I 

NUREG/CR-6814

ANL-03/4

Final Report on Advanced

Nondestructive Evaluation for

Steam Generator Tubing for

the Second International

Steam Generator Tube

Integrity Program

Argonne National Laboratory

U.S. Nuclear Regulatory Commission

Office of Nuclear Regulatory Research a 4

Washington, DC 20555-0001
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NUREG/CR-6814

ANL-03/4

Final Report on Advanced

Nondestructive Evaluation for

Steam Generator Tubing for

the Second International

Steam Generator Tube

Integrity-ProgramX

Manuscript Completed: December 2002

Date Published: July 2003

Prepared by

S. Bakhtiai, J. Y. Park, D. S. Kupperman,

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
NUREG/CR-6814, has been reproduced

from the best available copy.
Final ISG-TIP-2 Report on Advanced Nondestructive Evaluation

for Steam Generator Tubing

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

Abstract

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

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

Regulatory Commission. As a follow-up to preceding publications on the assessment of

advanced nondestructive evaluation (NDE) techniques for nservice 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 data. 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. Recent efforts on implementation of

more useful data analysis tools are briefly described. As a benchmark for NDE accuracy, the

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

described. Analysis of eddy current inspection data from a limited set of field-degraded pulledtube sections is also presented. The effect of uncertainties in NDE estimates of flaw size on the

prediction of tube structural integrity is addressed. -

iii
Contents

Abstract . ... iii

Executive-Summary .. . . xv

Acknowledgments .. . . xvi

Acronyms and Abbreviations.. . xvii

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

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

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 ........................... 10

2.3.2 Signal Conditioning ...................... 1..... 1

2.3.3 Signal Enhancement and Restoration .12

2.3.4 Signal Enhancement by Least Squares Method .31

2.4 MultlparameterDataAnalyss .. 31

2.4.1 Least Squares Regression and Factor-Based Techniques .32

2.4.2 Multifrequency Mixing Techniques .33

2.4.3 2-D Suppression of Unwanted Signals..................................................... 34

2.4.4 Multivariate Analysis for Estimation of Flaw Depth .36

2.5 Rule-Based Flaw Identification .. 38

2.6 Data Quality....................................................................................................... 39

3 Data Analysis Results . . .41

3.1 Comparison of Original and Processed NDE Profiles . .41

3.2 Analyses of Laboratory-Grown SCC Specimens .. 41

3.3 Assessment of Sizing Accuracy in Presence of Artifacts . .67

3.4 Analysis of Field-Degraded Tubes .. 74

4 Benchmarking of Multiparameter Algorithm . . .82

4.1 Stress Corrosion Cracks Used in Benchmark Tests .82

V
4.2 Comparison of Crack Geometries Determined by Fractography and

Multiparameter Algorithm .............................. ....................................... 82

4.3 Comparison of Failure Pressures Predicted by Use of NDE and

Fractography Data ..................................................................... 93

5 Concluding Remarks ..................................................................... 95

References.......................................................................................................................... 97

Appendix Characterization of Corrosion and Stress Corrosion Cracks in

Steam Generator Tube Specimens .98

A. 1 Scope and Application .98

A.2 Dye Penetrant Examination .99

A.3 Installation of Reference Markers .99

A.4 -Nondestructive Examination .100

A.5 Fracture of Specimen ......................................................... 102

A.6 Digital Fractography .103

A.7 Comparison of Destructive Fractography with NDE Measurements .103

References for Appendix .. 105

vi
Figures

1. Schematic diagram showing basic structure of computer-aided data analysis algorithm 3

2. Display of GUIs for main ECrool menu with data conversion and segmentation

submenus, and strip chart and Ussajous display of segmented raw data ........................ 5

3. Display of GUIs for main ECTool menu with data calibration submenus. raw data and

trigger channel, and calibrated data in strip chart and Ussajous display format ........... 6

4. MATIIAB-based GUI tools for computer-aided analysis of EC inspection results

acquired with standard commercial instruments ............................. ............................. 8

5. MATLAB-based GUI tool for analysis of EC rotating probe data currently under

implementation...............................9............................................................................. 9

6. Typical amplitude response of RPC to 100% IW, 6.35-mm-long axial EDM notch at

300 kHz and normalized Gaussian and Lorentzian impulse responses having

approximately same parameters .15

7. Spatial domain 2-D kernels for Gaussian and Lorentzian Impulse responses

constructed by two orthogonal -D functions and rotation of 1-D function about

vertical axis ....... 16

8. Flaw geometries in set of 24 laser-cut specimens .... ................................... 17

9. OrigInal and restored signals with 2-D Lorentzian kernels of Fig. 7 for Type-1

laser-cut specimen #5528-1-1 with nominal OD flaw depth of 80%W .......................... 19

10. Original and restored signal for Type-2 laser-cut specimen #5516-4-3 with

nominal OD flaw depth of 80%IW ......................... 20

11. Original and restored signal for Type-3 laser-cut specimen #5528-3-3 with

nominal OD flaw depth of 40% 7W ............... 2.............................

21

12. OrigInal and restored signals with 2-D Lorentzian kernels for Type4 laser-cut

specimen #5469-2-1 with nominal OD flaw depth of 80%9IW ............. .......................... 22

13. Original and restored signal for Type-4 laser-cut specimen #5469-2-2 with nominal OD

flaw depth of 40%'IW .23

14. Original and restored signal for Type-5 laser-cut specimen #5469-2-4 with nominal OD

flaw depth of 80% TW................................................................................................. 24

15. Original and restored signal for Type-8 laser-cut specimen #5469-4-1 with nominal OD

flaw depth of 40%'TW ..................................................................... - 25 

vii
I

16. Original and restored signals with 2-D Lorentzian kernels for rype-9 aser-cut

specimen #5469-4-3 with nominal OD flaw depth of 80% 1W ............. .......................... 26

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

terrain plot of relative OD depth for cracked zone and restored data by inverse filtering 28

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

terrain plot of relative OD depth profile for cracked zone and restored data by inverse

filtering........................................................................................................................ 28

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

terrain plot of relative OD depth for cracked zone and restored data by inverse filtering 29

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

showing terrain plot of relative OD depth for cracked zone and restored data by inverse

filtering........................................................................................................................ 29

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

showing terrain plot of relative OD depth for cracked zone and restored data by inverse

filtering .......................................................................... 30

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

dented TSP intersection showing terrain plot of relative ID depth for cracked zone and

restored data by inverse filtering................................................................................... 30

23. Demonstration of 2-D signal suppression on RPC traces of simulated raw data at

400 kHz and 200 kHz frequencies composed of a 40% IW axial OD notch at an

arbitrarily selected location within a 2700 tube support ring and processed data using

400 kHz primary and 200 kHz auxiliary frequency and 300 kHz primary and 100 kHz

auxiliary frequency....................................................................................................... 37

24. Demonstration of 2-D signal suppression on RPC traces of simulated raw data at

400 kHz and 200 kHz frequencies composed of a 40% TW axial OD and 40% TW

circumferential ID notch at arbitrarily selected locations within a 270° tube support

ring and processed data using 400 kHz primary and 200 kHz auxiliary frequency and

300 kHz primary and 100 kHz auxiliary frequency ........................................................ 37

25. Conventional isometric plot of +Pointm probe amplitude response and multiparameter

sizing data for same specimen with laboratory-grown circumferential ODSCC ............... 43

26. Conventional isometric plot of +Pointf probe amplitude response and multiparameter

sizing data for same specimen with laboratory-grown circumferential ODSCC ............... 44

27. Conventional isometric plot of +Pointfm probe amplitude response and multiparameter

sizing data for same specimen with laboratory-grown axial ODSCC .......... .................... 45

vii
28. Conventional isometric plot of +Point' probe amplitude response and multiparameter

sizing data for same specimen with laboratory-grown ODSCC....................4............... 46

29. Conventional Isometric plot of 4 Pointf probe amplitude response and multiparameter

sizing data for a specimen with laboratory-grown SCC indications at roll-transition -

region ...... . - 47

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

circumferential ODSCC showing pre- and post-processed images of normalized data

and terrain and cross-sectional plots of estimated flaw size ....................................... 48

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

shallow circumferential lDSCC showlng terrain and cross-sectional plots, taken over a

point with maximum depth, of estimated flaw size .. 49

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

cireumferential ODSCC showing terrain and cross-sectional plots, taken over a point

with maximum depth, of estimated flaw size .. 50

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

axial IDSCC showing terrain plot and cross-sectional view of estimated flaw size .51

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

axial ODSCC showing pre- and post-processed images of normalized data and terrain

and cross-sectional plots, taken over a point with maximum depth, of estimated flaw

size . .52

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

circumferential ODSCC showing terrain plot and cross-sectional views of estimated

flaw size . ... , . 53

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

axial ODSCC showing terrain plot and cross-sectional views of estimated flaw size . 54

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

axial ODSCC showing terrain plot and cross-sectional views of estimated flaw size . 55

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

axial ODSCC showing terrain plot and cross-sectional views of estimated flaw size ....... 56

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

axial ODSCC showing terrain plot and cross-sectional views of estimated flaw size . 57

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

ODSCC degradation showing image display, terrain plot. and cross-sectional views of

estimated flaw size ... ......... . -. ,.,.58

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

ODSCC degradation showing image display, terrain plot, and cross-sectional views of

estimated flaw size ......................................................................... 59

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

ODSCC degradation showing image display, terrain plot, and cross-sectional views of

estimated flaw size ......................................................................... 60

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

ODSCC degradation showing image display, terrain plot. and cross-sectional views of

estimated flaw size ......................................................................... 61

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

ODSCC degradation showing image display, terrain plot. and cross-sectional views of

estimated flaw size ......................................................................... 62

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

ODSCC degradation showing image display, terrain plot, and cross-sectional views of

estimated flaw size ......................................................................... 63

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

ODSCC degradation showing image display. terrain plot. and cross-sectional views of

estimated flaw size ............. 64

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

ODSCC degradation showing image display, terrain plot, and cross-sectional views of

estimated flaw size ......................................................................... 65

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

IDSCC degradation showing image display, terrain plot, and cross-sectional views of

estimated flaw size ......................................................................... 66

49. Data analysis results for specimen with laboratory-grown ODSCC showing Image

display of scanned region of standard and flawed tube with TSP collar placed over

crack............................................................................................................................ 68

50. Data analysis results for specimen with laboratory-grown ODSCC showing Image

display of scanned region of standard and flawed tube with TSP collar placed over

crack............................................................................................................................ 69

51. Data analysis results for specimen with laboratory-grown ODSCC showing Image

display of scanned region of standard and flawed tube with sludge collar placed over

crack............................................................................................................................ 70

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

display of scanned region of standard and flawed tube with sludge collar placed over

crack............................................................................................................................ 71

x
53. Data analysis results for specimen with laboratory-grown ODSCC showing Image

display of scanned region of standard and flawed tube with magnetlte collar placed

over crack ...... . 72

54. Data analysis results for specimen with laboratory-grown II)SCC showing Image

display of scanned region of standard and flawed tube with magnetite collar placed

over crack ................... . . - . . . , .73

55. Data analysis results for McGuire pulled-tube section with axial ODSCC degradation

at dented TSP ntersecton .76

56. Data analysis results for McGuire pulled-tube section with axial ODSCC degradation

at dented TSP Intersection. . ................... . . 77

57. Data analysis results for McGuire pulled-tube section with axial ODSCC degradation

at dented TSP intersection .. 78

58. Data analysis results for McGuire pulled-tube section with axial ODSCC degradation

at dented TSP intersection .79

59. Data analysis results for McGuire pulled-tube section with axial ODSCC degradation

at dented TSP intersection .80

60. Data analysis results for McGuire pulled-tube section with axial ODSCC degradation

at dented TSP ntersection.8.................. - . 8

61. NDE and fractography profies for specimen AGL 224 with circumferential IDSCC...83

62. NDE and fractography profiles for specimen AGL 224 with circumferential EDSCC......83

63. NDE and fractography profiles for specimen AOL 288 axial IDSCC ................ 83

64. NDE and fractography profiles for specimen AGL 394 with circumferential ODSCC.....84

65. NDE and fractography profiles for specimen AGL 533 with axial ODSCC ............ 84

66. NDE and fractography profiles for specimen AGL 535 with axial ODSCC ............ 84

67. NDE and fractography profiles for specimen AGL 536 with axial ODSCC ............ 85

68. NDE and fractography profies for specimen AGL, 503 with axial 0ODSCC ............ 85

69. NDE and fractography profies for specimen AOL 516,with axial ODSCC ........ 85

70. NDE and fractography profiles for specimen AGL 517 with axial ODSCC ...................... 86

71. NDE and fractography profiles for specimen AGL 824 with axial ODSCC ....................... 86
72. NDE and fractography profiles for specimen AGL 826 with circumferehial. ODSCC ....... 86

73. NDE and fractography profies for specimen AGL 835 with axial ODSCC ...................... 87

74. NDE and fractography profies for specimen AGL 838 with circumferential ODSCC ....... 87

75. NDE and fractography profiles for specimen AGL 854 with axial ODSCC ...................... 87

76. NDE and fractography profiles for specimen AGL 855 with axial ODSCC ......... ............. 88

77. NDE and fractography profiles for specimen AGL 861 with axial ODSCC ...................... 88

78. NDE and fractography profiles for specimen AGL 874 with axial ODSCC ...................... 88

79. NDE and fractography profiles for specimen AGL 876 with axial ODSCC ...................... 89

80. NDE and fractography profies for specimen AGL 883 with axial ODSCC ...................... 89

81. NDE and fractography proffles for specimen AGL 893 with circumferential ODSCC ....... 89

82. NDE and fractography proffles for specimen AGL 816 with axial IDSCC ..................... 90

83. NDE and fractography profiles for specimen AGL 816 with axial IDSCC ........................ 90

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

determined by fractography ......................................................................... 90

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

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

function of multiparameter depth estimate ................................................................... 92

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

fractography ......................................................................... 93

87. Comparison of predicted ligament rupture pressures and unstable burst pressures of

specimens with SCC using crack depth profles from fractography and advanced NDE

technique ......................................................................... 94

Al. Inscribed identification of tube specimen ...................................................................... 99

A2. Dye penetrant examination of tube specimen SGL 865 ................................................. 100

A3. Wire markers attached to tube specimen ...................................................................... 1 00

A4. EC NDE evaluation of tube specimen ......................................................................... 101

A5. Tube specimen SGL 413 with OD axial SCC after pressure-leak test ............................. 102

xii
A6. Cross-sectional optical metallogmphy........................................................................... 103

A7. Scanning electron microscopy for fractography of tube specimen SGL 413 .104

AS. Scanning electron microscopy of ODSCC ............................. 14...... 104

A9. Size and shape of OD axial SCC in tube specimen AGL 536 determined by

EC NDE and fractography .105

-0 - - - :-: - ~~~~~~~~~~~~~. -

xIi
Tables

1. Available laser-cut tube specimens and their nominal flaw dimensions ........................... 18

2. RMSE for depth estimates by multiparameter algorithm as a function of the

fractographic crack depth............................................................................................... 92

3. RMSE for depth estimates by multiparameter algorithm and by regression fit in

FIg. 85 as a function of predicted crack depth................................................................. 92

3dv
Executive Summary

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

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

inservice inspection (ISI of steam generator (SO) tubing. The work was carried out at Argonne

National Laboratory (ANIJ as part of the Steam Generator Tube Integrity Program sponsored by

the U.S. Nuclear Regulatory Commission. Results of efforts to date on implementation of

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

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

containing 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 mplemented at

ANL to explore the applicability of such algorithms for more accurate and efficient processing of

eddy current rotating probe data. More recent efforts on improving the functionality of such

tools through better graphical user interfaces are briefly discussed. Key objectives of this work

have included characterizing flaws in the ANL tube bundle mock-up in order to minimize the

expense of tedious destructive examinations, and further assisting 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. This is followed by a general description of signal

processing and data analysis schemes adapted for manipulation of C-scan recordings with highresolution probes. he issue of data quality and Its implications on reliability and uniformity of

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

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

illustrate the NDE results. Data analysis results are also presented for a- limited set of pulledtube sections from a retired steam generator removed from the McGulre Nuclear Plant.

Comparisons of estimated flaw profile by NDE with true state by fractography are then

presented for laboratory-degraded specimens, following a brief description of the destructive

examination procedure. Finally. results of lmited statistical analysis to determine the

significance 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 tube failure pressure from NDE and destructive profiles.

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 M cal Engineers

ASTM American Society for Testing and Materials

EC eddy current

EDM electro-dscharge-clned

EPRI Electric Power Research Institute

FBH fiat-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

HR ifinite-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

PLS partial least squares

POD probability of detection

RMSE root mean square error

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

'1W throughwall

xvil
1 Introduction

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

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

Commission is provided in this report. It is a follow-up report to preceding publications on the

-assessment of advanced -nondestructive evaluation NDE) techniques for inservice inspection

(ISI) of steam generator (SG) tubing.1 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. along with production and destructive characterization of specimens with laboratoryinduced cracking. used to benchmark NDE accuracy. Finally, the effect of uncertainties In NDE

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

In this report, the basic structure of a computer-aided data analysis algorithm that was

implemented at ANL to provide more accurate and efficient processing of EC rotating probe data

is described. First, we address implementation of data conversion and calibration routines for

off-line manipulation of EC data. This is followed by a general overview of signal processing and

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

rotating probes. The issue of data quality and its Implications on reliability and uniformity of

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

batches of laboratory-produced specimens with chemically induced cracks to illustrate the NDE

results. Results of data analysis are included for a limited set of field-degraded pulled-tube

sections from a retired steam generator removed from the McGuire Nuclear Plant. Estimated

flaw profiles by NDE are compared with true state, by fractography for lab-produced specimens,

following a brief description of the destructive examination procedure. Finally, results of

statistical analysis are given to determine the significance of NDE uncertainties on predicting

the structural integrity of tubing. Reference is provided to reports with a detailed description of

analytical formulations used here to estimate the failure pressure from NDE and destructive

profiles.

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 when dealing with complex forms of degradation such as

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

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

this context, noise is defined as anyrandom or coherent signal 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 to 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

1
analysis is a viable means of overcoming many of the challenges associated with reliable

processing of data acquired with high-resolution probes.

Presented below is 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. 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 structure

of the algorithm mplemented to allow 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. is the digitized recordings of a

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

in use at ANL are a Mlz-30TM (Zetec. Inc.) remote data acquisition unit controlled under the HPUX-based EddynetTm (Zetec, Inc.) environment. To facilitate development of algorithms for

evaluation of EC inspection results, codes have been developed to convert the raw inspection

data to a standard file format. All data analysis and signal processing algorithms have been

implemented by using the PC-based software MATLAB, a high-level scripting language that

provides an efficient 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 MAI1AB program, ECToL 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

2
 w 

Ampex c allhg

asedignm ent:, 

: -- 2;jo .*-: 4.

(A'l!-l.fl -

ndustry-for?,.

Standard format .. ;

. ,~~~~~~~~~~io

Acqulsition

Multiple frequency-

-EC Inspecon-. .~ ;A .g-0 On- .

rm

i- -.-:Pr st e ssinsg ...a 

. at s-e gmhnentationt

-0

Or7_7-E' i :7

FIgure 1. Schematic diagram showing basic sructure of computer-aided data analysis

algorthnL- Blocks associated with operations described in this report are

nwnberes

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

commercial Instruments.

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

editable text, and pushbuttons that activate various 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

MIZ-30 instruments), such as the number of channels and their associated frequencies, from

the original Eddynet-formatted files. The decoded header information contains the frequency

and channel configuration that is subsequently used to sort out raw EC readings. The

reformatted data -matrix. along with the header and coil configuration information, is then

stored in a user-defined data file. Normalization values of all the available channels are

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

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

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

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

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

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

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

3
raw data file. To avoid Incorrect application of normalization factors, the codes initially check

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

data files that are subsequently loaded.

2.2.1 Data Conversion

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

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

header is the data stream that sequentially contains the horizontal and vertical signal

components for all channels that are stored in 2-byte signed integer format. Me 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 ies 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 require 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 nterpretation in the presence of noise (i.e., background fluctuations and artifacts).

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

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 and the 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 an 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 Indication for amplitude scaling it calculates the phase angle rotation, null

value, and scaling factors for uniform normalization of all of the data ifies that belong to the

same calibration group.

4
(a)

Figure 2. DMsplay of GUs for (a) main ECol menu with data conversion and

segmentation submers, and [b) strip chart and Ltssajous display of

segmented raw data Eddy current data were acquired on an 18-

notch standard with a 2.92-mm, (0.1 15-in.)-dlameterprimary pancake

coil of a the-coil rotating probe.

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 n

strip chart and Lissajous display format. Eddy current data were

acquired on an 18-notch standard with a 2.92-mm (0. 1 15-in.)-

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

6
2.3 Sgnal Processing, Data Analysis, and Display Routines

In reference to Fig. 1, the data analysis section (shown by blocks 3-6) has three basic

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

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

channels, apply filters 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 SIN ratio is

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

detectable amplitude from a calibration standard tube. Subsequently, this nformation Is used

to implement a series of filters that suppresses artifacts and baseline fluctuations and provides

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

both the coil configuration and the sampling frequency of the inspection data. Flaws and their

origin are identified by a series of rules 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. Multivarlate analysis is used to estimate the NDE depth profile of the tube

based on the available Information from multiple channels. he following sections contain brief

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

fundamental operations for multiparameter analysis of EC rotating probe data.

A series of algorithms has been implemented as MAThAB scripts to provide a real-time

profile of flaw depth in an 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 Eggr, which incorporates various algorithms for the processing of raw EC data. Pull-down

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

various stages of data analysis process. A series of modifications was 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. After the depth profile is calculated, estimated values are converted

to percent of tube wall thickness.: Reconstruction of helically scanned data into C-scan format

allows 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 direct deduction of flaw extent along the tube. Test case results on analyses of

various tubing specimens with this computer-aided data analysis tool are provided later this

report.

To date, computer-aided data analysis routines discussed in this report have been utilized

as a research tool to assess sizing capability with a particular EC coil design. To provide

improved functionality and ease of use, recent efforts in this area have focused In part on

implementation of better user interfaces. ncorporating future algorithm modifications without

major restructuring of -the codes and analyzing EC inspection results from various probe

designs requires creation of more modular routines. A brief discussion of some major areas of

software improvement for computer-aided analysis of EC inspection data is provided next.

These on-going efforts will be carried out in collaboration with the industry.

7
Figure 4. MAT1AB-based GUI tools (top three rows) for computer-aided analysis of EC

inspection results acquired with standard commercial nstruments. Also

shown (bottom row) are several graphical display formats such as mage,

terrain, and cross-sectional NDE profiles of tube test sections at various stages

of analysts.

One important area of software modification is the creation of a common GUI screen. The

aim here is to allow all Interactions with the user to take place from a single screen. In this

manner, selection of scripts to be executed in a desired sequence and viewing of data in the

available display formats at intermediate stages of the process do not require any internal

modification of the codes. Figure 5 displays the primary GUI screen currently under

implementation to aid in future studies on computer-aided analysis of EC inspection data.

Typical contents of the screen at two intermediate stages of the process for a calibration

standard tube are shown.

Another area of improvement is the functionality of the data processing scheme. This

includes carrying out operations such as incorporating modular filters. scripting, mid-process

modification of filter parameters, saving of partially processed data, and undo features. The

ability to undo each individual process when inconsistencies are detected during some

intermediate stage of the process is especially Important when dealing with large data segments

from rotating probe Inspections. Mid-process saving allows partially processed data to be stored

and recalled for further processing at a later time if necessary. This option is also necessary

when the data from the calibration standard and the flawed tube sections are to be

8
Figure 5. MATLAB-based GUI tool for analysis of EC rotating probe data

currently under LmpleentatiorL All maniulattons and display of

data are carried out from a single user _treface screen. Shown

here are typical displays of tntemnedfate stages of analysts for

rotating probe datafrom a calibration standard tube..

9-
processed separately. Filter modularity allows the user to call processing operations without the

need for detailed knowledge about the GUI structure. In this way, a series of operations can be

executed sequentially by simple scripting. This capability is of great mportance to future

investigations in this area that may involve processing of data from other probe designs that

may require incorporating other processes better suited for that particular coil configuration.

2.3.1 Application of Digital Filters

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

They may be implemented in either the hardware or software and serve 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 any given 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 filters

with no apparent analog equivalent. The discussion here is concerned only with the application

of digital filters for processing EC inspection data. Several filtering operations that were adopted

for this specific application are described. They are implemented in either the original signal

domain (i.e.. spatial or time) or the Fourier domain (i.e., frequency). The choices for selecting

the proper domain are discussed below.

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

filtering may be written as

g(±nAx)=Yzf(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 also represents the discrete form of convolution

integral in which h(x) would then represent the convolution kernel. 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(w) = F(a)-h(), (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. can be

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

M-1 N-I

A~X, ) = 7- E; fn, n)h(x - m, y - n), (3)

m=0 n=O

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

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

10
In regard to processing of eddy current WDE 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 SIN ratio. In this context,

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

Tranisformations of the form shown in Eq. 2 are 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 domqii tihe 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

filtering, 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 well separated,

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

2.3.2 SignalConditioning

Pre- and post-conditioning of data are among 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 present at-multiple

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 filtering have been

Incorporated into the data analysis routine -to reduce the level of such noise. hey 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 by applying the filter

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

the basis of data sampling rate and frequency content of noise. A -brief description of FIR filters

that serve the same purpose is given below.

-Frequency domain bandpass FIR filters are nonrecursive filters -that offer certain

advantages and limitations over recursive infinite-duration impulse response OMR) filters. The

primary advantages are their linear phase, stability, finite-duration transients, and efficiency of

implementation. FIR filters are always stable, but require longer sequences to meet the same

requirements as their UR counterparts. his latter property, a direct consequence of their

simpler transfer function, often translates into more computation time for nonrecursive filters.

As with all fequency-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-domaii difference equation-for a linear filter with input sequence x(k) and output

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

11
I

M N

y(n) = Z; b(k) x(n - k) + Y, a(c) y(n - k), 4

k=o k=l

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

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

M

I b(k)z -

H(z) =Y() k)= , (5) XWz N k

ao + I a(k)zk

k=l

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

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

frequency response, which is a rational function in Z7 for an EER filter and represents a discrete

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

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

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

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

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

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

background fluctuations. 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 signal enhancement and signal

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

degradation phenomenon. Signal restoration, on the other hand, assumes 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 is the original data. Although the two approaches are

clearly differentiated in most standard signal 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 signal 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

12
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 morphologles 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 filters 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. This could be particularly

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

response at the base and auxlliary 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 where that flow

is governed by a diffusion-type 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 nfluence the degree to which a signal can be recovered is the lack of separation

between the spectral content 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 of 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-filtering schemes. Finally, it

is important to note -that physically realizable optimal inverse filters are often unstable. For this

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

restoration of EC flaw signals for practical IST 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. 'ypical 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:-

13
M-lN-I

g(x, y) = E X f(m, n)h(x - m, y - n) + q(x, y)

m=O n=O (6)

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

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

coil impulse response, 7 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-filtering operation in the Fourier domain

could be expressed as

f(x, y) [ GQ( v)] (7)

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

G(uv) and Probe transfer function H(uv) are defined as

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

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

in which the transformed variables uv) 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.

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

narrow axial flaw is shown in Fig. 6(a). The coil is a standard 2.92-mm (0.1 15-in.)-diameter

pancake excited at 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. 6(a). two different functions were considered in this study. Figure 6(b) displays

Gaussian and Lorentzian functions of the form

(x) = Ae(XP)2 (10)

L(x) = A (1

I +a(x _/)

in which (I 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 13 in Fig. 6(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 close

approximations to the coil impulse response, the Gaussian function can be adjusted to better

14
(a) O3

Fgre 6. (a) Typtcal amplitude-response of RPC (2.92-mm 10.115-in.1

pancake) to 10096 TW, 6.35-mm (0.25-n.)4ong axial EDM notch

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

Lorentzlan (dashed line) &mpulse responses having approxmately

same parameters.

match the measured response. This is expected, because coil impedance is a complex

exponential function fI.e., a sinusoidal response). While the Gaussian function is often the

principal choice for simulating the coil Impulse response, it may not be the optimal choice for

inverse-filtering applications. The sharp taper of this function could result in amplification of

high-frequency 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 7 displays a series of 2-D kernels 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 -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. he geometries of all

the available laser-cut specimens are depicted In Fig. 8. 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 Lorentzlan Impulse response are presented

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

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

calculate these parameters.

15
(a)

F'] d'a A,

o o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~g

Fdt. ~ C* O 0x 

(b)

Fygure 7. Spatial domain 2-D) kernels for Gaussian (left andi Loreniin (right

inwuLse responses constructed by (a) tw orthogonal -Djuntions

and {bI rotion of I-Dfunction about Vertical axds.

Figure 9(a) shows a terrain plot of the original C-scan data from a Type-l laser-cut

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

of 80%1IW. Figures 9b) and (c) display the restored signals subsequent to application of the

2-D kernels shown in Fgs. 7(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. Mhs s also reflected in the Lissajous trace displayed alongside the terrain plot.

Similar mprovement 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 0. I15-injl pancake) data.

Figures 10-16 display the results of inverse filtering on the remaining samples from the

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

restored data show mproved spatial resolution and reduction of the baseline level. It s

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 maximnize the

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

16
IMI-

* Loll re ; ; ELTVI , -t Ih ._ -

Type 

I I I

\ Rype

X - r - -

T - I

Id Is_ ==T=e 8-====M I=

Type 3

TYPe 5

I* i .- a I$

I

-

4 6

-INfL

.

Type 7

A W-A I -

TAM a - M .

FYgure 8. Flaw geometries n set of 24 loser-cut specimens. Flaw dimensions are

listed n Table 1.

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 original readings. A more detailed description of analyses of EC inspection results

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

earlier report.2

17
-

Table 1. Available laser-cut tube specimens and their nomirnalflaw dinensions.

No. of Length Depth Ligament

Tube ID Type Notches (mm/degrees) (% TW) Width (mm)

5528-1-1 1 1 6.35 80 NA

5528-1-2 1 1 6.35 80 NA

5528-1-3 1 1 8.89 80 NA

5528-1-4 1 1 8.89 80 NA

5528-2-1 1 1 12.7 80 NA

5528-2-2 1 1 12.7 80 NA

5516-4-3 2 2 12.7 80 0.25

5516-4-2 2 2 12.7 80 0.13

5528-3-1 3 6 12.7 80 0.13

5528-3-2 3 6 12.7 80 0.25

5528-3-3 3 6 12.7 40 0.25

5528-3-4 4 6 12.7 80 0.13

5469-2-1 4 6 12.7 80 0.25

5469-2-2 4 6 12.7 40 0.25

5469-2-3 5 6 12.7 80 0.25

5469-2-4 5 6 12.7 80 0.51

5469-3-1 6 6 12.7 80 0.25

5531-3-1 6 6 12.7 80 0.51

5469-3-3 7 2 3600 80 0.13

5469-3-4 7 2 3600 80 0.25

5469-4-1 8 6 3600 80 0.25

5469-4-2 8 6 3600 80 0.13

5469-4-3 9 2 180 x 12.7 mm 80 NA

5469-4-4 10 2 180 x 12.7 mm 80 NA

18
-b.

(a)

UO IJ 4~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..; . ...'..' 

...1 h S 01 : ::: ---:--:--::::: .... --1:X:-- ::::f--:-::::::-::::::::~~~~~~~~~. ... .. ...... ..... ID

I-~~~~~~~~~~~~~~~~~~~~~~~~~~I

o 46 tr f : O ,., . , , ,._

U -LI ~~~ ~ .......... ~~~~~~~~~~~~.. .. ...

* e- 4 -1J *1 0 o A. I 1.5

HFw

(b) - ~ ~ ~~~~ ~ ~ I L . I

'U ~ ~ ~ ~ C -:

P~gure 9. (a) Origin ad~ (b)-(c).restored sgal wtt - oetfnbreso

1;g. 7for Type-I lser-cut-spechnen lf5528-1-1 wh nomnarl ODflawl

dep of 806 7W. In both ces, deconodled sgnal displays higher

spatial resoluton and lower baseline varedor. IE i nxmu depth

estinates were 58% by bobbin coil (phase-angle) and 75% by

mltanter analysis of RPC (2.92-rnm 10. I15-in.] panae) dta19
I

2

0

-0

2 ................... .5~ ~ . .

o -

15 ...... ...... .... ................ ...... .....

-1 -; .5 a 0.5 ; 15

6oN~

4

30 20 10

AK Or.

2

(a)

II.-W

1.0

12D

80 40

ZO ~~~~~~~~~30

A.-di,.

>I

1.5

0 

-1 0.5 0

HaWp.

0.5 I

b

Figure 10. (a) Original and (b) restored signal for Type-2 laser-cut specimen

#5516-4-3 with nominal OD flaw depth of 80% 1W. NDE maximum

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

multiparameter analysis of RPC (2.92-mm 10.11 5-in.) pancake) data.

20
. '. .. .. . . .. .. . . .. .. .. . .. . ..

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

0 - ~~~~~~............ ........ ..... '_......''.:

¶45 0 05 I 1.5,s

(a) O ................. .

la)~~~~~~~~~~.......... ..... .....

40 U

0 

C=*.

(b)

Figure 11. (a) Original and (b) restored snal for Type-3 laser-cut specimen

#5528-3-3 with nominal OD flaw depth of 40% TW. NDE maximun

depth estimates were 4296 by bobbin coil (phase-angle) and 61% by

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

21
i

A

2.52

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

. -15 .1 45 0 O I .5 I 2

H p.

(a)

2

. . , . : . - ': - .

1.5 . ... ..... ... j . u . . .... ......

. '' ,,tl

ioa 7

45 ; 0 so

-1 45 0 M 4 05 1

b

140,

I

Iso0,~ 

U0 ~ 1

J0-5W

0~~~~~~~~~~~~f~t

05

I.. .....

.0.5

-2 -1.5 -1 045 0 05 1. I 2

(C)

FYgure 12. (a) Original and (b)-fc) restored signals with 2-D Lorentzlan kernelsfor

Type-4 laser-cut specimen #5469-2-1 with nominal ODflaw depth of

80% 7W. NDE maximum depth estimates were 45% by bobbin coil

(phase-angle) and 74% by multiparameter analysis of RPC (2.92-mm

10.1 15-zn.] pancake) data

22
I

....... ....... . _ ........................ ........ ................

1J::............ : .............. ....... ..

'S ,....,.._< ' ~~~~~~~~~~~~......

e........... .............................

isf A 4). a GA OJ l

40

I

ads.

(a)

lc

A3L*.

(b)

Fygure 13. (a Original and (bJ restored signal for Tpe-4 laser-cut specimen

#5469-2-2 wIth nominal ODJiaw depth of 4096 1W. NDE maximum

depth estimates were 4396 by bobbin coil (phase-angle) and 6096 by

multparmeter analysis of RPC (2.92-mm 10. 15-I1n pancake) data.

23
4.5,, . ' E

140.

120,

100.

! O0

40.

20.

e0

3.

a

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

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

... . ... . . . .

................... :§

...... ............ .. '

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

01

20

1.I

0.

A-dr.

40

(a)

.05 

q _s ^ s o~~~~~~~~~~~~~~

-Z .1 o 1 Z

2.5

140

120

1W

so

40

20

0

E0

2

20 _ _ ; ...-- 40

0

- 4

......... ..

..... .... .......i1, 

. ... .... ... ''-'',.

-2 1 4.5 0 05 1 IS 2 H.p

4S

-O

-1.

-2

(b)

FIgure 14. (a) Original and (b) restored signal for Type-5 laser-cut

specimen #5469-2-4 with nominal ODflaw depth of 80% 7W.

NDE maximum depth estimates were 64% by bobbin coil

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

(2.92-mm 10. 115-IL pancake) data.

24
140

I40

Io.

40

40

A

2.1

U

I0U

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

,,,...... ..

......... ..sM

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

A 

-, a II

A10*

(a)

4 -1 0 2

m I= 

140,

120

40

e0 14 e 0 

ck .4w A1r

I

.

I 45 a 0.5 ' !

H-OMV.

(b)

FIgue 15. (a) Original and (b) restored signal for ype-8 laser-cut

specimen #5469-4-1 with nominal ODlaw depth of 4096

IW. NDE maxirmum depth estimates were 3996 by bobbin

coa (phase-angle) and 7096 by multparameter analysis of

RPC (2.92-mm (l0 15-in.)pancake) data

25
4.5

140

120

40.

0

Ute

o

I

I I

2.8

4

2.5

IIA

0.5

0

I ... .. .. - .... ... . .. . .. . . ...

i lt. 

I ...... . |. .....

' .- ' ',\\''' 00

f .. '. ... ;' .'.. ......... ... 0 .. - ..

olo - 0 0 A.,

-2 -I 0 I 2

H-V

(a)

2

I

1.5

> 05

a

-0.5

2 1 -8 -

... .... i..._. 40

-1.5 -1 -0.5 0 0.5 1 1.5

J. ~ ~ ~ ~ .M (b)

2.5 .

2

1.5 ....

I i I

, . V . . . .

* 0 - i i

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

..................... ...... f ' . < h: . . :

* , 00 ... .. ... ; X, ....... .. ........

.. ... ... ., ., \t .. ... ....... ...... ....

: : : s:t : : :

... ' %t , - '

* ...E . .. .... A\ . .... .E .. .... .. <

E : "",' ' 0

;

0.5 . ..

0

*0.5

40

-2 -I 5 -I 0.5 0 OS I 1.l 2

(C)

FYgure 16. (a) Original and (b)-(c) restored signals with 2-D Lorentzian

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

nominal ODflaw depth of 80% TW. NDE maximum depth

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

by multiparameter analysis of RPC (2.92-mm 0. 1I15-in.

pancake) dat

26
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

laboratory-produced cracking were analyzed by using the 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 (0.1 15-

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

electro-discharge-machined I(EDM) notches of axial. and circumferential orientation originating

from the OD and ID of the tube and ranging in depth from 20%o to 100% IW. -All notches were

6.35 mm (0.25 in.) long. Data analysis results are presented as an isometric display of relative

depth for the flawed region of the tube. Estimated flaw sizes are-shown with and without signal

restoration by inverse filtering.

Figure 17 shows the normalized depth 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 n the laboratory-degraded

specimen was estimated to be >80% W. Figures 17(a) and (b) display, respectively. the

estimated 2-D sizing data for the flawed segment without and with the deconvolution process

being applied. Unlike the gradual tapering of the flaw depth seen in Fig. 17(a), restored data of

Fig. 17(b) shows the flaw to have a rather uniform depth in the center, with a sharp drop in.

depth at each end of the crack. -

Figure 18 shows the results of analyses for the second axial ODSCC with estimated

maximum depth of -80% '1W. Comparison of the restored data in Fig. 18(b) with that in

Fig. 18(a) suggests the crack is composed of a series of axial segments, with one segment nearly

separated from the rest Similar results are shown in Fig. 19 for another specimen with an axial

ODSCC flaw. Terrain plots of the relative depth, 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%'1-W.

Figures 20 and 21 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 dominant signal feature in the amplitude trace.

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

terrain plots in Figs. 20(a) and 21(a), both cracks extend nearly 1200 around the circumference.

The depth estimates for the restored signals shown in Figs. 20(b) and 21(b) suggest that both

circumferential cracks consist of multiple segments. The maximum depth of the flaw displayed

in Fig. 20(b) appears to be near-the center.^ Terrain plots of the relative depth for the restored

data in Fig. 21(b), however, suggest that the circumferential crack consists of four closely

spaced short segments with similar maximum depths.

Finally, Fig. 22 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% ITW. In

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

Comparing the restored data of Fig. 22(b) with that of 22(a) Indicates some improvement of

27
-

I

(a) (b)

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

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

(b) restored data by inversefiltering. Maximum crack depth is estimated to

be >80% 7W. Isometric display of results shows finer details In restored

data

-. I  MMM M-ME a,>"-.gxl .sb. XXJ=

OA--

02

0

--- - - -----

0 2 -- - -- -- - ---- 

02

30

30

20

50

00 to -Qt

(a) (b)

Figure 18. Data analysis results for specirnen with aboratory-grown longiuinal

OrDSC sowing a) terrain plot of relaive OD depthfor cracked zone and

lb) restored data by nersefiltern. Maximwsn crack depth s estiatd o

be =-80% W. Isometric display of results shows finer details in restored

data

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

signals is clearly evident in both cases.

28
a I .14 4"PRIP"

" -, 1'11

- V -. V

U -u

(a) ~ - ~ - (b)

F~gwre19. Data anaysis resultsfor specwn with laboratory-grown skewed ODSCC

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

(b) restored data by riverseflering. Maximurm crack depth Is estimated to

be =80% TW. IsometrIc display of results shows finer details in restored

data-

 fE Km

__) =.E

(a) (b)

FIgure 20. Data analysis results for specimen with kboratory-grown circumferential

ODSCC showing (a) terrain plot of relatve OD depth for. cracked zone and

Ib) restored data by Inersefiltering. Maxnium crack depth is estimated to

be =60% 7W. -Isometric display of results showsfiner details in restored

datL

29
I . - ;vy-11PIT-11y"MP

.,

.' ~~~~~~~~~~

h ----

(a) (b)

FIgure 21. Data analysis results for specimen with laboratory-grown circumferential

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

(b) restored data by inversefltering. Maximum crack depth s estimated to

be =80% 7W. Isometric display of results shows finer details n restored

dataL

rM I P Tt 

,A s..:g iA, C. .. , .. MUeJ~

U

(a) (b)

Ffgure 22. Data analysis results for specimen with laboratory-grown longitudinal

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

for cracked zone and (b) restored data by nversefiltering. Maximun crack

depth s estimated to be <40% rW. Isometric display of results shows finer

details n restored data.

30
2.3.4 Sgnal Enhancement by Least Squares Method

Two basic operations carried out in the spatial domain for signal enhancement are

smoothing and peak detection. Polynomial fitting based on the method of least squares can be

implemented to effectively carry out these operations. Primary advantages of this method in

comparison to the frequency domain methods noted earlier are ease of implementation and

processing speed. Polynomial fitting can be done as a one-step convolution operation in the

spatial domain. Convolution kernels In this -case are pre-stored coefficients and weights

(noimalization factors) that are calculated by least squares polynomial fitting. Equation (3) from

the previous section describes the general form of the 2-D convolution operation. Symmetric

kernels are used to ensure that the -location of signal peak is not shifted. Two major

requirements for reliable application of this method are uniformity of the digitization rate and

continuity of the data. For EC inspection results, data may have to be resampled off-line when

the digitization rates in the axial and circumferential directions deviate from one other.

Removal of random fluctuations with mnimal distortion of signal shape is a fundamental

operation when dealing with noisy EC data. Simple techniques such as use of a moving average

filter that gives equal weights to all data points within a fixed window can result in loss of

information. Least squares fitting, on the other hand,- reduces random noise and

simultaneously preserves the underlying information. Variations in rotation and translation

speed of rotating probes during tube inspections can result in misalignment of data when

helically scanned data is converted Into a rectangular grid. Least squares polynomial fitting by

a short kernel can significantly reduce this problem. Excessive smoothing will always result in

loss of spatial resolution. The polynomial order and the approximate size of the kernel should

thus be determined on the basis of the coil impulse response. Accordingly, as an alternative

approach to the frequency-domain signal restoration technique described in the previous

section, polynomial fitting can also be used to restore signal peaks. For EC rotating probe data

collected according to standard field practices, quadratic or cubic convolutes provide sufficient

flexibility for the restoration of coil response from crack-like indications.

2.4 Multlparameter Data Analysis

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

processing 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 to

suppress 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 IPCR), 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

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

techniques is presented next.

31
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 [XI and

the parameter matrix ll, MLR develops a linear combination of variables in [l that minimizes

errors in reproducing I[Y. This is accomplished by constructing a relationship between [XI and

III such that

[Y] = [X][fi] + [E]. (12)

where [1P1 is the matrix of regression coefficients and [El is the matrix of errors associated with

the MLR model. The matrix [PI 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

N M _ 2 N M 2

[El= (Ymn .n) I Emn (13)

n=l m=1 n=1 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 I[Y and independent variable [XI. The outer relationships can be

written in matrix form as

N

[X] =[71[PI +[E] = 2 tPn +[El (14a)

n=1

N

[Y]= [U][Q + [F] = II Unqn +] (14b)

n=I

32
with the inner relation defined by

- -" bntni - -S- 4 , (I 4c)

where ..UE. I. and 1g9 are the elgenvectors and eigenvalue matrices and f1B is the vector that

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

both blocks. The intention -is to minimize Ill (i.e.i Norw4flF, -and hence to construct a useful

relationship between X1 and --.

2.4.2 Multifrequency Mixing Techniques

Processed channel Information from multiparameter mixing is commonly utilized in

analyzing multifrequency EC nspection- 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 (signals to be suppressed) on a calibration standard tube.

Mix frequencies are chosen 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

always contain residual signals from unwanted artifacts. This could in part be attributed to

background fluctuations, as well as to 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 field) 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 amplitudes

and phases of the signals 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 SN 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 mixdng algorithms that incorporate

more than two frequencies could be unreliable -for interpreting 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. -

33
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 in analyzing 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.

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 l-D mix procedures for

analysis of rotating-probe EC inspection results. Preliminary evaluations of the two techniques

discussed here were based on analyses of simulated rotating probe composite signals 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 in processing bobbin coil readings. However, independent evaluations at

ANL suggest that -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 rotating-probe

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

34
rotating probe. To improve rotating-probe mixes, specially designed simulated TSP rings (e.g.. a

2700 ring rather than the axisymmetric 3600 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 nitiated 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 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 an LS

regression approach with two frequencies.

Spectral decomposition methods serve as the fundamental numerical technique in a wide

range of applications from solution of a 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-elgenvalues. 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

preservation of relevant signal features and in effect elimination of background noise. Factor

analysis methods that use spectral decomposition, together with multivarlate 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 the LSbased factor analysis method used here for the processing of rotating probe data was described

earlier in Subsection 2.4.1.

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 described above, namely, single-frequency spectral

decomposition and two-frequency decomposition with LS-based regression, were initially tested

on simulated composite signals from rotating pancake coil RPC) readings on a 22.2-mm (7/8-

in.)-OD tube standard with machined notches and a 270° support plate slip ring. Various

combinations of OD and ID notches within the TSP region were produced 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

35
expected that such nonuniform and overwhelming EC indications could be encountered in the

field from external 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 Subsection 2.4.1, on the other hand, rendered a systematic approach for

repeatable suppression of TSP indications with acceptable degradation of flaw signals. Figures

23 and 24 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.

2.4.4 Multivarlate Analysis for Estimation of Flaw Depth

Multivariate analysis is also used in the final stage of data analysis (shown in block 5 of

Fig. 1) for estimation of the depth in 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 measurements on a calibration standard tube are used to determine 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 means that the adequacy of the results is strongly

dependent on the range of flaw sizes, types, and origin in the calibration standard tube. In

addition, availability of a useful set of frequencies is of utmost importance. Studies have been

carried out at ANL to detennine the sizing accuracy based on the available number of channels.

Although a minimum of two frequencies is required for constructing a two-parameter model, our

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 IST applications.

36
M~~~~~~~~~~~~- - -- -- . -

0I

§2310

-f

I

"40O1_ __ ,__ .: __. . I MD 1000 1500 2000 -250 - 300 36

'm de- por

a 0 O SOD 10D0 150D 2000 2s0 3000 3500

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0 5W 103 1O 200i 2 300 3500 da pait

(a) (b)

Figure 23. Dernonsb-ation of 2-D signal suppression on RPC traces of (a) sinulated raw

data at 400 kHz (top) and 200 kHz (bottom) frequencies composed of a 409

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 auxilary

frequency bottom). In both cases, the processed data Indicate substantial

Improvement of S/N ratio over original radings.

.e0100: .80 250030 . _._ -0 = 

£ ~ 4i~~ 0 _ - _ __.£ 

I. ' 0' o

-201~~~~~~~~ 

500 1000 1500 2000 2500 3000 1000 1 3 

'datepo PO,91 ; :- dtpoMn

(a) (b)

Ftrure 24. Demonstration of 2-D signal suppression on RPC taces of (a) sirnulated raw

data at 400 kHz (top) and 200 kcz (bottorrUfrequencies composed of a 406

TW axial OD and 4096 W circumferential ID notch at arbitrarily selected

locations within a 2700- -tube support ring and (b) processed data using

40 kHz prinary and 200 kHz auxiliaryfequency (top) and 300 kHz prirnary

and 100 kz auxiliary frequenc (bottorm). In both cases, the processed

data indicates substantial improvement of SIN ratio over the original data.

37
2.5 Rule-Based Flaw Identification

Eddy current testing of conducting materials is intrinsically a relative measurement

technique, unlike some conventional NDE methods for which absolute-type measurements are

possible. Calibration of data with respect to known artificial 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 EC 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 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 identifying flaws are based on changes in 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 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 skin depth, or the characteristic depth of 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

greater phase lag from OD indications also results in a higher degree of phase rotation for ODinitiated indications. Depending on the calibration procedure in place for ISI of thin-wall SG

tubing, clockwise (CW) 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 lesser 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 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

38
statements (i.e., IF-THEN) sequentially applied to the selected data segments. Calculation of

SIN from the earlier stages of the analysis is used to set the minimum threshold for sorting of

signals to be examined. Combined amplitude and phase-based mles are Implemented to better

discriminate between potential flaws and noise. Rules currently in place are set to be generally

conservative for identifying 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 semiautomated 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 examining a

wider range of SG tubing degradations will aid modification and upgrading of the current set of

rules as necessary.

2.6 Data Quality

As noted earlier, EC 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 data quality affects both detection and sizing, it 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, 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. he 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 a wide range of damage

mechanisms rely on strict adherence to some minimum requirements 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 generally dealt with

by numerical processing of data. On the other hand, degradation of S/N in the form of

39
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 reporL The relationship between sample interval Ax, and maximum observable frequency

fax according to the Nyquist theorem is given by

fmax = 9Ar (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 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.

40
3 Data Analysis Results

NDE results from machined flaws and lboratory-produced specimens have been utilized in

various studies for the evaluation and refinement -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 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 profiles on a batch of

specimens used to benchmark NDE accuracy through comparisons with destructive

examination results Is provided in Chapter 4. Fnally, sample calculations are presented on a

subset of tubes to assess multiparameter sizing of flaws in the presence of simulated artifacts.

3.1 Comparison of Original and Processed NDE Profiles

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 +PointyW. and 2.03-mm (0.080-in.) high-fiequency pancake coil. Original

amplitude profiles are all from the +PointTM coil at a single channel. Processed results, on the

other hand, are the estimated depth profiles obtained -by using multichannel information from

the mid-range pancake coil.

Figures 25-29 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, a

conventional Isometric plot (Eddynet m) of the +Pofnt probe amplitude response at a single

frequency is displayed, along with the multiparaneter sizing profile of the same specimen.

Image display of the sizing results Is also shown in Figs. 26-28. 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

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. Al specimens used so far were made of

22.2-mm (0.875-in.)-diameter Alloy 600 tubing. The primary pancake coil readings from a

three-coll rotating probe was utilized for- multiparameter data analysis. Manufactured flaws in

the first batch were composed primarily of circumferential cracking, and those in the second

41
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 Section 3.3 in connection with

destructive examination results.

Figures 30-34 display the results for selected samples from the first batch of NDE samples

containing OD- and ID-initiated cracks of primarily circumferential orientation. Figure 30

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 30(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 30(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% TW. Figures 31 and 32 display the sizing

results for two other specimens with circumferential cracks. Finally. Figs. 33 and 34 show

proffles for two axial cracks from the same batch of tubes. Analyses of EC inspection data on

this set suggest that the majority of flaws are quite complex in nature.

Figure 35 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 35(a) shows a terrain plot of the estimated flaw depth

profile from an arbitrarily chosen azimuth and elevation angle. Figure 35(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% 'W.

Figure 36 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% 'W. Figures 37-39 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

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.

42
4t t X~~~~~~~~~~~~~

_s .~~~~~a

arc.scale (dog.)

20

-Ax. scale (mn)

(b)

FIgure 25. (a) Conventional sometric plot EddynePi of +PItbnf probe amplitude

response and (b multparameter sizing data for same specimen with

laboratory-grown circumferential ODSCC. Results suggest presence of

=360- staggered cracking wih maximum depth >0% 7W. 

43
. Ot .4*

Ca

0-

(a)

ooS

100, 1-- ----- C 

80, ~ ------ ~ ~ ~ ~ ~ ~ ~ * -'

20

00

20

r~T ;- W .! -1

I

1

4C

Ac

5m

INE

Circ scale (deg) Ax. scale (mm)

(b

Figure 26. (a) Conventional isometric plot (Eddynee') of +Point' probe amplitude response

and (b) multparameter sizing data for 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° parallel cracking with maximur depth ;1 00% 7W.

44
(a)

MiMMAV 0ft. - ...

100 ------

60 -..--------- ~-''T~',. 4

80 -- '--t : 

arccscala 

_ l__w- (dea} °- O AX sCale (MM)

(b)

Ftgure 27. (a) Conventional sometric plot (Ecdynee) of +PoinfU probe amplitude response

-and b) multiparameter sizng datajfr same specimen with laboratory-grown

axial ODSCC. - The image display axes In this case are pixel locations n axial

(horizontal) and circumferential (vertical) directions. DE results suggest

presence of axial cracking with nmaxium depth >9096 7W. Flaw exhibited no

bobbin and uncharacteristically low rotating probe anWlitude response.

45
- -

1 | 

/~~~~~~ W a 

- J- - *- l -~~~~~~5 5

(a)

- ~ ~ - -= 21 x-I 

100

80

SB0

t 40

a 20

0

rZPMOT

I

300

Circ. scale (dog.) 0 0 Ax. scale (mm)

(b)

Fgure 28. (a) Conventional isometric plot (Eddynee') of +Point' probe amplitude response

and (b) multiparameter sizing data for same specimen with laboratory-grown

ODSCC. Image display axes in this case are pixel locations n axial (horizontal)

and circumferential (vertical) directions. Results suggest presence of multiple

axial cracking with maximur depth =10096 7W.

46
(a)- 

arc. scab (cls) Ax. scale (mm)

-

Fligure 29. (c4 Conventional soretric plot (Eddyneteu) of +Pontr probe

amplitude response and lb) multiparameter sizing data for a

speciren with laboratory-grown SCC Indications at rolltransition region. Results suggest presence of crcumferental ID cracking with maximm depth <3096 W.

Figures 40-48 display the analysis results for a subset of the 23-tube laboratory-degraded

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 depth in the vicinity

of the flawed region. The terrain plot is also displayed from two different view angles to provide

axial and circumferential profiles. For the subset of samples shown here, the proffle of degraded

regions suggests the presence of OD-initlated 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 single

axial/circumferential to multiple and mixed-mode cracks. Additional Information on NDE

results for this set of tubes is provided in Chapter 4.

47
Ift' - = = =I 

20

50A

50 1X0 150 200 11

20 40 e00 15 l

40 r11 40~~~~~~~~~~~~~~~0 

* 1 so

: I

(a)

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40. ~ ~ ~ ~ ~ 4 04rcci~e0 50 1----2- 50 30 5

:~x Ax~~ Czrc sca e(ds.)

(b)

Figure 30. 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 estimatedflaw size. Results indicate

-360° circumferential cracks, with maximum depth of =8096 SW.

48
NOMM". APM" --I- - -a 11IM-11-

Orc. scale (degj -- : Ax. scale (rffn)

(a)

M~~~ . . .

s I- : SF SC o ---- --1I- _ -_ --- - ----- ........................

0 --------------- ,' --------- ---------- ---------- I

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20 .. ... . .. .... ... ... . .. --- - -

OK- 20 40 eo s 100

AX. scale (min)

IOD

*S ----------------

80 -------- A---4--- i ------ ----- 4 ----- 4

40-,__j_ 

20 ----- L.......----- ----- -- ______ ---------- ---

O 50 100 150 200 250 300 350

O. scale (dNg

Figure 31. Representative display of data analysis results for

specimen with laboratory-grown shallow circumferential

lDSCC showing (a) terrain and (b) cross-sectional plots.

taken over a point with maximum depth, of estimated

flaw size. Results indbate =360° circumferential cracking

with maximum depth of >2096 7W.

49
MK7Mf= -

2000

irccb[e

40

00

3

20

Ax. scale (rim)

(a)

M=ZZC=

iI

Is

Ww^-o,4

so -- -- -- - - -- - - - --- ..................... .|______| _ ...,_

80 - -- ---

40 ------- -I - -

0 5 10 15 20 25 30 35 40

Ax scale (rvn)

00 t m ____ ..

E

*

V1

0 -------- - - ..

s0 ---- -0 150 00- ----

Vr- - -- - -- -- --

0 50 100 150 200 250 300 350

I

Cirt. scale (deg.)

(b)

Flgure 32. 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

estimatedflaw size. Results indicate =3600 staggered

circumferential cracking with maximum depth of >8096

7W.

50
-~~~~~~~~~~~~~~~~~~~~~- 1111111 I

M--WlAx scae on)

(a)

U~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

i(dgJ

100 ................. .---.......... P~**--- ......... .......... _

90 -..-..-...-- - -- ----- - - ---- --- --F- -- ---- -- - ---- ----

80 TO .... ---- ;- . ...... ,. _ _-*-- r--- -- T-..---

:50 - - ......... - ........ 

40- ... --------- ---------.--------- a--------.-

30 --------- - -------- ..... ... -------- ---' 

20 ---------- ------ ----------

10 ---------- ...

0 20 40 60 80 100

Ax scae (mm) -

(b)

Figure 33. 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

size. Results ndicate dominant single axial crack with

maximum depth of >80% . - -

51
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30

40

so

80

100

20

40

80

so

100.

40

so

80

18

(a)

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40 . ------

20 .. .. --- ----- -- - - -- -- 00 -------3

Circ. scale (do) 0 0 Ax. .0

-

scale &= 0 50 -100 150 200 250 300 350

Ome scab (do%)

lb)

FIgure 34. Representative display of data analysis results for specimen with

laboratory-grown axial ODSCC showing (a) pre- (top) and post-processed

(bottom) images of normalized data and (b) terrain and cross-sectional

plots, taken over a point with maximum depth, of estimated flaw size.

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

_9O% rw.

52
rl~ I= - I

(a)

kIMM".44 I . ____ .Nmm=mm= 121111

P-,Af1W

o

7 ....... -------- I 4........a -j. I -------- i- --- - --- -- ---

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7(t

IC

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o . j- .-- -- i- -

7 . . .

10 20 30

Axsc O m)

40 Om rc. e two -

1

-Figure 35.

: - 1 

Representative dsplay of data analysis results for specftwn wth

laboratory-grown curffrential ODSCC showing (a) terrain plot and

(b) cross-sectional vews of estimated fa size. Results inie -360'

rfumfferera cracs wth md ,deptf7 *906 W. -

53
--

EUXM3=3= 1111111111MME

I

(a)

MMMM=~~~ I MALVA - -

ONNIMMINNIPM10f Km

100 ---------------------- ----------------------------- 100-

70 ------------- ___ _ '----------- ------ + 708 

70____ ....... _ _. .. ............. OR _____ 0 __

50 .__ .I _..______. ----- _40

30 ________ - -----------. . 30 -

20 ___- ____ l | l_--------r----- -- ------ 20 _

o0 - ---- 0 .. ......... . 10o ~~~~~~~~~~0 -

0 10 20 30 40 0

AK scab m)

-------*---------------------------------

i----j-- ------ i----- --

,, --- -- - -- -- - -- -- - ................ ,.,,.

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

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'--''-' 'l -.--- -------- ------ '---------

------ '------ --.......---- 1------ --------

-:----- ....... ..........

so 100 ISO 200 250 300 350

Orc. scale(deg)

(b)

FYgure 36. Representative display of data analysis results for specimen with

laboratory-grown axial ODSCC showing (a) terrain plot and (b) crosssectional views of estimatedflaw size. Results show two indications with

maximum depths >80% 7W.

54
RM = =-- OA

-= = =:E~~~~~~~~m;

Crc.uCde(dog)

20 a

Ax scab Cm)

(a)

1-1 . . _I

100

50

70

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...._...'_ _

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

10 20 30 AX cae (m)40 0 50 100 150 200 250

Orc.scw (.) 300 350

--J'

b)

Figure 37. Representatve display of data analysis resultsfor specimen with laboratorygrown axial ODSCC showing (a) teain plot and lb) cross-sectional views of

estimated flaw size. Results indicate two closely spaced parallel (axially

offset cracks with mamum depths reaching 100 IW.

-55
I

'PRU. aI

I

d

a

I

I

0 0

Crc. scae (dog.) Ax scale (m1n)

(a)

100 , ----------------------------------------- 100 ------T ------------------------------------

90 ;_ _ - _ ;--------i i , 90 _------ j ------------ 

80 _ , _, _80. w o. y .

70 __ ._ I I- ----- - .--- - . 70 _- ' . . __ . . ----------

rE ----------------- t - ----------------- ......... F 5 0 -------* ---------------- -- ------ - ---------

4 0 -- -- - .___ -- t ---- -- -- ----- i- ---- _-.-- 50 ---- --- -- _, -- -- - I- -- ---- -- -- ---

0 | --- .-------- j--- .* .... -------- 30 ----- .-------- 4- ,- -- __ . - ----

20 .. .. . .. .. .. 2 0 ----- ' l

i tj 1[ ^§ S| ffi o i~~~~~~~~~~l 1 ffi .----- 10 20 30 40 50 60 . 0 so I 00 150 200 250 300 350

Ax scle(bTmn) Om. scale dwg

(b)

Figure 38. Representative display of data analysis results for specimen with

laboratory-grown axial ODSCC showing (a) terrain plot and (b) crosssectional views of estimated flaw size. Results indicate multiple axial

cracks with maximum depths >80% 7W.

56
00*4

0 0 0 Axscae=nvX

Orrtscae(deg A.sal fn

(a)

(b)

FIgure 39. Representative display of data analysis results for specinen with

laboratory-grown axal ODSCC showing ( terrain plot and (b) crosssectional views of estimated flaw size. Results indlcate mutiple axial

cracks with murdmum depths >80% 1W.

57
bl'

JkAt -1, -, 

4

4

¶010 IDD

(a)

c4 = _ _. kl ..

Crc scale (dog.) Ax scae ffn)

(b)

I -s 

Ut,^' A )";>;;Sift--l" _ I _ _ s _ w~~~~~~~

o --- ------ ------

F 60 --- ------------ - ------- ------------------ 

20 ,. j si i 

50 ... . .. . . .. . . ..... . .... .... ...

0 10 20 30 40 50 0D 70 Ax scis( (m)

t

I

I! 1 411

3

2

1

O-. ; . ---- 

60 . . .. . . ... I . ......

. ......

..... . ....... r.... 1 ------------- s- . ............... _

20 . . ., jt . i i - t~~~~~~~~~~-- - -s--- 

0;

o0_....... 

o j. e ........ I

V DU 10 ,D 20 A 25 U JIM 350

Cic sc (dog)

(C)

Figure 40. Representative display of data analysis results for specimen with

laboratory-grown ODSCC degradation showing (a) image display, (b)

terrain plot and (c) cross-sectional views of estimated flaw size.

Results indicate single crack with maximum depth >90% 7W.

58
... 1-1-1-1- MonommomosL

a

10

-a

10

(a) 

9 '"MR."FORM

10 . .. . ....

4.~~~~~~:

20- 

o 500

MM al~e(do. 0 10 0 Ax scale (m) I

tb)

70 3 - -- - -- --- --- --- - -- 0 -- ---- - -- - - -- -- - -- - - 70 --- .... ........... ....... .. .

50 f- so .......... ... ....

30--- ---- I--- ---- s +.... 14 4

10 ..... .................... ---- 0 -

= ~~~~~~~~~~~~~~~~~~~~~_0

O 5 tO 15 20 25 30 35 40 0 50 100 150 200 250 300 350 - Ax scajmm -- ac.uimfdeg) -

(C)

Figure 41. Representatie display of data analysis resultsjbr specimen with laboratorygrown ODSCC degradation showing (a) image display. (b) terrain plot; and

(c) cross-sectional views of estimated flaw size. Results ndicate multiple

cracks around circumference, with maximum depth of dominant flaw

reachIng 10096 7W.

59
. I I 11 MAajftm '- ..... ,

SI

1a

2D [iflIS 1' -N7 M

UT IN

MS

ys0 *Y 10 200 250 so 1_ 0 1 0 D

(a) t

100 -- -- ----

.=~~~~~~~~~0 -- , _-----

90 ~ ~ ~ ~ q----- ~ ~~4 --------- -------- I------------i---i'--bi 

80~~~~~~~~2 t--?-d.-----1------- - ----- e8 __ __.l ?---------?---?

80

200

80

,arc.sca (og-) 00 Ax. scale (mmu)

(b)

700 ---- __ ------- r--- |-----.--------------- ----- 7D ---------~~~i~ --- ------- -- ------ ------- --- --- -

S i; --'---------- --- ----- ' 1 l --- -------------- 50 ------- ------ - f ------- -------------- ------ -

0 ---- - ---- i-- ------- j ----- ;_ ------ _ _ 0 _-------i---- U ------- ---- ------ F ----.i-j

70 . ,:- :70 : a : :- 30 ------- ~ ------ E- t ---- 70 ----------- -------- ---- . '--- ~----------- 20 ------ ------ .---- ------------------ - - ..... ............... - - - -

40 ---- . . .. .......... I--- --------...------ 40 ------- ------ --_ ------- ------- ------- ------ -

20 ---- ...... 20 ---- ---- ....... I.... ...'... .. ...

0-----I T-0-

10 10

0 10 20 30 40 50 80 70 80 0 50 100 150 200 250 300 350

AX cal (mm1) arc. scab .(g.)

(c)

FIgure 42. Representative display of data analysis results for specimen with

laboratory-grown ODSCC degradation showing (a) inage display,

(b) terrain plot, and (c) cross-sectional views of estimatedflaw size.

Results indicate two circumferentially offset cracks, with maxinum

depth reaching =1 0096 W.

60
7e

I

I

-(a)

it .. "-" -. . .. . .. .. ......-. 

AX Scde OM) -

(b)

,,g .0 . . .: . _ : .

l_§C - - 1- I - - 1 1 N . . -11 111

. M kaz :2A.X7.a:.3Ji:B=..._ 'C.a_13.P-taX' f. _.@f-,8.:S - !2W_ ffi 

1w ------. T''- ' ----.............-- ----- fi---- ,- 0 --- -.- --

so --- -4--1- ----- ;.------ ....... ......- 

70 --_ -- .... 

e 0 t -U .... ... ...... .... ..

s-- . ........

X 40 '-''--- f-.,--- ------- ------ 30 --- ----

20 ---------- ....

1 1- .... ---- - --

0 10 20 30 40 S0 S0 70 Ax wle (mm)

to ---------- ------- ------- I

So ------- - ----- - ' -i

't 4i- ---------- - ---------l ........... ---- ;-------- ---

3 . .. --- ---- i---* l u[ ---- I-i---- ---- --' 1 .- --------- '.

la -----............ .JR . ....... . .... 

ci

0 S0 100 10 200

Sc, (do9g 

2S0

' (uc) 

FIgure 43. Representative dsplay of data analysis results for specimen with

laboratory-grown ODSCC degradation showing (a) image display.

(b) terrain pot and () cross-sectional views of estimated flaw size.

Results idicate segmented crack with maximum depth of I1 0096 7W.

61
IMMUKAU'AMI., , !"

e

I0

20

a6

(a)

M 11=-

r .^8b--........ 0 . 20

60

-~~~~~~ AL -V--)to

(b)

I-1

.- I IMN

-11: ;

100

70

850

1 40

30

20

10

0

,^

..... .. l. ,...... . . . . . ....................... .

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

---- - -- --- - - - -

10 20 30 40 50 o^0

00

ao

80

70

.60

9 40

30

20

0

.- -------------------..........---- ------- T----- 

.... ........ @. .. --------- , _

-- -. ,-- .. . .... . .. . -. .... -- -- --

-------------.- ----------------T- 

................ ----------------T - j

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

... .... ..... .... . .......... :

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

0 Ii ~~~~~~Ax. scale (flMn 50 100

Orc WA"(degj

(C)

Figure 44. Representative display of data analysis results for specimen with

laboratory-grown ODSCC degradation showing (a) nage display,

(b) terrain plot, and (c) cross-sectional views of estimatedflaw

size. Results indicate single axial crack with maximum depth

>90% 7W.

62
- -- - -- - - - -~~~~~ -N

-2

I4 0

AD:WCDm 0

:

(a)

bi VJi ti 

00 - ---------- so - --- ----- '.''''" '"

2 0 - 0 4,-------i '

c -d (dg)A St M

- ')

o w l . , 11 . ---. 

i.l

,.

10 ........ r ....... . ...... .- ----- - ------ -------

---- 0 -- '--- ..... . .. - - . - TO.... .4......' . ._.--_.... '- -_......

. p-- --- -- - ---- ------- --- --- -

----x ------------ - ------:

tOM --------- -------- ----------------------- ;-

90 ---- 4------F*-----* O---

so . ., _.,.-..4.----------------- ............

[ - 4~~.._-......:. .. .... _...

S

2

I

0

1

c

c

-----r--------

II. F---- ; - -- .. ------ ... -- --- - .... T... ...-- I+--- -. $ , : -.- . ' 4.-.- i

r -o,---r 

r ...........

so-'''' ........... '''' L. 

40 -----------

30~ ~ ~ ~~~~~~~5 lo ----- .....- 11 1 ---------- 1u ------------- ---------------------

I rL - - - 6 10 20 30 40 50 8o 1

A-_cOWIM)

0 SD O0 150 200 C. e(dog.) 25D

I

(C)

FIgure 45. Representative display of data analysts resultsfor specimen with laboratorygrown ODSCC degradation showing (at display, (b) terin pl o.age and

(c) cross-sectional views of estimated flaw size. Results ndicate multiple

cracks around crcumfrerence, with maximum depth >6096 7W.

63
11A -=X

!O so too ISO 200 250

1- 50 100 150 - 250

(a)

b

1D r-------------- --- -------- --- ---------- -------

10 ------ -

30 ------- 1

20-

70

1o4Q .. r---- *. .. ..

0 20 40 80 80 100 120

Ax scale (M)

00 ........--- -- - ---- i-- ---- - -- ----- ---- . ---- -- 90 ---- --- ---- j-------- - . i

70 ., . , -

o ... .. .. .. -- --- - -- - --- ------

4 0o, --- 

0 --- ---- ---- I 1 ---- --- j ----I-

. 0 ---- ....t- 0 50 100 150 200 250 300 350

Clic cle. (dog

(C)

FYgure 46. Representative display of data analysis results for specimen with

laboratory-grown ODSCC degradation showing (a) mage display,

(b) terrain plot, and (c) cross-sectional views of estimated flaw size.

Results indicate multiple cracks around circumference, with maximum

depth of dominantflaws reaching =10096 7W.

64
WaIWJ+'i ^1- 

a

10-

.4

a

a~ 

(a) - - -

~I Mt

i~ --------- -- .

so.---~~ ~i--- -------

- --------- - 0 -

Ax WAGOin)

(b)

pI. .: :.' ' t. :'w lIftVMMM10 .. ,- 

1a0 -- ---- o- I------7 -------- ------ ---- T'

SO {--- ---- ---- - ------- ------- - ---- -

70 T._....... ._ .._4_..._,_____................_, ... __.... 

F ___________. ............ _.. ___.. .-.

so5 ...... - ---- -- -- ---- --.......... _ .

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

20 ---- -------

l o ---t------' ----- - . ..... .....

0 la 20 30 40 50 e 0 AX ando j )

0 -------- -- ---- - .- -.-

CO-- - ------------ 1--- ---------- ---------------

--- ----- -i- - -- 

40 ------F-----*-i f. . .. .......---^§

,2 ---- 4..... i- - - ------

100 . . . _. . ,

50 -100 150 200 250 300D - 350 f irt SCO dg 

- (C)

FIgure 47. Representative display of data analysis results for specimen with

laboratory-grown ODSCC degradation showing (a) image display,

(b) terrain plot, and (c) cross-sectional views of estimated flaw size.

Results indicate multiple cracks at several axlol locations around

circumference, with maxmnum depth >60% 7W.

65
ICA- -

4

10

00

tC 1C0 151>

40 il _ 

tiO 5 1S0 ISO0 1 250

(a)

._2A3z

100 --

i 

,Y,,,,4it ; 

Ax scale (mm)

10 -- -- --

93 -- - - - - - - - -- - - ----- - -- - - - -- - - -

80 -- - - - - -- - - - - - -- -- - -- -- - -- -- --- -- -

20 -0 20 _- ,-

20 -20 4A i (L n - Ax sale {Me

=';1=

10

90

so

70

£50

j 40

30

20

10

0

0

_ . .. . _..,. .. . . .. _... ... . . . .

e r _ _ t --->------+- ----- -

---------- -- --- -- --

i . . . .. .. .. . . -- - -i-- 

----- --------t-------t-----------t---t

------ -- --- -- --- -I -- ------- --- - -------- 1

50 100 150 20D 250 300 350 rm Sca x (deg-)

(C)

Figure 48. Representative display of data analysis results for specimen with laboratorygrown IDSCC degradation showing (a) image display, (b) terrain plot and

(c) cross-sectional views of estimatedflaw size. Results indicate multiple cracks,

with dominant crack depth <35% 7W.

66
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 were 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 the degraded region. Eddy current data for each test case of

simulated OD artifact were 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 artifact geometry and composition as well as on signal amplitude at a

given excitation frequency.

FIgures 49 and 50 show sizing results for the case of TSP simulation. Terrain plots of

estimated flaw size are displayed for a small section of the tube in the vicinity of the indication.

Variation of maximum depth for the deep crack shown in Fig. 49 was relatively insignificant for

all the simulated collars used in the study. For the test case of shallower ODSCG shown in

Fig. 50. 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 51 and 52 show representative test cases of deep and shallow ODSCC, respectively,

in the presence of a sludge artifact Finally, Figs. 53 and 54 display the profiling results for OD

and ID cracking. respectively, for the case of magnetite simulation. The estimated maximum

depth of cracks varied by <1096 of tube wall thickness for all the test cases considered here. As

expected, the effects of OD simulated collars are lower for larger-amplitude signals and greater

-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 speeds). Examtion 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 artifact geometry

and composition, as well as on signal amplitude at a given excitation frequency.

67
-

~___ 20

2020

40 40

60 6

100

50 100 150 200 250 100

so 10o 150

1050 1 00 10 20 20 100

(a)s~edg Oe 0Asc~smui cI.de 50 0 I f l)

2ettQ:=~~~~~~~~~~~~~~~~~~1 ...... =

b lc)

Figure 49. 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 sizing data over

approximately same region of tube (b) without and (c) with TSP collar.

Estimated maxmur depth (>90% TWfrom plain tube) varies by <10% 7Wfor all

simulated artifacts.

68
.5

O . = = = e I., .....a I---- -~ I . -1 ''.. I.- ~ .I _. - ....1-.1 _- -1 .... I........ .., I .-.- '' -.1

AC0 - AC

-. soC

- -1 0

I

I

50 X 100 - 150

- (a)

LMM 9

( -- : (C)

FlIure 50. Data analysis results for specimen with laboratory-grown O)SCC showing

(a) Image display of scanned region of standard andjlawed tube with TP collar

placed over crack. Also shown are terrain plots of sizing data over

approximately same region of tube b) without and (c) with TSP collar.

Estimated maximun depth (<5096 Wfrom plain tube) varies by <106 lWfor all

simulated artifacts.

69
mm-E I - -  I . I 11.1. O=

1X~~r..._=.. .D ::__ IC6Wt_ o

40

SC

a( 60(

2C

4C

OC

8C

100

(a)

L~~~~~R ~ ~ ,~ ~ ~ .l _ _1 il . .11I -1 

I -, .I -.- . - I g--. I .-- ...- K-. s~-i

Zl;,

.&A _ 

Circ scale (dog) 0 o hc scale(nus)

° - e sae'40

Circ. scale dg]J

(b) (C)

FIgure 511. Data analysis results for specimen with laboratory-grown ODSCC showing

(a) Image display of scanned region of standard and flawed tube with sludge

collar placed over crack. Also shown are terrain plots of sizing data over

approximately same region of tube (b) without and (c) with sludge collar.

Estimated maxirum depth (>9096 7Wfrom plain tube) varies by < 10% 7W for all

simulated artifacts.

70
IC2

:; :;-20 S 

X( il) 150

a

-

-C

la X

-(a)

- _ 4 . . ... . ..1 W .1111.1. -

e ,- rar -IEM

: . 1-1-- I . - 1. - I ~ - . - . _*112-

W _

Cire.Sal(d@9O e o

~40

sale (niN

(b) (C)

Figure 52.. Data analysis results for specimen with aboratorygrown ODSCC showing

(a) Unage display of scanned region of standard andflawed tube with sludge

collar placed over crack. Also shown are terrain plots of sizing data over

approximately same region of tube (b) without and (c) with sludge collar.

Estlmted maximm depth (=4096 7Wfrom plain tube) varies by <1096 7Wfor all

simulated artifacts.

71
-E.IC7I.MISII].t ~ ~ ~ ~ ~ ~ ~ ~I . - -.1 ... .

a =_M~- =E B _ _~~~~~~~~~~~~,;A

20

40

60

100 50 100 150 200 250

20

40

0

so

100

(a)

1-_ , . .. g ~~~~M _ .*_ M... . ..MM ,Z... . M . M - ...... ge- ;, JIXAIPAP%1"tA*VIIi" %UIW. LII.,"'6

i

,

(b) (c)

FIgure 53. Data analysis results for specimen with laboratory-grown ODSCC showing

(a) image display of scanned region of standard andflawed tube with magnetite

collar placed over crack. Also shown are terrain plots of depth profile over

approximately same region of tube (b) without and (c) with magnetite collar.

Estimated maximum depth (<50% IWfrom plain tube) varies by <10% TWfor all

simulated artifacts.

72
-- 3- '1 . ' L : = . . . -.. , , , -

;tr~~~~~~~~~~~~~~~~~V ~ 14p ~ p - I - -

20~

s 140 150

so

140w

= g. V 

(a)

I ,-MO ~ U1 =MM ~-,-

m. scde (dog.) 0 0 A.Saft (rm)

(b) (c)

Flgure 54. Data analysis results for specimen with laboratory-grown 1DSCC showing

- (a) image display of scanned region of standard andflawed tube with magnetite

collar placed over crack. Also shown are terrain plots of depth profile over

approximately'same region of tube (b) without and (c) with magnetite collar.

Estfimated maximum depth (<4096 7Wfom plain tube) varies by <1096 2WJbr all

simulated artifacts.

73
-

3.4 Analysis of Field-Degraded Tubes

Eddy current inspection results on pulled tube sections from a retired SG removed from the

McGuire Nuclear Plant collected In the glovebox facility with bobbin and rotating probes were

analyzed. Initial estimates of flaw depth and extent were made by conventional analysis of the

data with the Eddynetm software. The computer-aided multiparameter analysis routines

developed at ANL were modified to size flaws in 19.1-mm 0.75-in.)-diameter 1.09-mm ((0.043-

in.) nominal wall-thickness tubing. Initial results from the conventional and multiparameter

analyses of the NDE data are provided here. Further investigations are currently under way to

study the reliability and accuracy of sizing results in thinner wall tubing.

NDE results on a selected set of six McGuire pulled tube sections are presented here. Preand post-pull inspections identified all flaws as ODSCC at dented TSP intersections. Data on

this set of 19.1-mm (0.75-in.)-diameter tubing were collected with both bobbin and 3-coil

rotating probe containing a mid-range pancake, +Pointm' and a high-frequency pancake coil.

The tubes were first decontaminated and then were scanned in a glovebox with and without a

simulated TSP ring over the degraded zone of each tube. In addition to an ASME standard, an

in-line EDM 18-notch standard was used. This standard contains a series of 9.5-mm (0.375-

in.)-long notches of axial and circumferential orientation, varying In depth from 20% to 100%

'1W, originating from both OD and ID of the tube. To allow comparison of the NDE results with

pre-pull inspections. both bobbin and rotating probe data were acquired with procedures similar

to those used during the field inspections. Some intermediate frequency channels were added

for further assessments.

Estimates of flaw depth by bobbin coil were made from the 550/130 kHz mix process

channel. Flaw maximum depth and extent were estimated by rotating probe using the 300 kHz

channel. The probe gain had to be reduced during acquisition to avoid saturation of pancake

coil signal from 100% 7W EDM notches. The noise levels in all of the pulled-tube test sections

were significantly higher (i.e.. lower S/N ratio) than in laboratory-produced specimens. The

noise was primarily ID-originated. Comparison of S/N from all coils showed that the +PointTm

signal was the least affected by the noise.

Except for one case, all flaws detected by the surface-riding coils of the rotating probe were

also detectable by the bobbin coil. Corruption of bobbin coil signal from the tube section in

which the rotating probe indicated there was a >50% TW flaw is attributed to presence of a large

signal from tube deformation that masked the flaw signal. For the other five specimens, there is

no apparent correlation between flaw depth and the bobbin coil signal magnitude (peak-to-peak

voltage), which varied from 0.75 V to > 4 V. Maximum flaw depths estimated by +PointTM varied

from < 10% TW to < 90%'IW with axial crack lengths of 3.5 to 10mm (0.13 to 0.40 in.). The

shortest axial flaw had the largest circumferential extent based on the rotating probe C-scan

display. In tubes without the simulated TSP collar, there is reasonable agreement between

bobbin and rotating probe estimates of maximum crack depth based on phase angle information

for the majority of detectable flaws.

Flaw sizing results were also generated by multiparameter analysis of mid-range pancake

coil data. The minimum necessary modifications of the algorithms, which have been used to

date for 0.875-in. (22.2-mm)-dlameter tubing, were made to allow sizing of flaws in 0.75-in

(19.1 mm)-diameter tubing. The rotating probe data was resampled off-line to produce

74
approximately equal digitization rates in axial and circumferential directions. Figures 55-60

show the results for all the tube sections examined so far. Sizing data are shown for a small

zone around the degraded region of each tube. Except for one case, the maximum depth

estimates are generally In agreement (I.e., ±20%) with those from the +Point~u coil. It is more

difficult to make estimates flaw sizes In 1.09-mm 0.043-in.)-thick wall tubing than in thicker

wall (1.27-mm or 0.050-In.) tubing. This Is due to fundamental limitations that are

independent of the coil design and the analysis method. -As a direct consequence of skin depth

effect, the same upper and lower frequency limits for thinner wall tubing result in less ID-OD

phase separation at each frequency, as well as less variation of phase angle between the primary

and auxiliary frequency channels. This narrower dynamic range in effect reduces the sizing

accuracy and increases the ambiguity in determination of flaw origin. Future studies on thinner

wall tubing will Include optimization of frequencies -to minimize the effects of the inherently

smaller dynamic range on sizing accuracy. The effect of OD- and ID-originated noise and

artifacts on the sizing accuracy will also be evaluated.

75
w , -gw ' I ; .. o z l z, -

2

a

a

(a)

a a 

>. w., * .. F... ~~~~~~~.. ... ....

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

m. ' . . ' . '. '-'.-- ' '' 

I.___.______-_._..________,,1_-_________:_-

(b)

FIgure 55. Data analysts results for McGuire pulled-tube section with axil

ODSCC degradation at dented TSP intersection. Maximumflaw

depth s estimated to be <8096 1W. Graphics shows (a) amplitude

Image display of standard andflawed tube at various stages of

the process and (b) age and terrain plots of sizing results in

vicinity of degraded zone.

76
a -

3o-

: -

i :f- g < a : 

_ v 41Ka

!~~~~~-. *_ -; . . S

...........

-.~~~~~~~~. . ..... e. 

*~~~~ 'r -

* W 

£ s, a3 ee

(b)

Figure 56. Data analysts resultsfor McGuIre pulled-tibe section wIth axial

ODSCC degradation at dented TSP Intersection Maximumflaw

depth is estirated to be <406 7W. Graphics shows (a) amplitude

image display of standard andflawed ube at arious stages of

the process and (bJ -mage and terrain plots of sizing results n

vicunity of degraded zone.

7-7
WW = Vt -- __. .:..... . _ ............ :. @ ....... :1:i. _2L.,-.-6.fi4-. .i_. , I - C

m

a

W

I go MD 2D 30D

(a)

= __ia.. O ^ W .. mk f : ... - - ...... ~

".. ,

i

(b)

Figure 57. Data analysts results for McGuire pulled-tube section with axial

ODSCC degradation at dented TSP intersection. Maximurmflaw

depth Is estimated to be >9096 1W. Graphics shows (a) amplitude

image display of standard and flawed tube at various stages of

the process and (b) image and terrain plots of sizing results n

vicinity of degraded zone.

78
I 

a

a

I - a

U 1W - a- m m -

(a)

.,, -1 =1%4m

m -^ X _, a. . w . -, , .-.< -.s .= .>z1-2 .-. e=

I

-

i-

. lAP._

. .

CICghW)i a a A 

(b)

FIgure 58..- Data analysis results for McGuire pulled-tube section with axial

ODSCC degradation at dented 7SP ntersection. Maximumflaw

depth Ls estimated to be <70962W Graphics shows (a) amplitude

bmage display of standard andflawed. bube at various stages of

process and (b) Irage and terrain plots of sizing results n viuiy

of degraded zone. -

79
111 1 I ,   5 a. I. .7 7 .  i I i,  '- - 'j_ "

2

A

a

11 I D40

a0 lsaI ml

30

so

*I

(a)

mmmm rmm.

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

Cm.

' A. -- 

ee0 

I

(b)

FYgure 59. Data analysis results for McGuire pulled-tube section with axial

ODSCC degradation at dented TSP ntersection. Maxlmumflaw

depth s estinated to be <20% 7W. Graphics shows (a) amplitude

inage display of standard andflawed tube at various stages of

the process and (b) nage and terrain plots of sizing results in

vicinity of degraded zone.

80
-

-~~~~~~~~~~~~ 0 a a VC :

- - (a)-.

Fitwre 60.

(b)

Data analysis results for McGulre pulled-tjbe section with axial

ODSCC degradation at dented 7'SP Intersection. Maximum flaw

depth s estimated to be >5096 7W. Graphics shows () amplitude

Image display of standard and flawed tube at various stages of

process and (b) Biage and terrain plots of sizing results incinity

of degraded zone.

81
4 enchmarking of Multiparameter Algorithm

4.1 Stress Corrosion Cracks Used In 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 M 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 6001C for 48 h, prior to the stress

corrosion cracking (SCC), to produce microstructures 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 were 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 examintion, 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. 61-83. Figure 84 compares the maximum depths 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 are 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.

82
100

to -

F 60 

1 40

20 

0

la

1 40

20

to

F 00

20.00

0.00

.10

Crack Length (mm)

Figure 61.

NDE andfractography proflesfor

spectmen: AGL 224 with

circumferential IDSCC (crack #1).

Figure 62.

NDE and fractography profies for

specimen AGL 224 with

circumferential IDSCC (ack #2).

10 20 30 40 So so 70

Crack Length (mm)

Figure 63.

NDE and fractography profiles for

spectien AGL 288 axial IDSCC.

0 10 20 - 30 - 40

Crack Length (mm)

50 60

83
120.00

F 80.00 

6 0.00 

a 40.00

20.00

0.00

20

Fture 64.

NDE and factography profiles

for specimen AGL 394 with

circumferentia ODSCC.

25 30 35 40 45 50 55 60

Crack Length (mm)

120.00

.

-2 0 2 4 6 a 10 12

Crack Length (mm)

FIgure 65.

NDE and fractography profiles

for specnen AGL 533 wIth axial

ODSCC.

Figure 66.

NDE and fractography profiles

for specinen AGL 535 with axial

ODSCC.

20.00

-2 0 2 4 a

Crack Length (mm)

8 10

84
120.00

00

1 0.00

FIgure 67. :

NDE and fractography profiles

-for specinen AGL 536 with axial

ODSCC - ':

2 0 2 4 6 S 10 12

Crack Length (mm)

120.00

F .00

e -

I40.W

20.00

0.00

.Figure 68.

NDE and fractography profiles

for specimen AGL 503 with axial

ODSCC.

-4 0 5 10

Crack Length (mm)

15 20

120.00

I

Figure 69. -

NDE and fractography profies

for specimen AGL 516 with axial

ODSC..

-5 0 5 10

Crack Length (mm)

15 20

85
10000

80.00

* 60.00 .-

a 40.00

20.00

0.00

120.00

Figure 70.

NDE and fractography profles

for specren AGL 517 with axial

ODSCC.

.5 0 s 10

Crack Length (mm)

15 20

80.00 

A 40.00 _

20.00

0.00

-10

120.00 ,

0 10 20 30 40 50 60 70

Crack Length (mm)

Figure 71.

NDE and fractography profles

for specimen AGL 824 with axil

ODSCC

Figure 72.

NDE and fractography profles

for specimen AGL 826 with

circumferentir ODSCC.

F 80.00

60.00 ~ 

.

i 40.00

20.00

0.00 . I a . .I

-10 0 tO 20 30 40 50

Crack Length (mm)

60 70

86
12000

100.00 

l 000 

20.00 

0.00 

-10

100.00 _

80.00 

0.0

a 40.00

20.00

0.00

Figure 73. 

NDE and fractography profiles

for specimen AGL 835 with axial

ODSCC.

0 10 20 30 40

Crack Length (mm) o so

Fwgure 74.

NDE and fractogphy profiles

for specimen AGL 838 wth

circumferentil ODSCC.

0 10 20 30

Crack Length (mm)

.40 so

120.00

100.00

v80.00

e- 000

40.00

20.00

000

FIgure 75.

NDE and fractography profiles

for specimen AGL 854 with axial

ODSCC.

-5 0 5 10

Crack Length (mm) 15 20
120.00

100.00

^ 80.00

d o

140.00

20.00

0.00

-5 0 5 10 15 20 25

Crack Length (mm)

80.00

B0.00

6 40.00

20.00

0.00

FITgure 76.

NDE and fractography profiles

for specimen AGL 855 with axial

ODSCC.

F;gue 77.

NDE and fractography profiles

for specimen AGL 861 with axial

ODSCC.

Figure 78.

NDE and fractography profles

for specimen AGL 874 with axial

ODSCC.

!S 10 15 20 25 30 35 40 45

Crack Length (mm)

120

100

g 80

50

CL

40

20

0

-5 0 5 10 15 20 25 30 35

Crack Length (mm)

88
80.00

rF 8000

fL 0.00

-5

20.00

0.00

120-

100 _

F o

el 60

£ 40

20

0

100 -

so 

CM 40

a

20

0

0 5 - 10 15 20

Crack Length (mm)

FYgure 79.

-NDE and fractography profiles

for specimen AGL 876 with axial

:-1OWCC.

ODSW.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Fwure 80.

NDE and fractography profiles

for specimen AGL 883 wIth axial

=ODSOC.

FIgure 81.

NDE and fractography profiles

for -specimen AGL 893 with

circuferentI 01)CC.

20 40_- 60 So

Crack Length (mm)

-5 0 5 10 15

- Crack Length (mm)

20

89
10000

B0.00 ,

F 60.00 .

1 4000

20.00

0.0 _

-10

100.00 -

80.00 .

F 60.00

1 40.00

Figre 82.

NDE and fractography profiles

for specimen AGL 816 with axial

IDSCC.

0 10 20 30 40

Crack Length (mm)

50 s0

EC NE

Fragraphy

Figure 83.

NDE and fractography profiles

for specimen AGL 816 with axial

LDSCC (2)

20.00

0.00

'~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ a 5 10

Crack Length (mm)

15 20

100

80

.60

20

I T I 1 I 1 | r s g ^ s , ^I T

.10'' H~

_~~~~~~~~et% 21. + 07 -dep

Figure 84.

Comparison of maximum depth

determined by multtparrneter

algorithm with that determined

byfractography

I 20 40 60

DepUita (%)

80 100

90
Because the field of view of the rotating pancake probe is limited. the depth measurements

at points 5 mm (0.2 In.) apart along the crack profile are essentially Independent, and

additional comparisons of the estimated depth with that determined by fiactography were made

at various points along the crack profile. To avoid observer bias in the selection of the data for

comparison. the intersections of the crack profiles with the the major grid lines in the graphs of

the superimposed profiles were chosen as the points for comparison. This corresponds in most

cases to a spacing of 5-10 mm between points. Figure 85 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 are

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. 84 and 85, 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.

T-he 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% '1W 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 fit shown in Fig. 85. 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 instead represent cracks that

were simply 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. 86, 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.

91
100

so

I 

20(

* 0 0 ° ;w85

O 0' Comparison of depths

- O o O < ~determnned by multIparameter

-. algorithm with those by

fractography, and regression

fit and estimated 95% bounds

for observed depth as a

depUI- = 13.8+ function of multiparameter

dep. 13.8 + 0.78,ept, depth estfmate.

40 60 80 100

Table 2. RMSE for depth estimates by multarameter

algorithm as a function of the fractographic crack

depth.

Depth Range RMSE Max. Crack Depth RMSE Crack Profiling

(%w (s"n MM 

0-100 13.7 15.5

30-100 9.7

50-100 8.2 -

(ODSCC only)

0-20 - 11.9

20-40 - 15.9

40-60 - 20.5

60-80 - 18.7

80-100 - 9.8

Table 3. RMSE for depth estimates by multiprameter algorithm (MP) and

by regressionfit in Fig. 85 as a function of predicted crack depth.

Depth Range RMSE Crack Depth MP RMSE Crack Depth

(%6 1W) (%6 'W Regression (% W)

0-20 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

92
100

0 ,* 3 g0 Igure 86. -

80so . / - O o 0o .> Comparison of depths determined

°b O -) ,' 0 by multparameter algorithm with

E e _ 0 0 0 0 those by fractography.- In this

j 0 ~ case, observed and predicted

40 8 results are shown n termns of to0 /8> oO / V pefect agreemenr 1:1 line and

00o 0/ \ > - - 0 estirnated 95% bounds that

account for variation in

______ :_____. _____-:_____ uncertainty with depth.

0 20 40 10 80 100

Depths (- , :

4.3 Comparison of Failure Pressures Predicted by Use of NDE and

A- Fractography Data

Although the accuracy of measurements of SCC crack depths by EC is usually determined

by comparing them with the depths measured by fractography, a more useful comparison, at

least from the structural ntegrity 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 profiles 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

ldata is shown in Fig. 87a.- A similar comparison for the unstable burst pressures is shown in

Fig. 87b. 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% confidence 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.

93
-10 * I 10 -'I...

z 8 .^w -/*E :- / M05.2' , :

i ~~~~~~0 

{ 2 < / ' 1 sS M 2 X / ~~~~~~~~~~~~~~~~~1:1 'Tru.

2 4 j 8 10 2 4 4 8 10

gament Rupur Pressure rm Frcaphy M Unstable ursl Pressu hwn Fraclogrphy (ks

(a) (b)

P~gure 87. Coprisn of predicted (a lament rupture pressures ad (bl unstable

burst pressures of specimnens wth SCC using crack depth profiles fr-om

fractography and advanced techntque. saDE Regression fits and 95%

confidence bounds are shown along wIth 1:1 line that would denote perfect

agreement between NDE and fracography.

94
5 Concluding Remarks -

Eddy current Inservice Inspection (ISI) of steam generator (SG) tubes with rotating probes is

carried out to overcome known limitations associated with conventional bobbin coils. Highresolution inspections with small coils Inherently provide more detailed information about a

tube's condition. These so-called supplementary nondestructive evaluation NDE) methods are

in place both to help resolve nonquantiflable 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 multiftequency eddy

current (EC) 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.

More accurate and detailed NDE information about crack profile and distribution can help to

better assess the structural integrity of SO 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 by analyzing 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 on data quality 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 data processing can be achieved by integrating suitable algorithms for computer-aided

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

rotating pancake coils; however, many of the fundamental processes described In this report are

95
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 include

additional studies in several key areas, some of which are noted here. Suppression schemes

that are more global are better suited to dealing with a wider range of unwanted signals from

various SG designs and associated degradation mechanisms. Also, rule-based algorithms

should be adapted to handle existing levels of variability in NDE test parameters. Furthermore,

it is desirable for such algorithms to accommodate differences in probe response due to coil

designs and configurations 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 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 cois 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 felddegraded tubes.

96
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 ntegrity Program," Semiannual Report. Oct. 1998-

March 1999, NUREG/CR-6511, ANL-0/4,-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.

97 -
Appendix: Characterization of Corrosion and Stress Corrosion Cracks In

Steam Generator Tube Specimens

A.1 Scope and ApplIcatlon

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

to radioactively contaminated tubes. 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 Precautions

Use protective sleeves for tubes to prevent scratches or other mechanical damage. Wear

clean gloves when handling tubes to avoid contaminating the surface. When transferring a

specimen, record your name, location or name of person to whom you are transferring, purpose

of transfer, date, and your initials.

Tube Identification

Verify the alphanumeric identification (ID) that is permanently inscribed on the OD at each

end of each tube (Fig. Al). This ID must be carried through the entire procedure. Any

subsections of tube specimens resulting from cutting or fracture must be permanently marked

with the ID. All documentation should be referenced to the ID.

A.2 Dye Penetrant Examination

Dye penetrant examination is used for OD degradations. After completion of the

degradation process. the tube specimen is ultrasonically cleaned in high-purity water and dried.

Dye penetrant examination is performed in the vicinity of the degradation.

Magnaflux SpotcheckSKC-S Cleaner/Remover is sprayed onto the test area to clean the

surface and is wipedoWrepeatedly until the surface is clean.

Magnaflux Spotcheck SKL-SP Penetrant is sprayed over the test area and left for I to

30 minutes; usually 10 minutes is sufficient. 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 20-30 cm (8-12 n.) from the surface. Wait 5 to 15 minutes, then inspect.

98
Figure Al. Inscribed denffiaton oftbe specimen

If the above procedures are not satisfactory, -clean the surface with Magnaflux Spotcheck

SKC-S Cleaner/Remover, then ultrasonically clean the tube in high-purity water, dry, and clean

again with Magnaflux Spotcheck SKC-S Cleaner/Remover. Then spray Magnaflux Zyglo 2L-27A

Penetrant over the test area and wait for 1 to 30 minutes; usually 10 minutes is sufficient. Wipe

off the penetrant with a clean dry paper towel. To remove excess penetrant, SKC-S

Cleaner/Remover is sprayed onto a clean dry paper towel that is used to lightly wipe the test

area.

Magnalux Zyglo 2P-9f Developer is lightly sprayed over the test area by holding the spray

can 20-30 cm (8-12 in,) from the surface. After 10 minutes. the-test area is Inspected by using

a fluorescent light to reveal degradation.

Results of the dye penetrant examination are documented by photography at 0.5-5X

magification. The photograph should include a calibrated scale so that the magnification

factor may be measured directly from the photograph (Fig. A2).

A.3 Installation of Reference Markers

NDE-detectable markers are attached to the OD in the crack area to provide reference

positions of degradation. These markers are used to locate the NDE ndications, to decide

proper sectioning procedure and locations (Section 9), and to verify relative position between

degradations (Section 14).

Four pieces of platinum wire (about 0.25 mm or 0.010 in. diameter), each of a different

length (3.2, 6.4. 9.5 and 12,7 mm 10.125, 0.250, 0.375, and 0.500 In.), are spot-welded,

properly spaced, in the crack area (Fig. A3). Denting or scribe marks may be used Instead. Be

careful not to loosen the attached markers from the specimen throughout the process.

NDE Indications for -the markers and degradations must be recorded simultaneously so

that relative positions of the markers and the degradation can be determined.

;99-
JFiure A2. Dye penetant exanation of tube specinen SGL 865.

FIgure A3. Wire markers attached to tube specimern

AA4 Nondestructive Examination

Eddy current or other NDE measurements are performed on the tubes with the markers

attached. The results should include a profile of degradation In real-space coordinates, as well

as EC voltages. The profile should also include Images that properly Identify the four different

markers with respect to degradation in real-space coordinates (Fig. A4).

Heat-Tinting

To discriminate SCC and fracture area, the specimen Is heat-tinted for 1-5 min at 6000C in

air. This makes the SCC appear In darker contrast to the fracture area.

Pressure-Leak Testing

Pressure-leak testing may delineate SCC more clearly, particularly for tight, shallow

secondary cracks and networks of cellular cracks. The applied pressure is higher than that

100
.8 -__ .-I-1----_1

1 |E_ ~~~~~~~~~7 --- --. -x4:_

30 3

J_ e .. ...-.X

: . ...... . ... X x o

_ to 30D "0 

i mes..g.. 4 f , s. ; - -rts-ur . t I - by the principal

* F IC ,_, ,,, 3 ._ 0 . F00 ,-- 1013 303 -1-0-0*s

used t~~~~o produce deraaton Hwvr- ap-co of too muc prssr shol be a:- vded.......... --

investigator with consideration of the flow stress of the material, prior degradation process

history, and type and extent of degradation. Test results should be documented by photographs

(Fig. A5).

Dye Penetrant Examination after Pressure-Leak Testing

Dye penetrant examination Is performed according to the procedure n Section A2. The

'degradation might have opened more during pressurization and leak testing and additional dye

penetrant indications may be seen. The results should be properly documented.

Sectioning of Tube Specimen

General mappingde of thegtend degradatted fom therEC/NDE anddye

penetrant examination results. Maps should include sizes and locations. of degradations, and

distances between degradations. The tube Is sectioned Into convenient,-pieces for crosssectional metallography or fracture. Each map should include the detailed sectioning plan.

101
Fgure A5. Tube specimen SGL 413 wffh OD axaln SCC after pressure-leak test

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 0.1 mA/mm2 for 5-30 s. 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).

A.5 Fracture of Specimen

After a degradation is opened by fracture, 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 chilled in a liquid nitrogen bath and then fractured by fatigue loading.

which may be applied manually or by a fatigue machine. 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.

102
Branched Axial ODSCC

Figure A6. Cross-sectional optical rnetalogrcphy.

A.6 Digital Fractography

The fractured surfaces are recorded by digital photography at 0.2-lOX 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 ilumination 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

interfere with the degradation.

'i.+ Each digital photograph must be identified with a unique and proper file name that is

traceable to a particular degradation and tube.

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

- (ig. A8). SEM may be performed at 5-2000X magnification, depending on the features of

)Iinterest. For size measurements, calibrated size references should be attached near 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 identified with a unique

and proper file name that is traceable to a specific degradation and specific tube ID.

A.7 Comparison of Destructive Fractography with NDE Measurements

The profile of a crack determined -by fractography and NDE evaluation is digitized by using

software such as Datathief. The sampling Interval for determining the profile depends upon the

complexity of the crack geometry. Small sampling intervals should be used for a complicated

geometry over a small scale, while larger Intervals may be used for simpler geometries, e.g.,

-103
Figure A7. Scannn electron miroscpyforfrctogphy of tbe specimen SGL 413.

SEM of axial ODSCC SEM of circumferential ODSCC

Figure A8. Scanntng electron microscopy of ODSCC.

straight line or smooth profiles. Digitized data are plotted for graphical presentation 'With

software such as KaleidaGraph. Fractography and NDE results may be plotted simultaneously

for comparison (Fig. AS).

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.

104
.U

*.' 60.00 

0.

0

04000

20.00

0.00

-2

FIgure A9.

U 2 4 6 8 10 12

Crack Length (mm)

Size and shape of OD axial SCC n tube

specimen AGL 536 determined by EC NDE

and fractography.

References for Appendix

Al Metallography and Microstructure, Metals Handbook, 9 Ed., Vol. 9 American Society for

Metals, 1985.

2 Fractogaphy. Metals Handbook. 9 h Ed.. Vol. 12, American Society for Metals, 1987.

*ii..,, . - ; 

105
NRC FORM 335 U.S. NUCLEAR REGUlATORY COMMISSION 1.REPORT NUMBER

=-es) -I{(AssWdbYNRr Ad VoL. SWp Rev.

NRCM I 10Z W~~~~~~~~~~~~~~~~~~adAddm zMvibeM ifwy) =nMaaM BIBUOGRAPHIC DATA SHEET

(See hisfrztc an ft "St0~~~~~~Z)~~ANL-03/4 reverse)

2. TITLE AND SUBTM.E

Final Report on Advanced Nondestructive Evaluation for

Steam Generator Tubing for the Second International Steam Generator a DATE REPORT PUBUSHED

Tube Integrity Progrm MONTH YEAR

July 2003

4. FIN OR GRANT NUMBER

5. (S) 6 TYPE OF REPORT

S. Bakhtlari, J. Y. Park, D. S. Kupperman, S. Majumdar, and W. J. Shack Technical

7. PERIOD COVERED (hive Dabts)

8. PERFORMING ORGNIZATION-NAMEAND ADDRESS(ipmv Aordeglam aIwb OfNe US. McbarRAACfsk u I

name a addfw Iessj - , ,

Argonne National Laboratory

9700 South Cass Avenue

Argonne, IL 60439

S. SPONSORING ORGANIZATION-NAMEANDADDRESS ( NNC , SameasaboaveSffo* , proide NeCDMn Orce orbRegkn U.S Mw~erRedataqComrs&o

andmafltnads

Division of Engineering Technology

Office of Nuclear Regulatory Research

U. S. Nuclear Regulatory Commission

Washington, DC 20555-0001

la SUPPLEMENTARY NOTES

J. Muscara,.NRC Project-Manager

11. ABSTRACT (M words or bss)

This report describes research activities carried out at Argonne National Laboratory CANL) as part of the

Steam Generator Tube Integrity Program sponsored by the U.S. Nuclear Regulatory Commission. As a

follow-up to preceding publications 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 data. 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. Recent efforts on

implementation of more useful data analysis tools are briefly described. As a benchmark for NDE

accuracy, the production and destructive characterization of specimens with laboratory-induced

cracking are described. Analysis of eddy current inspection data from a limited set of feld-degraded

pulled-tube sections is also presented. The effect of uncertainties in NDE estimates of flaw size on the

prediction of tube structural integrity is addressed.

12. KEY WORSIDWESCRIPTORS (Lst words orprases #W RC eassstwseartws i ocawtns repot) 11 AVALABILTY STATEMENT

Steam Generator Unlimited

Tubes 14. SECURITY CLASSIRCATION

Stress Corrosion Cracking -h Page)

Eddy Current Testing -Unclassified

Nondestructive Evaluation (TS Repo)

In-service Inspection Unclassified

Round Robin 15. NUMBER OF PAGES

Alloy 600, Inconel 600

16 PRICE

NRC FOM 35 (2-9)
Federal Recycling Program
NUREG/CR-6814 FINAL REPORT ON ADVANCED NONDESTRUCTIVE EVALUATION FOR STEAM

GENERATOR TUBING FOR THE SECOND INTERNATIONAL STEAM GENERATOR TUBE

INTEGRITY PROGRAM

JULY 2003

UNITED STATES

NUCLEAR REGULATORY COMMISSION

WASHINGTON, DC 20555-0001

OFFICIAL BUSINESS