

-Multiple frequency.: -.EC Inspection

Pre-processing

: ::ata .- aignnment-..- .. :-

Pre-conditioning

rtif actsuppression

Resolution enhancement 

_ _ _ _ _ _ _ . _ _ _ Visualiza

- ~0Detectionlldentification. - Image

S/N calculation - Stripchc

Signal detection - Lissajoi :1.Rule-based identification. ii. Pr

2 -0 A~-'-; :::E -L -S izin g P rl

ation; :-

lart-:-

us

oricl - :e , rofile:

-.t; E ;

fiE; Post-p'rcesing-:'n"-' X

Post-condltioning:

-Smultiparameter fitting .

Data segmentation

Figure 1.- Schematic diagram showing basic structure of computer-aided data

analysis algorithL Blocks associated with operations described in this

report are numbered.

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

editable text, and pushbuttons that activate yarious functions for the manipulation of the NDE

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

routine extracts the essential header information (currently assumed to be from MIZ-18 or MIZ30 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 userdefined 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 userdefined 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 flle. To avoid

incorrect application of normalization factors, the codes initially check the header information

3

J__
for consistency between the original calibration standard tube and new data files that are

subsequently loaded.

2.2.1 Data Conversion

Eddynet provides inspection data in MIZ-18 and MIZ-30 formats. The header sizes for MIZ18 and MIZ-30 are currently preset to contain a fixed number of bytes. Following the header is

the data stream that sequentially contains the horizontal and vertical signal components for all

channels that are stored in 2-byte signed integer format. The necessary information can be

extracted from the header 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 fie.

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 manipulations. Figure 2 shows an

intermediate display of the tools under conversion and segmentation functions that are

activated from the main menu.

2.2.2 Automated Data Calibration

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 ratio (S/N). as well as analyst judgment, could

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

Because estimates of flaw depth from phase angle information of multifrequency inspection data

depend heavily on initial calibrations, it is expected that computer-aided data calibration

routines will play an essential role in uniform and accurate normalization of raw EC data.

Automatic calibration of multifrequency EC inspection results could provide more efficient

data analysis and more importantly 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 ECTooI that

display the acquired data in standard graphical formats. Signals from machined flaws 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. Alignment of the phase angle at each frequency is typically made in

reference to either a shallow inside-diameter (ID) or a throughwall flaw.

Figure 3 displays the user interfaces along with intermediate and final stages of calibration

of raw EC readings collected with the 2.92-mm (0.1 15-in.)-diameter primary pancake coil of a

three-coil rotating probe on a 18-notch standard. The code sequentially asks the user to

interactively provide information on the approximate positions of lift-off signal, 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.

4
(a)

(b)

Figure 2. Display of GUIsfor (a) main ECTool menu with data conversion and

segmentation submenus and (b) strip chart and lissajous display of

segmented raw data. Eddy current data was acquired on a 18-notch

standard with a 2.92-mm (0.11 5-in.)-diameter primary pancake coil of

a three-coil rotating probe. .~~r .e. .

5
(a)

(b)

(c)

Figure 3. Display of GUIs for (a) main ECTool menu with data calibration

submenus. (b) raw data and trigger channel, and (c) calibrated data

in strip chart and lissajous displayformaL Eddy current data was

acquired on a 18-notch standard with a 2.92-mm (0.1 15-in.)-

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

6
2.3 Signal Processing, Data Analysis, and Display Routines

In reference to Fig. 1 the data analysis section (shown by blocks 3-6) is composed of three

basic stages: pre-processing, flaw detection and identification, and post-processing of data.

These blocks consist of various scripts that successively perform the calculation of S/N for all

channels, apply fllters for pre- and post-processing of data, and ultimately combine multiplefrequency information from all processed channels to provide an estimate of the depth profile for

the entire tubing test section. Both frequency and spatial domain filters are incorporated for

signal conditioning, baseline reduction, and resolution enhancement. Initially, the S/N ratio is

calculated from a user-defined approximate location along the trace baseline and minimum

detectable amplitude from a calibration standard tube. Subsequently, this Information is used

to implement a series of flters that perform suppression of artifacts, baseline fluctuations, and

signal enhancement or restoration. Filter characteristics are determined by taking into account

both the coil configuration and the sampling frequency of the inspection data. Identiflcation of

flaws and their origin Is made by a series of rules that are applied to the multiple-frequency EC

data. Both amplitude and phase relationships among the processed channels are used at this

stage of the process. Finally, the phase information at multiple frequencies is combined to

calculate the depth profile for the tube test section in reference to known indications on a

calibration standard tube. Multivariate analysis is used for estimating the NDE depth profile of

the tube using the available information from multiple channels. In the following sections, brief

descriptions of various signal processing and data analysis schemes that perform the

fundamental operations for multiparameter analysis of EC rotating probe data is provided.

A series of algorithms have been implemented as MATLAB scripts to provide in real-time

profile of flaw depth in a SG tube test section from NDE inspection results. These codes are

executed through a user interface tool to automatically process EC inspection results at the

multiple frequencies acquired with rotating probes. Figure 4 shows the main window of the GUI

tool ETProf, which incorporates various algorithms for the processing of raw EC data. Pull-down

menus, pushbuttons, and edltable text areas on the display can be activated to perform the

various stages of data analysis process. A series of modifications were recently incorporated to

allow for more quantitative mapping between NDE estimates and destructive examination

results. Figure 4 also shows several forms of graphics currently in place for the visualization

data at various stages of analysis. Subsequent to calculation of the depth profile, estimated

values are converted to percent of tube wall thickness. Reconstruction of helically scanned data

into C-scan format allows for the observation of sizing results from any azimuth and elevation

view angle and for any axial or circumferential cross section of the tube.- Scaling of data in axial

and circumferential directions allows for direct deduction of flaw extent along the tube. -Test

case results on analyses of various tubing specimens' using this computer-aided data analysis

tool are provided later this report.

7
Figure 4. Series of MATLAB-based Graphical User Interface (GUI) tools (top three rows)

for automated analysis of EC inspection results acquired with standard

commercial instruments. Also shown (bottom row) are several graphical

display formats such as image, terrain, and cross-sectional NDE profiles of

tube test section at various stages of analysis.

2.3.1 Application of Digital Filters

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

They may be implemented either in the hardware or software and serve in essence to

discriminate between real signals and noise. Although digital filters in theory are the numerical

counterparts of analog devices, there are many practical cases in which the two cannot be

substituted for one another. A fundamental difference that separates the two is that at a given

point in time, in-line analog filters operate only on the basis of information available up to the

point at which data have been collected, in contrast to off-line filters that also have access to

information on data points ahead. This additional information would allow implementation of

various digital flters with no apparent analog equivalent. The discussion here is concerned only

with the application of digital filters for the processing of EC inspection data. Several filtering

operations that were adopted for this specific application are described. They are Implemented

either in the original signal domain (i.e., spatial or time) or in the Fourier domain (i.e.,

frequency). The choices for selecting the proper domain are discussed below.

8
In its most basic form, the' one-dimensional (1-D) discrete form of a spatial domain digital

filtering may be written as

g(±nAx)=I,f(x)h(x±nAx), n= 0,1,2,... (1)

x

in which f(x) is the original signal, h(x) is the filter function, g(nAx) is the filtered signal, and Ax

denotes the sampling interval. Equation 1 also represents the discrete form of convolution

integral in which h(x) would then represent' the convolution kemel. A fundamental

mathematical theorem states that convolution in the time domain is equivalent to multiplication

in the frequency domain. This can be represented as

g(x) =f(x) *h(x) (2a)

G(o) = F(o-h(o). (2b)

in which Eqs. 2(a) and 2(b) denote the transformation pairs that show the equivalency of the

convolution and multiplication operations in the original and Fourier domain, respectively. With

EC rotating probe data being collected in a two-dimensional (2-D) fashion, Eq. 1 can be

expanded to represent the discrete form 2-D convolution or filtering equation as

M-1 N-1

g(X, Y) = E f(m, n)h(x-m, y - n), (3)

m=O n=O

where (x,y) is the spatial coordinate of a pixel in a matrix of C-scan data. For EC data, f and g

are complex quantities that represent in-phase and quadrature components of a signal.

In regard to processing of eddy current NDE data, digital filters generally serve three basic

purposes: noise reduction, artifact suppression, and signal enhancement or restoration. All of

these operations, however, are fundamentally aimed at improving the S/N ratio. In this context,

noise is characteristically defined as any unwanted signal, regardless of its source.

Transformations of the form shown in Eq. 2 are quite widespread in signal and data processing

applications. In general, they provide information that may not readily be available in the other

domain. When frequency domain filtering is the desired method, data are transformed to the

Fourier domain, processed, and then reverse-transformed into the original domain. The choice

for using either a frequency or spatial domain operation depends on the signal features that

need to be suppressed or preserved. No strict rules can be given as to use of spatial or

frequency domain filtering. For EC signals, frequency domain operations are often better suited

for reducing low-frequency baseline fluctuations and high-frequency noise. Spatial domain

fltering, on the other hand, is typically more appropriate for enhancing subtle features in the

data. In general, when the' frequency content of the signal and noise are wel separated,

frequency domain operations are the preferred method for'noise suppression.

2.3.2 Signal Conditioning

Pre- and post-conditioning of data are some of the most fundamental operations in any

signal processing application. The aim of such data manipulations is to minimize the

contribution from known sources of signal distortion. Coherent noise that is present at multiple

9

, A ., 4 1  I  .

I , , i . ; Ij .I . , I : i -
channels occurs commonly in EC testing of SG tubing. Low-frequency baseline variations and

high-frequency noise are also always present, to various degrees, in any EC inspection data.

Typical sources of low-frequency signals, particularly for absolute impedance coils, are probe

wobble, probe wear, temperature fluctuations, and gradual dimensional and geometry changes

of the tube. Tube ID conditions and electronic noise, on the other hand, are typical sources of

high-frequency noise. For both these sources of signal degradation, frequency domain filters

can be designed to effectively suppress unwanted signals. Two types of fltering have been

incorporated into the data analysis routine to reduce the level of such noise. They consist of

standard Fourier domain filters and the more elaborate finite-impulse response (FIR) filters. In

reference to Eq. 2, conventional filtering in frequency domain is performed through application

of the filter transfer function to the Fourier transform of the signal. Filter characteristics are

determined based on the data sampling rate and the frequency content of noise. A brief

description of FIR filters used to serve the same purpose is given below.

Frequency domain bandpass FIR filters are nonrecursive filters that offer certain

advantages and limitations over recursive inflnite-duration impulse response (IIR) flters. 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 some degree of perturbation of

amplitude and phase of the original signal.

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) = E b(k) x(n - k) + a(k) y(n - k), (4)

k=O k=l

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

in z domain, which is the z-transform of the time-domain difference Eq. 4, can be described by

Y(z) YE b(k)z

H(z) = (Z) N k-° (5)

X()ao + F, a(k)zk

k=l

where a= 1 for the case of a FIR filter, and the summation in the denominator of Eq. 5

vanishes. The difference between the two filtering type can be seen by examining the flter

frequency response, which is a rational function in z71 for an IIR flter and represents a discrete

Fourier transform for a FIR filter. Regardless of flter 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 flltered 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

10
background variations. Filters for EC signal conditioning often require a sharper transient 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. Studies so far have suggested 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 may complicate conventional data analysis procedures.

2.3.3 Signal Enhancement and Restoration

Two basic approaches for improving data quality are the signal enhancement and signal

restoration methods. Signal enhancement refines the quality of data without any knowledge of

the degradation phenomenon. Signal restoration, on the other hand, is founded on the

assumption that the degradation source is well understood so that an inverse process could be

implemented to recover the true response. The principal objective in both cases Is to treat the

data so that the result is more suited for a specific application than the original data. Although

the two approaches are clearly differentiated in most standard data restoration applications,

distinction between the two becomes less apparent in practical applications associated with the

EC testing of SG tubing. This is because of the difficulty in accurately modeling the underlying

degradation phenomenon that arises from the combined influence of many factors, in addition

to the blurring effect of the coil impulse response. Both data enhancement and approximate

restoration schemes have been incorporated into the multiparameter data analysis algorithm.

Studies to date suggest that comparable results can be achieved by the two techniques for the

processing of EC rotating probe data. Signal restoration, referred to here as approximate

deconvolution or Inverse filtering, is implemented in the frequency domain. Signal

enhancement, on the other hand, is implemented directly in the original spatial domain.

Although deconvolution-based methods are generally expected to produce more accurate

results, they are also computationally more intensive.

The application of approximate deconvolution schemes to real-time processing 'of

multifrequency inspection results has been investigated at ANL. This study was prompted by

the need for more accurate characterization of complex cracking morphologies in SG tubing.

Deconvolution techniques are used in a wide range of signal processing applications, primarily

to recover signals distorted by the sensing environment. In EC inspection applications,

pseudoinverse fiters could be effective for enhancement of spatial resolution that is degraded by

the finite spread of the coil-induced field. Better separation of flaw indications from extraneous

signals could, in turn, improve the estimation of flaw depth determined from the phase-angle

information of multifrequency data. Frequency-dependent signal restoration could also help

reduce differences in probe response at different frequencies. his could be particularly

beneficial for multifrequency mixing techniques in which the differences in the coil impulse

response at the base and auxiliary frequencies could significantly contribute to the level of mix

residuals.

- The fundamental limitation of data enhancement and restoration techniques in application

to EC signals could be attributed to the flow of current in a conducting medium that is governed

by the diffusion phenomenon. This suggests that distortion Is not simply a modulation of signal

amplitude. Instead, the waveform could experience a complete alteration of structure that is not

linearly dependent on Its original form. 'Another factor that could significantly influence the

degree to which a signal can be recovered is the lack of separation between the spectral content

;11
of flaw indications and extraneous signals. Internal/external artifacts and design

discontinuities (e.g., conducting and magnetic deposits, tube dimensional variations, external

support structures, etc.) could produce signal trajectories with spectral components that are

close to those from flaw indications. Furthermore, practical sampling rates that are typically

used for acquisition of ISI data with rotating probes do not render continuous smooth signals

that are essential for optimal restoration by inverse-fitering schemes. Finally, it is important to

note that physically realizable optimal inverse filters are often unstable. For this reason,

approximate deconvolution algorithms may provide the best alternative for real-time restoration

of EC flaw signals for practical ISI applications.

Signal Enhancement- When the exact form of the degradation process can only be approximated,

signal enhancement techniques are often the first choice for the treatment of data. Because

such techniques can be implemented directly in the original domain, they are computationally

efficient. Data enhancement is achieved by applying pre-stored kernels to the EC C-scan data

in accordance with Eq. 3. Both smoothing and peak restoration are attained in this manner.

The kernels in effect perform least squares polynomial fitting of the data. Because of the

availability of Information around any center pixel, a symmetric convolution process Is applied.

Typical 2-D kernels are shown in the following discussion on signal restoration.

Signal Restoration- Degradation of the probe's spatial resolution in two dimensions could, in

general, be described by the following discrete convolution model:

M-1 N-1

g(x, y) = F, f (m, n)h(x - m, y - n) + t7 (x, y)

m=O n=O (6)

3 h(x, y) * f(x, y),

wherefis the nondistorted signal (probe input), g Is the measured signal (probe output), h is the

coil impulse response, ii is the additive noise, and * denotes the convolution operation. With

prior knowledge of the distortion model, the original signal may then be recovered through

deconvolution, either directly by polynomial division in the spatial domain, or more efficiently by

transformation into frequency domain. The inverse-flltering operation in the Fourier domain

could be expressed as

f( , IFG(u,v)1()

fx, Y) F H(u, v)]

where f is the new estimate of the original signal. The distorted frequency-domain signal

G(u,v) and Probe transfer function H(u,v) are defined as

G(u, v) = F[g(x, y)] (8)

H(u, v) = F[h(x, y)], (9)

in which the transformed variables (u,v) are inversely proportional to the sample spacing in the

original spatial domain. Equation 7 suggests that the level to which a signal can be restored is

dependent on knowledge of the degradation function.

12
Representative amplitude response (1-D circumferential trace) of a rotating probe to a

narrow axial flaw is shown in Fig. 5(a). The coil is a standard 2.92-mm (0.1 15-in.)-diameter

pancake excited at a frequency of 300 kHz. The machined flaw is an axial notch of length 6.35

mm (0.25 in.) and depth of 100% TW. The trace in this figure was resampled at approximately

twice the minimum prescribed ISI sampling rate of rotating probes. As an approximation to the

coil response in Fig. 5(a), two different functions were considered in this study. Figure 5(b)

displays Gaussian and Lorentzian functions of the form

5(x) = Ae (XP) (10)

A , ~ ~~~~~~~~~~~~~~~~~ 1

1+a(x -,) 2

in which (B determines the peak position, and the parameter a is inversely proportional to the

variance for the Gaussian function and to the width at half-height for the Lorentzian function.

-Values of the parameter ,B in Fig. 5(b) were selected so that the two functions have

approximately the same widths at half-height.

Although both Gaussian and Lorentzian functions could be considered as close

approximations to the coil impulse -response, the former can be adjusted to better match the

measured response. This is expected, because coil impedance is a complex exponential function

(i.e., a sinusoidal response). However, the Gaussian function, which is often the principal

choice for simulating the coil impulse response, may not be the optimal choice for inversefiltering applications. The sharp taper of this function could result in amplification of highfrequency signal components and false Indications. The smoothly decreasing Lorentzian

function, on the other hand, could resolve this problem associated with the use of Gaussian

function as the impulse response of the coil.

Figure 6 displays a series of 2-D kernels that was constructed from 1-D Gaussian and

Lorentzian functions of Fig. 6(b).- The two types of kernels considered in this study consisted of

(1) two superimposed orthogonal 1-D functions and (2) true" 2-D impulse response made by

rotating a 1-D function about its vertical axis; Although both the Gaussian and Lorentzian

functions have been implemented as part of a 2-D deconvolution algorithm, in all test cases

considered so far the results have pointed to the consistency and improved stability of the

Lorentzian over the Gaussian impulse response for restoration of rotating pancake coil (RPC)

signals. -

Test Case Results on Signal Restoration for Machined Specimens- Preliminary evaluation of the

2-D deconvolution scheme that is integrated into the multiparameter data analysis algorithm

was carried out by using a subset of laser-cut notch specimens. The geometry of all the

available laser-cut specimens is depicted in Fig. 7. -Flaw dimensions are listed in Table 1.

Inspection results acquired with a standard mid-range pancake coil were preprocessed by using

both types of 2-D kernels described in the previous section. To display the outcome of the

inverse-filtering scheme, intermediate results with a Lorentzian impulse response are presented

at a single frequency. However, for multiparameter data analysis, the code automatically

calculates the impulse response parameters for all the available excitation frequencies. A user13
defined segment of the EC trace, typically from the available tube standard, is initially used to

calculate these parameters.

Figure 8(a) shows a terrain plot of the original C-scan data from a Type-1 laser-cut

specimen (#5528-1-1) containing a single axial notch of length 6.35 mm and nominal OD depth

of 80% 'W. Figures 8(b) and (c) display the restored signals subsequent to application of the

2-D kernels shown in Figs. 6(a) and (b), respectively. In both cases, the deconvolved signals

display better spatial resolution and lower baseline variation. The improvement in spatial

resolution could most clearly be observed from comparison of the original and restored response

at opposite ends of the axial flaw in which the flaw ends in the deconvolved signal are better

defined. This is also reflected in the lissajous trace displayed alongside the terrain plot. Similar

improvement in spatial resolution is also expected in the circumferential direction. NDE

maximum depth estimates for this sample were 58% by bobbin coil (phase-angle) and 75% by

multiparameter analysis of RPC (2.92-mm 10.1 15-in.] pancake) data.

Figures 9-15 display the results of inverse filtering on the remaining samples from the

subset of laser-cut tubes with one or both types of 2-D Lorentzian kernels. In all cases, the

restored data show improved spatial resolution and reduction of the baseline level. It is

important to note that the primary objective of the proposed deconvolution scheme as a

preprocessing tool for multiparameter analysis of EC inspection results is to maximize the

separation between crack-like indications and extraneous signals with minimum loss of

information from the original data. For all test cases shown here, the results also indicate less

ambiguity In the measurement of the phase-angle of restored indications in comparison to those

from the originaf readings. A more detailed description of analyses of EC inspection results

using bobbin and +Poinfm probes, as well as multiparameter data analysis, was provided in our

earlier reporL2

Test Case Results on Signal Restoration for Laboratory-grown SCC Specimens- Eddy current

readings on a collection of 22.2-mm (0.875-in.)-diameter Alloy 600 tubes with laboratoryproduced cracking were analyzed by using a multiparameter data analysis scheme described in

this report. Flaws in this small set of six samples consisted primarily of OD axial cracking in

free-span and circumferential cracking in roll-transition regions, plus a single specimen with

axial ID cracking at a dented TSP region. Multiple-frequency NDE data used in this study were

acquired with a standard three-coil rotating probe containing a 2.92-mm (0.115-in.) pancake, a

mid-range +Point, and 2.03-mm (0.080-in.) high-frequency pancake coil. The primary pancake

coil readings were utilized for analyses. The calibration standard contained 18 EDM notches of

axial and circumferential orientation originating from the OD and ID of the tube and ranging in

depth from 20% to 100% 'W. All notches were 6.35 mm (0.25 in.) in length. Data analysis

results are presented as an isometric display of relative depth profile for the flawed region of the

tube. Estimated profiles are shown both with and without signal restoration by inverse fltering.

Figure 16 shows the normalized depth profile for a specimen with laboratory-grown

longitudinal ODSCC. For all test cases, only the portion of tube in the vicinity of the flaw is

displayed. In reference to the calibration standard, the maximum flaw depth in the laboratory14
(a) (b)

(a) Typical amplitude response of RPC (2.92-mm 0.115-in.lpancake) to 100% TW, 6.35-mm (0.25-in)-long axial EDM notch

at 300 kHz and (b) normalized Gaussian [solid linel and

Lorentzian (dashed line) impulse responses having approximately

same parameters.

A

(a)

II

J0

l.. i0;

02

W----20

10

r* 0 0 d N .

(b)

Figure 6. Spatial domain 2-D kernels for Gaussian (left) and Lorentzian

(right) inpulse responses constructed by (a) two orthogonal 1-D

functions and (b) rotation of 1 -Dfunction about vertical axis.

-15

Figure 5.

0.0

I ,

j0.4.

02

a

II

OJ

II

GA.

02

is

I
I L

,*- L -*

I I

Ty

I I

! I = =

Type 2

f* L - ,

IType It

Type 3

a L

LI =| Tye = 

Type 4

L~Type 6

W-3. 1,-

Type

Type 10

Figure 7. Flaw geometries in set of 24 laser-cut specimens. Flaw dimensions are

listed in Table 1.

16

'V

1

i ~~~~~~~~~I I I-

- - T==

Type 5

Type 7

Type 9

S . .

W-J-H-F,

_ .

_

, I

a

-

LL

. 11
Table 1. List of all available laser-cut tube specimens and their

nominalflaw dimensions.

Tube |D No' of|

Tube ID # Tpe Notches|

5528-1 -1

5528-1 -2

5528-1-3

5528-1-4

5528-2-1

5528-2-2

5516-4-3

5516-4-2

1

1

1

1

1

1

2

2

1

1

1

1

1

1

2

2

Length

(in./deg)

1 0.25"

0.25"

0.35"

0.35"

0.5"

0.5"

0.5"

0.5" 

Depth

(%TW)

80.00

80.00

80.00

80.00

80.00

80.00

80.00

80.00

|Ligament 

Wdth I (In.)l

NA

NA

NA

NA

NA

NA

0.010

0.005

5528-3-1 3 6 0.5 80.00 0.005

5528-3-2 3 6 0.5" 80.00 0.010

5528-3-3 3 6 0.5" 40.00 0.010

5528-3-4 4 6 0.5" 80.00 0.005

5469-2-1 4 6 0:5" 80.00 0.010

5469-2-2 4 6 0.5" 40.00 0.010

5469-2-3 5 6 0.5" 80.00 0.010

5469-2-4 5 6 0.5" 80.00 0.020

5469-3-1 6 6 0.5" 80.00 0.010

5531-3-1 6 6 0.5" 80.00 0.020

5469-3-3 7 2 3600 80.00 0.005

5469-3-4 7 2 3600 80.00 0.010

5469-4-1 8 6 3600 80.00 0.010

5469-4-2 8 6 3600 80.00 0.005

5469-4-3 9 2 1800 x0.5" 80.00 NA

5469-4-4 10 2 180°x0.5" 80.00 NA

17

-

-

: q e .
I:

3.5

2.5

1.0

0.5 

.0.0 

-1

A,u. -2 -1 0

H-p.

1 2

(a)

140,

100,

40,

2 0

0~~~~~~~~~~~~~~~~~~~~2

53; 35

CL-W. ° o A-d.

2

1.5

0.5

4

0

-0.5

-l

-2

.......... .... ..... ...... ..... ;

........'.,,, 

~~~~~~~~~..... ........ 

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

.15 -1 40.5 0 0.5 1 1. ; 4p.

(b)

140

120

,

!82

40,

20

so

40 35

20 20 252, 

0 C d r ° ~ O 

2

1.5

0.5

0

-0.5

-1

(C)

Figure 8. (a) Original and (b)-(c) restored signals with 2-D Lorentzian kernels of

Fig. 6for Type-I laser-cut specimen #5528-1-1 with nominal ODflaw

depth of 80% TW. In both cases, deconvolved signal displays higher

spatial resolution and lower baseline variatiorL NDE maximum depth

estimates were 58% by bobbin coil [phase-angle) and 75% by

multiparameter analysis of RPC (2.92-mm 10.115-in.l pancake) data.

18

.,'

.......... ......._',_

................... .. ..... ..... .'.. .. ...,

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

- : 

...... :,; N:.; : V :~' 

-2 -1.5 -1 45.5 0 05 1 1.5

Hp

.. .......

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

. . .. .. ... . ..... .. . .. . b .

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

2.5,

21

2

I

It

I
1

.0

10

Ax-dr.

25 ~~~~~~~~~~~~~. ,... ,.,...... ..

IS ........ .... .. * ...... .. . ... .. .

0~~~~~~~~~~~~~~~.- ..... '....

.2 1.5 * .5 0 0 5 I Is 

H-.

(a)

(b)

Figure 9. (a) Original and (b) restored signalfor Type-2 laser-cut specimnen #5516-

4-3 with nominal OD flaw depth of 80%6 TW. NDE maximum depth

estimates were 35% by bobbin coil (phase-angle) and 72% by

nultparameter analysis of RPC (2.92-un 0. 11 5-in.] pancake) data.

-19

40 N

20

Cim-dr.

E

c4rc*.

2

.L

I

At4ir.
E

50

50

40

Ck-&.

30

4 ' ,

A.-.

-1.5 .1 40.5 °

H-c=ms

(a)

40~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~4

20~~~0

AX-di,.10 O'.5 1

(b)

Figure 10. (a) Original and (b) restored signalfor Type-3 laser-cut specimen #5528-

3-3 with nominal OD flaw depth of 40% TW. NDE maximum depth

estimates were 42% by bobbin coil (phase-angle) and 61% by

multLparameter analysis ofRPC (2.92-mm (0 115-in. pancake) data.

20

.1

2.5,

0.5 1 1.5
120 2

120~~~~~~~~~~~~~~~~~~~~~~~~~~.

t eo :1| il.t t ;: ......... ~~~~~~~~~~~~~~~~~~~~~~~........ ........ ......... . .................. X

40

20 t1 "R'@'t ~~~~~~~~~~~~~~~~~~~~~~~~~~ .............. ~0.5 ....... >4

eo '. ..... ... ....... .. .

O,l-dk. 00 10 - 1.5 .1 -0.5 0 0.5 1 15 2 A. H-Co.

(a)

1<0 1 ;'.5

SIr ,i,il :§ , ~ ~ ~~~~~~~ ~ ........ ~ ~ ~~~~~~~~~~~~~~~~~.. ......................... . ..

120,

100,

40 z. ....

* eoZ 6o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. . . ......... .. .. .... . .. ..... ... .

1~~~~~~ 0.5 .

20

0 0 ......~~~~. ........ ... ..... .............

so~~~~~~~~~~~~~~~~~~s

40 _ ;~~~~~~~ e o . -~~~~~4

2D so~~~~~~~5

cso..*. 0 o 10 .2150= -I 50. 0 0 . 1 1.

- ' b-1t -0. . -. ' 1x1

(b)

100

40~~~~~~~~~~~~~

2D2

20 .1 -~~~~~~~~~~~~~~~~.. U - 1 U . - I - . 2

(c)

Figure 1 1. (a) Original and (b)-(c) restored signals with 2-D Lorentztan kernels for

-Tjpe-4 laser-cut specimen #5469-2-1 with nominal OD flaw depth of

8096 IW. IVDE ma.ximum depth estimates were 45% by bobbin coil

(phase-angle) and 74% by multiparamneter analysis of RPC (2.92-m

10. 11 5-in. pancake) data.

21

3 7 

'~~~~~~~~~~~ ' I 
S

60

10

A-dr.

I.5

(a)

-.5 -I . ... 5

30

20 ZO

Ch:-di. O O -1 a . 5 -1 0.5 0 0.5

(b)

Figure 12. (a) Original and b) restored signalfor Type-4 laser-cut specimen #5469-

2-2 with nominal OD flaw depth of 40% W. NDE racimum depth

estimates were 43% by bobbin coil (phase-angle) and 60% by

rnutiparaieter analysis of RPC (2.92-mm (0. 11 5-in.l pancake) data

22

Clrc.dr,
140 4 . .. .......

Az6cir.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I I 1111. II 120, 3.5 1.. .... . .... . .....

60 . 2 ''',''' -','^ '' . '

40 - -

20-7 30 ~~~~~~~~~~~~~~~~~~~~~~~~~~~.. ......

cin.dfr. 0 0 ' .5 -2 0 O .I IS2:. !

NDE maximum depth estbnat~~~~~~~ ~~~~~~es ........... bin oi

(2.92-mm[0.115-irLIpancake)~~~~~~~~~~~..... .......... ............

140

so ~ ~ ~ ~ ~ ~ ~~~~i 

a60 

~~~~60 *~~~~~~~~2

no~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~- j1 60 

40, 

120~~~~~~~~~~~~2

100,~~~~~~~~~~1

20 00.... 0 ....... ..... 05 15I

AM.di,.H-oV

(b)

Figure 13. (a) Original, and (b) restored signal for Type-5 laser-cut

specimen #5469-2-4 with nominal OD.fRaw dphof 80% TW.

NDE maximum depth estimates were, 64% by bobbin coil

(phase-angle) and 78% by multipararmeter analysis of RPC

(2.92-mm 10.1I 15-In.) pancake) data

23
-40 20 1 0

0 0

M-4*.

(a)

140

120

100~

40

20

0 40

40 30

20 20

Om-dir. ° AO-dir1

4 r

4 ..... .. .............

3 .5. ........... .. . ........ .... ............... ....

23 ....... .......... . ---- .............. ;. .

1 2

~05 .... ...

45. -2 -.. 0 0 1 I- 2

25~~~~~~~~~~~~ 

2 . .. .. . . ... .. ....

4 .5 .. .. . ...... .....

'2 -1.5 -I -0.5 0 0.5 1 1.5 2

H_C_V.

(b)

Figure 14. (a) Original and b) restored signal for Type-8 laser-cut

specimen #5469-4-1 with nominal ODflaw depth of 40% 7W.

NDE maximum depth estimates were 39% by bobbin coil

(phase-angle) and 70% by multiparameter analysis of RPC

(2.92-mm 10.11 5-in.] pancake) data

24

140

120

100

80.

60

40

20

50

40

Gs, dr.
140.

120,

100.

. W

40.

20.

0O,

80 ;

20

CL--' 00 -1 - 0 .- I

.5

4 ... .. . .. ...-.-. .... .... . ... . ....

35,l............... ............

1.5 ,:g,..:

O.' _ t 4 1'~~~~~~~~..........

.0.

-2 -1 0 I . 1 2

H -".

(a)

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

.... .. .. .;L . .. .. ... .. . .. ... .

2+.~~~qj

.. . .. . .... 1 ...... %

0.5 v , , FW , _ 5

.0.5,,-__ .. . . ..1 .... .........

(b)

-15 .1 0.5 0 0 5

H-comp.

1.5

W.

140

12D0

100

' ' I' O I . . . ' 40o

I20

01

80~~~~~~~~~~~~~~~~~~~~s

,2 .1.5 -I .. 0 0.5 I 1.5 2

- (C)

Figure 15. (a) Original and (b)-(c) restored signals with 2-D Lorentzian

kernels for Type-9 laser-cut .specimen -#5469-4-3 with

nominal OD flaw depth of 80% 7W. NDE maximum depth

estimates were 48% by bobbin coil (phase-angle) and 69%

by multiparameter analysis of PPC (2.92-mm 10.115-in.)

pancake) data.

25

,6
S L . * ..' " l 1 i '

(a) (b)

FYgure 16. Data analysis results for specimen with laboratory-grown longitudinal

ODSCC showing (a) terrain plot of relative OD depth profilefor cracked zone

and (b) restored profile by inverse filtering. Maximum crack depth is

estimated to be >80% TW. Isometric display of results showsfiner details in

restored profile.

0 a ....................

0.61 ... .. . ....... ' \

021~ ~ ~ ~ ------ -- 0.8-r---!^

t. .... .... . ... .. 0 >wz., ..........- 

3~~~~~~~~~~~~~4 5 ,.*' sr va OS-w-|6, ..... . -'-i4s 

50 5

10 30 30

-I t" s3 5 _ ="14" ol OMENONMAIW

ieff.a^JI

t ,,,,,,, ,,,,,, .......... , . ........-. 

041 ~ ~~~~~ ~ ................... ?

3 0tffl ° |...........

' 4 0~~~~~~4 50 0-T 0 1O 20 U O

(a) (b)

FYgre 17. Data analysis results for specimnen with laboratory-grown longitudinal

ODSCC showing a) terrain plot of relative OD depth profilefor cracked zone

and (b) restored protlIe by inverse filtering. MaxiTnum crack depth is

estUnated to be =80% IW. Isometrc display of results showsftner details in

restored profile.

grown specimen vwas estimated to be >80% TW. Figures 16(a) and (b) display, respectively, the

estimated 2-D profies of the flawed segment without and with the deconvolution process being

applied. Unlike the gradual tapering of the flaw depth seen in Fig. 16(a), restored depth profile

of Fig. 16(b) shows the flaw to have a rather uniform depth in the center, with a sharp drop in

depth at the two ends of the crack.

Figure 17 shows the results of analyses for the second aial ODSCC flaw with estimated

maximum depth of 80% IW. Comparison of the restored depth profile in Fig. 17(b) with that in

26
60

(a) (b)

FYgure 18. Data analysis results for specimen with laboratory-grown skewed ODSCC

showing (a) terrain plot of relative OD depth profilefor cracked zone and b)

restored profile by inversefiltering. Maximum crack depth is estimated to be

-80% 7W. Isometric display of results showsfiner details in restored profile.

-fl. .* t I .i 

04. .....

(a) - (b)

Figure 19. Data analysis results for specimen with laboratory-grown circumferential

ODSCC showing (a) terrain plot of relative OD depth profile for cracked zone

and (b) restored profile by inverse filtering. Maximum crack depth is

estimated to be 60% 7W. Isometric display of results showsfiner details in

restored profile.

Fig., 17(a) suggests the crack to be composed of a series of axial segments with the one segment

being nearly separated from the rest. Similar results are shown in Fig. 18 for another specimen

with an axial ODSCC flaw. Terrain plots of the depth profile, however, suggest the orientation of

the crack to be approximately at 450 with respect to the tube axis. Maximum flaw, depth in this

case was estimated to be =80% IW.

Figures 19 and 20 show the results of analyses for two specimens with circumferential

ODSCC at the roll-transition region of the tube. The processed data clearly indicates complete

suppression of the roll-transition that is the primary signal feature in the amplitude trace. The

;27

U 0

=._ I ftINMP1" A PI, il-nlf . ,10 ,,,_ .,

zi^.^z;D:_ai; -
U "II

(a) (b)

Figure 20. Data analysis results for specimen with laboratory-grown circumferential

ODSCC showing (a) terrain plot of relative OD depth profile for cracked zone

and (b) restored proftle by inverse ftltering. Maximum crack depth is

estimated to be -809 TW. Isometric display of results showsfiner details in

restored profile.

maximum flaw depths were estimated to be =60% and =80% TW, respectively. Based on the

terrain plots in Figs. 19(a) and 20(a), both cracks extend nearly 1200 around the circumference.

The depth profiles of the restored signals shown in Figs. 19(b) and 20(b) suggest both

circumferential cracks to be composed of multiple segments. The maximum depth of the flaw

displayed in Fig. 19(b) appears to be near the center. Terrain plotting of the estimated depth for

the restored signal in Fig. 20(b), however, suggests that the circumferential crack consists of

four closely spaced short segments with comparable maximum depths.

Finally, Fig. 21 shows the results of analysis for an ID axial crack in a dented region at the

tube support plate intersection. The maximum flaw depth was predicted to be =40% TW. In

this case, a terrain plot of the defected segment was constructed from the estimated ID profile.

Comparing the restored profile of Fig. 21(b) with that of 21(a) indicates some improvement of

spatial resolution following signal restoration. Once again, complete suppression of the TSP and

dent signals is clearly evident in both cases.

2.4 Multiparameter Data Analysis

Multivariate data analysis algorithms serve as the foundation for various operations for the

processing of EC data. Both regression and factor-based techniques fit into this category of data

manipulation. More prominent areas of application to EC ISI data are in algorithms used for

suppression of unwanted signals and in predictive models that attempt to correlate NDE

parameters to single or multiple independent variables such as flaw size or tube structural

integrity. Three separate algorithms were investigated earlier in this program and have been

adapted for use in various areas of research associated with the EC testing of SG tubing. They

consisted of multiple linear regression (MLR), principal component regression (PCR), and partial

least squares (PLS) techniques. Conventional least-squares-based regression has been used

primarily in standard artifact suppression schemes, commonly referred to as mixing algorithms.

The more versatile PLS algorithm has been used in a wider range of applications, including more

28

20 0 0

_____________________________________________________________________________________ J

0.8- ------

6 .......

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

04-

0.2-

0

40 20
0 o'

(a) (b)

Figure 21. Data analysis results for specinen with laboratory-grown longitudinal

IDSCC at dented TSP intersection showing (a) terrain plot of relative ID depth

proftle for cracked zone and (b) restored profile by inverse fltering.

Maximum crack depth is estimated to be <40% TW. Isometric display of

results shows fner details in restored profile.

sophisticated suppression schemes and predictive models. A brief description of these

techniques is presented next.

2.4.1 Least Squares Regression and Factor-Based Techniques

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 the 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 X1 and

the parameter matrix [I', MLR develops a linear combination of variables in [Xl that minimizes

the errors in reproducing [YL.' This is accomplished by constructing a relationship between XI

'and IYl such that

: (Y]= [X][f3] + [E], ' ' ' f ' - (12)

where 1/31 is the matrix of regression coefficients and [lj is the matrix of errors associated with

the MLR model. The matrix 13] 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

M ^ 2 N M 2 [El E (Ymn-Ymn) = XE-mn.* 

n=l m=l . n=l m=l

29

(13)

E . n x~~~~~~~~~- I PPN. _ "M

I _
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. 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) have also been 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 [Yl and independent variable [XI. The outer relationships can be

written in matrix form as

N

[X] =[T] [P] +[El]= tnPn + [E] (14a)

n=l

N

[Y] =[U][IQ]+[IF] = E nqn + [F], (14b)

n=l

with the inner relation defined by

u = bntn, (14c)

where 71, [, [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 IIFI (i.e., Nonrd1Dj), and hence to construct a-useful

relationship between [Xl and [].

2.4.2 Multifrequency Mixing Techniques

Processed channel information from multiparameter mixing is commonly utilized in

analyzing multifrequency EC inspection data. So-called mixing techniques, originally

implemented electronically on analog signals by using rotation, scaling, and subtraction, were

devised to enhance detection of EC signals of interest by suppressing the effect of unwanted

signals such as background fluctuations and artifacts. Conventional digital mixing techniques

use multifrequency signal components from two (or more) channels to construct a new mix

channel based on known artifacts (signal to be suppressed) on a calibration standard tube. Mix

frequencies are chosen so as to provide independent information on the signal to be eliminated.

Current mix algorithms generally use a linear least squares method in which the output (mix

channel) signal is a weighted linear combination of horizontal and vertical components of input

signal components at different frequencies. Because independent mix coefficients can be

calculated for each signal to be suppressed (often based on simulated artifacts on a calibration

standard), in theory an unwanted signal could be eliminated by successive implementation of

different sets of mix coefficients. However, except under ideal conditions, the mix signal will

30
always contain residual signals from unwanted artifacts.' This could in part be attributed to

background fluctuations as well as material and geometrical differences between the simulated

and actual artifacts. Furthermore, variation in the probe's field of view (i.e., spread of the

induced fleld) at different frequencies could also contribute to a nonzero mix residue. It is

important to note that the mixing process could affect to different degrees both the amplitude

and phase of the signal from defects of interest.

Recovery of defect-induced signals in the presence of strong background interference is an

important and often challenging NDE problem when analyzing NDE results for ISI of SG tubing.

Data analysis is further complicated when degradations are accompanied by tube ID variations

such'as'design-related tube diameter changes or the presence of significant denting. These

difficulties are associated with such factors as low S/N ratio and small phase separation

between ID and deep OD indications. Multifrequency mixing is routinely applied to improve

detection of flaw signals that are obscured by tubing artifacts. However, the mix channel

information should be analyzed discerningly, particularly when more than two frequencies are

used in the process. Conventional two-frequency least-squares (LS)-based mix algorithms that

are optimized primarily to suppress a single OD artifact generally provide consistent outcomes.

On the other hand, processed channel information from mixing algorithms that incorporate

more than two frequencies could be unreliable for the interpretation of signals outside the

segment where the mix coefficients were calculated. This is due in part to uneven perturbation

of the signal amplitude and phase information that is introduced by higher-order (nonlinear)

mix models. This issue is of particular importance when dealing with rotating probe data.

Direct (independent) and indirect (dependent) mixing algorithms, for suppressing multiple

unwanted indications from composite signals have also been examined. In an indirect mix,

regression coefficients are determined by using a data segment from a simulated artifact such

as a TSP ring on a reference standard tube, which is expected to closely resemble those present

in the actual SGs. The aim of the regression model is to best reproduce the primary/base

frequency signal by combining signal components from auxiliary frequency channel(s). This is

the conventional approach used for the analysis of EC ISI results. Alternatively, independent

mix procedures have the potential to suppress unwanted signals by using multiple-frequency

readings on the same tube. This approach; primarily suggested for suppression of dominant

signal features, is of particular interest when tube standards with simulated artifacts that

resemble field-induced signals are not readily available.

Various two- and three-frequency 'regression fits have been implemented at ANL.

Preliminary results indicated that two-frequency mixes are generally more consistent, so the

approach was to sequentially apply two-frequency mixes for suppression of two unwanted

indications at the same axial location. To improve the mix outcome, several modifications were

made to our previously developed standard mix algorithm. These consisted of energy-scaling

each trace, resampling in frequency domain, and phase-angle referencing. Frequency

resampling was done to increase the number of available independent variables. Phase-angle

tracking was done to help reduce phase ambiguity from multiple application of linear regression

coefficients. Based on cases tested so far, the amplitude renormalization consistently exhibited

improved quality of mix outputs. This is believed to be a result of the weighting introduced by

the energy-scaling process.

31
2.4.3 2-D Suppression of Unwanted Signals

Test case results are presented here from an investigation on the application of signal

decomposition and factor analysis techniques for selective suppression of unwanted indications

from rotating-probe data. Initial studies suggest that 2-D multiple-frequency factor-based

techniques could provide substantial improvement over conventional 1-D mix procedures for

analysis of rotating-probe EC inspection results. Preliminary evaluations of the two techniques

discussed here was based on analyses of simulated composite signals from a rotating-probe that

were produced from the available readings on a calibration standard.

Conventional regression-based mix algorithms were initially developed to aid in suppression

of unwanted signals (e.g., external support structures and, to a lesser extent, tube dimensional

variations) from EC signals. Multifrequency mix procedures that typically involve combining the

information from a primary/base frequency and one or two auxiliary frequency channels have

been fairly effective for the processing of bobbin coil readings. However, independent

evaluations at ANL suggest that 1-D mix algorithms are not as effective for the processing of

higher-resolution rotating-probe inspection results. In particular, unlike the situation for the

bobbin coil, mixes for rotating-probe C-scan data are quite subtle and routinely fail to produce

consistent outcomes. Also, when mix coefficients are applied to the entire signal trace,

significant distortion of both the amplitude and phase-angle information of the original readings

could result. The nature of instability of traditional mix schemes for the processing of rotatingprobe data becomes somewhat apparent when one compares a 1-D bobbin coil signal (i.e., each

measurement point along the tube axis is an integration of coil response over the circumference)

with 2-D readings (i.e., measurements along both axial and circumferential directions) from a

rotating probe. To improve rotating-probe mixes, specially designed simulated TSP rings (e.g., a

2700 ring as opposed to an axisymmetric 360° ring used for bobbin mixes) are often utilized.

Nevertheless, the mix results could vary substantially, depending on the selected portion of

signal for the calculation of mix coefficients. For this reason, mix channel information is not

currently used for reporting rotating-probe inspection results.

A study was initiated at ANL to evaluate potential alternatives to 1-D conventional mix

procedures for RPC data. Two approaches based on 2-D factor analysis techniques were

considered. Both methods utilize spectral decomposition, also referred to as singular value

decomposition (SVD) or outer product expansion. However, they differ significantly in their

analysis of factors. The first method is based on a standard decomposition at a single-frequency

and subsequent selection of appropriate coefficients. The second method, on the other hand,

uses a least-squares (LS) regression approach with two frequencies.

Spectral decomposition methods serve as the fundamental numerical technique in a wide

range of applications from solution of linear system of equations to problems in signal and

image processing such as restoration, compression, denoising, pattern recognition, and feature

extraction. Such transformation algorithms basically attempt to decompose an often illconditioned data matrix to an outer product expansion of left- and right-hand unitary matrices

of orthonormal eigenvectors with a matrix of pseudo-eigenvalues. With the underlying

assumption that the basis functions that span the signal and noise subspaces are orthogonal,

this mapping should then provide improved separation of relevant signal features from

inconsequential trends. Following the decomposition, selection of appropriate factors allows for

the preservation of relevant signal features and in effect elimination of background noise.

32
Factor analysis methods that use spectral decomposition together with multivariate regression

schemes offer a more robust method for the analysis of factors through recursive regression

between the independent and dependent variable' matrices. The basic formulation of LS-based

factor analysis method used here for the processing of rotating probe data was described earlier

in this report.

In practice, selection of appropriate factors often poses the greatest challenge when spectral

decomposition alone is used. This is particularly true for the EC inspection results with rotating

probes in which flaw and artifacts at one frequency are often described by common factors.

This means that it is not always possible to select a set of factors that reasonably separate

consequential indications from artifacts and background fluctuations. Therefore, factors cannot

be applied blindly, and some form of discretion is necessary in deciding which factors to keep.

For this reason, the multiple-frequency LS-based factor analysis method should provide a more

robust and systematic approach to suppression of unwanted signals in rotating-probe data.

The two approaches that were de'scribed above, namely, single-frequency spectral

decomposition and a two-frequency decomposition with LS-based regression, were initially

tested on simulated composite signals from'-RPC readings on a 22.2-mm -(7/8-in.)-OD tube

standard with machined notches and a 2700 'support plate slip ring. Various'combinations of

OD and ID notches within the TSP region were produced. This was done by vectorial addition of

data segments with notches at various frequencies with that from the TSP segment. It Is worth

noting that the nonaxisymmeteric geometry of the TSP ring used here poses a greater challenge

to any signal suppression scheme than do axisymmetric drilled support plates. However, it is

expected that such nonuniform and overwhelming EC indications could be encountered in the

field from extemal structures such as broached support plates.

Initial results on the suppression of TSP indication at a single frequency by using a

standard SVD algorithm did not produce acceptable results. Specifically,. in several test cases a

reasonable degree of suppression of TSP indications introduced substantial degradation of the

flaw signal. As mentioned earlier, the primary limitation of this method arises from the lack of

separation of factors that describe artifact and flaw signals.

With the same 'simulated data, the two-frequency 'LS-based factor analysis method

described in Sec. 2.4.1, on the other hand, -rendered a systematic approach for repeatable

suppression of TSP indications with acceptable degradation of flaw signals. Figures 22 and 23

show the results for two test cases by using the composite signal described above. In both

cases, single and multiple flaws that were arbitrarily placed within the TSP region were

recovered with a high level of suppression of TSP indications. Selection of primary and auxiliary

frequencies for 2-D suppression is analogous to that used by standard 1-D mix procedures.

Also, in a similar manner to conventional mix procedures, multiple unwanted indications could

be suppressed by successive application of this method. Further studies in this area will be

carried out by using specimens with laboratory-produced flaws and with various forms of

simulated artifacts.

33
s~~~~~~ i - ON AgL

0~5

-

0 500 1000 1500 2000 2500 3000 3500

data poirt

0 500 1000 1500 2000 2500 300O

data port 3500

u4

02

0 500. 1000 1500 2000 2500 3000 3500

data poirt

0A.

20.2 -

IY .Iiii 1 .

- 0 500 1000 1500 2000 2500 3000 3500

data poirt

(a) (b)

FYgure 22. Demonstration of 2-D signal suppression on RPC traces of (a) simulated raw

data at 400 kHz (top) and 200 kHz (botton frequencies composed of a 40% TW

axial OD notch at an arbitrarily selected location within a 2700 tube support

ring and (b) processed data using 400 kHz primary and 200 kHz auxiliary

frequency (top) and 300 kHz primary and 100 kHz auxaiary frequency (bottor).

In both cases, the processed data indicate substantial inprovement of S/N ratio

over original readings.

- - - -- --__) I j;

0 500 1000 1500 2000 2500

data poirt

0

0 500 1v0 1500 2000 2500 TW

date pout I 1 .-

(b)

0 500 1000 1500 2000

dat poit 2500 3000

(b)

Figure 23. Demonstration of 2-D signal suppression on RPC traces of (a) simulated raw

data at 400 kHz (top) and 200 kHz (bottorrmfrequencies composed of a 40% 7W

axial OD and 40% 7W circumferential ID notch at arbitrarily selected locations

within a 2700 tube support ring and (b) processed data using 400 kHz primary

and 200 kHz auxiliary frequency (top) and 300 kHz prinmary and 100 kHz

auxiliary frequency (bottomn). In both cases, the processed data indicates

substantial improvement of S/N ratio over the original data.

34

N VA 4 IJ IW J~ J

s 0.5

t i0o0.s

S00 1000 1500 200 2500 30

date poirt

(a)

-1 _.

1, . . . . .

.2A 

-.s
2.4.4 Multivariate Analysis for Estimation of NDE Profile

Multivariate analysis is also used in the final stage of data analysis (shown in block 5 of

Fig. 1) for estimation of the depth profile of a test section. 'Processed data from multiple

channels is simultaneously used to construct a model that correlates the NDE results to flaw

size and origin. In reference to Eqs. 14(a) and (b), dependent variables in this case consist of

phase angle information at multiple frequencies. The independent variables are flaw size and

origin. Eddy current measurement on a calibration standard tube is used for the determination

of regression coefficients.

Reliability of any predictive correlation depends heavily on the calibration 'data used to

construct that model. Both the range and composition of training data play crucial roles. In

regard to the analysis of EC data, this translates to the composition of the available range of

-flaw size, type, and origin in the calibration standard tube. In addition, availability of a useful

set of frequencies is of outmost importance. Studies have been carried out at ANL to determine

the sizing accuracy based on the available number of channels. With a minimum of two

'frequencies required for constructing a two-parameter model, assessments so far suggest that

the combined information from three frequencies generally produces more consistent results.

Although, incorporation of a larger number of channels is also feasible, acquiring rotating probe

data at a greater number of frequencies is generally prohibitive for practical ISI applications.

2.5 Rule-Based Flaw Identification

Eddy current testing of conducting materials Is intrinsically a relative technique, unlike

some conventional NDE methods for which absolute-type measurements are possible.

Calibration of data with respect to known artiflcial signals in a tube standard that closely

resemble field-induced degradations and artifacts Is necessary in order to allow discrimination

and quantitative characterization of consequential indications. Once EC readings at multiple

frequencies are calibrated, the.behavior of signals, typically extracted from their impedance

plane trajectory, can be utilized for their characterization. The two most fundamental features

that are attainable from the impedance plane are the changes in signal amplitude and phase as

a function of frequency.-

In conventional eddy current data analysis of SG tubing, human analysts are trained to

recognize flaw-induced signals from artifacts by selective application of a' series of rules once

potential flawed regions are visually detected. Aside from well-defined rules for interpretation of

signal behavior from traditional forms of tubing damage, empirical rules are also systematically

applied that are founded on past experience and historical lessons learned in dealing with

similar SG units. Complexity, origin, and location of indications in an SG are typical markers

for detection of such flaws.

As stated above, conventional rules for identification of flaws are based on changes in the

signal amplitude and phase angle among the primary and auxiliary channels. Variation of the

signal level as a function of frequency can be used to identify flaws from artifacts. -Artifacts

would include any inconsequential indication such as -tube deformation or geometry changes

(e.g., dents, roll-transitions, and U-bend ovalization), conducting and ferromagnetic deposits

(e.g., sludge, magnetite, and copper deposits), and tube support structures (e.g., tube sheet and

tube support plates). As a direct consequence of the skin depth, or the characteristic depth of

35
penetration, the decay of eddy currents inside a conducting medium produces a sharper drop in

the amplitude of OD-originated flaws than of ID indications, as frequency is decreased. Stronger

attenuation of signals within the tube wall also renders the detection of OD indications more

difficult. The larger phase lag from OD indications also results in a higher degree of phase

rotation for OD-initiated indications. Depending on the calibration procedure that is in place for

ISI of thin-wall SG tubing, clockwise (CV) and counter-clockwise (CCW) rotation of the

impedance plane signal trajectory is commonly used to determine the flaw origin. For instance,

adjusting of the phase angle at all frequencies from a TW flaw to a fixed value results in

indications of OD origin to exhibit CCW rotations and those of ID origin having a smaller CW

rotation. Likewise, information at lower frequencies can be utilized to more effectively

distinguish OD deposits and permeability variations.

Analogous to the manual analysis of EC inspection data, characteristic behavior of EC

signals as a function of frequency can be utilized in computer-aided data analysis algorithms.

Because all available information is indiscriminately examined, such algorithms allow more

effective identification of subtle forms of degradation. Computer-based algorithms that imitate

some form of human decision making process are generally labeled as expert systems.

Automated identification of flaws and their origin is performed through implementation of a

series of rules that are applied to the pre-processed multiple frequency NDE results. This

intermediate stage of data analysis is shown in block 4 of Fig. 1. Rules are coded as a series of

conditional statements (i.e., IF-THEN) that are sequentially applied to the selected data

segments. Calculation of S/N from the earlier stages of the analysis is used to set the minimum

threshold for sorting of signals that are to be examined. Combined amplitude and phase-based

rules are implemented to better discriminate between potential flaws and noise. Rules that are

currently in place are set to be generally conservative for identification of flawed regions of the

tube. As a tradeoff between conservatism and sensitivity, they can be adjusted to some degree

based on the value of S/N for a particular test section. The current set of rules is fixed for a

specific coil configuration and range of frequency. Efforts are currently underway to facilitate

semi-automated implementation of rules to more effectively deal with the expected variability in

ISI data acquisition procedures. With the expansion of the NDE data base at ANL. it is expected

that lessons learned from examination of a wider range of SG tubing degradations will aid

modification and upgrading of the current set of rules as necessary.

2.6 Data Quality Issue

As it was noted earlier, eddy current testing is a comparative NDE method. Success of this

technique is highly dependent on the consistency of data acquisition parameters and proper

calibration results. Although the quality of data affects both detection and sizing, this issue is

of particular concern when quantitative estimates of flaw size are to be determined. Any

compromise in the setup of the instrumentation and the calibration procedures that are in place

could affect the quality of NDE data and in turn the final analysis of results. Minimum

requirements for acceptable data acquisition parameters are well documented in various EPRI

examination technique specification sheets and NRC generic letters for ISI of SG tubing. These

guidelines generally define the instrumentation setup (coil excitation frequencies, gain setting,

cable length, sampling rate, probe speed, etc.) and calibration procedures to achieve a certain

probability of detection (POD) for a given probe (e.g., bobbin, rotating, and array probes). With

only a handful of techniques being qualified for sizing of particular forms of SG tubing damage,

36
these techniques are primarily aimed at ensuring attainment of a certain POD. The data quality

issue has recently gained much attention, particularly in connection with the use of rotating

probes for estimation of flaw size. Efforts are being made by the industry to define a suitable set

of parameters that could be used to ascertain the appropriateness 'of data quality. One such

parameter is the S/N for a particular section of SG tubing. Because initial detection of potential

flaw signals during manual analysis of EC data is based on'visual identification, defining of a

particular value or range of values for' the S/N may seem appropriate for manual analysis for

the purpose of detection. However, in regard'to'sizing of flaws, manual or automated, the term

S/N carries a much broader connotation. The'quality of data that may seem appropriate for the

purpose of detection may not meet the requirements for reliable estimation of the flaw size.

Automated data analysis algorithms that are intended for sizing of a wide range of damage

mechanisms rely on strict adherence to some minimum requireme'nts for the quality of EC

inspection data. Detection probability of automated algorithms that incorporate signal

conditioning schemes,'in general, is less affected by the signal amplitude (i.e., S/N). However,

unlike conventional manual analysis, aimed primarily at conservative detection of flaws, in

which a certain level of compromise in data quality could be accommodated, rule-based

automated techniques for sizing of flaws are more sensitive to data variability. For instance,

degradation of S/N arising from baseline fluctuations and semiperiodic signals from tube

deformations that could significantly reduce visual flaw identification can be gen&rally dealt with

by numerical processing of data. On the other hand, degradation of S/N in the form of

acquisition-related corruption of data and insufficient sampling rate that may not substantially

affect detection may significantly influence the sizing results. Recovery of signal peaks in a

digitized data stream is governed by the Nyquist sampling rate that was mentioned earlier in

this report. The relationship between sample interval Ax. and maximum observable frequency

f, according to the Nyquist theorem is given by,

fmax (15)

For rotating probes, sufficient sampling rates should be acquired in both axial and

circumferential directions in accordance with the rotational and push/pull speed of the probe.

Although lack of sampling can be remedied'to some degree through interpolation'of the data,

significant undersampling will always result in loss of information. Other factors that are

crucial to successful operation of automated data analysis schemes are the availability of data

at appropriate set of frequencies and proper calibration standards. Importance of these factors

was described earlier in connection with rule-based identification and multivariate sizing

algorithms.

37
3 Data Analysis Results

NDE results from machined flaws and laboratory-produced specimens have been utilized in

various studies for the evaluation and refmement of algorithms that have been implemented at

ANL. Data analysis results using the multiparameter algorithm described in this report are

presented next on several samples from separate batches of tubes with chemically induced

cracking. Selected test cases are initially provided to demonstrate the level of resolution

enhancement that is achievable through processing of standard rotating probe data.

Subsequently, analysis results are presented on representative samples from three separate

batches of laboratory-grown specimens. Flaw types in these tubes are representative of those

incorporated into the ANL tube bundle mock-up. A more complete listing of the sizing proflles

on a batch of specimens used to benchmark NDE accuracy through comparisons with

destructive examination results are provided in Sec. 4. Finally, sample calculations are

presented on a subset of tubes to assess multiparameter sizing of flaws in presence of simulated

artifacts.

3.1 Comparison of Original and Processed NDE Profiles

As a means to demonstrate the level of enhancement in spatial resolution, the original and

processed rotating probe data from a selected set of tubes with laboratory-grown cracks are

presented here. The specimens were chosen primarily on the basis of complexity of the flaw

signal, and in one case on the atypical nature of the probe response. Multiple-frequency EC

data was acquired with a standard three-coil rotating probe that incorporates a 2.92-mm

(0.115-in.) pancake, a mid-range +PointTM, and 2.03-mm (0.080-in.) high-frequency pancake

coil. Original amplitude proffiles are all from the +Point coil at a single channel. Processed

results on the other hand are the estimated depth profiles by using multichannel information

from the mid-range pancake coil.

Figures 24-28 display a series of graphics on comparison of the original and processed

channel data on specimens with laboratory-grown SCC degradation. Cracks of both OD and ID

origin with axial and-circumferential orientations are included. For all cases shown here,

conventional isometric plot (Eddynel') of the +Poin1m probe amplitude response at a single

frequency is displayed along with the multiparameter sizing profile of the same specimen.

Image display of the sizing results is also shown in Figs. 25-27. The maximum depth obtained

by conventional analysis of original data was notably underestimated in the majority of cases

shown. Furthermore, complex geometry of cracks in some specimens resulted in difficulty of

determining the flaw orientation from the original signal. Improvement in the spatial resolution

of the processed data is clearly visible in all cases shown here. Comparison of estimated profiles

with destructive results also shows improved sizing accuracy by multiparameter data analysis.

3.2 Analyses of Laboratory-Grown SCC Specimens

Data analysis results are presented next on representative samples from three separate

batches of laboratory-grown specimens. Flaw types in these tubes are representative of those

incorporated into the ANL tube bundle mock-up. All specimens used so far were made of

22.2-mm (0.875-in.)-diameter Alloy 600 tubing. The primary pancake coil readings from a 3-coil

rotating probe was utilized for multiparameter data analysis. Manufactured flaws in the first

38
batch were composed primarily of circumferential and those In the second batch of axially

oriented cracking. A separate batch of tubes was also produced to carry out blind studies for

better assessment of the performance of alternate data analysis schemes in detection and sizing

of various flaw types. Results are also presented here on the analysis of EC rotating-probe data

on representative specimens from this collection of tubes. A detailed description of sizing

results on this batch of tubes is provided in the next section in connection with destructive

examination results.

Figures 29-33 display the results for selected samples from the first batch of NDE samples

containing OD- and ID-initiated cracks of primarily circumferential orientation. Figure 29

displays results of the analysis for a laboratory-grown specimen with circumferential ODSCC.

The sizing profile is shown'for two arbitrarily selected views of the estimated depth. The

estimated profile is shown for only a small section of tube in the vicinity of the crack.

Figure 29(a) shows a representative display of intermediate results in image format.

Preprocessed images at different stages of analysis for both the in-line standard and the flawed

tube. are also shown. Presentation of data in this format provides a useful tool for visual

identification of potential flaws that often show up as structured indications within the baseline

signal. Figure 29(b) shows terrain plots of the estimated flaw profile from two different azimuth

and elevation angles. Analysis results in this case indicate presence of =360 multiple parallel

circumferential cracks with maximum depth of =80% rW. Figures 30-33 display the sizing

results for three other specimens with circumferential cracks. Finally, Fig. 33 shows the proffle

of an axial crack from'the same'batch of tubes. Analyses of EC inspection data on this set

suggest that a majority of flaws are quite complex in nature.

Figure 34 displays the analysis results for specimen SGL-099 with laboratory-grown

circumferential ODSCC degradation. Estimated depth profiles are shown as a terrain plot, as

well as in axial and circumferential cross sections of the tube. Only the section of tube in the

vicinity of the crack is shown here. Figure 34(a) shows a terrain plot of the estimated flaw depth

profile from an arbitrarily chosen azimuth and elevation angle. Figure 34(b) shows the same

data from axial and circumferential viewpoints, respectively. Analysis results in this case

indicates the presence of 3600 multiple circumferential cracking with a maximum depth

exceeding 90% 'IW.

Figure 35 shows similar results for a specimen with multiple axial ODSCC degradation.

The dominant indications are two parallel axial cracks with maximum depths estimated to be

=90%1TW. Figures 36-38 display sizing results for three other representative specimens from

this sample set that contain multiple axial cracks. Eddy current inspection results from this

second batch of tubes generally indicate that the majority of flaws are less complex in their

morphology than those In the earlier batch of tubes that contained mostly circumferentially

oriented cracks.

A batch of 31 tubes containing various crack types and.sizes has also been utilized to

further assess the capability of computer-aided data analysis algorithms. Flawed tubes in this

set have been augmented with blank specimens having no visible damage, to better test the

techniques. Eddy current readings on these tubes were acquired with and without simulated

artifacts such as TSP, sludge, magnetite, and copper deposits that may be present to various

39
(a)

x | S s . 9 X

50 .-

0.

a0

3001

200

Circ. scale (deg.)

40

\!W?T~2~0 3

10

° 0 Ax. scale (mm)

(b)

FYgure 24. (a) Conventional isometric plot (Eddynet") of +Point" probe amplitude

response and (b) muttiparameter sizirg profile of the same specimen

with laboratory-grown circumferential ODSCC. Results suggest

presence of-=360° staggered cracking with maximum depth >80% TW.

40
* IMWQ I= ~~MON

00

40n

Circ. scale (deg.)

'11 - i2 A s 4U

- I I Ax. scale mm)

(b)

Figure 25. (a) Conventional isometric plot (EddynetT 7) of +PointTM probe amplitude

response and (b) multiparameter sizing profiles of same specimen with

.laboratory-grown circumferential ODSCC. Image display axes in this

case are pixel locations in axial (horizontal) and circumferential (vertical)

directions. Results suggest presence of -360° paraUel cracking with

maximum depth 10096 7W..

-41

'~atCl IAi~Jfla.l ltiI A.110 I C 0 1

(a)

I

. xo

20

30

, 40

u--aiSE-aa- 4 mS=B-~~~~~~QJX
(a)

lax

1-00 ------ _-1........

80.1 - ,---

60 1 --

Circ. scale (deg.)

--------------- ~ ~ ~ ~ ~ --

40 ou

Ax. scale mm)

(b)

Figure 26. (a) Conventional isometric plot (Eddynet) of +Poinem probe amplitude

response and (b) multiparameter sizing profiles of same specimen with

laboratory-grown axial ODSCC. The image display axes in this case

are pixel locations in axial (horizontal) and circumferential (vertical)

directions. NDE results suggest presence of axial cracking with max.

depth >90% 7W. Flaw exfhibited no bobbin and uncharacteristically low

rotating probe amplitude response.

42

40. 0

C0

20,

200

Pt'; - -

ta ~~~~~~IMOMM-R

I

__ __ ___------

_ __ __ 1----.- 

I -
OSCaLU~I 3~I~2 N.... ~tOOS C & 021 Ii,'I.

(a)

* .

100- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~1

, , . 20 40 o2

o~~ ~ . - 60 

0 .- - .

40

0 ~~~~~~~~~~~~~~~~~~~~~~~~~~60

20 ~~~~~~~~~~~~~~~~~~~~~go 100 ~~~~~~~~~~~~~~~~~~~~100

Circ. scale (deg.) 0 0 x cle mm20 o

(b)

Figure 27. (a) Conventional isometric plot (EddynetTM) of +Pointf probe amplitude

response and (b) multiparameter sizing profiles of same specimen with

laboratory-grown ODSCC. Image display axes in this case are pixel

locations in axial (horizontal) and circumferential (vertical) directions.

Results suggest presence of multiple axial cracking with maximum

depth 100% TW.

43

f~~~ If CE

a~~~~~~~~~.L : *S 

eta

y e fl ; l~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-I

..
Ug n ftn

16. 7 .z. . - .,_ . --.-__-_

(a)

(b)

Figure 28. (a) Conventional isometric plot Eddynet7

e) of +Pointem probe amplitude

response and (b) multiparameter sizing profiles for two specimens with

laboratory-grown SCC indications at roll-transition region. Results

suggest presence of circumferential ID cracks with maximum depth

<30% IW (left) and 100% TW axial cracks (right).

degrees during field inspections. The effect of tube dimensional variations on detection and

sizing of indications is also simulated by producing flaws within the transition zone of

mechanically rolled tube sections.

Figures 39-47 display the analysis results for a subset of the 31-tube laboratory-grown

specimens. In all cases, the results are shown as amplitude image displays of the standard and

flawed tube at intermediate stages of processing, along with the estimated 3-D depth profile in

the vicinity of the flawed region. The terrain plot is also displayed from two different view angles

to provide axial and cross-sectional profiles. For the set of samples shown here, the profile of

degraded regions suggests the presence of OD-initiated SCC in all tubes except one, which

contained cracks that originate from the ID side of the tube. Eddy current inspection results

from this batch of samples show a wide range of complexity in flaw geometries, varying from

44

I

... ...... .

100 .....

-- -- -- -

80 ---

60 ~~~~~-------

40.

0.

300 200~~~~~~~~~~0

100 4 0 8 0

Orc. scale (dog.) 0 0 0 Ax. scale (mm)

80

f=__~~'^: -__ r _ D'!! - -' " - . . i. _._

Z- R)r

I-._.

Ma I rINMMI<MM-U v1 , 

2 - --- :

> Lo<t

S w -- s

.. 

. .

=s .

I

o*
single axial/circumferential to multiple and mixed-mode cracks. A more detailed description of

NDE results on this set is provided in Sec. 4.

21

460

.c

Ce

I" ''

4

a

s0 100 10 200 10

(a)

U,

30 400.° Cec. soide-9)

Ax. scal (on)

o (mn)

, -ioo

90

70

320

'i_

20

60

50 100 150 200 250 300 350

Cin. scle (deg.)

(b)

FYgure 29. Representative display of data analysis results for specimen with

laboratory-grown circumferential ODSCC showing (a) pre- (top) and

post-processed (bottom) images of normalized data and (b) terrain

and cross-sectional plots of estimated flaw profile. Results

indicate -3600 circumferential cracks, with maximum depth of

-80% IW.

45

bwi

.ro I irm RPMR41-

_' =I a

. T.. . .. . . .. .. . . .. . . .. . .... . . .

--- e--,--.---...................... -_. 

~~~~~... ' .........

.. _ d l . ..-

.... l l l t~~~~......

mm
M121s. 5Irr,a. n - =

(a)

(b)

FYgure 30. Representative display of data analysis results for

specimen with laboratory-grown shallow circumferential

IDSCC showing (a) terrain and (b) cross-sectional plots,

taken over a point with maximum depth, of estimated

flaw profile. Results indicate 360° circumferential

cracking with maaxium depth of >20% 'W.

46

60 --

i 6.40 ...... -

x O.

0-

300

200

i Circ. scele (deg.)

I t 

V

80 ......-- ---- ' I. .

5 60- ~~3 * 

20 ------ ,--;f;\ .~ -- ------ ......... 0

0 20 40 S0 80 100

Ax. scale (mml

100

80 - --------

0

0 50 100 150 200 250 300 350

Circ. scale (deg.)

_i% = 11MP -

Ax. scal (mr)
Circ.scale(deg.) 0. 10

, . . .0 

20

Ax. scale (mm)

(a)

I,. X

Figure 31. Representative display of data analysis results for

specimen with laboratory-grown circumferential

ODSCC showing (a) terrain and (b) cross-sectional

plots, taken over a point with maximum depth, of

estimated flaw proftle. Results indicate 360°

staggered circumferential cracking with maximum

depth of >80% TW.

47

r2M.M. aKu~~~~~~~~~~~

80 ---- - --------- -- .......... ' . - -

A O --X--- - 1lt----@6- .... ...... 

20L -1 - - - ...

0 'A

0 5 10 15 20 25 30 35 40

Ax scale (mm)

0 . .. . .

20 - --- --- .........

0 50 100 150 200 250 300 350

Orc. scafe (deg.)

i
100 .. .

80 - -

60

t 40- 0

a

20,

---..- I

...---- '''....

0 1

200

100

Circ. scale (deg)

..... ~ ~ ....

, ,1''

ii t s tV

Ax. scale (mm)

(a)

m : A a XiS A 

(b)

Figure 32. Representative display of data analysis results for

specimen with laboratory-grown axial IDSCC showing

(a) terrain plot and (b) cross-sectional view of estimated

flaw profile. Results indicate dominant single axial

crack with maxinum depth of >80% 7W.

48

le (deg.)

100

90

-t. 80

70

'----------'----'''''' -''-- - -'-- ''T '''''''''T '--

,0 ... , A ,

70 * -- - - I.I- - - -- - - - - -- - - - -- - - 60 .50

C 40

a 30

20

10

--------- -------- r-----'

--------- ---------- -----I--- -

0 0 20 A0 40 .0 60 60A 0 100A AA. scale (mm)

MIS7-3ufIM -IO

nn __

''

I

I

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

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

.-. ---- I -- - -- -

80 100
-~~... .. -...* 

. . .............. I'll. 

2 ~ ~ ~ ~ ~ ~ ~ .....

(a)

100

80.

t 40.

20

r. sca

e Itg - 100I 0 0 20 3U 0~~~~h Axb, 6t(-) , 

60 * 4 -r .4 n -- e 

240 ........ 2't'''''. ... I. ..... t'- --............... ..... .... ......

20 ._ 

o 10 15 20 25 30 35 Ax scale (rmn)

100 . . - .

60------- ------ ---A- ........ ......---------

50 150 200 250 3w 350 Circ. scale (deg)

Figure 33. Representative display of data analysis results for specimen with

laboratory-grown axial ODSCC showing (a) pre- (top) and post-processed

(bottomf) images of normalized data and b) terrain and cross-sectional

plots, taken over a point with maximum depth, of estimated flaw profile.

Results indicate >15-mm (0.6-in.)-long axial crack with maximum depth of

-90% 7W.

49

20 

40 . 6

10 50 100 150 2o - r 10

2 0

40 isI I I

so o S5YI.

q'.- 56"

i~ -z 

I, ,,,,,, , ,, 11 a ---.. . . . . .. . .

80 . I ' 

:s: _
(a)

-- Ta"M -r i

100 ------ - - - - - - - -- - - - - -- - - - - -

70 ------

70.. . . . .

-- ---------- -- -

S 60 ------ ........ .- _

30 -- - - - - .. .. -- - - - - I 

20 ---- . -----

10 ----------- ----- ------

0--_ _ . __s__

0 10 20 30 40

Ax scale (nm)

100 ----- -------------------- ...- --

90 ... .. .. ..... .. .L ..... ...... 

- 70 

T 7 70 _ . , , ---------- S o6 . .... . . ........ .

0 IiA .'s i 'ii L . .'

300 350

(b)

Figure 34. Representative display of data analysis results for specimen with

laboratory-grown circumferential ODSCC showing (a) terrain and b) crosssectional views of estimated flaw profile. Results indicate 360°

circumferential cracks with maxium depth >90% IW.

50

100 -- -- .--- 

Circ. scale (deg.) 0 

19. 2 I.- I L" P M". m WhIll
-~~ 

''I" I -

(a)

M. 1 k| ig-LO _ fa Iba

100 -------- : --- -- ------------ ------- r-------

80 ,---- ........... -- ---- , ----- :_ 10 ..... . .----------

70 ----------

.30 40 ------ . ---------- ------------------ ;_

30 :......... -------j .

0 10 20 30 40

x. scale (i)

. 0 ............. :¢- . .....-- r-- -- ---- - ------- ,-

.o----- ------- ..-4-.. *------.---

. 70 -''''',----- . .................. ---- . ..... ... '

50 ------

A40 . . -,..f......... ............. _ 30 ------- ----- . ........... ------i--- ---t- 30 - - - .. . .. .

20 ----

O: ..._._.. . . . ... . . .. . . .......... ..

0

0 - 50 - 100 150 200 250 300 350

arc. scale (dog.)

(b)

Figure 35. Representative display of data analysis results for specimen with

laboratory-grown axial ODSCC showing (a) terrain and (b) cross-sectional

views of estimated flaw profile. Results show two indications with

maxinum depths >80% TW.,

.51

* t ..-~~~~~~~ .....

80 ....

60- ... 

. 40-

0

20

-0

20 100

Circ.scal (dg.) 0 0 AxL scale (ffwm)
UEl i

(a)

ha =, , _________ __ __

! 100 ....... .... .. . ........---- ----- 10 ------ ~ -- ------ - ------ .. . .. ^ . -. . .... . ..

90 ------ --- --- -- -- -- --- -- -- -- 90 -- ----- ... --- ------ t- -- -- t--------------------

° . E I I . - - . . 70 -- .............. 70----------- . . .

40 - - . - -- - S 40 _ t -

30 - - . - - _ 30 ----- ...... --- . ----- ------------- 4

20 ---------- -------- 2----0--------- * -------

10 j - - - 10 . - - ..........

_ _ _ _ _0 - - -

0 10 20 30 40 0 50 100 150 200 250 300 350

Ax. scale (rnm) Orc. scab (dog )

(b)

Figure 36. Representative display of data analysis resultsfor specimen with laboratorygrown axial ODSCC showing (a) terrain and (b) cross-sectional views of

estimatedflaw profile. Results indicate two closely spaced paraUel (axially

offset) cracks with maximum depths reaching 100% TW.

52

60 ---

S 40 -

0 20 -

300

200

Orc. scale (deg.) Ax. sca (n)

Z - ~~~~~~~~~~~~~~~~~1

i

I
100 --- ' ' -

0"

60

200

Circ. scale (deg.) I 00

E . 11,1.. ,-~~~~~~' i a M !Wl. M M.I.1 . ,

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

... .. . - -.:... ...............

.. . . . . ...... ... .. ..................--'-'

.. ... . lli'''''''' ' ----'--------

........ ... ....................--i------ 

..... . .....

Ax. scale ()

(a)

100 ---- -- ------- ........ .... .. ....... .......... ,;

90 ...... '-'..........-------'-' --------- '------

E0 ... . ............ . . .... . .. .....

70 .... ..... * . ..... r - r ...... o~e. 

4 0 ... ..... ,- .. ....... r...,_

0 -------------i--- -- .... ...........:',j_

0 -- - - -- - -- - - .... ------ '.

20 .... _.> ........... ' .... .',.

0 S0 I100 .tS0 200 250 300 350 ____ ~ ~~~~~~ O c- scale deg.) - . 10 20 . 30 1.40 so5 60

Ax. scale (mwm) 

; ,(b)

FYgure37. Representative display f data'analysis .results for specimen with

laboratoTy-grown axial ODSCC shouJirg (a) terrain and (b) cross-sectional

views o estimnatedflaw profiLe. Results indtcate mulltple aial cracks with

maxmu depths >8096 TW.

53

100

90

so

70

re60

t 50

ot 40

30

20

10
* - 1r I - . j X

CKw* -!* =z=

(a)

s; I l. ._M. L .... Is I tr>t i. . .Ill ._ a I II

MT P, L T= F-;-,

0 10 20 30 40

Ax scale (mm)

50

.1

10 -- --- - - -- ---- --- - ----- - -- - - --- - - --

80-------0------ ------------------------------

70 i - -- -. .... ------- i

70 ----. _ ...... _ ,-- .-- ...... ... .... ..-. _.-_.

5 e ...... I ...... ...... I...... .............

40 ._..... ..... ..... -- - - - - --- --

40 ----- .-- ----- 

a 0 . .,,U,, [..- 4.. ..> . ..........

lo ---- - ---- .. .. . . .............. .. ''

30

0 50 100 150 200 250 300 350

circ. SC (g.)

(b)

FYgure 38. Representative display of data analysis results for specimen with

laboratory-grown axial ODSCC showing (a) terrain and (b) cross-sectional

views of estimatedflaw profile. Results indicate multiple axial cracks

with maximum depths >80% 7W.

54

100 - .......... 

00

lrr rAlAf riAni 0 ° Ax. scale (ml)

0

90

70

t.50

4

30

20

10

0

........ . ........ _..... .. .. .. .. ... .. .. .. .. ..

.___ -.J--'--' t ........... ,.......... ---

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

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

_. ..w.._ vu9E,.,

I
(a)

100 ------------------------------.-------------- .............. ,,_

90 ------ i------- ; ...... -- -i-----. ................ J so -------....-------- ---

70 .__,...... ,,...' ..,_ @.

60 ................

so ---- ............ _ _... .I......... ... __ 

40 ------ ------- - --'---

30 ------ .... .... ...... i ....... - +-

20 ------- ".............

0 10 20 30 40 50 60 70 Ax scae (nan)

100,

go

80

70

60

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

... ......... .... .........i.!...i

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

F 0 ...... ...... ... -------- ...... ---- _ _

t 40 '-'- 1 '.-''. F-<

30 .4 i-----; f -i- - ------- 

20 -- -- ...... ... , . ............ ....... ... ..

.0 50 100 150 200 250 300 350 Ci. scale (dog.)

(C)

Figure 39. Representative display of data analysis results for specimen with

laboratory-grown ODSCC degradation showing (a) image display,

(b) terrain profile, and (c) cross-sectional views of estimated flaw

size. Results indicate single crack with Tnaximu depth >90% TW.

55

20

40

60

so

150

I -1M m .Fl nI- o 
90 ------------------ ------

so ---- I ....--- ...... ----

70 .... ... ......

40 ---- I...... ..... .|------ ------ ------t--,

30D --- ---- l..... -+---E 

.20 ------.

lo -- - -- - - ..... .... ......... . ..

E.50

0 5 10 15 20 25 30 35 40

Ax! scae (m)

(a)

(b)

100 ------ *------- ,--- *-.-.,,, -- - - . ,_

30 ........:.. .. ;...... . -- - j ... - - -

80 .. _ .. . . - . ..---

50 -- ------------

40 30o ... ._. . .... ----- .. .. -' ------.- .

20 .. --- ....

10 . .......... ... .......

0

0 50 100 150 200 250 300 350

Orc. scal (degI

(C)

FYgure 40. Representative display of data analysis resultsfor specimen with laboratorygrown ODSCC degradation showing (a) image display. (b) terrain profie, and

(c) cross sectional views of estimatedflaw size. Results indicate multiple

cracks around circumference, with maximum depth of dominant flaw

reaching 100% 7W.

56

40

Orc. scale (dog.) 0 0

La. 60--V- -V-b jzU'-

lit 00i -00 . ... .. 00 . 0 M .. R . . .. . . . . . ........ 

, Ax scale (mm)

i

1

i
FYgure 41. Representative display of data analysis results for specirnen with

laboratory-grown ODSCC degradation showing (a) rnmcge display.

(b) terrain profile, and (c) cross-sectional views of- estinated flaw

size. Results ndicate two circunferentially offset cracks, with

max.ni depth reaching 100% W.

57

ti W C. sX

! am_ _
(a)

3~~~~~~~~~~~~~~~~~~.0 

(b)

". 10 ...... 1 ,.---:-- 30 

-l---;- ...................... -r--. - - ........... - -...... 100 --1 ...... ........ ....... ..

90 ------ --- - - - 90 5- ------------------------- . -

70 -'70|1 ............... 7 , i --.------. - - - ......... ------- --------------

10 _ . 10 - - -- -:-::

0 ~ ~ ~ ~ ~ ~ ~ . o

10 20 30 40 S0 60 70 0 50 100 150 200 250

Ax scte (nm) Circ. scale (cl

(C)

Figure 42. Representative display of data analysis results for specirnen with

laboratory-grown ODSCC degradation showing (a) image display, (b)

terrain proftle, and (c) cross-sectional views of estimated flaw size.

Results indicate segmented crack with maximTum depth of -1 00%6 IW.

58

80 -----

-80 --- - - -- - --

40 - - - - -- - -

20 --- --- ---. ...

20

Circ. stele (dog)} 0 0 AS scal1e (mm)

-1-1

Ap.10'
o0 ...... .' ...................... '' - ----------------- go ..... 0.. ...-.....- - - 90 ---- '.----------t70- _ 

60 ..... . ... ...... ._ ... .... ... .. .

50 ...... . ...... . ................

rk40 ........ -*--s+ ....... --i..-..

230 --- --- --- -- -- ------ -------

20 ...... ----- ... .-

0 10 20 so 40 50 60 A $Ca (mr)

(a)

(b)

,loo - -------- ------- ---- ---------------------- @@@@e 

' 90 .......... ......

80 _. . . . ....... . . .

0 .... ...................... ...... . .. ........

60 ---------.. ... ........ .. ...... .._

40o ----..----- ''T'.......................... .. ......

30 -- --- - - - - -.- -- -- -- - .. ......... ..

20 ....................... ,, ........ j._

10 --------- '' .' ....... .......... '' .. ........

O - S 100 S IOrc.s ale o9c

(C)

Figure 43. Representative display of data analysis results for specimen with

-laboratory-grown ODSCC degradation showing (a) image display.

(b) terrain profile, and (c) cross-sectional views of estbnatedflaw

size. Results indicate single axial crack with maximum depth

>9096 TW.

59

20 4 4~.5 o

I s 100 10 20 250

10

100 --------

. . ..

20 .....

Orc sAxde sgc e m0 Ax. Scale (tin,)

...- - - ..-..- - -.. ...- 1 -.. - -_ - .._ ......... - .. .-...-- . - ..... ....-1 -.- Elk =Ml - MM

:

401

*, 1
90 ------- ------- ------ - -------------....

80 *@ve- ............. .~. .... .... ,, ....... ...... ...

R 50 ..........

40 -- - - -- - - --- ..... ------ --------. ------- .. 

I 30 . L------------- --- .... I ...------- ;- ------ 4

20 .',.,,,,,,, --- ... ...--- ,-----------

10 10 20 30 40 50 70

Z hc~~~~~~~~A scal (min)

(a)

(b)

100------- ------------------------------ -

- 0 --------- ---------- --- ----- ---------- ---------1-

70 --------- ....... ._,,.................. _...___,___------

70 ..... _ .*@o........... .......

50 ................... _

40 ---- -- - A ---------

30 - - 4'' .. .........

tc---------- ----- 1 | 0 ----- .. _.____....... _ _ ..._.....

D 0 100 150 200 250 Circ scale (dog)

(c)

Figure 44. Representative display of data analysis results for specimen with laboratorygrown ODSCC degradation showing (aj image display, (b) terrain profile, and

(c) cross-sectional views of estimated flaw size. Results indicate multiple

cracks around circumference, with maximum depth >60% TW.

60

2

a4

a

Is

100 . .. . ...

80, --- - - - -

0 0 6

Orc. scale (dog) ~Ax. scale (mm)

1 141-1

: 0 50 10 1 50 200

I20 ii i 

100 S , 11 Ei zi 

50 100 150 200 250

-.-.....~. I. ..- . I .. - - ....I .... ... ~ ... .1. ... _ -~ -I ..~
2

-4

a

a

to

3

10

3

a

(a)

(b)

00--?-:-

so ..... . .. ... .. 'S ... .. . .... . . 80 ........ .... ;

30-. .. . .

-20 . ...

10 ........ ...

0 20 40 .0 O 100 120 A scale (rrn)

t 0 ..r . ......-. -...... . ......

go ...... .... ...... .....

80

1 70 ~~~~~~~~~. .... , '.....~'1l'-''

so T ---- -t----- --- --.... .. .... s .... . .... ........ .......

so40 ... .. .. l..... ... ... ---- 40 - - ------

l8 bE .| 30 .... .....

20 --- ---

lo

0

0 so 100 150 200 250 300 30 tirc. scale (deg -

(C)

Figure 45. Representative display of data analysis results for specimen with'

laboratory-grown ODSCC degradation showing (a) image display, (b)

terrain profile, and (c) cross-sectional views of estimatedflaw size. Results

- indicate multiple cracks around circumference, 'with maximum depth of

dominantflaws reaching t100% TW.

61

Ax scale (m)

P0-JA-W-q---

] Ir I-| | IAI 1. ;1.i' _ lS1] 

.1
(a)

I_

(b)

0 10 20 30 40 Ax. scale (rnt)

50 60

100 --- -- -r- --- -- -- ---- --- --- ----- -- ----- -- -- ----

50 ------ - -- - -- -- -- --- --- ---- -- -: ---- ---- -- ---

80 ....... ....... ,,,,, ., . ...... ...

5 0 .'' ...... . . ... .....

140 

30 --- ---------. E

0 ........

0 50 100 150 200 250 300 350 am scMe (dag)

(C)

Figure 46. Representative display of data analysis results for specimen with

laboratory-grown ODSCC degradation showing (a) image display, (b)

terrain profile, and (c) cross-sectional views of estimated flaw size.

Results indicate multiple cracks at several axial locations around

circumference, with rmaiximum depth >60% 7W.

62

2

8

la

2

4

ID

100------ - 8 4 . .. .

40 4 .. .........

20 -- . -- -- --

Ax scale (mm)

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

80 _

70 .. . . ... . .. . .. .. ... .

30

g60 ', . ' " . '.

,! 0 --- J----- ---- -F--- -' --- - 0

-11, ' W so.* -r
1 V

(a)

1~~~~~~~~........

-0 ,.....

60 - -------------

~~ ~' j 100 T*w ^ 1 f * ~~~~~~. ...... I........ =@@Be

~~~~~~~~~~~. 40~ -- .*,.; .......... .... ,--------- J.-

3 2 40 , ........ : 20 -----------------'-........... 0 .-,- l--t--^-,,^---

0

20..

*0 -~0 

D 20 40 * 0 805, 0 5 0 5 0 5

Circ. scal e (deg) 0i0Ac scale (mm)

(b) 1 100 ------------------------ . results for specimen . laboratoryso -- ..-. .. - .-.-- - -- .. . .. . . - -- 90 -. . . . '-- .. . . . .. . .... .. - ----- -- - -

20 ....... 4)............ 20.... .. ... ... ......

10 .. 10. ... .. . .. .. . .. .

I 2 40 60 80 0 50 100 150 20 25 30 30 Ax. scale (in) Circ scde(det)

(C)

Figure 47. Representative display of data analysis results for specimen with laboratorygrown IDSCC degradation showing (a) image display, (b) terrain proJUe, and (c)

cross-sectional views of estimatedflaw size. Results indicate multiple cracks,

with dominant crack depth <35% 7W.

63

21

41

SC

SC

101

P-l"M~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~&$z

-.... ...... ....... ..... ......I....... -.-.- .- --.. .. ...... .... ...... ... - -

s-__i=fl 7_~~~~~~~~~6a
3.3 Assessment of Sizing Accuracy in Presence of Artifacts

In continuation of studies to demonstrate the effect of various artifacts on flaw signals,

results of the analyses on a subset of specimens from the 31-tube set are given here. Data on

these tubes was collected with and without the removable collars that simulate artifacts such as

TSP, sludge, magnetite, and copper deposits that may be present to various degrees during field

inspections. Representative test cases from profiling of six tubes are displayed as amplitude

images of the standard and flawed tube with simulated artifact and terrain plots of sizing profile

in the vicinity of degraded region of the tube. Eddy current data for each test case of simulated

OD artifact was collected separately, each time scanning the entire batch of tubes. Acquisition

frequencies represent typical primary and auxiliary channels that are used for the inspection of

thin-wall SG tubing. Studies so far suggest that the degree of signal distortion depends

primarily on the artifact geometry and composition, as well as signal amplitude at a given

excitation frequency.

Figures 48 and 49 show the profiling results for the case of TSP simulation. Terrain plots of

estimated profiles are displayed for a small section of the tube in the vicinity of the flaw.

Variation of maximum depth for the deep crack shown in Fig. 48 was relatively insignificant for

all the simulated collars used in the study. For the test case of shallower ODSCC shown in Fig.

49, the dominant indications were placed near the edge of the collar. Although this scenario

generally results in maximum perturbation of the flaw signal, the profiles for the case of plain

tube and TSP simulation are in good agreement.

Figures 50 aid 51 show representative test cases of deep and shallow ODSCC, respectively,

in the presence of a sludge artifact. Finally, Figs. 52 and 53 display the profiling results for OD

and ID cracking, respectively, for the case of magnetite simulation. The estimated maximum

depth of cracks varied by <10% of tube wall thickness for all the test cases considered here. As

expected, the effect of OD simulated collars are less for larger amplitude signals and more

significant for the smaller amplitude portions of the flaw. For the specimens shown here,

variations in NDE flaw depth and length are within the range that is expected from such factors

as probe alignment and data acquisition parameters (i.e., variations in sampling rate, rotational

and push/pull speed)-.-Examination of additional samples from this data set with various

degrees of flaw complexity, origin, dimension, and orientation is currently underway. Results of

this study should help understand the uncertainties in the accuracy of this sizing technique.

Studies so far suggest that the degree of signal distortion depends primarily on the artifact

geometry and composition, as well as on signal amplitude at a given excitation frequency.

64
" .U -I .. .- . .. .I ... - ... .-1N . ......... ..... I- -- .. _ ... ... . .' --' - -... . .- . _ -.. .I. ~ 1 .1 11 - -.... I.. .. _ ... --

-(a)

50 0 Azscale(0 yDu a-rc. scale (deg.) o 2 20 . . Ax scale (mm)

100

Crc. scale (deg.)

, 20

0 0 Ax. scale (mm)

(b) (c)

Figure 48. Data analysts results for specimen with laboratory-grown ODSCC showing (a)

Image display of scanned region of standard andflawed tube with a TSP coUar

placed over crack. Also, shown- are terrain plots of depth profile over

approximately same region of tube (b) without and (c) with TSP collar.

Estirnated maximum depth (>90% TWfrom plain tiube) varies by <10% 7Wfor all

simulated artifacts.

65

40

60

SC8

'.'. l00

9= to _ 
11.1~~~ I V21

41

1

20

40

60

80

100

20

40

60

80

100

(a)

(b) (c)

Data analysis results for specimen with laboratory-grown ODSCC showing (a)

Image display of scanned region of standard andflawed tube with TSP collar

placed over crack. Also shown are terrain plots of depth proftle over

approximately same region of tube (b) without and (c) with TSP collar.

Estimated maximum depth (<50% TWfrom plain tube) varies by <10% 7Wfor all

simulated artifacts.

66

0

Ax. scae (n)

Figure 49.

. .--l 1 1 ........ .I.I ---- ...... .... ... ..... .- .1- . .. ... .. ..11 in.l. 10"14"PIRMP MOM~~~- 

N ---- --
N 

(a)

K,, ' . I- ,i. .

(b) (c)

Figure 50. Data analysis results for specimen with laboratory-grown ODSCC showing (a)

Image display of scanned region of standard andflawed tube with sludge collar

placed over crack. Also shown are terrain plots of depth profLle over

approximately same region of tube (b) without and (c) with sludge collar.

Estimated maximum depth (>90% TWfrom plain tube) varies by <10% TWfor all

simulated artifacts.

67

I

IC

20

40

60

80

100

10

40 o ''.,''.

20

0

200

Circ. scae (deg.) o0 2 Ax scale (w 
(a)

U', I I *' a

0

40

Ax scale (rrm)

(b) (c)

Data analysis results for specimen with laboratory-grown ODSCC showing (a)

Image display of scanned region of standard andflawed tube with sludge collar

placed over crack. Also shown are terrain plots of depth profile over

approximately same region of tube (b) without and (c) with sludge collar.

Estimated maximum depth (40% TWfrom plain tube) varies by <1096 7Wfor all

simulated artifacts.

68

Circ. scale (deg.)

20

40

60

so

100.

40

80 ~~~~~~~~~~~~~60

80 80~~~~~~~~~~~~~s

Figure 51.

Dw~~~~~~~~~~~~~~~ 
la .f

(a)

Clrc. scale (deg.) 0 0

20'

Ax scale (mm)

(b) (c)

Figure 52. Data analysis resultsfor specimen with laboratory-grown ODSCC showing (a)

Image display of s'canned region of standard andflawed tube with magnetite

collar placed over crack. Also shown are 'terrain plots of depth proftle over

approxlmately same region of tube (b) without and (c) with magnetite collar.

Estimated maximum depth (<50% Wfrom plain tube) varies by <10% TWfor aU

simuilated artifacts.

69

2C

4C * 8

4O

60

100-~

100

Circ. scale (deg-) e O Ax sca ()

.- . .1- --11 ..... ......... ... ...... - l- -1.l-.11 I...'.- .' .. .. .. .......... *j. .- - -. ........ - ... . _ ....... - . ........ - --- -. 1 .-, -... - 1. - ..... .. j--

I 1 11IMM"
(a)

Circ. scale (deg.) 0 0

=60

7 -40

20

Ax. scale (rrr)

100,

80 '-'" '.- .',', ' . .

60, i

40~

20,

0

20

50 Circ. scale (dog.) 0 0

(b)

, 40

20

Ax. scale (mm)

(C)

Data analysis results for specimen with laboratory-grown IDSCC showing (a)

Image display of scanned region of standard andflawed tube with magnetite

collar placed over crack. Also shown are terrain plots of depth proftle over

approximately same region of tube (b) without and (c) with magnetite collar.

Estimated maximum depth (<40% IW from plain tube) varies by <10% IWfor all

simulated artifacts.

70

20

40

60

80

100

20

40

60

80

100

50 100 150 200 250

50 100 150

Figure 53.

;T- a A- S. -1;11-

. |. ..! I - ....- - .! ..~ I 1 -. ... 1 . -...1 . .I I 1 ... I ~ 1.." .1 . .. - .-x1 . . -- - -.. II . I. .I lir 

I 'PrMMM - rMV ry
4 Benchmarking of the Multiparameter Algorithm

4.1 Stress Corrosion Cracks Used in the Benchmark Tests

In the development of the multiparameter algorithm, the results from the algorithm have

been compared to fractographic results on a wide variety of SCC flaws and EDM and laser-cut

notches. To provide an objective benchmark, however, an additional set of 29 SCC flaws were

produced and used for a blind test of the predictions of the algorithm against fractographic

measurements of the crack geometry.- Six of the benchmark samples have not yet been

destructively analyzed because they will also be used for leak and ligament rupture tests that

have not yet been performed.

The stress corrosion cracks for the blind test were produced in a 1M aqueous solution of

sodium tetrathionate at room temperature and atmospheric pressure. The Alloy- 600 tubes used

to produce the specimens were sensitized by heat treating at 6000C for 48 hours (h), prior to the

stress corrosion cracking (SCC) to produce microstructures that are susceptible to cracking in

the sodium tetrathionate. Masking by coating areas of the tubes with lacquer was used to limit

or localize the cracking area. The tubes were internally pressurized for hoop stress to produce

axial cracks and 'axially loaded to produce circumferential cracks. The times to produce

cracking ranged from 20-1000 h,- depending on the type of crack being produced. A variety of

OD and ID crack geometries were produced; axial, circumferential, skewed, or combinations of

these. Many of the specimens contained multiple cracks separated by short axial or

circumferential ligaments. Cracked tubes were examined by dye penetrant techniques,

conventional EC NDE, multiparameter algorithm, and destructive methods.

For the destructive examination, the samples were first heat-tinted before fracture to permit

differentiation of the SCC and fracture opening surfaces, then the specimens were chilled in

liquid nitrogen and the cracks were opened by fracture. The fracture surfaces were examined

macroscopically and with optical and scanning electron microscopy. Details of the procedure

are given in the Appendix. The fractography and NDE data were digitized to obtain tabular and

graphical comparisons of the depths as a function of axial or circumferential position. Welldefined markers on the test sections provided a means to accurately overlap the profiles.

4.2 Comparison of Crack Geometries Determined by Fractography and

Multiparameter Algorithm

Crack profiles from the destructive analyses are compared with those obtained from the

multiparameter algorithm in Figs. 54-76. Figure 77 compares the maximum depths as

determined by fractography to the maximum depths determined by the multiparameter

algorithm. A linear regression fit and 95% confidence bounds for,the observed data as a

function of the multiparameter estimates is shown in the figure. The overall root mean square

error (RMSE) in the predicted maximum depths is 13.7%. If the comparison is limited to deeper

cracks, the RMSE Is smaller, 9.7% for depths 30-100%, and 8.2% for ODSCC of depths

50-100%. The data are too few, however, to determine whether the apparent variation of the

RMSE with depth Is statistically significant.

71
Because the field of view of the rotating pancake probe is limited, the depth measurements

at points 5 mm apart along the crack profile are essentially independent, and additional

comparisons of the estimated depth with that determined by fractography were made at various

points along the crack profile. To avoid observer bias in the selection of the data for

comparison, the intersecffons of the crack profiles with the the major grid lines in the graphs of

the superimposed profiles were chosen as the points for the comparison. This corresponds in

most cases to a spacing of 5-10 mm between points. Figure 78 shows the results for 89 points

from 20 different cracks, axial and circumferential, ID and OD. A linear regression curve and

95% confidence bounds for the observed data as a function of the multiparameter estimates is

shown. The intercept is 13.8 in this case, somewhat less than that generated from the

maximum depth data, but the slope of 0.79 is almost identical to the linear regression line slope

for the maximum depth data.

The RMSE values from the data of Figs. 77 and 78, for various binned depth ranges, are

presented in Tables 2 and 3. In Table 2, the depth ranges are given in terms of the

fractographic depths. This is useful when assessing the capability of the multiparameter

algorithm for cracks of a certain depth. In Table 3, the depth ranges are given in terms of the

predicted depths. This is more useful when assessing the uncertainty in predicted depths.

The overall RMSE for all cracks of all depths is 15.1%, but this is somewhat misleading

because there is significant variation in the RMSE with depth. The RMSE value is significantly

better for the 80-100% 'IW bin than for the other depth bins.

In Table 3, two sets of RMSE values are given. One set is based on the values obtained

directly from the multiparameter algorithm and the other on corrected" values obtained from

the regression flt shown in Fig. 78. For the shallowest cracks, the corrected' values give a

significantly lower RMSE value, but when all the data are considered the differences in the

RMSE for corrected and uncorrected predictions are small. This indicates that there is little

systematic bias in the predictions of the multiparameter algorithm, i.e., the errors are random.

The regression fit is very sensitive to the values at zero depth. However, these reflect more

of a problem of detection; the errors are not really in sizing, but rather represent of cracks that

were just not detected. Thus, direct comparison of the multiparameter and observed values may

be a better measure of the sizing capability of the algorithm. This comparison is shown in Fig.

79, where the direct multiparameter predictions are used as the best estimate of the crack

depth, and the 95% confidence bounds in the figure account for the variation of the RMSE with

crack depth.

These results can be used to estimate the uncertainty in POD curves if the multiparameter

algorithm is used to determine the "true" state of the mockup for the NDE round-robin. Instead

of characterizing the error in the depths in terms of the overall average for all depths (=15%), the

error will be taken as a function of depth. Analytically, the values of RMSE given in Table 3 are

assumed to apply at the midpoint of the depth range for each bin. The error at other depths is

then estimated by linear interpolation of these values.

72
4.3 Comparison of Failure Pressures Predicted by Using NDE and Fractography Data

Although the accuracy of measurements of SCC crack depths by EC is usually determined

by a comparison of the depth data with those measured by fractography, a more useful

comparison, at least from the structural integrity standpoint, is that between the failure

pressures predicted from data obtained by the two methods. Therefore, the ligament rupture

pressures of all the SCC specimens were predicted by using the equivalent rectangular crack

method discussed in Ref. 3. Equivalent rectangular cracks were calculated from the crack

depth proflles measured by fractography, as well as those by the multiparameter algorithm. A

comparison of the ligament rupture pressures predicted by using the multiparameter algorithm

and fractography data is shown in Fig. 80a. A similar comparison for the unstable burst

pressures is shown in Fig. 80b. The "1:1" line shows where the data would fall if there were

perfect agreement between the NDE and fractographic results. Regression lines and 95%

confldence bounds are also shown for the actual results. For shallower cracks (higher failure

pressures), the multiparameter algorithm estimates tend to overpredict the failure pressure

compared to the fractography data.

100

|. EC NE I. Frdctgrhy 

so

FYgure 54.

60 Comparison of NDE and

at fractography profiles for

tL 40 specimen AGL 224 with

circumferential IDSCC (crack

20 .< .......... , , ... ~#1). ..- 20 

0 " 

0 10 20 30 40 50 60 70

Crack Length (mm)

73
Figure 55.

Comparison of NDE and

fractography profiles for

specimen AGL 224 with

circumferential IDSCC (crack

#2).

0 0 20 30 40 50 60 70

Crack Length (mm)

0 10 20 30 40

Crack Length (mm)

FYgure 56.

Comparison of NDE and

fractography profiles for

specimen AGL 288 axial

IDSCC.

50 60

Figure 57.

Comparison of NDE and

fractography profiles for

specimen AGL 394 with

circumferential ODSCC.

0.00I I . I.. .,. I , . I I 1

20 25 30 35 40 45 50 55 60

Crack Length (mm)

74

100

80

.

0

60

40

20

100.00 -

80.00

a 60.00

r- 40.00

C: 0

20.00

0.00 

-10

120.00 

100.00 _

8 60.00 _

I0

C

D 40.00

20.DO
-2 0 2 4 6

Crack Length (mm)

-2 0 2 4 6

Crack Length (mm)

Figure 58.

Comparison of NDE and

fractography profiles for

specimen AGL 533 with axial

. nODSCC.

8 10 12

FiYgure 59.

Comparison of NDE and

fractography profiles for

specimen AGL 535 with axial

. | ODSCC. 

8 10

Figure 60.

Comparison of NDE and

fractography profiles for

specimen AGL 536 with axial

ODSCC.

.2 0 2 4 6 8 10 12

Crack Length (mm)

75

120.00

VOUv

- 60.00

0.

O 40.00

20.00

0.00

120.00

100.00

- 80.00

- 60.00

S

C)

O 40.00

20.00

0.00

120.00

100.00

i 80.00

.8 60.00

C)

a 40.00

20.00

0.00
-5 0 5 10

Crack Length (mm)

FYgure 61.

Comparison

fractography

specimen AGL

ODSCC.

of NDE and

profiles for

503 with axial

15 20

5 0 5 10

Crack Length (mm)

-5 0 5 10

Crack Length (mm)

Figure 62.

Comparison of NDE and

fractography profiles for

specimen AGL 516 with axial

ODSCC.

15 20

Figure 63.

Comparison

fractography

specimen AGL

ODSCC.

of NDE and

profiles for

517 with axial

15 20

76

120.00

100,00

- 80.00

I-.

0

- 60.00

C] 40.00

20.00

0.00

120.00 .

100.00

_ 80.00

°. 60.00

a. C

C 40.00

20.00

0.00

100.00

80.00

I60.00

8 40.00

20.00

0.00
120.00

100.00 

Figure 64.

Comparison of NDE and

fractography profiles for

specimen AGL 824 with axial

ODSCC . .

u.U I. . . . .

-10 0 10 20 30 40 50 60 70

Crack Length (mm)

Figure 65.

Comparison of NDE and

fractography profiles for

specimen AGL 826 with

circumferential ODSCC.

O.W I ,I, i *, 

-10 0 10 20 30 40 50 60 70

Crack Length (mm)

0.00 .1

-10 0

Figure 66.

Comparison of -NDE and

fractography profiles for

specimen AGL 835 with axial

ODSCC.

10 20 30 40 50 60

Crack Length (mm)

77

80.00

60.00

S

0 40.00

20.00

a I I ~ ~~~I I I I

| EC NDE I Flaphy

. , , ' , . . k ~~~.. 

I ~~~~~~~~~~~q I %

JII

* ~~~~~~~~I

- ~~~~~~~I

4~~~~1

120.00

100.00

- 80.00

- 60.00

0 40.00

20.00

120.00

100.00

5 80.00

I 60.00

C 40.00

20.00

n
10 20 30 40 50

Crack Length (mm)

.5 0 5 10

Crack Length (mm)

-5 0 5 10 15

Crack Length (mm)

Figure 67.

Comparison of NDE and

fractography profiles for

specimen AGL 838 with

circumferential ODSCC.

Figure 68.

Comparison of NDE and

fractography profiles for

specimnen AGL 854 with axial

ODSCC.

15 20

Figure 69.

Comparison of NDE and

fractography profiles for

specimen AGL 855 with axial

ODSCC.

20 25

78

too.oo

80.00

, 60.00

a

w. 40.00

20.00

0.00

120.00

100.00

- 80.00

a

° 60.00

a.

D 40.00

20.00

0.00

120.00

100.00

- 80.00

60.00

C 0

O 40.00

20.00

0.00
100.00

80.00

60.00

:

O) 40.00

i0

20.00

0.00

120-

I 100

3 80_

IC. 60

CI 0)

40

20

5 10 15 20 25 30 35

Crack Length (mm)

Figure 70.

Comparison of NDE and

fractography profiles for

specimen AGL 861 with axial

ODSCC.

40 45

Figure 71.

Comparison of NDE and

fractography profiles for

- . specimen AGL 874 with axial

ODSCC.

.5 0 5 10 1 5 20 25 30 35

Crack Length (mm)

80.00 -

60.00

40.DO

20.DO

0.00

0 5 10 iS

Crack Length (mm)

Figure 72.

Comparison of NDE and

fractography profiles for

specimen AGL 876 with axial

ODSCC.

20

79

100.00

C)

0

I *ECNDE 

FracogrphY

I

l

_ _ _ _ . _ _ _ _ . .
0 20 40 60 60

Crack Length (mm)

-5 0 5 10

Crack Length (mm)

0.00 -

-10

Figure 73.

Comparison of NDE and

fractography profiles for

specimen AGL 883 with axial

ODSCC.

Figure 74.

Comparison of NDE and

fractography profiles for

specimen AGL 893 with

circumferential ODSCC.

15 20

FYgure 75.

Comparison of NDE and

fractography profiles for

specimen AGL 816 with axial

IDSCC.

0 10 20 30 40 50 60

Crack Length (mm)

80

120

100

R B0

-

a)

0 40

20

0

100

80 

60 _

40-

Q

0.

a)

M

----- EC NDE Fractography

... ,,. .,",,Jo.

20

0

100.00

80.00

1 60.00

1-

Ci 40.00

0

20.00
100.00

80.00

60.00 Figure 76.

Comparison of NDE and

fractography profiles for

0. 40.00 speciTnen AGL 816 with axial

c - IDSCC (2)

20.00

0.00

0 5 10 1 5 20

Crack Length (mm)

100 r- -I I I 

80 - ,. 

at~0' -0, > Figure 77.

F 60 -

. - / .- -O Comparison of maximum depth

S - °/ - , determined by multipararneter

40 - algorithm with that determined

- °{ -' \ byfractography

20 - depthb = 21.8 + 0.79 depth,,

0 -

0 20 40 60 80 100

Depthm.t.. (% ,-,

80 ~ ~ ~ ~ ~ ~ (

so 0 0 o>, Fgure 78.

°0 . ° Comparison of depths

F 60 _ . determined by multiparameter

-0 ,. . algorithm with those by

0:L ° 8 ' , , /{ - -fractography, and regression 8 IA * / o ° c° :fit and estimated 95% bounds

-for. observed depth as a

2fu-nction multiparameter depth

- .' * depthd,, 13.8 + 0.78-depth estiMate.

0 20 40 60 80 100

Depthmo.wa (%

81

I a . --- EC NDEI

Fracto5graptyI
Figure 79.

Comparison of depths

determined by multiparameter

algorithm with those by

fractography. In this case,

observed and predicted results

are shown in terms of perfect

agreement' 1:1 line and

estimated 95% bounds that

account for variation in

uncertainty with depth.

2 4 6 8 10 2 4 6 8

Ugament Rupture Pressure from Fractography (ksi) Unstable Burst Pressure from Fractography (ksi)

(a) (b)

10

Figure 80. Comparison of predicted (a) ligament rupture pressures and b)

unstable burst pressures of specimens with SCC using crack depth

profiles fromfractography and advanced NDE technique. Regression

fits and 95% confidence bounds are shown along with 1:1 ltne that

would denote perfect agreement between NDE andfractography.

82

i60

a

0

Cs0

DepthMuItate (/°)

UJ

z

E

0

0

2

C) EaU
Table 2 Comparison of RMSE for depth estimates by

multiparameter algorithm as function of the

fractographic crack depth.

Depth range RMSE Maximum crack depth

(00/0 13.7

0-100 13.7

30-1009-;

50-100 8.2

'(ODSCC only)

Depth range RMSE Crack depth

(% T h. - _ ( % _ 1 W ) _ . _ _ _ _ _ _

0100 15:5

-0-20 11.9

20-40 15.9

40-60 20.5

60-80 18.7

80-100 9.8

Table 3 -Comparison of RMSE for depth estirnates by multiparameter algorithm (MP) and

by regressionfit in Fig. 78 as afunction of predicted crack depth.

-Depth - range RMSE Crack depth MP RMSE Crack depth

(%lIW) (%'PW) Regression (% TW0-20 19.5 12.8

20-40 21.0: 23.0

40-60 16.3 =16.1

60-80 :12.2 10.6

80-100 9.8 9.5

83

,

. ..

. ..

. .

. ,

. .

. . .
5 Concluding Remarks

Eddy current inservice inspection of steam generator tubes with rotating probes is carried

out to overcome known limitations associated with conventional bobbin coils. High-resolution

inspections with small coils inherently provide more detailed information about a tube's

condition. These so-called supplementary nondestructive evaluation methods are in place both

to help resolve non-quantifiable calls made by the initial bobbin coil inspections and as the

recommended technique for examining difficult regions of tubing that historically have been

more susceptible to cracking. Detailed manual analysis of multifrequency eddy current data

collected with rotating probes can be laborious, particularly when dealing with complex and

distributed cracks. Additionally, influence of undesired signals from support structures,

deposits. and tube geometry variations can substantially complicate the decisionmaking process

during manual analysis of data.

Studies carried out here and elsewhere have shown that EC signal amplitude is not always

a reliable indicator of flaw size, particularly for cracks with complex geometries. A typical

example is the apparent difference between the amplitude response from a machined notch and

that from a tight or ligamented crack of comparable length and depth. Flaws that behave

similarly from the structural integrity standpoint could produce EC signals with amplitude

profiles that are quite dissimilar. Also, complexity of crack geometry and variability in influence

of many coherent sources of signal distortion could lead to unreliable estimates of flaw size by

conventional methods of data analysis. More accurate and detailed NDE information about

crack proffle and distribution can help to better assess the structural integrity of SG tubes for

ISI applications.

Computer-aided data analysis techniques can provide reliable and efficient processing of

multifrequency EC data acquired with high-resolution probes. The basic structure of a data

analysis scheme for the processing of EC recordings with a standard rotating probe was

described in this report. -Implementation of data conversion, calibration, and analysis routines

for off-line manipulation of inspection results was discussed. Sample calculations were

provided in some cases to demonstrate the outcome of intermediate stages of the process.

Selected examples from analyses of various batches of machined and laboratory-produced

specimens with chemically induced cracking were provided to illustrate the results.

Comparisons of estimated flaw profile by NDE with true state by fractography were presented to

benchmark the NDE results. The influence of undesired signals from support structures and

deposits on the sizing results was assessed through analysis of a subset of tubes with simulated

artifacts. Effect of uncertainties in NDE estimates of flaw size on the prediction of tube

structural integrity was also addressed. Certain key issues regarding the quality of data and its

implications on reliability and uniformity of NDE results were discussed. Through comparative

studies, results of these investigations have demonstrated that improved sizing accuracy and

efficiency in processing of data can be achieved by integrating suitable algorithms for computeraided analysis of EC inspection results.

A key objective of this work has been to characterize flaws in the ANL tube bundle mock-up

in order to minimize the expense of tedious destructive examinations and to further assist in

parallel studies under this program on prediction of tube structural integrity from NDE

estimates of flaw profiles. NDE assessments so far have been limited to analyses of data from

84
rotating pancake coils; however, many of the fundamental processes described in this report are

applicable to other probe geometries and coil configurations. .Evaluation of signal processing

and data analysis schemes to date have dealt exclusively with laboratory-grown and machined

flaws that represent damage types present in the mock-up. Accordingly, simulated support

structures and deposits used here closely resemble those present in the mock-up. These

artifacts simulate some of the most common sources of signal distortion: however, they

reproduce only a subset of those encountered during field inspections. -

Investigations at ANL on computer-aided analysis of EC inspection data collected with highresolution probes are currently aimed at improving the generalization capability of the

algorithms implemented for this purpose. Future investigations in this area should involve

further studies in several key areas, some of which are noted here. Suppression schemes that

are more global are better suited to deal with a wider range of unwanted signals from different

SG designs and associated degradation mechanisms. Also, rule-based algorithms should be

adapted to handle the existing levels of variability in NDE test parameters. Furthermore, it

would be desirable for such algorithms to accommodate differences in probe response due to

coil designs and conflgurations used for ISI applications. Standard probes generally include

either impedance or transmit/receive elements that may be configured as rotating or array

probes. Rotating probes that are commonly used for the inspection of SG tubing often

incorporate multiple coils within the probe head. Depending on coil design, they could provide

multimode (differential and absolute) capability and directional sensitivity (axial or

circumferential orientation of flaw). Combining readily available information from multiple coils

could improve reliability in characterizing flaws. The foundation for implementing more

versatile data analysis algorithms rests on the availability of data bases that encompass broad

variability in parameters that influence NDE results. Efforts are currently under way to

augment the NDE data base at ANL with a wider range of flaw morphologies that will also

include EC inspection results from field-degraded tubes.

85
References

1. S. Bakhtiari and D. S. Kupperman, Advanced NDE for Steam Generator Tubing,"

NUREG/CR-6638, ANL-99/9, Jan. 2000.

2. D. R. Diercks, S. Bakhtiari, K. E. Kasza, D. S. Kupperman, S. Majumdar, J. Y. Park, and

W. J. Shack, "Steam Generator Tube Integity Program," Semiannual Report, Oct. 1998 -

Mar. 1999, NUREG/CR-6511, ANL-00/4, Vol. 7, 2000.

3. S. Majumdar, K. Kasza, and J. Franklin, Pressure and Leak-Rate Tests and Models for

Predicting Failure of Flawed Steam Generator Tubes," NUREG/CR-6664, ANL-99/23, Aug.

2000.

86
Appendix: Characterization of Corrosion and Stress Corrosion Cracks in

Steam Generator Tube Specimens

A.1 Scope and Application

This procedure is for metallurgical and destructive examination of laboratory-degraded steam

generator (SG) tubes with corrosion attack or stress corrosion cracks. It is not applicable for the

tubes contaminated with radioactive substances. The procedure includes method of

preparation, examination, and disposition of SG tubes to be examined. Examination techniques

include dye penetrant examination, pressure and rupture, fractography, cross-sectional

microscopy, optical macro/microscopy, and scanning electron microscopy.

This procedure is written in a general sequence of operation. Portions of the procedure may be

skipped when judged unnecessary by the principal investigator.

General Precaution

Use protective sleeves for tubes to prevent scratch or other mechanical-damage. Wear clean pair

of gloves to handle tubes not to contaminate surface. When transferring specimen, record onto

data; your name, location or name of person you are transferring to, purpose of transfer, date,

and your initial.

Identification of Tube

Verify the alphanumeric identification (ID) that is permanently inscribed on the OD at both ends

of each tube (Figure Al). This ID must be carried -through the entire procedure. Any

subsections of tube specimen resulting from cutting or fracture must be permanently marked

with the ID. All documentation should be referenced to the ID.

Figure Al. Inscribed identfication of tube specimen - .~~~~~~~~ l v . , , , - . ~~~~~~~~~~~~~

87
A.2 Dye Penetrant Examination

Dye penetrant examination is performed for degiadations on-the OD. After completion of

degradation process, tube specimen is ultrasonically cleaned in high-purity water and dried.

Dye penetrant examination is performed in the vicinity of degradation.

Magnaflux Spotcheck SKC-S Cleaner/Remover is sprayed onto test area to clean surface, and

then wiped off repeatedly until the surface is clean.

Magnaflux Spotcheck SKL-SP Penetrant is sprayed over the test area and left for 1 to 30

minutes; usually 10 minutes is sufficieit. To remove excess penetrant, SKC-S Cleaner/Remover

is sprayed onto a clean dry paper towel that is then used to lightly wipe the test area.

Magnaflux Spotcheck SKD-S2 Developer is lightly sprayed over the test area by holding the

spray can about 8 to 12 inches from the surface. Wait for 5 to 15 minutes, then inspect.

If the above procedures are not satisfactory, clean the surface with the Magnaflux Spotcheck

SKC-S Cleaner/Remover, then ultrasonically clean the tube in high-purity water, dry, and clean

again with the Magnaflux Spotcheck SKC-S Cleaner/Remover. Then Magnaflux Zyglo 2L-27A

Penetrant is sprayed over the test area and left for 1 to 30 minutes; usually 10 minutes is

sufficient. Wipe off the penetrant with a clean dry paper towel. To remove excess penetrant, the

SKC-S Cleaner/Remover is sprayed onto a clean dry paper towel that is used to lightly wipe the

test area.

Magnaflux Zyglo 2P-9f Developer is lightly sprayed over the test area by holding the spray can

about 8 to 12 inches from the surface. After 10 minutes, the test area is inspected by using a

fluorescent light to visualize degradation.

Results of the dye penetrant examination are documented by photography at 0.5-5X

magniflcation. The photograph should include a calibrated scale so that the magnification

factor may be measured directly from the photograph (Fig. A2).

. r .e I. In I .fe

I;Ygure A2. Dye peneb-art exarnfrton of tube specirnen SGL 865.

88
A.3 Installation of Reference Markers

NDE-detectable markers are attached on the OD in the crack area to provide reference positions

of degradation. These markers are used to locate the NDE indications, to decide proper

sectioning procedure and locations (Section 9), and to verify relative position between

degradations (Section 14).

Four pieces of platinum wire (about 0.0 10 inches diameter), each of a different length (0.125,

0.250, 0.375. and 0.500 inches), are spot-welded, properly spaced, in the crack area (Fig. A3).

Denting or scribe marks may also be used for the purpose. Be careful not to loosen the

attached markers from the specimen throughout the process.

NDE indications for the markers and degradations must be simultaneously presented so that

relative positions between the markers and degradation can be determined.

Figure A3. Wire markers attached to tube specimnen.

A4 Nondestructive Examination

Eddy current or other NDE measurements are performed on the tubes with the markers

attached. The results should nclude a profile of degradation in real space coordinates, and EC

voltages. The profile should also simultaneously include images that properly identify the four

different markers with respect to degradation in real space coordinates (Fig. A4).

:89
- IPI~ I~~~~~. 

s o . .. .. B M ... ._ ',,, ,._ ,,..... -. 4 ._, t ...... ... ..... , ,., ; ,,,,_,

70 __|i Z l __ s* *~~~~~7

60 . ......... .. ......... 

50

[ X < _~~~~~~~~~~~~~~t .......... ... _ .XlO... ............

40O__ ...... s _*. ...... 0 ................ -O_;---_- **_.--- -_

20 _ ......... ... ...Z' --- _---

*o~ -~ ~~~~~~~t- --------- ' -- '---Z2 *' x--Y o

x~~~~~~~~~~~~~~~~~2 .. . . ;...**.!'.

0 10 20 30 40 o 50 0 100 150 200 250 300 230

M d c o .r .r w. ( 0

FYgure A4. EC IVDE evaluation o tbe specimrnen

Heat Tinting

To discriminate SCC and fracture area, the specimen is heat tinted for 1-5 minutes at 600°C in

air atmosphere. This makes the SCC appear in darker contrast to the fracture area.

Pr'essure-Leak Testing--:

Pressure-leak testing may delineate SCC more clearly, particularly for tight, shallow secondary

cracks and networks of cellular cracks. Applied pressure is higher than that used t produce

degradation. However, application of too much pressure should be avoided because gross

plastic deformation of the tube and degradation can introduce signiflcant inaccuracies in

measuring flaw size and shape. The correct pressure is selected by the principal investigator

with consideration of the flow stress of the material, prior degradation process history. and type

and extent of degradation.

Document the pressure-leak test results by photographs (Fig. A5).

90
Figure A5. Tube specimen SGL 413 wit OD axial SCC afer pressur-leak test.

Dye Penetrant Examination after Pressure-Leak Testing

Dye penetrant examination is performed according to the procedure in Section A2. The

degradation might have opened more during the pressure and leak testing and additional dye

penetrant indications may be seen. Document the results.

Sectioning of Tube Specimen

General mapping for the location of degradation is constructed from the EC/NDE and dye

penetrant examination results. The map should include the size and location of degradations,

and distances between degradations. The tube is sectioned into convenient pieces for crosssectional metallography or fracture. The map should also include the detailed secUoning plan.

Metallography of Cross Section

Cross-sectional microscopy is performed on a metallographically polished surface. The

specimen may be etched to delineate grain boundaries and other microstructural features, by

electrolytic etching in a 5% nitric acid-alcohol solution at 01 mA/mm2 for 5-30 seconds, or by

other techniques.Al The etching may also enhance the image'contrast, but the tip'of a tight

intergranular crack could be confused with a grain boundary. Photographic images are

recorded at 10-50OX magnification (Fig. A6).

91
Pigure A6. Cross-sectional optical metalography.

A.5 Fracture of Specimen

Degradation is opened by fracture, and the fracture surface is examined by optical or scanning

macro/microscopy. The size of the undegraded ductile fracture area may be minimized by

properly selecting the size of the fracture specimen or introducing notches near the ends of

degradation. Nevertheless, sectioning or cutting should not destroy any part of the degradation.

The specimen is first chilled in a liquid nitrogen bath, and then fractured by fatigue loading,

which may be applied by a fatigue machine or manually. The loading should ensure that

degraded surfaces are not rubbed against each other or disturbed. In some instances, the

specimen may further be plastically deformed to delineate degradations that were not detected

by NDE or dye penetrant examination.

Pieces of the specimen resulting from the fracture should be clearly identified, marked with new

identification numbers, and documented.

A.6 Digital Fractography

The fractured surfaces are recorded by digital photography at 0.2-1OX magnification (Fig. A7). A

calibrated scale must be included in the digital photography of the fracture surface in the same

frame. The optimal magnification factor is selected, depending on the size and shape of the

degradation. Methods of illumination of fracture surface play an important role in obtaining the

optimal image quality; the best illumination may be found by trial-and-error. For a large crack,

photographs may be taken of partial areas, and then a composite photograph may be

constructed later. Reference markers are needed for partial photography, but should not

disturb the degradation.

All digital photographs must be identified with a unique and proper file name that is traceable to

a particular degradation and tube.

92
Figure A7. Scanning electron microscopyforfractography of tube specinen SGL 413.

Scanning electron microscopy (SEM) of a fracture surface will provide size and shape of

degradation, and information on the type of degradation, in a three-dimensional perspective

(Fig. A8). SEM may be performed at 5-2000X magnification, depending on the features of

interest. For size measurements, calibrated size references should be attached close to the area

of interest and photographed simultaneously with the fracture surface in the same frame. The

overall SEM magnification factor alone will not be enough to accurately determine the

degradation size.

All SEM photographs must include the magnification factor and be identtifed with a unique and

proper file name that is traceable to a specific degradation and specific tube ID.

SEM of axial ODSCC SEM of circumferential ODSCC

Figure A8. Scanning electron microscopy of ODSCC.

A.7 Comparison of Destructive Fractography with NDE Measurements

The contours of a crack determined by fractography and NDE evaluation are digitized by using

software such as Datathief. Sampling distance depends on the complexity of the crack

geometry. Short sampling distances should be used for a complicated geometry over a small

scale, while longer distances may be used for simpler geometries, e.g., straight line or smooth

93
120.00 , .

|ECND..

Fractgah 

100.00

80.00

0

60.00

a40.00

20.00 -

0.00

-2 0 2 4 6 8 10 12

Crack Length (mm)

Figure A9. Size and shape of OD axial SCC in tube

specimen AGL 536 determined by EC NDE and

Fractography.

contours. Digitized data are plotted for graphical presentation with software such as

KaleidaGraph. Fractography and NDE results may be plotted simultaneously for comparison

(Fig. A9).

Digitized data are stored with proper file names that are traceable to a specific degradation and

tube.

Disposition and Documentation

All subsections and remainders should be clearly marked with ID and stored in the tube

specimen rack and in the respective metallography specimen box. The storage is recorded in

the main data log.

Results of examination and characterization are documented as a part of the scientific notebook

and maintained for the period required by the program.

References for Appendix

Al Metallography and Microstructure, Metals Handbook, 9th Ed., Vol. 9, American Society for

Metals (1985).

A2 Fractography, Metals Handbook, 9th Ed., Vol. 12, American Society for Metals (1987).

94

.
NRC FORM 335 U.S. NUCLEAR REGULATORY COMMIS.

(249)

NRCM 1102,

3201,3202 BIBLIOGRAPHIC DATA SHEET

(See instructions on the reverse)

2. TITLE AND SUBTITLE

Advanced Nondestructive Evaluation for Steam Generator Tubing

1. REPORT NUMBER

(Assigned by NRC, Add Vol., Supp., Rev.,

and Addendum Numbers, IH any.)

NUREG/CR-6746

ANL-01/21

3. DATE REPORT PUBLISHED

MONTH I YEAR

October 2001

4. FIN OR GRANT NUMBER

W6487

5. AUTHOR(S) 6. TYPE OF REPORT

S. Bakhtiari, J.Y. Park, D.S. Kupperman, S. Majumdar, W.J. Shack Technical; topical

7. PERIOD COVERED (Indusive Dates)

8.PERFORMING ORGANIZATION - NAME AND ADDRESS (IINRC,provideOivision.OfrEceorRegion. US. NuclearRegulatoryComission, andmaiTng addressffrontractor,

provide narne 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. US. Nuclear Regulatory Commission.

9. SPONSORING ORGANIZATION - NAME AND ADDRESS (IfNRC, type Sarna as above-:ffrontratorprovideNRC Divisin, Office or Region, US Nuclear Regulatory Cormission,

and mailing address.)

Division of Engineering Technology

Office of Nuclear Regulatory Research

U.S. Nuclear Regulatory Commission

Washington, DC 20555-0001

10. SUPPLEMENTARY NOTES

J. Muscara, NRC Project Manager

11. ABSTRACT (200 words or less)

This report describes research activities carried out at Argonne Natonal Laboratory (ANL) as part of the Steam Generator (SG)

Tube Integrity Program sponsored by the U.S. Nuclear Regulatory Commission (USNRC). As a follow-up to a preceding

publication on the assessment of advanced nondestructive evaluation (NDE) techniques for inservice Inspection of steam

generator tubing. This report provides an overview of more recent studies associated primarily with signal processing and dataanalysis techniques applied to eddy current NDE. Results of efforts made to date on implementation of various algorithms that

pertain to computer-aided analysis of eddy current rotating probe data are discussed. As a benchmark for NDE accuracy,

production and destructive characterization of specimens with laboratory-induced cracking are described. The effect of

uncertainties in NDE estimates of flaw size on the prediction of tube structural integrity is addressed.

12. KEY WORDS/DESCRIPTORS (Ltst words orphrases that wlvl assist searchers In locating the repot) 13. AVAII-ABILITY STATEMENT

Steam Generator unlimited

Stress Corrosion Cracking 14. SECURITY CLASSIFICATION

In-service Inspection (This Page)

Eddy Current Testing unclassified

Nondestructive Evaluation (ThIS Repot

RotaUng Probes . unclassified

Signal Processing 15. NUMBER OF PAGE

Computer-aided Data Analysis

16. PRICE

NRC FORM 335 (2-89)

.

I
Federal Recycling Program
NUREG/CR-6746 ADVANCED NONDESTRUCTIVE EVALUATION FOR STEAM GENERATOR TUBING SEPTEMBER 2001

UNITED STATES

NUCLEAR REGULATORY COMMISSION

WASHINGTON, DC 20555-0001

OFFICIAL BUSINESS