Application of Automated 

Analysis Software to Eddy

Current Inspection Data 

from Steam Generator Tube 

Bundle Mock-up

Office of Nuclear Regulatory Research

NUREG/CR-7217

ANL-11/60
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Application of Automated 

Analysis Software to Eddy

Current Inspection Data 

from Steam Generator Tube 

Bundle Mock-up

Manuscript Completed: January 2012 

Date Published: September 2016

Prepared by:

S. Bakhtiari, W.J. Shack and T.W. Elmer

Argonne National Laboratory 

Argonne, IL 60439

M. Rossi, NRC Project Manager

NRC Job Code Y6582

Office of Nuclear Regulatory Research

NUREG/CR-7217

ANL-11/60

 

iii

ABSTRACT

This report documents the results of evaluations of computerized data screening software used 

for analyzing eddy current data obtained during the inspection of steam generator tubes. The 

work was performed at Argonne National Laboratory (ANL) as part of on-going activities under 

the International Steam Generator Tube Integrity Program (ISG-TIP) sponsored by the U.S. 

Nuclear Regulatory Commission (NRC). Following the initial review of the available literature, a

software-based tool widely used by industry for in-service inspection applications was acquired 

and used to automatically analyze bobbin probe data collected previously from the ANL-NRC 

steam generator tube bundle mock-up. A subset of that data was initially used to establish data 

screening parameters. The results of those tests were used to iteratively adjust the data 

screening parameters to further evaluate their influence on the detection and classification of 

signals. Alternative configurations of the data screening parameters for different regions of the 

SG mock-up were further examined. The detection probability and false call rate were 

evaluated to help determine the influence of selected data screening parameters on the 

performance of automated data analysis software. The data in each chosen configuration was 

sorted out using special-purpose algorithms generated at Argonne with standard spreadsheet 

software. The results of analyses performed on eddy current inspection data from different 

elevations of the SG mock-up are discussed in this report. Comparisons are made between the 

detection probability results based on automated analysis with that based on manual analysis of

data from a previous round-robin exercise. Some general remarks are finally provided 

regarding advantages and limitations of conventional automated data analysis programs for inservice inspection of steam generator tubes based on bobbin probe examinations.

v

FOREWORD

This report documents a study conducted by Argonne National Laboratory (ANL) under contract 

with the U.S. Nuclear Regulatory Commission (NRC), to study licensee inspection programs 

that utilize automated analysis of eddy current signal data. Inspection reliability is important to 

properly assess the condition of steam generator tubes.

Steam generator tubes, which are a few millimeters thick, comprise the thinnest portion of the 

primary coolant boundary in pressurized water reactors. Therefore, regular inspection for flaws 

or defects is required. Eddy current analysis is the industry standard method for in-service 

inspection of these tubes. During an outage, hundreds or thousands of tubes may be tested by 

a certified inspector. Automated analysis software, which processes and evaluates eddy current 

data is sometimes used in combination with or in place of manual (human) analysis. However, 

use of this software in lieu of human inspectors may lead to difficulty in detection of new 

degradation modes or other issues, such as movement of physical supports during operation.

This NUREG/CR documents an evaluation of a widely used commercial software product that 

industry utilizes for analyzing eddy current signals. Argonne National Laboratory performed this 

evaluation on a mock-up steam generator tube assembly. This mock-up was constructed prior 

to this study and inspected with bobbin coil and motorized rotating pancake probes. The mockup bundle contained artifacts that would be encountered in an operating steam generator, such 

as support structures and geometric tube variations. Hundreds of flaws were added to the 

bundle to simulate the types of flaws commonly observed in the field. For instance, the bundle 

contained corrosion deposits, dents, wear, and stress-corrosion cracks. An expert task group 

with members from industry, ANL, and the NRC reviewed the eddy current signals from the 

mock-up flaws to ensure realistic simulation of flaws found in the field.

This report documents the research findings with regard to the use of automated analysis of 

eddy current inspection signals. A discussion of the detection probability of automated analysis 

compared to those based on manual analysis of data from a previous round-robin exercise is 

included. This report also includes discussion regarding advantages and limitations of 

conventional automated data analysis programs for in-service inspection of steam generator 

tubes based on bobbin probe examinations. The information in this report is of value for NRC 

regional inspectors, because at the time of publication several US licensees are making use of 

these automated analysis software programs to analyze their steam generator tube inspection 

data.

vii

TABLE OF CONTENTS

ABSTRACT ........................................................................................................... iii

FOREWORD........................................................................................................... v

TABLE OF CONTENTS........................................................................................ vii

LIST OF FIGURES................................................................................................. ix

LIST OF TABLES ................................................................................................ xix

EXECUTIVE SUMMARY ..................................................................................... xxi

ACKNOWLEDGMENTS .................................................................................... xxiii

ABBREVIATIONS AND ACRONYMS................................................................ xxv

INTRODUCTION............................................................................................. 1

AUTOMATED DATA ANALYSIS TOOLS FOR STEAM 

GENERATOR TUBE EXAMINATIONS.......................................................... 3

STEAM GENERATOR TUBE BUNDLE MOCK-UP....................................... 7

APPLICATION OF COMPUTERIZED DATA SCREENING 

TO SG TUBE BUNDLE MOCK-UP .............................................................. 11

4.1 Basic Structure of Computer Data Screening (CDS) Tool ............................ 11

4.2 Implementation of Generic Setups for Performance Evaluations.................. 13

4.3 Automated Analysis of EC Inspection Data from the Entire Mock-up ........... 70

4.3.1 Analyses of Data for Tube Support Plate (TSP) Elevations............. 70

4.3.2 Analyses of Data for Free-span Elevations...................................... 89

4.3.3 Analyses of Data for Tubesheet Elevations ................................... 115

SUMMARY AND CONCLUSIONS ............................................................. 133

REFERENCES............................................................................................ 137

ix

LIST OF FIGURES

Figure 3.1 Schematic drawing of the SG tube bundle mock-up. .....................................9

Figure 4.1 Display of Landmark Table generated for identification of discontinuities 

and structures in the tube bundle mock-up. .................................................15

Figure 4.2 Display of setup parameters from (a) Locating Selectables dialog box 

used for the adjustment of landmark detection parameters, and (b) 

Landmark Location Output window for more detailed examination of the 

locating parameters......................................................................................16

Figure 4.3 Display of representative bobbin probe data for a full-length mock-up tube 

(a) before and (b) after application of the automatic landmark locator.........17

Figure 4.4 Display of CDS Editor window for setting up the location, detection, and 

classification parameters for different regions of the tube bundle mock-up. 

Shown here are the initial settings for the (a) free-span and (b) TSP 

elevations of the mock-up. ...........................................................................20

Figure 4.5 Display of different stages of the CDS process for representative bobbin 

probe data from a mock-up tube with single and multiple SCC indications 

at two separate free-span elevations for the (a) intermediate and (b) final 

stage of analysis with the detected indications marked (arrows) in the 

landmark column of the analysis window. ....................................................21

Figure 4.6 Display of different stages of the CDS process for representative bobbin 

probe data from a mock-up tube with fatigue cracking at a TSP for the (a) 

intermediate stage of analysis, and (b) recall of the indication following 

termination of the analysis process. .............................................................22

Figure 4.7 Manual analysis of bobbin probe signal displayed in the main analysis 

window for an ID indication at a dented TSP intersection. ...........................23

Figure 4.8 Manual analysis of bobbin probe signal displayed in the main analysis 

window for a free-span indication.................................................................23

Figure 4.9 Manual analysis of bobbin probe signal displayed in the main analysis 

window for a free-span indication.................................................................24

Figure 4.10 Manual analysis of bobbin probe signal displayed in the main analysis 

window for a small OD indication at a TSP intersection...............................24

Figure 4.11 Display of CDS Editor window for setting up the location, detection, and 

classification sorts for different regions of the tube bundle mock-up 

settings for the (a) free-span and (b) TSP elevations of the mock-up..........28
x

Figure 4.12 Display of the CDS Editor window used for setting up the parameters 

associated with the location, detection, and classification algorithms. 

Shown here are the sorts for the (a) TTS and (b) the TS elevations of the 

SG mock-up. ................................................................................................29

Figure 4.13 Display of the CDS results for the same tube shown in Fig. 4.7 with the 

detected indications marked with arrows in the landmark column of the 

analysis window. ..........................................................................................30

Figure 4.14 Display of the CDS results for the same tube shown in Figs. 4.8 and 4.9 

with the detected indications marked with arrows in the landmark column 

of the analysis window. ................................................................................30

Figure 4.15 Results of the CDS analysis displayed by the Report Editor dialog box for 

all thirteen tubes in the training data set.......................................................31

Figure 4.16 Display of the CDS results for the entire length of a mock-up tube from 

the test data set for the (a) final CDS results with the detected signals 

marked with arrows in the landmark column of the analysis window, (b) a 

large ODSCC signal near the second TSP intersection, and (c) a dent 

signal at the first TSP intersection................................................................32

Figure 4.17 Display of the CDS results for the entire length of a mock-up tube from 

the test data set for the (a) final CDS results with the detected signals 

marked with arrows in the landmark column of the analysis window and 

(b) an ODSCC signal at the first TSP intersection........................................33

Figure 4.18 Display of the CDS results for the entire length of a mock-up tube from 

the test data set. Shown here are (a) final CDS results with the detected 

signals marked with arrows in the landmark column of the analysis 

window and (b) signal from a shallow ODSCC flaw located in the freespan region of the tubing..............................................................................34

Figure 4.19 Display of the CDS results for the entire length of a mock-up tube 

from the test data set. Shown here are (a) final CDS results with the 

detected signals marked with arrows in the landmark column of the 

analysis window and (b) signal from an IDSCC flaw at a dented tube 

support region. .............................................................................................35

Figure 4.20 Display of the CDS results for the entire length of a mock-up tube from 

the test data set. Shown here are (a) final CDS results with the detected 

signals marked with arrows in the landmark column of the analysis 

window and (b) signal from one of two closely spaced axial IDSCC flaws 

in the free-span region of the tube................................................................36

Figure 4.21 Results of the CDS analysis displayed by the Report Editor dialog box for 

all twelve tubes in the testing data set..........................................................37

Figure 4.22 Display of the CDS results for two free-span ODSCC indications................38
xi

Figure 4.23 Examples of ferromagnetic markers near the tube end identified as 

indications by CDS. ......................................................................................39

Figure 4.24 Display of the CDS results for the entire length of a mock-up tube from 

the test data set. Shown here are (a) final CDS results with the detected 

signals marked with arrows in the landmark column of the analysis 

window and (b) an example of a false call at a TSP elevation due to a low 

voltage threshold. .........................................................................................40

Figure 4.25 An example of a missed call for IGA at a TSP intersection. Shown here 

are the signals measured manually from (a) the 400|100 kHz TSP mix 

channel and (b) multiple Lissajous with expanded strip chart display of the 

same signal from four different channels......................................................41

Figure 4.26 Display of the CDS Editor window used for setting up the 

parameters associated with the location, detection, and 

classification of free-span signal sorts for detection of (a) closelyspaced signals and (b) long axial signals.....................................................47

Figure 4.27 Display of the CDS Editor window used for setting up the parameters 

associated with the location, detection, and classification of indications at 

TSP elevations. Shown here are the sorts for detection of closely-spaced 

flaw signals at TSPs using (a) 200 kHz (channel 3) and (b) TSP 

suppression mix (P1) as the test channel. ...................................................48

Figure 4.28 Display of the CDS Editor window used for setting up the 

parameters associated with the location, detection, and 

classification of flaw and non-flaw-like signals at TSP elevations. 

Shown here are the sorts for detection of (a) flaw signals at a 

dented TSP using TTS suppression mix (P3) for detection and (b) 

tube geometry change associated with denting. ..........................................49

Figure 4.29 Display of the CDS Editor window used for setting up the parameters 

associated with the location, detection, and classification of flaw signals at 

or near the top of the tube-sheet. .................................................................50

Figure 4.30 Display of the CDS results for the entire length of a mock-up tube from 

the test data set of a relatively long indication at the first TSP elevation, 

which was identified (a) as two separate signals by one sort and (b) as 

one signal by another sort. ...........................................................................51

Figure 4.31 Display of the CDS results for the entire length of a mock-up tube from 

the test data set of a relatively long indication in the free-span, which was 

identified (a) as two separate signals by one sort and (b) as one signal by 

another sort. .................................................................................................52
xii

Figure 4.32 Display of the CDS results for the entire length of a mock-up tube from 

the test data set example of a dented TSP region with separate calls for 

the (a) dent by one sort and (b) potential flaw signal by another sort at the 

second TSP elevation. .................................................................................53

Figure 4.33 Results of the CDS process displayed by the Report Editor dialog box for 

all twelve tubes in the testing data set using the modified sorts...................54

Figure 4.34 Display of the CDS results for a mock-up tube with an indication at a 

dented TSP region, which was classified as (a) a 1.19v ID indication by 

one sort and (b) a 0.77v OD indication by another sort................................56

Figure 4.35 Display of the CDS results for a mock-up tube with an indication at a 

dented TSP region with separate calls for the (a) dent by one sort and (b) 

an ID flaw signal by another sort. .................................................................57

Figure 4.36 Display of (a) the CDS results for the entire length of a mock-up tube and 

(b) baseline noise at the fourth tube section.................................................58

Figure 4.37 Display of the CDS results for a mock-up tube with an indication near a 

TSP edge. The measured signal amplitude varies between (a) 2.59v by 

free-span sort and (b) 4.13v by TSP sort. ....................................................59

Figure 4.38 (a) Display of the CDS results for the entire length of a mock-up tube (b) 

Five separate entries were made for the same signal associated with an 

indication near a TSP edge by different sorts with the measured signal 

amplitude varying between 0.54v and 1.95v. ...............................................60

Figure 4.39 Display of the CDS results for (a) dented TSP and (b) baseline noise in 

the free-span region of the tube. ..................................................................62

Figure 4.40 Display of the CDS results for (a) the left excursion and (b) the 

right excursion of a differential signal associated with a dent at 

a TSP elevation. ...........................................................................................63

Figure 4.41 Display of manual data analysis results of ID indications at (a) dented 

free-span (ding) and (b) dented TSP elevation. ...........................................64

Figure 4.42 Display of the CDS Editor dialog box used for setting up the parameters 

associated with the location, detection, and classification of flaws in the 

tube bundle mock-up. Shown here are the modified (a) free-span and (b) 

TSP sorts with a reduced number of successive screening configurations 

for the same region of the tube. ...................................................................65

Figure 4.43 Display of the CDS results for representative SG mock-up tubes with 

missed calls at TSP elevations due to an increase in the detection 

threshold. The displayed signals, measured manually, are 

associated with (a) a shallow OD and (b) an ID indication at different 

TSP elevations. ............................................................................................67
xiii

Figure 4.44 Display of the CDS results for representative SG mock-up tubes 

with missed calls at free-span regions due to increase in detection 

and classification thresholds. The displayed signals, measured 

manually, were identified as OD indications at the (a) second and 

(b) ninth tube section....................................................................................68

Figure 4.45 The displayed signal, reported as an indication by the previous sort 

settings, was not reported as a result of modifying the detection threshold 

value for the TSP sort. The signal in this SG mock-up tube was later 

identified as a false indication.......................................................................69

Figure 4.46 Display of the CDS results for representative manually measured missed 

signals associated with (a) the dent part of the probe response detected 

by CDS in a TSP zone also containing shallow ID cracking and (b) a low 

amplitude OD flaw identified as fatigue cracking..........................................76

Figure 4.47 Display of the CDS results for manually measured missed signals, 

identified as shallow (a) ODSCC and (b) volumetric OD flaw at the 

TSP region. ..................................................................................................77

Figure 4.48 Display of the CDS results for manually measured missed signals at TSP 

regions, both identified as IGA. ....................................................................78

Figure 4.49 Display of the CDS results for a signal at a dented TSP elevation of the 

SG mock-up. Shown above are two separate detection calls made using 

the multiple sort configuration.......................................................................79

Figure 4.50 Display of the CDS results for a signal at a dented TSP elevation of the 

SG mock-up. Shown above are two separate detection calls made using 

the multiple sort configuration.......................................................................80

Figure 4.51 Display of the CDS results for a signal at a dented TSP elevation of the 

SG mock-up. Shown above are two separate detection calls made using 

the multiple sort configuration.......................................................................81

Figure 4.52 Display of the CDS results for two separate signals at a dented TSP 

elevation of the SG mock-up. Shown above are the detection calls made 

using the multiple sort configuration.............................................................82

Figure 4.53 Display of the CDS results for two separate signals at dented TSP 

elevations of the SG mock-up. Shown above are the detection calls 

made using the multiple sort configuration...................................................83

Figure 4.54 Display of the CDS results for two separate low-amplitude signals at TSP 

elevations of the SG mock-up. Shown above are the detection calls 

made using the multiple sort configuration...................................................84
xiv

Figure 4.55 Display of the CDS results for two separate signals at TSP elevations 

of the SG mock-up. Shown above are the detection calls at (a) a 

non-dented and (b) a dented TSP elevation made using the multiple 

sort configuration..........................................................................................85

Figure 4.56 Representative POD curves as a function of depth for flaws at the TSP 

elevations identified as LIDSCC using the CDS configuration with 

multiple sorts. ...............................................................................................86

Figure 4.57 POD curves for flaws at the TSP elevations identified as LIDSCC using 

the CDS configuration with multiple sorts (TSP2), CDS configuration with 

single sort (TSP1), composite round-robin manual analysis (RR), and 

expert analyst (expert) results. .....................................................................87

Figure 4.58 POD curves for flaws at the TSP elevations identified as LODSCC using 

the CDS configuration with multiple sorts (TSP2), CDS configuration with 

single sort (TSP1), composite round-robin manual analysis (RR), and 

expert analyst (expert) results. .....................................................................88

Figure 4.59 Display of the CDS editor dialog box for the setups used for 

screening free-span indications. Shown here are the two sorts under 

the Indications list and their associated setup parameters. The 

detection logic line displays (a) a differential detection channel used 

for the first and (b) an absolute detection channel used for the 

second setup, respectively. ..........................................................................94

Figure 4.60 Display of data analysis results for representative signals at level-B 

elevation of the SG mock-up which were not reported using the CDS 

setup displayed in Fig. 4.59..........................................................................95

Figure 4.61 Display of data analysis results for representative signals at level-H 

elevation of the SG mock-up which were not reported using the CDS 

setup displayed in Fig. 4.59..........................................................................96

Figure 4.62 Display of data analysis results for representative signals at (a) level-F 

and (b) level-H of the SG mock-up which were not reported using the 

CDS setup displayed in Fig. 4.59. ................................................................97

Figure 4.63 Display of data analysis results for a signal at the level-F elevation of the 

SG mock-up which was not reported using the CDS setups displayed in 

Fig. 4.59. The probe response is shown in the (a) main analysis window 

and (b) in multiple Lissajous window at different frequencies. .....................98

Figure 4.64 Display of data analysis results for representative signals at the level-I 

elevation of the SG mock-up which were not reported using the setups 

displayed in Fig. 4.59. ..................................................................................99
xv

Figure 4.65 Display of data analysis results for representative signals at (a) level-B 

and (b) level-H elevation of the SG mock-up which were not reported 

using the setups displayed in Fig. 4.59. .....................................................100

Figure 4.66 Display of data analysis results for a signal at the level-H elevation of the 

SG mock-up which was not reported using the CDS setups displayed in 

Fig. 4.59. The probe response is displayed in the (a) main analysis 

window and (b) in multiple Lissajous window at different frequencies. ......101

Figure 4.67 Display of data analysis results for representative signals at the level-F 

elevation of the SG mock-up which were not reported using the CDS 

setups displayed in Fig. 4.59......................................................................102

Figure 4.68 Display of data analysis results for representative signals at the level-H 

elevation of the SG mock-up which were not reported using the CDS 

setups displayed in Fig. 4.59......................................................................103

Figure 4.69 Display of representative CDS analysis results for a long axial OD flaw for 

two separate entries in the free-span of the SG mock-up. .........................104

Figure 4.70 Display of the CDS analysis results associated with representative false 

calls in the free-span of the SG mock-up using the sort configuration of 

Fig. 4.59(b). ................................................................................................105

Figure 4.71 Display of data analysis results for representative signals in the free-span 

of the SG mock-up which were not reported using the CDS setup 

displayed in Fig. 4.59(a). ............................................................................106

Figure 4.72 POD curves for free-span (FS) flaws identified as LODSCC using the 

CDS sort configurations shown in Fig. 4.59. ..............................................107

Figure 4.73 Display of the CDS editor dialog box for the modified configuration used 

to screen the free-span region of the SG mock-up. Shown here are the 

setup parameters for the sort using (a) a differential channel and (b) an 

absolute channel for detection. ..................................................................108

Figure 4.74 Display of the CDS editor dialog box for the modified configuration used 

to screen the free-span of the SG mock-up. Shown here are the setup 

parameters for the sort configuration using (a) a differential channel and 

(b) an absolute channel for detection. ........................................................109

Figure 4.75 Display of CDS results for representative free-span signals. 

Shown here are flaws identified as axial ODSCC at (a) level-B and 

(b) level-H...................................................................................................110

Figure 4.76 Display of CDS results for representative free-span signals. Shown here 

are flaws identified as (a) circumferential ODSCC at level-B and (b) axial 

ODSCC at level-C. .....................................................................................111
xvi

Figure 4.77 Display of CDS results for representative free-span signals. 

Shown here are flaws identified as axial ODSCC at (a) level-H and 

(b) at level-F. ..............................................................................................112

Figure 4.78 POD curves for free-span (FS) flaws identified as SCC (axial 

and circumferential SCC of both OD and ID origin) and LODSCC 

(axial ODSCC) using the optimized CDS configuration shown in 

Fig. 4.59. ....................................................................................................113

Figure 4.79 POD curves for free-span (FS) level flaws identified as SCC (axial and 

circumferential SCC of both OD and ID origin) using the optimized 

configuration shown in Fig. 4.59 (FS2) and the modified configurations 

shown in Figs. 4.73 (FS2_T) and 4.74 (FS2_D) for (a) linear-logistic and 

(b) log-logistic fits. ......................................................................................114

Figure 4.80 Display of the CDS editor dialog box for the setups used for 

screening of the tube-sheet level for (a) TS Indications and (b) 

TTS Indications. .........................................................................................118

Figure 4.81 Display of bobbin coil inspection data (low baseline noise) peak-to-peak 

measurement of the baseline signal at an arbitrary TS location from (a) 

P3 in the main analysis window and (b) P1 and P3 in a multiple 

Lissajous display. .......................................................................................119

Figure 4.82 Display of bobbin coil inspection data (high baseline noise) peak-to-peak

measurement of the baseline signal at an arbitrary TS location from (a) 

P3 in the main analysis window and (b) P1 and P3 in a multiple 

Lissajous display. .......................................................................................120

Figure 4.83 Display of bobbin coil inspection data (high baseline noise) peak-to-peak 

measurement of the baseline signal at an arbitrary TS location from (a) 

P3 in the main analysis window and (b) P1 and P3 in a multiple 

Lissajous display. .......................................................................................121

Figure 4.84 Display of bobbin coil inspection data (smooth expansion transition) 

peak-to-peak measurement of the residual expansion transition signal 

from (a) P3 in the main analysis window and (b) P1 and P3 in a multiple 

Lissajous display. .......................................................................................122

Figure 4.85 Display of bobbin coil inspection data (smooth expansion transition) 

peak-to-peak measurement of the residual expansion transition signal 

from (a) P3 in the main analysis window and (b) P1 and P3 in a multiple 

Lissajous display. .......................................................................................123

Figure 4.86 Display of bobbin coil inspection data (smooth expansion transition) 

peak-to-peak measurement of the residual expansion transition signal 

from (a) P3 in the main analysis window and (b) P1 and P3 in a multiple 

Lissajous display. .......................................................................................124
xvii

Figure 4.87 Display of CDS results from a representative TS level tube section (rough 

TTS expansion transition) from (a) P3 in the main analysis window and 

(b) P1 and P3 in a multiple Lissajous display.............................................125

Figure 4.88 Display of CDS results from a representative TS level tube section (rough 

TTS expansion transition) from (a) P3 in the main analysis window and 

(b) P1 and P3 in a multiple Lissajous display.............................................126

Figure 4.89 Display of CDS results from a representative TS level tube section (rough 

TTS expansion transition) from (a) P3 in the main analysis window and 

(b) P1 and P3 in a multiple Lissajous display.............................................127

Figure 4.90 Display of manual data analysis results for a TS level tube section with a 

flaw near the expansion transition region in the (a) main analysis window 

and (b) in multiple Lissajous window at P1 and P3. ...................................128

Figure 4.91 Display of manual data analysis results for a TS level tube section with a 

flaw near the TTS region in the (a) main analysis window and (b) in 

multiple Lissajous window at P1 and P3. ...................................................129

Figure 4.92 Display of manual data analysis results for a TS level tube section with a 

flaw near the expansion transition region in the (a) main analysis window 

and (b) in multiple Lissajous window at P1 and P3. ...................................130

Figure 4.93 Display of manual data analysis results for a TS level tube section with a 

flaw below the TTS in the (a) main analysis window and (b) in multiple 

Lissajous window at P1 and P3..................................................................131

Figure 4.94 Display of manual data analysis results for a TS level tube section with a 

flaw below the TTS in the (a) main analysis window and (b) in multiple 

Lissajous window at P1 and P3..................................................................132

xix

LIST OF TABLES

Table 3.1 Distribution of flaw types in the SG tube bundle mock-up. ..................................8

Table 4.1 Tabulated results of the CDS process for combined training and testing data 

sets that was exported from the EddynetTM Report Editor dialog box..................55

Table 4.2 Results of the CDS process using the modified sorts for the combined 

training and testing data sets. ..............................................................................61

Table 4.3 Results of the CDS process using the modified sorts for the combined 

training and testing data sets, which was exported from the Report Editor

dialog box and converted to text format...............................................................66

Table 4.4 Sample Excel spreadsheet containing the analysis results for a subset 

of SG mock-up tubes, which were sorted using a Macro generated for 

this purpose. ........................................................................................................74

Table 4.5 Graded results for the sample data set shown in Table 4.4 using an ExcelTM

Macro generated for this purpose. .......................................................................75

xxi

EXECUTIVE SUMMARY

Automated data analysis software may be employed in field applications as a replacement for 

the primary, secondary, or both human analyst teams. The use of automated analysis (AA) 

tools can help to significantly improve the efficiency and consistency of data analysis during inservice inspection (ISI) of steam generator (SG) tubes. Reliable application of such tools that 

commonly employ threshold based detection and rule-based classification algorithms require 

access to an extensive database of representative signals encountered during field application. 

Optimization of the setup parameters of these tools in general is a tradeoff between detection 

probability and false call rate. Better understanding of the algorithms and the associated setup 

parameters as well as the intrinsic limitations of AA tools in general can help improve ISI 

reliability.

This report documents the results of evaluations of computerized data screening software used 

for analyzing eddy current (EC) data obtained during the inspection of SG tubes. The work was 

performed at Argonne National Laboratory (ANL) as part of on-going activities under the 

International Steam Generator Tube Integrity Program (ISG-TIP) sponsored by the U.S. Nuclear 

Regulatory Commission. Following the initial review of the available literature, a prominent 

software-based tool, the Computer Data Screening (CDSTM) module of the EC data analysis 

software Eddynet®Suite, from Zetec Inc., was acquired and used to automatically analyze 

bobbin probe data collected previously from the ANL-NRC steam generator tube bundle mockup. A subset of the mock-up data was initially used to establish data screening parameters. 

The results of those tests were used to iteratively adjust the data screening parameters to 

further evaluate their influence on the detection and classification of signals. Alternative 

configurations of the data screening parameters for different regions of the SG mock-up were 

further examined. The detection probability and false call rate were evaluated to help determine 

the influence of selected data screening parameters on the performance of AA software. 

Bobbin probe inspection data from all elevations (free-span, tube support plate, tubesheet) of 

the SG mock-up were analyzed using the CDS program. Data from all free-span and TSP 

elevations were subsequently used for comparative probability of detection (POD) studies. 

Comparisons were made between the detection probability results based on automated analysis 

with that based on manual analysis of data from a previous round-robin exercise. Unlike the 

free-span and the TSP elevations, analysis of bobbin probe inspection data from the TS region 

was deemed unreliable due to excessive level of EC noise associated with the mechanical 

expansion of the tubes. It is worth noting that bobbin probe techniques are not generally 

considered reliable for detection and characterization of flaws in the TS region. As such, the 

CDS results for TS elevations of the SG mock-up were evaluated only in a qualitative manner. 

The data in each flaw location was sorted out using the special-purpose algorithms generated 

by Argonne, with standard spreadsheet software. In general, the results of analyses based on 

EC inspection data from the free-span and TSP elevations suggest that automated data 

screening tools can provide comparable or better detection capability than that achieved by

manual analysis when comparatively higher false call rates are tolerated. The higher POD 

values by AA software were achieved through incorporation of multiple data screening setups 

for the same region of tubing. The results also suggest that, in comparison with other elevations 

of the SG mock-up, flaws near the tube-to-tubesheet region may be detected with comparable 

probability to other elevations but with significantly higher false call rates.
xxii

Some general remarks are also provided in this report regarding advantages and limitations of 

conventional automated data analysis tools used in the evaluation of SG tube bobbin probe EC 

data. Although AA algorithms could be optimized to provide improved detection capability over 

manual analysis, the possibility exists for visually discernable signals to go undetected using 

automated data screening tools. A number of factors that could affect the performance of 

conventional AA programs include inadequate or overly constrained algorithms, modification of 

previously qualified setup parameters, data quality issues, influence of noise (low S/N), 

incomplete tube coverage, incorrect identification of support structures (landmarks), and 

inherent limitations of a particular NDE technique. Intrinsic to the rule-based nature of expert 

system algorithms, the possibility of missing new or unexpected forms of degradation exists 

when representative signals are not included in the qualification database used for optimization 

of the software. As such, direct application of a generic setup provided by an examination 

technique specification sheet (ETSS) and qualified for particular test conditions may not be 

applicable for field applications. Furthermore, inclusion of manual analysis as part of the data 

analysis process or as oversight for automated data analysis results can help improve the 

detection probability of potential flaw signals with atypical probe responses. This is particularly 

important when AA tools are employed as replacement for both the primary and the secondary 

analyst team. Inclusion of manual analysis as an integral part of the ISI data analysis process 

can both help resolve potential discrepancies between independent AA tools and between 

reported redundant entries by alternative or overlapping data screening configurations of the 

same AA tool. Incorporation of adjustments to the measured signals by AA algorithms may also 

be necessary particularly when alternate repair criteria are implemented.
xxiii

ACKNOWLEDGMENTS

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

Regulatory Commission (NRC), under Job Code N6582. The NRC Project Manager is M. 

Rossi. The authors thank K. Karwoski and E. Murphy for their useful guidance in the 

performance of this work. The authors also wish to acknowledge C. Mathison (Zetec, Inc.) for 

providing technical support and C. Smith (EPRI), for providing helpful suggestions. The staff at 

Zetec is also thanked for their assistance and valuable comments.

xxv

ABBREVIATIONS AND ACRONYMS

AA automated analysis

ANL Argonne National Laboratory

AAPDD automated analysis performance demonstration database

ASME American Society of Mechanical Engineers

BC bobbin coil

CDS Computer Data Screening

EC eddy current

EDM electro-discharge machine

EPRI Electric Power Research Institute

ETSS examination technique specification sheet

FBH flat-bottom hole

ID inner diameter

IDSCC inner diameter stress corrosion crack/cracking

IGA intergranular attack

ISG-TIP International Steam Generator Tube Integrity Program

ISI in-service inspection

NDE nondestructive examination/evaluation

NRC U.S. Nuclear Regulatory Commission

OD outer diameter

ODSCC outer-diameter stress corrosion crack/cracking

POD probability of detection

POPCD probability of prior cycle detection

PTW percent throughwall

ROI region of interest

RR round robin

SCC stress corrosion crack/cracking

SG steam generator

S/N signal-to-noise ratio

SSPD site-specific performance demonstration

TS tubesheet

TSP tube support plate

TTS top of the tube sheet

TW throughwall

1

INTRODUCTION

Manual analysis of multi-frequency EC inspection data collected during shutdown periods is a 

challenging and laborious process. Computerized data analysis tools have been routinely 

employed during SG ISI to help improve the process efficiency and the reliability of data 

analysis. Since bobbin probe examinations have historically served as the primary EC 

inspection method for full-length examination of SG tubes, software development in this area 

has revolved mostly around automated analysis of data acquired with bobbin probes. 

Computerized or AA software has been employed as replacement for the primary analyst, the 

secondary analyst, or both. Initial adjustment of variables that determine the performance of AA 

tools is performed by a skilled analyst familiar with the software. Depending on the extent of 

examinations and the types of degradation mechanisms of concern, the complexity of data 

screening setups can vary significantly among different sites. Intrinsic to the rule-based nature 

of the majority of such algorithms, variable settings are applicable to a specific set of test 

conditions. Readjustment of the variables may be necessary if the test conditions vary. The 

degree of conservatism in setting the data screening variables determines the probability of 

detection and in turn the number of false calls. 

The primary objective of this work was to assess the capability of conventional automated EC 

data analysis tools used in the review of SG tube EC inspection data. The efforts were carried 

out as part of on-going activities under the ISG-TIP sponsored by the NRC. Initial studies at 

ANL focused on gaining a better understanding of the important features of such algorithms 

including the critical parameters for detecting and classifying a wide range of degradation and 

non-degradation signals. Subsequently, a commercially prominent data screening software was 

acquired. Vendor-supplied information and other accessible literature on this subject were 

reviewed. The software was first tested on representative EC inspection data collected with a 

bobbin probe from the ANL-NRC SG tube bundle mock-up containing tubes with primarily 

laboratory-produced flaws (a small number contained machined flaws). These initial tests

helped determine the proper setting of variables for screening of data at different locations 

(elevations) and for different flaw types present in the SG mock-up. Following optimization of 

the data screening parameters, the software was used to evaluate all of the EC inspection data 

from the mock-up. The effect of changing the screening parameters on the detection and 

classification of indications in the SG mock-up was examined. Their influence on detection as a 

function of flaw type and location was also evaluated.

In the subsequent sections, a general description of AA tools used for SG ISI applications is 

provided. The basic structure of common data screening software including the type of 

algorithms and test logic is first described. The design of the SG tube bundle mock-up and the 

type of flaws present is then provided. Application of commercial AA software to bobbin probe 

data collected from the SG mock-up is subsequently described. The results of comparative 

assessments on the effect of setup parameters and screening configurations on POD are 

presented. Finally, some general remarks are provided regarding the results of studies as well 

as a discussion on advantages and limitations of automated data analysis tools.

3

AUTOMATED DATA ANALYSIS TOOLS FOR STEAM 

GENERATOR TUBE EXAMINATIONS

Analysis of eddy current inspection data has traditionally been performed in a manual fashion by 

teams of qualified analysts. Software-based tools for automatic analysis of EC examination

data from SG tubes have been in use since the early 1980s and have helped to speed up the 

analysis process for the large amount of data collected during an ISI. Since bobbin probe 

inspections have historically served as the primary nondestructive examination (NDE) method 

for the full-length examination of SG tubing, software development in this area has 

predominantly revolved around tools for data acquired with bobbin probes. Computerized or 

automated analysis software has been used in the field as replacement for the primary analyst, 

the secondary analyst, or both. Initial adjustment of variables that determine the performance of 

AA tools is performed by a skilled analyst familiar with the software. Depending on the extent of 

examinations and the types of degradation mechanisms of concern, the complexity of data 

screening setups can vary significantly among different sites. Intrinsic to the rule-based nature 

of the majority of such algorithms, the data screening parameters are established for a specific 

set of test conditions. As such, readjustment of the variables may be necessary if the test 

conditions vary. The degree of conservatism used in setting the data screening variables 

influences the probability of detection and in turn the number of false calls (overcall rate). 

Among other factors, the number of overcalls is also dependent on the type of damage or 

degradation and its location in the SG.

Automated data analysis software for SG tube examinations are provided by a number of 

vendors in the U.S. including AREVA (US-AIDATM), Corestar (AutoVISIONTM), Westinghouse 

(ADSTM), and Zetec (CDSTM). An AA module is typically available as an integrated feature of a 

commercial software package, which commonly includes data acquisition, analysis, and 

management components. Depending on the vendor, the software product may run under the 

Windows or the HP-UX operating system. Because of the significant improvements in 

capabilities of personal computers, the general trend on the preferred environment for future 

software development is toward the Windows operating system. Software development in this 

area has focused primarily on the processing of data acquired with bobbin probes. 

Conventional AA tools can be broadly categorized as expert system algorithms employing 

amplitude threshold schemes and rule-based logic to detect and classify signals. Differences 

among such tools may be associated with the EC signal features used by the detection and 

identification algorithms and the number of steps applied for classifying discontinuities at 

different elevations within the SG. Options are typically provided to operate in an interactive or 

in a fully automated mode. In the latter mode of operation, the AA software automatically 

performs the analysis and the final results are generated without human intervention. In the 

interactive mode, recorded signals by the AA software may be reviewed during the process and 

the results may be reviewed by a human analyst prior to their final acceptance.

Detection and classification of EC signals by AA algorithms is done using a combination of 

threshold-based routines and rule-based algorithms (logic statements) that partly emulate the 

process followed by a human data analyst for interpreting EC probe response. Automatic 

calibration of data is often recommended to help ensure consistency of the analysis results. 

Pre-defined landmark tables specific to each particular SG design are used to automatically 

locate different regions of the SG that may then be screened for known degradation or nondegradation modes of interest. The landmark table maps the expected location of tube support 

structures and transitions to the position of their associated signals in EC data. A low-frequency 
4

channel with strong probe response from extraneous discontinuities is commonly employed by 

landmark detection algorithms to locate the position of support structures and transitions. The 

probe speed and digitization rate are used to locate the approximate location of a known 

landmark. More accurate positional information is determined through application of a series of 

rules and thresholds by landmark detection algorithms. Adjustment of detection variables is 

often necessary for proper identification of all landmarks.

A series of signal preprocessing routines (filters) are first applied to data from specific analysis 

channels to help suppress unwanted signals and in turn improve the signal-to-noise ratio (S/N). 

The initial detection of signals of potential interest is commonly based on an amplitude (voltage) 

threshold criterion. Depending on the expected signal characteristics at a particular test 

frequency and probe excitation mode, different peak detection algorithms may be selected to 

identify the signals of interest. Detected signals above a user-defined threshold are marked for 

further evaluation. Subsequently, signals that conform to a set of predetermined criteria (rules) 

for a particular mode of degradation are classified accordingly. The final classification may be 

based on satisfying one or a number of criteria that are sequentially tested on a signal. The 

expert system rules for characterizing signals could vary significantly depending on the 

complexity of the signal and its location in the SG. 

A number of measurement options are thus provided to deal with differences in probe response 

at different test frequencies. A fixed or a variable measurement window length can be chosen,

depending on the expected probe response to a flaw at a particular location in the SG. Signal 

features such as the expected range in variation of phase angle and its impedance plane 

response that are captured by the prescribed measurement type are used as the main criteria 

for classifying signals. In certain cases a nested series of conditions have to be met before an 

indication is positively identified. The degree of conservatism in setting up the rules is 

application dependent and could be site-specific. The extent and complexity of computerized 

data screening settings for detection and classification of EC indications often follows a parallel 

path as that used for manual analysis of data, which depends on the SG operating history and 

in turn on degradation and non-degradation mechanisms of potential concern. As such, the level 

of EC noise present in a particular SG will have a direct influence on the adjustment of data 

screening parameters. 

Field experience with automated data analysis tools over the years has demonstrated that they 

can provide comparable or better detection performance to that achieved by manual analysis of 

EC inspection data. In theory, the detection sensitivity of AA software may be adjusted to flag

low-amplitude signals that may not be readily detectable by manual analysis. However, the 

tradeoff for improving the POD is an increase in the number of false calls. Another limitation of 

conventional AA tools used in evaluating data collected with the bobbin probe is associated with 

the detection capability of the probe itself. The underlying assumption for all rule-based 

algorithms is that the signal characteristics associated with a particular mode of degradation is 

relatively well defined. This may not always hold true particularly in presence of high levels of 

noise and interfering signals from extraneous discontinuities. Furthermore, while there is no 

minimum amplitude threshold for reporting of flaw-like signals during manual analysis of ISI 

data, reporting of signals by AA algorithms is dependent on a globally applied minimum 

threshold. Thus, the possibility exists that visually discernible signals which do not conform to 

the characteristic probe response from a particular flaw type could go undetected when the data 

is analyzed in an automated manner. Finally, it is worth noting that analyst expertise in setting 

up the AA algorithms for site-specific applications will influence the performance of the software.
5

Performance demonstration requirements for automated data analysis tools are defined by the 

EPRI PWR steam generator examination guidelines (Ref. 1). Generic qualification of AA 

software products is based on the automated analysis performance demonstration database 

(AAPDD). The type of damage mechanisms included in the AAPDD is listed in the Appendix G 

of the EPRI guidelines. Appendix G also defines the procedure for demonstrating the 

performance capability of AA software for site-specific performance demonstration (SSPD) 

applications. The types of damage mechanisms included in the database used for qualification 

of AA software are similar to those used for qualification of human analyst. The initial 

qualification using AAPDD is done to demonstrate the capability of AA software to detect and 

characterize (i.e., classification and sizing) known types of damage mechanisms. The generic 

qualification on the AAPDD is commonly performed by the software vendor. Acceptable 

performance demonstration of AA software involves achieving a minimum POD of 80 percent at 

a 90 percent confidence level for all flaw categories included in the AAPDD. The number of 

false calls, while recorded, does not influence the generic qualification process. Acceptability of 

the number of false calls by a particular AA algorithm may be taken into consideration on a sitespecific basis. Analogous to training and testing of a human analyst, the dataset used for sitespecific qualification of AA software is expected to include a sufficient number of plant-specific 

signals associated with active and potentially latent degradation mechanisms. Accordingly, 

information about the SG design and operating history for a particular site as well as that from 

similar plants is included in the SSPD for qualification of AA software. Automated analysis 

software that satisfies both AAPDD and SSPD requirements may then be implemented in the 

field. Performance demonstration may not be required for application of AA software to detect 

signals associated with non-degradation mechanisms such as tube deformations and deposits. 

According to the EPRI guidelines for automated data analysis, independent software systems 

should be used when AA tools are employed as replacement for both the primary and the 

secondary analyst teams. Independence in this context refers to the type of algorithms 

incorporated into the software for detecting and characterizing signals. As noted in the EPRI 

guidelines, AA software products from different vendors that employ similar detection algorithms 

may not be considered as independent systems. It is also recommended by the EPRI 

guidelines for AA that the signal characterization from the AA systems be manually reviewed by 

at least one human analyst team to verify proper operation of the data analysis algorithms.

Following the performance demonstration process, a qualified examination technique 

specification sheet (ETSS) may be implemented during the ISI at a particular site. The number 

of applicable ETSSs depends on the number of active and potential degradation and nondegradation mechanisms in the SGs at that site. The ETSS document applicable to the sitequalified AA software commonly provides instructions for setting up and analyzing EC 

inspection data. This includes information about the calibration procedure (manual or 

automatic), location of SG support structures (landmarks), mode of analysis (interactive or fully 

automated), manual review procedure, reporting requirements, and applicable AA algorithms for 

screening of data from different SG elevations. The setup parameters are also provided in the 

ETSS for each data screening algorithm and include the threshold values and test logic for 

locating, detecting, and characterizing signals associated with each degradation and nondegradation mechanism of interest. To assist with implementation of AA tools, drawings are 

commonly included in the site-specific data analysis guidelines that identify the SG regions

covered by each data screening setup. While supplementary NDE techniques are used in 

regions of the SG tube where bobbin probe examinations are not qualified, AA algorithms for 

bobbin probe data are typically set to provide complete coverage of the entire tube length. 

Identification of landmarks is the first stage of AA process and is critical to proper operation of 
6

the software. Data is analyzed manually when all the landmarks for a tube cannot be 

automatically located. Setting up the site-qualified AA software configurations for 

implementation at a particular plant is commonly carried out by a designated EC analyst. The 

same analyst may also be responsible for verifying, and if necessary validating (through SSPD), 

of any modifications made to the data screening configurations. Modifications made to the AA 

data screening configurations needs to be reviewed and approved by an independent qualified 

data analyst to conform to SSPD requirements.

Improving the efficiency and reliability of computerized data analysis tools has been an active 

area of research and development during the past two decades. Improving the performance of 

data analysis results has been a major focus of the emerging AA software. More accurate 

characterization of flaw signals is also of great importance. Availability of automated data 

analysis tools is particularly important for more widespread deployment of array probe 

technology. In view of limitations with computational resources, employment of more 

computationally efficient AA algorithms is necessary to extract information from the large 

amount of NDE data collected from full-length examination of SG tubes with array probes. A 

viable approach for improving the effectiveness of bobbin probe AA software is to incorporate 

information from complementary EC examination techniques. Versatile AA algorithms that 

include the review of EC inspection data from different probe types could help increase the POD 

and the accuracy in classifying indications in SG tubes. Emerging AA software packages

employ more elaborate signal processing and data analysis algorithms which could help 

improve the performance of AA systems. The use of advanced filtering schemes can help 

increase the POD while keeping the number of false calls to an acceptable level. Such filters 

can provide significant improvement in S/N in certain cases, thus improving the capability to 

detect difficult signals. Incorporation of enhanced feature extraction and classification 

algorithms could help better discriminate between flaw signals and inconsequential signals. 

Modern AA software tools are expected to employ more reliable automatic file recognition and 

tracking capabilities, thus helping to reduce the possibility of unintentional errors caused by 

human intervention. The performance of the AA software products being developed by the 

industry will ultimately be determined through field trials following their qualification in 

accordance with the EPRI guidelines. 

In the following section, a description of the SG tube bundle mock-up design and the type of 

flaws present in the mock-up is provided. In the succeeding section, application of a 

commercially available automated data screening tool to EC inspection data from the SG mockup is provided. The initial generic setups and the tests carried out to optimize AA screening 

configurations on a subset of tubes from the available database are first presented. The results 

on the application of the AA software to all the EC inspection data from the SG tube bundle 

mockup are subsequently discussed.
7

STEAM GENERATOR TUBE BUNDLE MOCK-UP

Eddy current bobbin probe inspection data collected previously from the SG tube bundle mockup at Argonne was used to assess the capability of conventional AA software. As will be 

discussed in the following section, AA tools employ location-dependent data analysis 

algorithms. Correct identification of landmarks (hot/cold leg tube ends, tube support structures, 

dimensional/geometry transitions, etc.) in a particular SG design is therefore critical to their

proper operation. A detailed description of the SG mock-up design and the types of flaws 

present in the tubes is provided in other reports (Refs. 2, 3). For the purpose of completeness,

a brief description of the tube bundle mockup is again presented here.

A schematic drawing of the SG tube bundle mockup is shown in Fig. 3.1. It consists of 400 fulllength straight tubes which include multiple test sections each. The test sections are Alloy 600 

tubing material with nominal 22.2-mm (0.875-in.) diameter and 1.27-mm (0.05-in.) wall 

thickness. Each full-length tube consists of 9 test sections, each 0.3 m (12 in.) long and a 0.91-

m (36-in.)-long probe run-out section at the top. The test sections are denoted as level A 

through I in Fig. 3.1. The center-to-center spacing of the tubes is 3.25 cm (1.28 in.). Tube 

sections are butted end-to-end to form a continuous tube, held together by 19-mm (0.75-in)-

thick high-density polyethylene plates. The lowest level (A) in the mock-up simulates the tubesheet (TS) section with the tubes being fully expanded inside a 30.5-cm (6-in.) -long carbon 

steel-collar. A few tubes in the TS level are only partially expanded. Levels 4 (D), 7 (G), and 

9 (I) simulate drilled hole tube support plate (TSP) intersections. The TSPs are 19-mm (0.75-in.) 

-thick carbon-steel plates. The other five levels (B, C, E, F and H) of the mock-up simulate SG 

tube free-span regions.

The majority of test sections in the SG mock-up contain laboratory-produced flaws. A small 

number of test sections contain machined flaws (EDM notches and laser-cut slots). The 

distribution of flaw types present in the mock-up is provided in Table 3.1 (Ref. 2). The flaw 

types include intergranular attack (IGA), outer-diameter stress corrosion cracking (ODSCC), 

primary-water or inner-diameter stress corrosion cracking (PWSCC), wear/wastage, fatigue, and 

machined flaws. The TSP sections of the mock-up contain stress corrosion cracks (SCCs) of 

both OD and ID origin. Cracks in the TSP region may be associated with dents. Dents were 

also produced in flaw-free tube sections. The crevice region of some TSP intersections is 

covered with magnetic tape or a ferromagnetic fluid to simulate magnetite deposits. Axial and 

circumferential outer diameter (OD) and inner diameter (ID) SCCs were produced for the TS 

expansion transition region. In some elevations, a mixture of magnetite and copper bonded with 

epoxy simulating sludge deposits was placed above the expansion transition region and TSP 

intersection. Simulated deposits were also placed at a number flaw-free tube sections. Flaws 

in the free-span regions are mostly axial ODSCC, both planar and segmented. A small number 

of other flaw types such as IGA and wear were also placed in various locations in the tube 

bundle. 

In reference to Table 3.1, the depths of flaws in the mock-up are distributed into three ranges, 

0–50 percent throughwall (PTW), 51–80 PTW, and 81–100 PTW with the distribution 

intentionally skewed toward deeper flaws (Ref. 2). This distribution is expected to increase 

confidence in the POD values for deep flaws. Also, the mock-up contains short sections of 

discontinuous tubes. The EC probe response from tube-end discontinuities resembles the 

signal from a through-wall 360° circumferential notch. The use of short tube sections was

necessary to generate realistic flaws and to further allow reconfiguration of the tube bundle as 
8

needed. As will be noted in the following section, the large EC probe response from 

discontinuities in the mock-up that are not representative of those observed in the field could 

create a challenge when data is to be analyzed in an automated manner.

The EC inspection data from the mock-up were collected with magnetically biased (“magbiased”) bobbin probes. Magnetically biased probes were used so that the signals from 

sensitized and non-sensitized test sections would have similar EC responses. Because of the 

wider use of non–magbiased probes in the field, equivalency was demonstrated between the 

two probe types.

A detailed description of the EC data acquisition parameters for examination of tubes in the 

mock-up is presented in Ref. 2. The ETSS for acquisition with a bobbin probe is also provided 

in that report. In summary, bobbin probe inspection data were taken at 400, 200, 100, and 20 

kHz in both differential and absolute mode. Data were collected on the pull with a probe speed 

of ~53 cm/s (21 in./s) and a digitization rate of 800 samples/s, thus maintaining a sampling rate 

of around 15 samples/cm (38 samples/in). Data were calibrated using an ASME standard with

100, 80, 60, 40 and 20 PTW flat-bottom holes, and a 10 PTW OD and a 20 PTW ID 

circumferential groove. A 1.9-cm (0.75-in.) carbon steel collar on the calibration standard was 

used for simulating the probe response from TSPs. Calibration of amplitude was based on 

setting the peak-to-peak voltage reading at 400 kHz primary test channel to 4.0v and 

normalized to all other channels. The phase angle for all test channels was set to 40 degrees. 

The phase angle rotation for the 20 kHz locator channel was set so that the probe response 

from the TSP was at 270°. In accordance with the site-qualified ETSS, three process channels 

were additionally generated. The process channels consisted of 400|100 kHz differential TSP 

suppression mix (P1), 400|100 kHz absolute TSP suppression mix (P2), and 200|400|100 kHz 

differential TTS expansion mix (P3). The three-frequency mix (EddynetTM “turbo mix”) was 

implemented for suppressing the probe response from TSP and TTS expansion transition. 

Phase-based calibration curves were also generated using as-built dimensions of the 100, 60, 

and 20 PTW holes to estimate flaw depths.

Table 3.1 Distribution of flaw types in the SG tube bundle mock-up.

Max. Depth 

Range (

PTW)

EDM & Lasercut Slots

IGA

ODSCC @ TS

ODSCC @ TSP

ODSCC 

@

Free

–span

PWSCC @ TS

PWSCC 

@TSP

PWSCC 

@

Free

–span

Wear/

Wastage

Fatigue

0–50 7 2 3 14 15 4 8 1 6 0

51–80 13 9 2 14 26 8 16 0 6 0

81–100 1 2 16 41 49 35 7 3 0 3
9

Figure 3.1 Schematic drawing of the SG tube bundle mock-up.

11

APPLICATION OF COMPUTERIZED DATA SCREENING 

TO SG TUBE BUNDLE MOCK-UP

Following the initial review of the literature on conventional automated data analysis tools for SG 

tube examinations, a prominent AA tool that is routinely employed for ISI applications was 

selected for the planned studies at Argonne. The latest version of the commercially available 

EC data analysis software Eddynet®Suite, from Zetec Inc., was acquired and installed on a HPUX workstation. The software package provides various tools for manual analysis and automatic 

screening of EC inspection data including the Computer Data Screening (CDSTM) module. 

Bobbin probe inspection data collected previously from the SG tube bundle mock-up was used 

for all the tests conducted in this work. Data from a subset of tubes in the training data set from

the NDE round-robin exercise was initially used to set the data screening parameters for 

detecting and classifying the type of flaws present in the mock-up. Following optimization of the 

setup parameters for a number of location-specific algorithms using the entire training data set, 

CDS was used to analyze the EC inspection data for all the test sections in the mock-up. 

In the following sections, a general description of the AA software used in the studies is 

presented. The main components of the software including algorithms for locating, detecting, 

and classifying signals are briefly described. The results of the initial performance evaluations 

of the algorithms using generic setups and their subsequent optimization for generating a set of 

mock-up-specific data screening configurations are then discussed. Subsequently, the results 

from application of the optimized CDS configurations to bobbin probe inspection data from the 

entire SG mock-up are presented.

4.1 Basic Structure of Computer Data Screening (CDS) Tool

The CDS program is comprised of a number of algorithms that employ threshold-based

schemes and rule-based test logic to detect and classify EC signals. A general overview of the 

main components of the CDS is presented here. A detailed description of the CDS software

with a complete list of embedded algorithms and the associated setup parameters is provided 

elsewhere (Ref. 4).

The CDS module of the Zetec EddynetTM data analysis software can be accessed through a 

user interface window which allows implementation of a wide range of user-defined screening 

criteria for automatic processing of EC data collected with bobbin probes. As noted in Ref. 4, 

CDS does not independently evaluate EC inspection data. Rather, data screening criteria are 

based specifically on user-defined parameters and acceptance criteria. Single or multiple data 

screening configurations (“sorts”) can be developed to analyze data from various regions of the 

SG tubing. The parameters of a sort for a particular region along the tube include the algorithms 

and test logic for locating, detecting, and classifying EC signals. A signal is reported when it 

meets all the prescribed criteria defined by that sort. 

Conventional automated data analysis tools commonly employ location-dependent algorithms to 

identify signals. Proper operation of such AA tools is therefore dependent on the availability of a 

landmark table with accurate information about known structures in a particular SG. Landmark 

tables for a number of standard SG designs with default positional information for tube support 

structures and transitions are provided under the EddynetTM environment. Modifications may be 

made to those tables as necessary for application to modified SG designs. Development of the 
12

landmark table for the SG tube bundle mock-up and the use of the table for identifying mock-up 

specific structures and discontinuities are discussed in the next section. 

Three different modes of operation are available for running CDS. In the two interactive modes, 

the process is halted each time a signal is detected along a tube or after the data over the entire 

length of a tube is processed. In the automated mode, unless a discrepancy causes the 

process to abort, the process will terminate once all the tubes in the current directory (calibration 

group) have been analyzed. The analysis results will be sequentially entered into the Report 

Editor file after completing the process for each tube. The reported information for signals that 

meet the data screening criteria typically includes the tube (row and column), voltage, phase, 

depth, reporting channel, flaw location (with respect to nearest landmark), classification code, 

analyst identifier code, and data file name. 

The user-defined input parameters for a sort configuration in the CDS editor window are 

associated with region, location, detection, and classification setups. Under the region menu 

one defines the positional information for the length of SG tube to be examined and a list of 

sorts to be applied to selected regions over that length. The positional input parameters must 

be consistent with the labeling and the relative locations listed in the landmark table. Multiple 

sort configurations may be generated under each region menu. Multiple regions with separate

set of sorts may also be created. The location parameters define the range for the regions of 

interest where the detection and classification processes will be applied. It is worth noting that 

the sorts under the region menu use OR logic while the detection and classification stages use 

AND logic. This simply implies that a signal will be reported if it meets the criteria set by any 

one of the sort configurations defined under region menu. A signal must, however, meet all the 

criteria set under the detection and the classification test logic to be reported. 

The detection setup parameters include measurement channel (test frequency), measurement 

type, detection algorithm, measurement window size, and amplitude threshold values. 

Selection of the measurement channel for detection will depend on the region of SG tube (freespan, TSP, TS, TTS, etc.) to be screened by the sort. Qualified test frequencies are commonly 

defined in the applicable ETSS. The measurement type can be based on the vertical

component of the signal, the horizontal component of the signal, or both. A class of peak 

detection algorithms is available under the detection algorithm menu. Selection of an 

appropriate detection algorithm depends on the region of interest defined by the sort and the 

expected EC signal characteristics for the degradation or non-degradation mode of concern. 

Detection of a potential signal may be based on simple amplitude threshold schemes or based 

on more elaborate peak detection schemes. For example, a commonly used algorithm, Filtered 

Peaks, detects the maximum value of the signal away from the null value and identifies that as a 

true peak when the impedance plane trajectory predictably traverses across two quadrants. 

The algorithm applies a cross-correlation filter to the data from the measurement channel in 

order to minimize the influence of unwanted signals. Detected signals that fall within the lower 

and upper limit of the prescribed amplitude (voltage) threshold will be kept for subsequent

processing by the classification test logic. 

The classification setup parameters include measurement channel, test channel, measurement 

algorithm, measurement window size, amplitude threshold values, phase angle window, and 

depth range. The measurement channel for classification is the channel used for initial 

measurement of signals passed on from the previous detection stage. The test channel is 

subsequently used for the evaluation of classification test logic using the measurement points 
13

on the signal obtained by the measurement channel. When activated, the test channel is used 

for reporting of the signals that satisfy the classification test logic. Selection of the optimum 

measurement and test frequency once again depends on the region of interest defined by the 

sort and the expected EC signal characteristics for the degradation or non-degradation mode of 

concern. As with the detection stage, qualified measurement and test channels are commonly 

defined by the applicable ETSSs. The same channel may be also used for both measurement 

and testing. A class of measurement algorithms is available for examination of signals by the 

classification test logic. Classification measurement algorithms may simply locate the peak-topeak points on a signal or employ more elaborate schemes. For example, a commonly used 

algorithm, Transition Amplitude Volts Peak-To-Peak, is employed when closely spaced signals

need to be identified individually. To identify a signal, the algorithm searches over the transition 

regions on both sides of the peak position and stops when the signal amplitude no longer 

varies. The window size, when applicable, determines the number of data points to search 

around the peak position of the signal by the classification measurement algorithm. Once the 

signal is identified, amplitude threshold, phase angle window, and depth range conditions 

defined in each classification logic line are used to ultimately accept or reject the signal. Any 

number of classification logic lines may be created for sequential application to the data. A 

signal will be reported by CDS if it satisfies the acceptance criteria set by all the classification 

logic lines. It is worth noting that more elaborate classification test logic with a larger number of 

data screening parameters normally translates to more stringent criteria for reporting of potential 

signals and in turn results in a lower number of false calls. 

4.2 Implementation of Generic Setups for Performance Evaluations

Initial studies on the application of CDS to bobbin probe inspection data from the SG tube 

bundle mock-up were associated with the examination of different setup parameters for 

detection and classification of flaws. The setup parameters include those for locating, detecting, 

and classifying different degradation and non-degradation types of interest. A general 

description of those parameters was provided in the previous section. As noted earlier, because 

CDS employs location-dependent algorithms, proper identification of SG structures (tube ends, 

TSP, TTS, etc.) through a Landmarks Table is vital to the operation of the program. As a 

prerequisite to the application of CDS, a landmark table for the SG mock-up was first created. A 

series of generic data screening configurations were initially used to review the EC bobbin 

probe data from a subset of SG mock-up tubes. The results of those studies are discussed

below.

A description of the SG tube bundle mock-up design and the type of degradation and artifacts 

present in the mock-up was discussed in Sec. 3. As noted earlier, each full-length tube in the 

mock-up is made up of nine 12-in. sections that are butted end-to-end to form a continuous 

tube. The lowest elevation simulates the TS section with each tube fitted with a 6-in. carbonsteel collar. Also present are drilled carbon-steel tube support plates at three different 

elevations in the mock-up. The mock-up does not conform to any standard SG design, so 

generic files in the available list of landmark tables under the EddynetTM environment could not 

be used. Additionally, the spacing between the tube end sections, resembling a 360o, 100 PTW 

notch, generates large flaw-like signals that must be taken into account during automatic 

screening of data. 

A mock-up-specific landmark table was generated at Argonne for automatic identification of 

structures and discontinuities at different SG mock-up elevations. To allow CDS to bypass flaw-
14

like signals associated with the tube end sections, a number of approaches including separate 

region designation for each tube section were examined. Ultimately, the approach taken was to 

label the intersections as one type of landmark. The landmark table (in inches) generated for 

the mock-up is shown in Fig. 4.1, with the labels denoting the bottom tube end (TEH), TTS

(TSH), three TSP levels (01H, 02H, and 03H), and nine tube-end sections (TE1-TE9). The 

three-letter codes for the type of landmark being used were selected from the available list of 

standard landmark types. Also provided in that table is the location (elevation) of each 

landmark in inches with respect to the lower tube end. The Locating Selectables dialog box was 

next used to adjust the detection parameters associated with the landmark labels. Figure 4.2(a) 

displays the setup variables in that window, which includes the locator type, detection channel, 

probe speed, and threshold values for the signals from the different support structures. A 

number of Locator algorithms are available for detecting and identifying extraneous structures. 

Depending on the Locator type, either amplitude or phase of the signal may be used to identify 

support structures. The probe speed is also used by certain locator algorithms. Other important 

setup parameters include the search limits (in number of data points) around the pre-defined 

landmark positions and, when applicable, the threshold values for detecting signals from 

different types of structures. Selection of the optimum algorithm is mainly dependent on the EC 

probe response from the type of structures present in a particular SG, which in turn influences 

the selection of Landmark Channel (locator channel/frequency). Although any absolute or 

differential channel may be used for locating landmarks, the most viable frequency is typically 

the lowest test frequency that generates a large probe response from extraneous discontinuities 

and has low sensitivity to flaws within the tube wall. 

Different Locator types were examined for automatic identification of landmarks in the mock-up. 

A type-5 Locator which employs a threshold-based algorithm for detection of intersections was 

eventually used. The 20 kHz absolute channel was selected as the Landmark Channel. It is 

worth noting that attaining an optimal set of threshold values for the entire data set is typically a 

recursive process that requires inclusion of data files that represent the possible range of 

variation in probe response from each landmark. Validity of the setup parameters for detection 

and identification of landmarks may also be examined more closely using the Landmark 

Location Output window shown in Fig. 4.2(b), which lists the landmark locations identified by the 

Locator algorithm and their positional deviations from the nominal values listed in the 

Landmarks Table. Application of the automatic landmark detection algorithm to bobbin probe 

data from a representative SG mock-up tube is shown in Fig. 4.3. The automatically added 

labels representing landmarks identified along the tube length are displayed in the landmark 

column on the left hand side of the analysis window. All the discontinuities and support 

structures in this case were correctly detected and labeled. 

It should be noted that threshold-based automatic landmark detection algorithms may not 

always correctly identify all the landmarks in a tube. In addition to such factors as tubing 

anomalies, variability in the geometry of interfaces between tube sections and in turn the probe 

response from those locations could necessitate occasional variable adjustments to ensure 

proper landmark detection. In those cases, either the detection thresholds may need to be 

adjusted or the landmarks may have to be placed manually. Other minor variable adjustments 

performed later for the SG mock-up included the number of points that determine the zone for 

each landmark; i.e., lead/lag (+/-), which is made in the Locating Selectables dialog box. 

Although this adjustment did not influence the results of analysis with regard to detection and 

classification of signals, it resulted in a more accurate reporting of the positional information for 

signals located in close proximity to the support plates.
15

Figure 4.1 Display of Landmark Table generated for identification of discontinuities and 

structures in the tube bundle mock-up.
16

(a)

(b)

Figure 4.2 Display of setup parameters from (a) Locating Selectables dialog box used for the 

adjustment of landmark detection parameters, and (b) Landmark Location Output

window for more detailed examination of the locating parameters.
17

(a)

(b)

Figure 4.3 Display of representative bobbin probe data for a full-length mock-up tube (a) before 

and (b) after application of the automatic landmark locator.
18

The CDS Editor was subsequently used to create location-specific sort configurations for

locating, detecting, and classifying flaw signals in the SG mock-up tubes from the training data 

set. Evaluations of generic data screening parameters were carried out by initially running the 

CDS in an interactive mode. As noted earlier, this function allows the user to step through the 

data and examine each detection call on potential indications. This operating mode is 

particularly useful when iterative adjustments to the sort parameters need to be made. Bobbin 

probe inspection data from a subset of mock-up tubes designated as the training data set were 

used for the initial assessments. The setups used typical values suggested by the CDS 

reference material for detection and classification of potential indications in those particular 

regions. Based on these initial tests, preliminary generic setups for the type of flaws present in 

the training data set were created and subsequently applied to all the tubes included in the 

training data set.

Figure 4.4 displays the CDS editor window with the preliminary settings for the analysis of data 

at two different regions of the tube bundle mock-up. The data screening configuration for 

indications in the free-span region is shown in Fig. 4.4(a). Also marked on that figure are the 

setup areas for defining the sort configurations (Regions and Indications list) and the associated 

parameters for locating, detecting, and classifying indications. The location setup for the freespan screens the entire length of each tube excluding the landmark zones. The free-span 

(Region 1) includes the entire length of the tube excluding the landmark zones. Activation of the

Not logic in that line causes the search routine to bypass landmark regions within the prescribed 

window size. The peak detection algorithm selected for this region is defined as Filtered Peaks, 

which automatically applies a first-order cross correlation filter primarily to eliminate baseline 

fluctuations in the data. To emulate the manual analysis procedure, the measurement channel 

for detection of signals was selected as the 400|100 kHz differential TSP suppression mix (P1). 

An amplitude threshold of 0.3v was used for detection of potential signals. Two separate logic 

lines were created for the classification of signals. The first test logic line uses the 200 kHz 

differential channel for both measurement and testing. Because the sorts are associated with 

free-span region of the tube, the classification measurement and reporting channels used in the 

second test logic are the 200 kHz differential and the P1 channel, respectively. In both cases, 

the measurement algorithm used is Transition Amplitude VPP, which was described in the 

previous section. Figure 4.4(b) shows the setup for the analysis of signals at TSP elevations. 

The TSP suppression channel in this case was used for both detection and classification of 

signals. For the preliminary evaluations, the screening zone for each TSP was extended by 1 

inch on both sides of the intersection to overlap the free-span region. In combination, the two 

regions cover the entire length of each tube. 

Bobbin probe data from a subset of tubes in the training data set were screened automatically 

using the CDS setups described above. Representative test cases are presented next that 

demonstrate the results of the initial evaluations. An intermediate and the final stage of the 

analysis process are shown in Fig. 4.5(a) and (b), respectively. The tube in this case contains

single and multiple cracks at different free-span elevations. With the current sort settings, all 

free-span indications were detected in this particular tube. As displayed in Fig. 4.5(b), the 

locations of detected signals are marked with an arrow in the landmark column of the analysis 

window. Similar results are shown in Fig. 4.6 for another tube identified previously as having 

fatigue cracking at the highest TSP elevation. Once again, the indications in this case were 

detected using the preliminary CDS setup. 
19

The data screening sorts for the free-span and the TSP regions of the mock-up were 

subsequently applied to all the tubes included in the training data set. Although the majority of 

free-span and TSP indications were detected by CDS, some indications went undetected using 

the initial setup variables. Figures 4.7 to 4.10 show the signals, identified through manual 

analysis of data, which went undetected using the preliminary setups. 
20

(a)

(b)

Figure 4.4 Display of CDS Editor window for setting up the location, detection, and classification

parameters for different regions of the tube bundle mock-up. Shown here are the 

initial settings for the (a) free-span and (b) TSP elevations of the mock-up.

Regions and Indications

Location

Detection

Classification
21

(a)

(b)

Figure 4.5 Display of different stages of the CDS process for representative bobbin probe data 

from a mock-up tube with single and multiple SCC indications at two separate freespan elevations for the (a) intermediate and (b) final stage of analysis with the 

detected indications marked (arrows) in the landmark column of the analysis 

window.
22

(a)

(b)

Figure 4.6 Display of different stages of the CDS process for representative bobbin probe data 

from a mock-up tube with fatigue cracking at a TSP for the (a) intermediate stage of 

analysis, and (b) recall of the indication following termination of the analysis process.
23

Figure 4.7 Manual analysis of bobbin probe signal displayed in the main analysis window for an 

ID indication at a dented TSP intersection.

Figure 4.8 Manual analysis of bobbin probe signal displayed in the main analysis window for a 

free-span indication.
24

Figure 4.9 Manual analysis of bobbin probe signal displayed in the main analysis window for a 

free-span indication.

Figure 4.10 Manual analysis of bobbin probe signal displayed in the main analysis window for a 

small OD indication at a TSP intersection.
25

In Figs. 4.7-4.10, the flaw signal was not detected with the initial CDS setting for the TSP sort. 

The probable causes for omission of these signals was attributed to the detection measurement 

type (Fig. 4.7), lower limit on the percent depth value for reporting with the classification logic 

(Figs. 4.8, 4.9) and lower voltage limit of the detection threshold (Fig. 4.10).

Modifications were subsequently made to the existing free-span and TSP sorts to help detect 

the signals that were missed during the initial screening of tubes in the training data set. The 

modifications included lowering of the voltage threshold for detection of low amplitude signals

and selection of an alternative detection algorithm for flaw signals in the TSP region. Additional 

sorts were also generated for screening of the TS elevation of the SG mock-up (special 

interest). As a result of high false call rate in the TS region, defined as the number of false calls 

divided by the number of unflawed test sections, the tube sections inside the TS were excluded 

from screening by CDS. Only the sort generated for the analysis of data over a small zone at 

the top of the tube-sheet (TTS) was activated. Figures 4.11(a) and (b) display the CDS editor 

window for the free-span and the TSP sorts, respectively. The free-span (Region 1) includes the 

entire length of the tube excluding the landmark regions. Three separate sorts were created 

under Region 2 for examination of TSP level indications. The CDS editor windows for TTS and

TS regions are displayed in Fig. 4.12(a) and (b), respectively. Analogous to manual analysis of 

bobbin probe data from the mock-up, the detection and classification of signals at the TS 

elevations was based on examination of the process channels consisting of two- and threefrequency mixes, which suppress the probe response from the TS and the TTS transition, 

respectively. In comparison to the previous setup, the voltage threshold values for TTS sort 

were increased in order to reduce the number of false calls in that region. 

Bobbin probe data for all tubes in the training data set was analyzed automatically using the 

modified sorts described above. Figures 4.13 and 4.14 show screen captures of the CDS 

results for three indications missed using the initial setups for the free-span and TSP regions. In 

Fig. 4.13, the previously missed signal in the TSP region of the fourth tube section was detected 

following modification of the TSP screening sort. In Fig. 4.14, the previously missed free-span 

indications in the second and third tube sections were detected following modification of the 

free-span screening sort. All three indications were subsequently detected using the modified 

sorts shown in Fig. 4.11. Figure 4.15 shows the final results of the processing by CDS for all 

tubes in the training data set in the Report Editor dialog box. With the exception of the signal 

shown in Fig. 4.10, all free-span and TSP indications were detected using the modified sorts. 

With only a small number of false calls, all TTS indications were also detected using the newly 

generated sort for that particular region of the mock-up. Following the termination of the CDS 

process, the reported entries associated with the flaw depth (column 6), the three-letter 

indication code (column 11), and the flaw origin (column 12) were manually edited to match the 

format prescribed for manual analysis of bobbin probe data from the SG mock-up. 

Following the initial evaluations based on the training data set, bobbin probe inspection data 

from a subset of mock-up tubes designated as the analyst testing/qualification data were used 

to further assess the CDS sort configurations. The sorts generated for three different regions of 

the tube bundle, namely, free-span, TSP, and TTS were subsequently applied to all twelve 

tubes included in the qualification data set. Screen capture of the CDS results for 

representative tubes in that data set are shown in Figs. 4.16 through 4.20. Figure 4.16(a) 

shows the CDS results over the full length of a tube with detected signals (marked by arrows) at 

the free-span, TSP, and TTS elevations. In reference to Figs. 4.16(b) and (c), the free-span 

indication near the second TSP was correctly detected while the signal from a dent at the first 
26

TSP elevation was incorrectly identified as a flaw. Although the latter reported signal may be 

eliminated by adjusting the lower limit of the phase angle in the classification logic line, it can 

potentially result in elimination of calls associated with shallow ID originated flaws. Examples of 

shallow ID flaws that could be affected in this manner are provided below. As noted previously, 

the challenge is to determine the optimum settings for the classification logic lines to best 

maintain a balance between conservatism in detection of potential indications and an 

acceptable false call rate.

Figure 4.17 shows the CDS results for another tube with flaws in different TSP elevations. 

Figure 4.17(a) shows screen capture of the final analysis results with all detected indications 

marked in the landmark column of the analysis window. Figure 4.17(b) shows a large ODSCC 

flaw detected in the first TSP. An indication identified as IGA at the second TSP elevation was 

not detected with the current settings of the TSP sort. Three other examples of CDS analysis 

results on tubes from the test data set are provided in Figs. 4.18 to 4.20. An example of a lowamplitude signal associated with a shallow ODSCC flaw in the free-span region of the tube is 

shown in Fig. 4.18. Examples of IDSCC signals detected at a dented TSP and in the free-span 

region of the tube are shown in Figs. 4.19 and 4.20. In all cases, the flaws were properly 

detected and identified using the modified settings of the sort parameters. 

The final result of the processing by CDS for all tubes in the test data set is shown in Fig. 4.21. 

All the entries made in the Report Editor dialog box are listed. The columns associated with the 

estimated flaw depth (column 6), reporting code (column 11), and flaw origin (column 12) were 

manually edited to more closely match with the report entry format prescribed for manual 

analysis of bobbin probe data from the mock-up. Based on the results of the analysis here, the 

free-span and TSP indications in all tubes from the test data set were detected with a high 

detection probability and with a low false call rate. The false call rate for the TTS indications, 

however, is rather excessive. 

Subsequent efforts were aimed at further optimization of the setup parameters based on the 

signals present in the training and the testing data sets. Alternative detection algorithms for

reducing the number of double calls on long indications were also further evaluated. Examples 

of signals from the qualification data set which were reported twice by CDS are shown in Fig.

4.22, as seen in the screening results in Fig. 4.21(a) and (b) from the report editor, where both 

flaws were called twice. This may be attributed to the detection algorithm used here, which is 

optimized for detection of closely spaced signals. Also, examples of ferromagnetic markers 

identified by CDS as flaw indications are shown in Fig. 4.23. The markers, which were placed 

within one inch of the tube end, were intended for more accurate reporting of positional 

information for rotating probes. The markers in a small number of tubes can produce a large 

probe response that could be misinterpreted as shallow ID indications. Although such signals 

do not pose a major problem with regard to the evaluation of EC detection capability for the SG 

mock-up data, they can increase the number of false calls if the entries are kept as reported. 

These false calls can be eliminated through implementation of more detailed sorts that would 

examine additional channels for characterization of such signals. Specifically, possible options 

for minimizing the number of such false calls include incorporation of additional logic lines in the 

free-span sort and creation of a separate sort for classification of such signals. A more 

elaborate sort may include classification logic that uses information from the lower frequency 

absolute channels and/or uses the three-frequency TTS mix channel for suppression of probe 

response associated with permeability variations.
27

As noted earlier, detection of EC signals is strongly dependent on the setting of amplitude 

threshold values that are used by both the detection and classification algorithms. These 

thresholds are initially set based on the level of noise present in the inspection data. The ability 

to detect a particular signal is therefore dependent on its S/N. The primary challenge is to 

optimize the threshold values to provide an acceptable tradeoff between detection and the 

overcall rate. To further evaluate the test logic for the free-span and TSP regions of the mockup, the voltage threshold values were slightly reduced to assess their effect on detection. 

Based on feedback from the earlier tests, the amplitude threshold values for the TTS signals 

were also significantly increased. In general, the adjusted threshold values for the free-span 

and TSP regions did not influence the detection of flaw signals in this data set. However, the 

slight reduction of amplitude threshold values resulted in a measurable increase in the number 

of overcalls particularly at the TSP elevations. Screen capture of the CDS results for a 

representative tube with overcalls at two TSP elevations is displayed in Fig. 4.24(a). The signal 

for one of the two overcalls is shown in Fig. 4.24(b). 

An example of a signal that was not detected with the current sort configuration is shown in Fig.

4.25. The degradation is IGA at a TSP intersection. It should be noted that detection of IGA 

with bobbin probes, using either manual or automatic analysis procedures, can be challenging. 

The difficulty is attributed to the complex morphology of the flaw which results in atypical phase 

angle response that cannot be properly characterized based on the conventional analysis logic 

for crack-like or volumetric indications. A separate sort may have to be generated for detection 

of such signals in the mock-up. Potential indicators of the flaw in this case are the relatively 

large amplitude of the TSP mix residual signal and subtle changes in probe response at other 

test frequencies in comparison to the probe response from a clean TSP (i.e., a TSP elevation 

with no flaws in the tube). This test case demonstrates the potential difficulty in characterizing 

IGA using bobbin probe.
28

(a)

(b)

Figure 4.11 Display of CDS Editor window for setting up the location, detection, and 

classification sorts for different regions of the tube bundle mock-up settings for the 

(a) free-span and (b) TSP elevations of the mock-up.
29

(a)

(b)

Figure 4.12 Display of the CDS Editor window used for setting up the parameters associated 

with the location, detection, and classification algorithms. Shown here are the sorts 

for the (a) TTS and (b) the TS elevations of the SG mock-up.
30

Figure 4.13 Display of the CDS results for the same tube shown in Fig. 4.7 with the detected 

indications marked with arrows in the landmark column of the analysis window.

Figure 4.14 Display of the CDS results for the same tube shown in Figs. 4.8 and 4.9 with the 

detected indications marked with arrows in the landmark column of the analysis 

window.
31

Figure 4.15 Results of the CDS analysis displayed by the Report Editor dialog box for all thirteen 

tubes in the training data set.
32

(a)

 

(b) (c)

Figure 4.16 Display of the CDS results for the entire length of a mock-up tube from the test data 

set for the (a) final CDS results with the detected signals marked with arrows in the 

landmark column of the analysis window, (b) a large ODSCC signal near the second 

TSP intersection, and (c) a dent signal at the first TSP intersection.
33

(a)

(b)

Figure 4.17 Display of the CDS results for the entire length of a mock-up tube from the test data 

set for the (a) final CDS results with the detected signals marked with arrows in the 

landmark column of the analysis window and (b) an ODSCC signal at the first TSP 

intersection.
34

(a)

(b)

Figure 4.18 Display of the CDS results for the entire length of a mock-up tube from the test data 

set. Shown here are (a) final CDS results with the detected signals marked with 

arrows in the landmark column of the analysis window and (b) signal from a shallow 

ODSCC flaw located in the free-span region of the tubing.
35

(a)

(b)

Figure 4.19 Display of the CDS results for the entire length of a mock-up tube from the test data 

set. Shown here are (a) final CDS results with the detected signals marked with 

arrows in the landmark column of the analysis window and (b) signal from an IDSCC 

flaw at a dented tube support region.
36

(a)

(b)

Figure 4.20 Display of the CDS results for the entire length of a mock-up tube from the test data 

set. Shown here are (a) final CDS results with the detected signals marked with 

arrows in the landmark column of the analysis window and (b) signal from one of 

two closely spaced axial IDSCC flaws in the free-span region of the tube.
37

Figure 4.21 Results of the CDS analysis displayed by the Report Editor dialog box for all twelve 

tubes in the testing data set.
38

(a)

(b)

Figure 4.22 Display of the CDS results for two free-span ODSCC indications.
39

(a)

(b)

Figure 4.23 Examples of ferromagnetic markers near the tube end identified as indications by 

CDS.
40

(a)

(b)

Figure 4.24 Display of the CDS results for the entire length of a mock-up tube from the test data 

set. Shown here are (a) final CDS results with the detected signals marked with 

arrows in the landmark column of the analysis window and (b) an example of a false 

call at a TSP elevation due to a low voltage threshold.
41

(a)

(b)

Figure 4.25 An example of a missed call for IGA at a TSP intersection. Shown here are the 

signals measured manually from (a) the 400|100 kHz TSP mix channel and (b) 

multiple Lissajous with expanded strip chart display of the same signal from four 

different channels.
42

A number of CDS sorts were generated and tested for the analysis of data from different regions 

of the tube bundle mock-up. To verify the results of analyses using the CDS sort configurations 

generated at Argonne and to further seek consultation on optimization of CDS setup parameters 

on alternative configurations, Argonne staff attended a two-day workshop at Zetec. Following 

consultation with the vendor, modifications were made to the aforementioned CDS setups and 

complementary data screening configurations were additionally generated. Further evaluations 

were subsequently carried out on alternative data screening setups that could help improve the 

detection probability of signals present in the SG mock-up and to further determine the 

possibility of overcoming some known limitations in dealing with difficult flaw types. The results 

of evaluations using the modified sort configurations are discussed below. 

The preliminary investigations suggest in general that the latest set of sorts in most parts 

provide redundancy checks for screening different regions of the mock-up. Other sorts included 

in this latest set are those for detection and classification of dent (non-degradation mechanisms) 

signals at TSPs and for generating single calls on long axial indications. It is worth noting that 

while increasing the number of sorts for screening the same region of tubing generally improves 

the detection probability, increasing the number of classification logic tests for a given sort could 

lead to less conservative detection results. As mentioned earlier, this is because the Indications

definition list follows an OR logic while the sort test logic follows an AND logic.

The CDS Editor window for representative sort configurations associated with different regions 

of the mock-up is displayed in Figs. 4.26-4.29. Two separate Indication identification regions

defined for screening of free-span elevations are displayed in Fig. 4.26. The primary sort in Fig. 

4.26(a) employs a differential channel (200 kHz measurement channel) while the secondary sort 

employs an absolute channel (100 kHz measurement channel) for detection. As it was noted 

earlier, the detection and classification test logic of the configuration shown in Fig. 4.26(a) is 

better suited for identifying closely spaced signals while the complementary sort shown in Fig. 

4.26(b) is set specifically for the measurement and reporting of relatively long axial signals. The 

detection algorithm shown in Fig. 4.26(b) employs a lower frequency absolute channel with a 

larger measurement window size in order to better capture such signals. Except for the 

difference in measurement class and window size, similar classification test logic is used in both 

cases. The main difference between the two sorts is the use of a lower frequency absolute 

channel with a larger window size for detection of long axial signals.

Representative setups for the sorts used to screen TSP elevations are shown in Figs. 4.27 and 

4.28. Four separate sort configurations under the Indications list were defined for detection and 

classification of signals at TSP elevations. Three of those are associated with detection of flaw 

signals and one is associated with detection of dent signals over the same region of tubing. 

Figures 4.27(a) and (b) display a two sort configuration for detection of flaw signals at the third 

TSP elevation. The main difference between the two setups is the use of 200 kHz differential 

channel (channel 3) in Fig. 4.27(a) and the use of TSP suppression mix (P1) in Fig. 4.27(b) as 

the test channel in the classification logic. Other detection and classification parameters for the

two TSP sorts are similar to those described earlier. Duplicate sets of sorts are created for all 

three TSP elevations in the mock-up. Additional setups for reporting of flaws and geometry 

deformations at dented TSP regions are shown in Fig. 4.28. The sort configuration shown in 

Fig. 4.28(a) is based on the TTS suppression mix (P3) that was created to specifically detect 

flaw signals in dented regions. The setup displayed in Fig. 4.28(b) on the other hand is

intended for reporting of dents at TSP elevations. In this case the horizontal component of the 

signal is used in the detection logic as the EC probe response from tube geometry change is 
43

expected to produce a large horizontal component. A duplicate set of sort configurations were 

created for screening the three TSP elevations in the SG mock-up. 

The CDS editor window for setting up the sorts to detect indications at or near the top of the 

tube-sheet (TTS) is shown in Fig. 4.29. The measurement channel for both the detection and 

the classification test logic in this case uses the three-frequency TTS mix (P3) for measurement 

and testing of signals in that region. Unlike the earlier setups in which the two-frequency TSP 

suppression mix was used for reporting of signals, the same three-frequency mix channel is

used in the modified setup for both measurement and reporting of signals. The text boxes for 

percent depth values were deactivated in this case because a viable calibration curve cannot be 

created for P3. It should also be noted that, when a calibration curve does not exist for the test 

channel used in the classification test logic, the text boxes for percent depth values need to be 

deactivated. Failure to deactivate the depth range in such cases could result in bypassing of 

the logic line and in turn missing potentially relevant signals.

The latest CDS setup parameters were evaluated on the qualification data set from the SG 

mock-up. Representative results following the analysis of all tubes in that data set are shown in 

Figs. 4.30-4.32. An example of detection of a long axial indication at a TSP elevation by two 

separate sort configurations is shown in Fig. 4.30. While two individual signals were reported by 

the fist setup, the second complementary setup for screening the same TSP region resulted in 

only one reported entry. Another example of multiple calls for the same indication is provided in 

Fig. 4.31. In this case, a relatively long signal in the free-span region was identified as two 

separate signals by one sort and as one continuous signal by another sort. It is worth noting 

that, the setup shown in Fig. 4.26(b) may not be used as an alternative to that shown in Fig. 

4.26(a). This is because the detection algorithm in Fig. 4.26(b) is not optimized for capturing

flaw signals that are in close proximity to other relevant or non-relevant signals. Figure 4.32

provides an example of signals detected at a dented TSP region with separate calls generated 

for the dent signal (non-degradation mechanism) and the potential flaw signal at the same 

intersection by two different sort configurations.

The final results of analysis for all tubes in the test data set with the modified CDS sort 

parameters are shown in Fig. 4.33. The columns associated with the estimated flaw depth 

(column 6), reporting code (column 11), and flaw origin (column 12) were manually edited to 

more closely match with the report entry format prescribed for manual analysis of the bobbin 

probe data from the SG mock-up. Calls associated with markers near the end of tube sections 

were also removed from the original list. The analysis results in general show comparable

detection capability to the previous sort settings for flaw signals in the free-span and the TSP 

regions. As expected, inclusion of the additional sorts resulted in a larger number of redundant 

calls. The number of calls in the TTS region of the mock-up was minimized by intentionally 

setting a large detection threshold. As noted earlier, while EC inspection data from the TTS 

region of the SG mock-up were analyzed, because of the high level of noise associated with 

mechanically expanded tubes, application of CDS to data from the TTS level may not in general 

be justified in view of the large number of overcalls.

Additional tests were next carried out to further examine the utility of applying multiple CDS sort 

configuration to the same region of tubing. A larger data set composed of both the training and 

the performance test data was used in this study. Data analysis results associated with the 

application of CDS to bobbin probe data from a subset of 25 tubes from the SG mock-up using 

the sort configurations shown in Figs. 4.26-4.29 are provided in Table 4.1. The columns 
44

associated with the estimated flaw depth (column 6), reporting code (column 11), and flaw origin 

(column 12) were manually edited to more closely match with the report entry format prescribed 

for manual analysis of the bobbin probe data from the SG mock-up. Calls associated with 

markers near the end of tube sections were removed from the original list. The results were 

exported from the Report Editor dialog box and converted to text format for further evaluation 

outside the EddynetTM environment. The sort configuration for screening of data within the TS

region of the mock-up was deactivated. As described previously, this was done to eliminate the 

large number of false calls associated with the excessive level of EC noise from the mechanical 

expansion of the tubes in the TS region. The reported data analysis results were again 

manually edited to more closely match the report entry format prescribed for manual analysis of 

the bobbin probe data from the SG mock-up. In addition, entries associated with markers near 

the end of tube sections were removed from the original list. Examination of the analysis results 

listed in the table indicates that while nearly all flaws were detected, the number of redundant 

calls associated with the same signal and the total number of overcalls increased using multiple 

configurations.

Figures 4.34-4.38 show examples of typical signals that were reported either multiple times by 

different sorts covering the same region of the tube or were determined later to be false calls. 

Figure 4.34 displays a potential indication at a dented TSP region, which was called as a 1.19v 

ID indication by one sort and as a 0.77v OD indication by another sort. In both cases the flaw 

depth was estimated to be relatively deep. Figure 4.35 displays the CDS results for another 

tube with an indication at a dented TSP intersection. Separate calls were reported for the dent 

and the flaw signal by different sorts. In this particular case the location of the flaw signal did not 

match with that identified through manual analysis of data. Examples of representative false 

calls reported in the free-span regions of the tube associated with the ferromagnetic marker 

near the tube end and with baseline noise are presented in Fig. 4.36. A typical example of 

multiple detections made by separate sorts for the same region of the tube is shown in Fig. 

4.37(a), (b). In this case, three separate entries were made for the same signal by different 

sorts due to overlapping regions. The measured signal amplitude varies between (a) 2.59v by 

free-span sort and (b) 4.13v by TSP sort. Another example of multiple calls for the same flaw 

signal is presented in Fig. 4.38. In this case, five separate entries were made for the same 

signal by different TSP sorts with the measured signal amplitude varying between 0.54v and 

1.95v. The tests here generally indicate that, while inclusion of multiple sorts for screening of the 

same region of tubing can produce more conservative data analysis results, it can also lead to 

increased number of redundant calls. Also, depending on the type of detection and 

classification algorithm being used, the reported signal amplitudes can in some cases vary 

significantly. 

Follow-on tests using the data screening configurations shown in Figs. 4.26-4.29 included

comparison of the CDS results from different combination of sorts. The purpose of this study 

was to find an acceptable trade-off between detection probability and overcall rate using 

multiple sort configurations. To reduce the number of redundant calls a number of tests were

conducted to identify the minimum number of sort configurations that would provide comparable 

detection probability while keeping the number of redundant and overcalls to an acceptable 

level. Other tests were also carried out to assess the effect of detection thresholds and other 

data screening parameters on the automated data analysis results. A brief description of the 

results based on a reduced number of data screening configurations is presented here.
45

For the next series of tests the number of sort configurations was reduced to one for the 

screening of free-span and TSP regions of the tube bundle mock-up. This was done by 

deactivating the previously-generated configurations from the Indications list in the CDS Editor

dialog box and keeping only the most conventional setups for each region. The only data 

screening configurations kept were the ones shown in Fig. 4.26(a) for the free-span region and 

in Fig. 4.27(b) for the TSP region, respectively. The CDS results using the modified set of sorts 

for the data set of 25 tubes composed of the training and testing data set is shown in Table 4.2. 

The final results were once again exported from the Report Editor dialog box and converted to 

text format for subsequent evaluations. The columns associated with the estimated flaw depth 

(column 6), reporting code (column 11), and flaw origin (column 12) were manually edited to 

more closely match with the report entry format prescribed for manual analysis of the bobbin 

probe data from the SG mock-up. Most calls associated with the markers near the end of tube 

sections were also removed from the original list. Comparison of the results between the 

complete set of sorts shown in Table 4.1 and the modified sort list shown in Table 4.2 indicates 

that with the exception of one indication all the previously reported signals were reported using 

the modified settings. The primary difference between the two screening configurations is the 

number of redundant calls. A total of 61 calls were reported using the previous settings for the 

free-span and TSP regions. While nearly all flaws were detected, the total number of reported 

entries was reduced to 35 calls with the modified CDS configuration.

Examples of the false calls using the latest sort configuration are shown in Figs. 4.39 and 4.40. 

The signal associated with a dented TSP region identified as a false call is shown in Fig. 

4.39(a). An example of a false call associated with baseline noise in the free-span region of the 

tube is shown in Fig. 4.39(b). Another set of false calls associated with a dent at a TSP 

elevation is shown in Fig. 4.40. Examples of missed indications at a dented TSP zone are 

shown in Fig. 4.41. In those cases, the flaw signal was missed due to elimination of a sort for 

that region of tubing. The results of these tests suggest that it is possible to achieve 

comparable detection probability using a smaller number of sorts. However, it should be noted 

that because of the statistically small number of tubes included in this study, the results may not 

apply to that for the entire SG mock-up.

The reduced set of sorts discussed above was next used to assess the effect of threshold 

values on detection. In this series of tests, the screening parameters were adjusted to simulate 

the influence of higher level of baseline noise on the detection and classification of signals. The 

CDS Editor dialog box for the modified sort configurations for screening of the free-span and 

TSP elevations of the SG mock-up are displayed in Fig. 4.42. Only the detection and 

classification threshold values were adjusted for these tests. As noted previously, the CDS 

employs separate threshold variables for the detection and the classification stages. Although 

reporting of a signal is commonly based on satisfying the conditions for both test logics, a signal 

may be reported based on the detection logic alone. The option to independently define the 

threshold variables thus provides more freedom with regard to adjusting the level of 

conservatism in reporting of signals. Figure 4.42(a) displays the modified CDS Editor dialog box 

for the free-span sort. In reference to the previous setup, the classification voltage threshold for 

all test channels was increased to 0.75v from its previous value of 0.5v. The parameter setting

for the TSP elevations is displayed in Fig. 4.42(b). For the TSP regions the detection threshold 

was raised to 0.4v from its previous value of 0.2v. All other variables were kept unchanged. 

From a practical standpoint, the threshold values are often adjusted to compensate for changes 

in the level of noise present in EC inspection data and in order to achieve an acceptable 

balance between the detection probability and the number of false calls.
46

The CDS results using the modified set of sorts for the data set of 25 tubes composed of the 

training and testing data set is shown in Table 4.3. The columns associated with the estimated 

flaw depth (column 6), reporting code (column 11), and flaw origin (column 12) were manually 

edited to more closely match with the report entry format prescribed for manual analysis of the 

bobbin probe data from the SG mock-up. Most calls associated with the markers near the end of 

tube sections were also removed from the original list. As in the past, the final results were 

exported from the Report Editor dialog box and converted to text format. Representative cases 

of missed signals due to modification of the threshold values for the free-span and TSP sort 

configurations are shown next. Figure 4.43 shows examples of missed indications at two 

different TSP elevations in connection with the higher classification threshold value. The signals 

were measured manually following the application of CDS. The low-amplitude signal shown in 

Fig. 4.43(a) is associated with a shallow ODSCC near a TSP edge. The signal in Fig. 4.43(b) is 

associated with an ID indication at a dented TSP region with only the dent signal reported by 

CDS in that region. Examples of missed free-span signals are shown in Fig. 4.44. Finally, an 

example of a false call which was eliminated because of using the higher threshold value is 

shown in Fig. 4.45. In all cases the signals were missed because of using a higher threshold 

value for reporting of indications in the classification stage. 
47

(a)

(b)

Figure 4.26 Display of the CDS Editor window used for setting up the parameters associated 

with the location, detection, and classification of free-span signal sorts for detection 

of (a) closely-spaced signals and (b) long axial signals.
48

(a)

(b)

Figure 4.27 Display of the CDS Editor window used for setting up the parameters associated 

with the location, detection, and classification of indications at TSP elevations. 

Shown here are the sorts for detection of closely-spaced flaw signals at TSPs using 

(a) 200 kHz (channel 3) and (b) TSP suppression mix (P1) as the test channel.
49

(a)

(b)

Figure 4.28 Display of the CDS Editor window used for setting up the parameters associated 

with the location, detection, and classification of flaw and non-flaw-like signals at 

TSP elevations. Shown here are the sorts for detection of (a) flaw signals at a dented 

TSP using TTS suppression mix (P3) for detection and (b) tube geometry change 

associated with denting.
50

Figure 4.29 Display of the CDS Editor window used for setting up the parameters associated 

with the location, detection, and classification of flaw signals at or near the top of the 

tube-sheet.
51

(a)

(b)

Figure 4.30 Display of the CDS results for the entire length of a mock-up tube from the test data 

set of a relatively long indication at the first TSP elevation, which was identified (a) 

as two separate signals by one sort and (b) as one signal by another sort.
52

(a)

(b)

Figure 4.31 Display of the CDS results for the entire length of a mock-up tube from the test data 

set of a relatively long indication in the free-span, which was identified (a) as two 

separate signals by one sort and (b) as one signal by another sort.
53

(a)

(b)

Figure 4.32 Display of the CDS results for the entire length of a mock-up tube from the test data 

set example of a dented TSP region with separate calls for the (a) dent by one sort 

and (b) potential flaw signal by another sort at the second TSP elevation.
54

Figure 4.33 Results of the CDS process displayed by the Report Editor dialog box for all twelve 

tubes in the testing data set using the modified sorts.
55

Table 4.1 Tabulated results of the CDS process for combined training and testing data sets 

that was exported from the EddynetTM Report Editor dialog box.

11 A 6 NDD TE9TEH user1 primary DHR00AC006I015

11 A 9 9.22 113 41 P1 03H -0.03 TE9TEH DNI OD user1 primary DHR00AC009I020

11 A 9 10.05 115 39 P1 03H 0 TE9TEH DSI OD user1 primary DHR00AC009I020

11 A 9 1.2 112 42 P1 TE4 6.93 TE9TEH NQI OD user1 primary DHR00AC009I020

11 A 9 3.95 75 75 P1 TE4 6.48 TE9TEH NQI OD user1 primary DHR00AC009I020

11 A 9 4.9 79 72 P1 TE4 6.54 TE9TEH NQI OD user1 primary DHR00AC009I020

11 A 9 4.9 75 75 P1 TE4 5.91 TE9TEH NQI OD user1 primary DHR00AC009I020

11 A 15 2.17 185 P1 01H 0.09 TE9TEH DNT user1 primary DHR00AC015I026

11 A 15 1.19 30 86 P1 01H -0.24 TE9TEH DSI ID user1 primary DHR00AC015I026

11 A 15 0.77 63 84 P1 01H -0.27 TE9TEH DNI OD user1 primary DHR00AC015I026

11 A 15 14.69 33 P3 TSH 0.66 TE9TEH DTI user1 primary DHR00AC015I026

11 A 15 24.64 44 P3 TSH 0 TE9TEH DTI user1 primary DHR00AC015I026

11 A 18 10.07 112 42 P1 02H 1.7 TE9TEH NQI OD user1 primary DHR00AC018I029

11 A 18 0.79 131 19 P1 02H 1.67 TE9TEH NQI OD user1 primary DHR00AC018I029

11 A 18 10.69 111 43 P1 02H 0.96 TE9TEH NQI OD user1 primary DHR00AC018I029

11 A 18 0.97 11 31 P1 01H 0.54 TE9TEH DNI ID user1 primary DHR00AC018I029

11 A 18 4.21 187 0 P1 01H 0.12 TE9TEH DNT user1 primary DHR00AC018I029

11 A 18 1.55 21 60 P1 01H -0.22 TE9TEH DNI ID user1 primary DHR00AC018I029

11 A 18 7.59 57 P3 TSH -0.15 TE9TEH DTI user1 primary DHR00AC018I029

11 B 2 NDD TE9TEH user1 primary DHR00BC002I037

11 B 4 NDD TE9TEH user1 primary DHR00BC004I039

11 B 11 8.08 80 72 P1 01H 0.21 TE9TEH DNI OD user1 primary DHR00BC011I046

11 B 11 2.26 94 60 P1 01H 0.24 TE9TEH DSI OD user1 primary DHR00BC011I046

11 B 11 8.78 78 73 P1 01H 0.06 TE9TEH DSI OD user1 primary DHR00BC011I046

11 B 11 8.78 78 73 P1 01H -0.24 TE9TEH DSI OD user1 primary DHR00BC011I046

11 B 13 NDD TE9TEH user1 primary DHR00BC013I048

11 B 18 0.84 109 45 P1 TE1 3.79 TE9TEH NQI OD user1 primary DHR00BC018I053

11 B 18 1.11 103 51 P1 TE1 3.12 TE9TEH NQI OD user1 primary DHR00BC018I053

11 B 18 1.84 102 52 P1 TE1 3.33 TE9TEH NQI OD user1 primary DHR00BC018I053

11 D 10 NDD TE9TEH user1 primary DHR00DC010I095

11 D 13 0.74 111 43 P1 TE8 2.65 TE9TEH NQI OD user1 primary DHR00DC013I098

11 D 13 0.74 106 48 P1 TE8 1.97 TE9TEH NQI OD user1 primary DHR00DC013I098

11 D 13 5.02 94 60 P1 TE5 7.83 TE9TEH NQI OD user1 primary DHR00DC013I098

11 D 13 5.02 92 62 P1 TE5 7.86 TE9TEH NQI OD user1 primary DHR00DC013I098

11 D 13 10.8 27 P3 TSH -0.15 TE9TEH DTI user1 primary DHR00DC013I098

11 D 23 0.59 128 23 P1 TE1 5.74 TE9TEH NQI OD user1 primary DHR00DC023I110

11 D 23 0.67 135 13 P1 TE1 5.91 TE9TEH NQI OD user1 primary DHR00DC023I110

11 E 9 NDD TE9TEH user1 primary DHR00EC009I123

11 F 17 12.23 115 39 P1 01H 0.29 TE9TEH DSI OD user1 primary DHR00FC017I158

11 F 17 9.4 111 43 P1 01H 0.26 TE9TEH DNI OD user1 primary DHR00FC017I158

11 F 17 2.56 121 32 P1 01H 0.03 TE9TEH DSI OD user1 primary DHR00FC017I158

11 F 17 19.42 91 63 P1 01H -0.16 TE9TEH DSI OD user1 primary DHR00FC017I158

11 F 17 17.55 89 64 P1 01H -0.19 TE9TEH DNI OD user1 primary DHR00FC017I158

11 F 17 12.01 52 91 P1 TE1 5.87 TE9TEH NQI OD user1 primary DHR00FC017I158

11 F 17 12.01 60 86 P1 TE1 5.96 TE9TEH NQI OD user1 primary DHR00FC017I158

11 F 22 3.17 181 P1 01H 0.11 TE9TEH DNT user1 primary DHR00FC022I163

11 F 22 0.52 20 57 P1 TE3 5.36 TE9TEH DNI ID user1 primary DHR00FC022I163

11 G 8 1.19 12 34 P1 TE3 2.37 TE9TEH NQI ID user1 primary DHR00GC008I174

11 G 8 28.75 70 P3 TSH 0.18 TE9TEH DTI user1 primary DHR00GC008I174

11 G 8 53.82 62 P3 TSH -0.15 TE9TEH DTI user1 primary DHR00GC008I174

11 G 13 5.21 26 P3 TEH 5.37 TE9TEH DTI user1 primary DHR00GC013I181

11 G 13 5.21 26 P3 TEH 5.31 TE9TEH DTI user1 primary DHR00GC013I181

11 G 13 4.12 19 P3 TEH 1.3 TE9TEH DTI user1 primary DHR00GC013I181

11 G 14 1.21 149 0 P1 TE2 6.32 TE9TEH NQI OD user1 primary DHR00GC014I182

11 G 14 1.33 139 7 P1 TE2 5.77 TE9TEH NQI OD user1 primary DHR00GC014I182

11 G 14 2.35 137 10 P1 TE2 5.95 TE9TEH NQI OD user1 primary DHR00GC014I182

11 G 14 0.92 148 0 P1 TE1 6 TE9TEH NQI OD user1 primary DHR00GC014I182

11 G 14 0.9 142 2 P1 TE1 5.97 TE9TEH NQI OD user1 primary DHR00GC014I182

11 G 14 9.26 27 P3 TSH -0.52 TE9TEH DTI user1 primary DHR00GC014I182

11 H 11 4.13 128 23 P1 03H 1.01 TE9TEH DSI OD user1 primary DHR00HC011I204

11 H 11 4.13 128 23 P1 03H 0.83 TE9TEH DSI OD user1 primary DHR00HC011I204

11 H 11 2.59 122 30 P1 03H 1.07 TE9TEH NQI OD user1 primary DHR00HC011I204

11 H 11 5.83 33 P3 TSH -0.18 TE9TEH DTI user1 primary DHR00HC011I204

11 J 14 0.98 146 0 P1 03H 0.73 TE9TEH DSI OD user1 primary DHR00JC014I264

11 J 14 0.82 165 0 P1 03H 0.67 TE9TEH DNI OD user1 primary DHR00JC014I264

11 J 14 0.54 158 0 P1 03H 0.38 TE9TEH DSI OD user1 primary DHR00JC014I264

11 J 14 1.95 128 23 P1 03H -0.13 TE9TEH DSI OD user1 primary DHR00JC014I264

11 J 14 1.88 141 4 P1 03H -0.16 TE9TEH DNI OD user1 primary DHR00JC014I264

11 J 22 2.21 184 P1 02H 0.15 TE9TEH DNT user1 primary DHR00JC022I272

11 J 22 0.73 34 97 P1 02H -0.31 TE9TEH DSI ID user1 primary DHR00JC022I272

11 K 13 1.64 74 76 P1 03H 0.03 TE9TEH DSI OD user1 primary DHR00KC013I291

11 K 13 1.48 74 76 P1 03H 0 TE9TEH DNI OD user1 primary DHR00KC013I291

11 K 13 2.18 136 11 P1 03H -0.24 TE9TEH DSI OD user1 primary DHR00KC013I291

11 K 13 1.94 133 16 P1 03H -0.27 TE9TEH DNI OD user1 primary DHR00KC013I291

11 K 13 0.4 139 7 P1 TE6 5.47 TE9TEH DSI OD user1 primary DHR00KC013I291

11 M 4 3.42 22 P3 TSH 0.86 TE9TEH DTI user1 primary DHR00MC004I332

11 M 4 32.47 50 P3 TSH 0.03 TE9TEH DTI user1 primary DHR00MC004I332

11 N 12 1.88 10 29 P1 TE5 6.21 TE9TEH NQI ID user1 primary DHR00NC012I365

11 N 12 4.37 13 37 P1 TE5 5.19 TE9TEH NQI ID user1 primary DHR00NC012I365

11 N 12 4.37 13 37 P1 TE5 5.25 TE9TEH NQI ID user1 primary DHR00NC012I365

11 N 18 5.36 26 P3 TSH -0.09 TE9TEH DTI user1 primary DHR00NC018I371
56

(a)

(b)

Figure 4.34 Display of the CDS results for a mock-up tube with an indication at a dented TSP 

region, which was classified as (a) a 1.19v ID indication by one sort and (b) a 0.77v 

OD indication by another sort.
57

(a)

(b)

Figure 4.35 Display of the CDS results for a mock-up tube with an indication at a dented TSP 

region with separate calls for the (a) dent by one sort and (b) an ID flaw signal by 

another sort.
58

(a)

(b)

Figure 4.36 Display of (a) the CDS results for the entire length of a mock-up tube and (b) baseline 

noise at the fourth tube section.
59

(a)

(b)

Figure 4.37 Display of the CDS results for a mock-up tube with an indication near a TSP edge. 

The measured signal amplitude varies between (a) 2.59v by free-span sort and (b) 

4.13v by TSP sort.
60

(a)

 

(b)

Figure 4.38 (a) Display of the CDS results for the entire length of a mock-up tube (b) Five 

separate entries were made for the same signal associated with an indication near a 

TSP edge by different sorts with the measured signal amplitude varying between 

0.54v and 1.95v.
61

Table 4.2 Results of the CDS process using the modified sorts for the combined training and 

testing data sets.

11 A 6 3.17 32 P3 TEH 5.32 TE9TEH DTI user1 primary DHR00AC006I015

11 A 9 10.05 115 39 P1 03H 0 TE9TEH DSI OD user1 primary DHR00AC009I020

11 A 9 1.2 112 42 P1 TE4 6.93 TE9TEH NQI OD user1 primary DHR00AC009I020

11 A 9 3.95 75 75 P1 TE4 6.48 TE9TEH NQI OD user1 primary DHR00AC009I020

11 A 9 4.9 75 75 P1 TE4 5.91 TE9TEH NQI OD user1 primary DHR00AC009I020

11 A 15 2.17 185 P1 01H 0.09 TE9TEH DNT user1 primary DHR00AC015I026

11 A 15 1.19 30 86 P1 01H -0.24 TE9TEH DSI ID user1 primary DHR00AC015I026

11 A 15 14.69 33 P3 TSH 0.66 TE9TEH DTI user1 primary DHR00AC015I026

11 A 15 24.64 44 P3 TSH 0 TE9TEH DTI user1 primary DHR00AC015I026

11 A 18 0.79 131 19 P1 02H 1.85 TE9TEH NQI OD user1 primary DHR00AC018I029

11 A 18 10.6 109 45 P1 02H 1.17 TE9TEH NQI OD user1 primary DHR00AC018I029

11 A 18 4.21 187 0 P1 01H 0.12 TE9TEH DNT user1 primary DHR00AC018I029

11 A 18 7.59 57 P3 TSH -0.15 TE9TEH DTI user1 primary DHR00AC018I029

11 B 2 NDD TE9TEH user1 primary DHR00BC002I037

11 B 4 NDD TE9TEH user1 primary DHR00BC004I039

11 B 11 2.26 94 60 P1 01H 0.24 TE9TEH DSI OD user1 primary DHR00BC011I046

11 B 11 8.78 78 73 P1 01H -0.24 TE9TEH DSI OD user1 primary DHR00BC011I046

11 B 13 NDD TE9TEH user1 primary DHR00BC013I048

11 B 18 0.84 109 45 P1 TE1 3.79 TE9TEH NQI OD user1 primary DHR00BC018I053

11 B 18 1.11 103 51 P1 TE1 3.12 TE9TEH NQI OD user1 primary DHR00BC018I053

11 D 10 NDD TE9TEH user1 primary DHR00DC010I095

11 D 13 0.74 111 43 P1 TE8 2.67 TE9TEH NQI OD user1 primary DHR00DC013I098

11 D 13 0.74 106 48 P1 TE8 1.99 TE9TEH NQI OD user1 primary DHR00DC013I098

11 D 13 5.02 94 60 P1 TE5 7.77 TE9TEH NQI OD user1 primary DHR00DC013I098

11 D 13 10.8 27 P3 TSH -0.12 TE9TEH DTI user1 primary DHR00DC013I098

11 D 23 0.59 128 23 P1 TE1 5.67 TE9TEH NQI OD user1 primary DHR00DC023I110

11 D 23 4.42 29 P3 TEH 5.36 TE9TEH DTI user1 primary DHR00DC023I110

11 E 9 NDD TE9TEH user1 primary DHR00EC009I123

11 F 17 12.23 115 39 P1 01H 0.32 TE9TEH DSI OD user1 primary DHR00FC017I158

11 F 17 2.56 121 32 P1 01H 0.06 TE9TEH DSI OD user1 primary DHR00FC017I158

11 F 17 19.42 91 63 P1 01H -0.13 TE9TEH DSI OD user1 primary DHR00FC017I158

11 F 17 12.01 52 91 P1 TE1 5.87 TE9TEH NQI OD user1 primary DHR00FC017I158

11 F 22 3.17 181 P1 01H 0.11 TE9TEH DNT user1 primary DHR00FC022I163

11 G 8 28.75 70 P3 TSH 0.18 TE9TEH DTI user1 primary DHR00GC008I174

11 G 8 53.82 62 P3 TSH -0.15 TE9TEH DTI user1 primary DHR00GC008I174

11 G 13 5.21 26 P3 TEH 5.37 TE9TEH DTI user1 primary DHR00GC013I181

11 G 13 5.21 26 P3 TEH 5.31 TE9TEH DTI user1 primary DHR00GC013I181

11 G 13 4.12 19 P3 TEH 1.3 TE9TEH DTI user1 primary DHR00GC013I181

11 G 14 1.21 149 0 P1 TE2 6.32 TE9TEH NQI OD user1 primary DHR00GC014I182

11 G 14 1.33 139 7 P1 TE2 5.77 TE9TEH NQI OD user1 primary DHR00GC014I182

11 G 14 0.9 142 2 P1 TE1 5.97 TE9TEH NQI OD user1 primary DHR00GC014I182

11 G 14 9.26 27 P3 TSH -0.52 TE9TEH DTI user1 primary DHR00GC014I182

11 H 11 4.13 128 23 P1 03H 1.01 TE9TEH DSI OD user1 primary DHR00HC011I204

11 H 11 4.08 133 16 P1 03H 1.07 TE9TEH NQI OD user1 primary DHR00HC011I204

11 H 11 5.83 33 P3 TSH -0.18 TE9TEH DTI user1 primary DHR00HC011I204

11 J 14 0.98 146 0 P1 03H 0.73 TE9TEH DSI OD user1 primary DHR00JC014I264

11 J 14 1.95 128 23 P1 03H -0.13 TE9TEH DSI OD user1 primary DHR00JC014I264

11 J 22 2.21 184 P1 02H 0.15 TE9TEH DNT user1 primary DHR00JC022I272

11 J 22 0.73 34 97 P1 02H -0.31 TE9TEH DSI OD user1 primary DHR00JC022I272

11 K 13 1.64 74 76 P1 03H 0.03 TE9TEH DSI OD user1 primary DHR00KC013I291

11 K 13 2.18 136 11 P1 03H -0.24 TE9TEH DSI OD user1 primary DHR00KC013I291

11 M 4 3.42 22 P3 TSH 0.86 TE9TEH DTI user1 primary DHR00MC004I332

11 M 4 32.47 50 P3 TSH 0.03 TE9TEH DTI user1 primary DHR00MC004I332

11 N 12 1.88 10 29 P1 TE5 6.18 TE9TEH NQI ID user1 primary DHR00NC012I365

11 N 12 4.37 13 37 P1 TE5 5.16 TE9TEH NQI ID user1 primary DHR00NC012I365

11 N 18 5.36 26 P3 TSH -0.09 TE9TEH DTI user1 primary DHR00NC018I371
62

(a)

(b)

Figure 4.39 Display of the CDS results for (a) dented TSP and (b) baseline noise in the free-span 

region of the tube.
63

(a)

(b)

Figure 4.40 Display of the CDS results for (a) the left excursion and (b) the right excursion of a 

differential signal associated with a dent at a TSP elevation.
64

(a)

(b)

Figure 4.41 Display of manual data analysis results of ID indications at (a) dented free-span 

(ding) and (b) dented TSP elevation.
65

(a)

(b)

Figure 4.42 Display of the CDS Editor dialog box used for setting up the parameters associated 

with the location, detection, and classification of flaws in the tube bundle mock-up. 

Shown here are the modified (a) free-span and (b) TSP sorts with a reduced number 

of successive screening configurations for the same region of the tube.
66

Table 4.3 Results of the CDS process using the modified sorts for the combined training and 

testing data sets, which was exported from the Report Editor dialog box and 

converted to text format.

11 A 6 3.17 32 DTI P3 TEH 5.32 TE9TEH user1 primary DHR00AC006I015

11 A 9 10.05 115 DSI P1 03H 0 TE9TEH user1 primary DHR00AC009I020

11 A 9 1.2 112 NQI P1 TE4 6.93 TE9TEH user1 primary DHR00AC009I020

11 A 9 3.95 75 NQI P1 TE4 6.48 TE9TEH user1 primary DHR00AC009I020

11 A 9 4.9 75 NQI P1 TE4 5.91 TE9TEH user1 primary DHR00AC009I020

11 A 15 2.17 185 DNT P1 01H 0.09 TE9TEH user1 primary DHR00AC015I026

11 A 15 14.69 33 DTI P3 TSH 0.66 TE9TEH user1 primary DHR00AC015I026

11 A 15 24.64 44 DTI P3 TSH 0 TE9TEH user1 primary DHR00AC015I026

11 A 18 0.79 131 NQI P1 02H 1.67 TE9TEH user1 primary DHR00AC018I029

11 A 18 10.69 111 DSI P1 02H 0.96 TE9TEH user1 primary DHR00AC018I029

11 A 18 4.21 187 DNT P1 01H 0.12 TE9TEH user1 primary DHR00AC018I029

11 A 18 7.59 57 DTI P3 TSH -0.12 TE9TEH user1 primary DHR00AC018I029

11 B 2 NDD TE9TEH user1 primary DHR00BC002I037

11 B 4 NDD TE9TEH user1 primary DHR00BC004I039

11 B 11 2.26 94 DSI P1 01H 0.24 TE9TEH user1 primary DHR00BC011I046

11 B 11 8.78 78 DSI P1 01H -0.24 TE9TEH user1 primary DHR00BC011I046

11 B 13 NDD TE9TEH user1 primary DHR00BC013I048

11 B 18 0.84 109 NQI P1 TE1 3.79 TE9TEH user1 primary DHR00BC018I053

11 B 18 1.11 103 NQI P1 TE1 3.12 TE9TEH user1 primary DHR00BC018I053

11 D 10 NDD TE9TEH user1 primary DHR00DC010I095

11 D 13 5.02 94 NQI P1 TE5 7.77 TE9TEH user1 primary DHR00DC013I098

11 D 13 10.8 27 DTI P3 TSH -0.15 TE9TEH user1 primary DHR00DC013I098

11 D 23 4.42 29 DTI P3 TSH -0.49 TE9TEH user1 primary DHR00DC023I110

11 E 9 NDD TE9TEH user1 primary DHR00EC009I123

11 F 17 12.23 115 DSI P1 01H 0.29 TE9TEH user1 primary DHR00FC017I158

11 F 17 2.56 121 DSI P1 01H 0.03 TE9TEH user1 primary DHR00FC017I158

11 F 17 19.42 91 DSI P1 01H -0.16 TE9TEH user1 primary DHR00FC017I158

11 F 17 12.01 52 NQI P1 TE1 5.87 TE9TEH user1 primary DHR00FC017I158

11 F 22 3.17 181 DNT P1 01H 0.12 TE9TEH user1 primary DHR00FC022I163

11 G 8 28.75 70 DTI P3 TSH 0.15 TE9TEH user1 primary DHR00GC008I174

11 G 8 53.82 62 DTI P3 TSH -0.18 TE9TEH user1 primary DHR00GC008I174

11 G 13 5.21 26 DTI P3 TEH 5.37 TE9TEH user1 primary DHR00GC013I181

11 G 13 5.21 26 DTI P3 TEH 5.31 TE9TEH user1 primary DHR00GC013I181

11 G 13 4.12 19 DTI P3 TEH 1.3 TE9TEH user1 primary DHR00GC013I181

11 G 14 1.21 149 NQI P1 TE2 6.32 TE9TEH user1 primary DHR00GC014I182

11 G 14 1.33 139 NQI P1 TE2 5.77 TE9TEH user1 primary DHR00GC014I182

11 G 14 0.9 142 NQI P1 TE1 5.97 TE9TEH user1 primary DHR00GC014I182

11 G 14 9.26 27 DTI P3 TSH -0.52 TE9TEH user1 primary DHR00GC014I182

11 H 11 4.13 128 DSI P1 03H 0.86 TE9TEH user1 primary DHR00HC011I204

11 H 11 5.83 33 DTI P3 TSH -0.18 TE9TEH user1 primary DHR00HC011I204

11 J 14 0.98 146 DSI P1 03H 0.73 TE9TEH user1 primary DHR00JC014I264

11 J 14 1.95 128 DSI P1 03H -0.13 TE9TEH user1 primary DHR00JC014I264

11 J 22 2.21 184 DNT P1 02H 0.15 TE9TEH user1 primary DHR00JC022I272

11 K 13 1.64 74 DSI P1 03H 0.06 TE9TEH user1 primary DHR00KC013I291

11 K 13 2.18 136 DSI P1 03H -0.22 TE9TEH user1 primary DHR00KC013I291

11 M 4 3.42 22 DTI P3 TSH 0.86 TE9TEH user1 primary DHR00MC004I332

11 M 4 32.47 50 DTI P3 TSH 0.03 TE9TEH user1 primary DHR00MC004I332

11 N 12 1.88 10 NQI P1 TE5 6.21 TE9TEH user1 primary DHR00NC012I365

11 N 12 4.37 13 NQI P1 TE5 5.19 TE9TEH user1 primary DHR00NC012I365

11 N 18 5.36 26 DTI P3 TSH -0.09 TE9TEH user1 primary DHR00NC018I371
67

(a)

(b)

Figure 4.43 Display of the CDS results for representative SG mock-up tubes with missed calls at 

TSP elevations due to an increase in the detection threshold. The displayed signals, 

measured manually, are associated with (a) a shallow OD and (b) an ID indication at 

different TSP elevations.
68

(a)

(b)

Figure 4.44 Display of the CDS results for representative SG mock-up tubes with missed calls at 

free-span regions due to increase in detection and classification thresholds. The 

displayed signals, measured manually, were identified as OD indications at the (a) 

second and (b) ninth tube section.
69

Figure 4.45 The displayed signal, reported as an indication by the previous sort settings, was 

not reported as a result of modifying the detection threshold value for the TSP sort. 

The signal in this SG mock-up tube was later identified as a false indication.
70

4.3 Automated Analysis of EC Inspection Data from the Entire Mock-up

Following the initial optimization and testing of the data screening configurations discussed in 

the previous section, CDS was next applied to the bobbin probe data collected previously from 

the entire SG tube bundle mock-up. Various configurations of the setup parameters and 

combination of sorts tested earlier on the training and qualification data sets were also applied 

to the SG mock-up EC inspection database. The optimal set of sorts was compared against 

alternative configurations to help determine the effect of critical data screening parameters on 

the detection probability and false call rate. The results from analyses of data from different

elevations of the mock-up are discussed below. 

As noted previously, the CDS Report Editor provides the positional information for the reported 

signal entries in reference to the nearest landmark location. Independent evaluation of the data 

analysis results, however, requires availability of the positional information in terms of data point 

values. When dealing with large data sets, manual inclusion of this information into the analysis 

report file would be impractical. To get around this issue, the epri_scan3 program was used to 

automatically perform conversion of relative positional information of the CDS results to data 

point values. The program is readily available as a separate utility outside the EddynetTM

environment. The output text file generated by the epri_scan3 program may subsequently be 

read and manipulated by other application software. 

Additionally, a number of data manipulation routines were implemented under the spreadsheet 

software Microsoft ExcelTM to allow efficient sorting and grading of the analysis results for POD 

calculations. Two separate Macros written in Visual Basic language were created for 

manipulation of the data analysis results. An Excel Macro was generated to initially sort, in 

ascending order, all the entries in the analysis results file based on the tube’s row and column 

position within the SG and the elevation of each reported entry. A sample spreadsheet file 

containing a subset of SG mock-up tubes that was processed by the sort Macro is shown in 

Table 4.4. A second Macro was implemented to read the sorted data and grade the results 

based on a user-generated truth table. In its current form, EC data analysis results may be 

graded for detection, signal amplitude (voltage range), flaw origin (ID/OD), depth, and phase 

angle range. Table 4.5 displays the sample truth table that was used to grade the analysis 

results shown in Table 4.4. Also listed in that table is the detection result for each flaw as well 

as the total number of detected signals and overcalls, which were automatically generated by

the Macro. In reference to Table 4.5, the grading algorithm also marks the false call entries in 

the analysis results file. The Macro is used to automatically calculate the number of detected 

and missed calls based on the information provided in the truth table spreadsheet.

4.3.1 Analyses of Data for Tube Support Plate (TSP) Elevations

Eddy current inspection data from all TSP elevations of the mock-up were analyzed by using the 

optimized set of CDS sorts described earlier. Flaws in tube sections which were located outside 

the TSP grading zone were eliminated from the list. Data screening configurations of Figs. 4.27 

and 4.28, which employ multiple setups for screening of all TSP elevations were initially used. 

The results generally indicate a high POD value for all flaws present at the TSP elevations of 

the SG mock-up. The small number of missed indications in this case is associated mostly with 

low-amplitude indications and those with atypical signal characteristics. False calls in this 

particular data set were associated mostly with dented intersections. 
71

Representative signals pertaining to undetected flaws are shown in Figs. 4.46-4.48. Eddy 

current probe response from two test sections with indications located near the edge of the TSP 

is shown in Figs. 4.46(a) and 4.46(b). The flaws in this case were identified as a shallow ID 

crack at a dented TSP region and a low-amplitude OD crack at the edge of the TSP, 

respectively. Only that part of the probe response associated with the dent was reported by 

CDS in this case. Undetected signals associated with a shallow OD crack and a volumetric OD 

flaw at two different TSP regions is displayed in Figs. 4.47(a) and 4.47(b), respectively. Finally, 

undetected signals associated with IGA are shown in Fig. 4.48. The signals shown in Figs. 4.47

and 4.48 were missed because of the uncharacteristic nature of the probe response for those 

particular flaw types. As noted previously, automated algorithms such as CDS rely on consistent 

signal behavior including proper initial excursion and phase response to correctly classify 

potential indications. Significant distortion of signals caused by such factors as tube geometry 

variations and conducting or magnetic deposits in close proximity to the flaw could lead to the 

mischaracterization of signals. The complex structure of degradation types such as IGA could 

also result in mischaracterization of potential flaw signals since the probe response from such 

degradation types do not conform to the expected probe response from either crack-like or 

volumetric flaws. Although the test logic of the data screening algorithm may be adjusted to 

report such signals as flaws, the reduction of constraints for improving the detection probability 

will in turn result in an increased number of false calls. Nevertheless, a conservative approach 

when using automated data analysis tools is to periodically evaluate and adjust the test logic to 

ensure proper detection of unexpected degradation types with signal characteristics that may be 

different from those present in the training data set.

Comparative tests were subsequently performed to determine the effect of different sort 

configurations on detection and classification of flaws in the TSP region. A subset of the

multiple sort configurations comprised of only one sort was subsequently used to analyze the 

TSP data. The CDS Editor dialog box for the alternative screening configurations was shown in 

Fig. 4.27(b). Only three data screening configurations for detection of flaw signals were 

activated (one for each TSP elevation) in this case. As noted previously, the test logic of the 

selected sort here can be considered as the most common setup for detection of flaw signals at 

the TSP elevations. Also used was the sort configuration to report the dent (non-degradation) 

signals at TSP elevations. 

Bobbin probe data for all TSP elevations of the SG mock-up were subsequently analyzed using 

the optimized data screening setups. To conduct comparative assessments, the analysis 

results in each case were converted by using the epri_scan3 program and subsequently sorted 

and graded automatically by using the Excel Macros generated for this purpose. Although the 

Macros allow grading to be done based on different reporting parameters (e.g., signal 

amplitude, estimated depth, flaw origin), the grading of bobbin probe data here is based on 

detection alone. A variable grading unit, defined in terms of the number of data points, is used 

to set the detection zone for signals. Multiple hits within a grading unit are treated as one 

detection call in that zone. It should be noted that counting of the number of overcalls is not 

currently based on any grading unit (i.e., all calls are counted even if they are potentially 

redundant calls). This can lead to slight over-estimation of the false call rate, particularly when 

a combination of sorts is used to screen the same region of the tube. 

In terms of the total number of entries made by CDS for TSP level indications, a total of 404 

calls were reported by the first configuration (multiple sort setups) and 204 calls by the second 

configuration shown (single sort setup), respectively. Based on the truth table for flaws present 
72

at the TSP elevations, application of the first configuration resulted in 12 missed signals and 93 

overcalls, while the second configuration resulted in 23 missed signals and 49 overcalls. 

Data analysis results for all unreported indications as a result of applying the second 

configuration with reduced number of sorts are shown in Figs. 4.49-4.55. The use of the first 

configuration resulted in a small number of missed indications at the TSPs with most of those 

being associated with either low-amplitude signals or with atypical signal characteristics. As 

expected, the number of missed signals using the second configuration was larger and the 

number of overcalls was smaller. Comparison of the two data analysis results indicates that 

nearly all the additional missed indications from the second configuration are associated with 

flaw signals at dented TSPs. Inclusion of the complementary sorts (in the first configuration), 

however, resulted in a noticeably higher overcall rate. 

Assessments of the CDS results were conducted next by comparing the probability of detection

(POD) for different flaw types at the TSP elevations of the SG mock-up. Data analysis results 

on the application of CDS to the analysis of bobbin probe inspection data from TSP elevations 

using two separate sets of configurations were presented above. The first configuration for 

those tests consisted of multiple sorts, which were optimized for detection of all flaw signals at 

TSP elevations of the SG mock-up. The second configuration, which was a subset of the first 

one and may be considered as a more common setup, consisted of a single sort for screening 

of the same elevations. The CDS analysis results using the two configurations noted above 

were first sorted and graded using a set of Excel Macros generated for this purpose. The 

detection results were then used to generate POD curves for flaws present at the TSP 

elevations of the SG mock-up. For these comparative studies, manual analysis results from the 

round-robin (RR) exercise were used to help better assess the POD results with the automated 

data screening software. 

Figure 4.56 shows the POD curves for the CDS detection results at TSP elevations using the 

configuration composed of multiple sorts. A subset of TSP flaws identified as longitudinal innerdiameter stress corrosion cracking (LIDSCC) was used for the initial calculations. For 

comparison both linear-logistic and log-logistic fits to the same data are plotted with the flaw 

depth as the independent variable. Detailed description of the two fitting methods have been 

provided in a number of past technical reports on the evaluation of RR data analyses results 

from SG mock-up (Refs. 2, 3). In brief, these past studies performed by the industry on the 

probability of detection suggest that the log-logistic curve is a more appropriate choice when the 

signal amplitude (voltage) is used as the independent variable. Conservatism of that approach, 

however, is not clear when the flaw depth is taken as the independent variable. The linearlogistic fit on the other hand is expected to produce a more conservative estimate of detection 

probability. It is worth noting that the flaw size distribution and in turn the statistical acceptability 

of a database can influence the fitting results and the confidence on the POD results. The 

graph shown in Fig. 4.56 displays the typical behavior when the same data was fitted with the 

two curves mentioned above. In general, the log-logistic fit provides a higher POD at smaller 

flaw depths and the linear-logistic fit provides a higher POD at larger flaw depths. Although the 

POD curves with both fitting methods have been generated, it should be noted that for these 

comparative studies either one of the two logistic curves may be used to assess the detection 

results. It should also be noted that the POD results so far have all been generated using fixed 

values for the false call rate near the tail end (zero depth) of the logistic fits to the data. The 

small false call rate values used in these preliminary evaluations were chosen based on the 

results from the manual analysis of the RR data.
73

Figures 4.57 and 4.58 display the POD curves for the TSP level flaws using the two data 

screening configurations noted above. The CDS detection results for the multiple sort

configuration (denoted as TSP2) and the single sort configuration (denoted as TSP1) are plotted 

along with the composite RR results and the results by an expert analyst (not a fully blind test). 

Separate sets of curves were generated for the LIDSCC and the LODSCC flaws at the TSP 

elevations. The POD curves for flaws identified as LIDSCC are shown in Figs. 4.57(a) and 

4.57(b) using log-logistic and linear-logistic fits, respectively. The POD results for LODSCC 

flaws using the same approach are shown in Fig. 4.58. In general, the CDS detection results 

using the multiple sort configuration provides the highest POD values in all cases. The single 

sort configuration on the other hand provided the lowest POD values. Based on the spread of 

the POD results from all teams that took part in manual analysis of the RR data (not shown 

here), the differences among the POD curves shown in Figs. 4.57 and 4.58 seem to be 

relatively small for this particular data set.
74

Table 4.4 Sample Excel spreadsheet containing the analysis results for a subset of SG mockup tubes, which were sorted using a Macro generated for this purpose.

SG Row Col Ampl (V) Ph (deg) Depth (%T Channel Location Charac. Code Is Flaw Overcalls

11 A 6 3.17 32 P3 3648 DTI 5.32 TEH 1 OC - 2

11 A 9 10.05 115 P1 576 DSI 0 03H 1

11 A 9 1.2 112 P1 2046 NQI 6.93 TE4 1

11 A 9 3.95 75 P1 2061 NQI 6.48 TE4 1

11 A 9 4.9 75 P1 2080 NQI 5.91 TE4 1

11 A 15 2.17 185 P1 2473 DNT 0.09 01H 0

11 A 15 14.69 33 P3 3650 DTI 0.66 TSH 1

11 A 15 24.64 44 P3 3672 DTI 0 TSH 1

11 A 18 0.79 131 P1 1144 NQI 1.67 02H 1 OC - 1

11 A 18 10.69 111 P1 1166 DSI 0.96 02H 1 OC - 1

11 A 18 4.21 187 P1 2396 DNT 0.12 01H 0

11 A 18 7.59 57 P3 3589 DTI -0.12 TSH 1 OC - 1

11 B 2 3740 NDD 0 0

11 B 4 2469 NDD 0 0

11 B 11 2.26 94 P1 2420 DSI 0.24 01H 1

11 B 11 8.78 78 P1 2436 DSI -0.24 01H 1

11 B 13 2436 NDD 0 0

11 B 18 0.84 109 P1 3332 NQI 3.79 TE1 1

11 B 18 1.11 103 P1 3354 NQI 3.12 TE1 1

11 D 10 2474 NDD 0 0

11 D 13 5.02 94 P1 1614 NQI 7.77 TE5 1

11 D 13 10.8 27 P3 3659 DTI -0.15 TSH 1 OC - 2

11 D 23 4.42 29 P3 3722 DTI -0.49 TSH 1 OC - 1

11 E 9 3820 NDD 0 0

11 F 17 12.23 115 P1 2249 DSI 0.29 01H 1

11 F 17 2.56 121 P1 2258 DSI 0.03 01H 1

11 F 17 19.42 91 P1 2264 DSI -0.16 01H 1

11 F 17 12.01 52 P1 3049 NQI 5.87 TE1 1

11 F 22 3.17 181 P1 2752 DNT 0.12 01H 0

11 G 8 28.75 70 P3 4003 DTI 0.15 TSH 1

11 G 8 53.82 62 P3 4014 DTI -0.18 TSH 1

11 G 13 5.21 26 P3 3909 DTI 5.37 TEH 1 OC - 2

11 G 13 5.21 26 P3 3911 DTI 5.31 TEH 1 OC - 2

11 G 13 4.12 19 P3 4046 DTI 1.3 TEH 1 OC - 2

11 G 14 1.21 149 P1 3145 NQI 6.32 TE2 1

11 G 14 1.33 139 P1 3163 NQI 5.77 TE2 1

11 G 14 0.9 142 P1 3547 NQI 5.97 TE1 1

11 G 14 9.26 27 P3 3942 DTI -0.52 TSH 1 OC - 1

11 H 11 4.13 128 P1 966 DSI 0.86 03H 1

11 H 11 5.83 33 P3 4124 DTI -0.18 TSH 1 OC - 1

11 J 14 0.98 146 P1 886 DSI 0.73 03H 1

11 J 14 1.95 128 P1 915 DSI -0.13 03H 1

11 J 22 2.21 184 P1 1650 DNT 0.15 02H 0

11 K 13 1.64 74 P1 957 DSI 0.06 03H 1

11 K 13 2.18 136 P1 966 DSI -0.22 03H 1

11 M 4 3.42 22 P3 3818 DTI 0.86 TSH 1

11 M 4 32.47 50 P3 3845 DTI 0.03 TSH 1

11 N 12 1.88 10 P1 2063 NQI 6.21 TE5 1

11 N 12 4.37 13 P1 2097 NQI 5.19 TE5 1

11 N 18 5.36 26 P3 4053 DTI -0.09 TSH 1

41 11
75

Table 4.5 Graded results for the sample data set shown in Table 4.4 using an ExcelTM Macro 

generated for this purpose.

FLAW ID FLAW TYPE ORIENT BC BC ID/OD Beginning End of MAX DEPTHAVE DEPTH LENGTH Expert TRUE CIRC POS FLAW ID

Row,Col,Leve TS Special VOLTS PHASE ACTUAL of flaw flaw %TW %TW mm BC Call STATE REL to

Axial Position Location Interest CALL MARKER (°) POD ID/OD Depth

(data point) Location

A09I 0576 80273 WASTAGE X 10.08 114 OD 561 593 60 NA 21.0 DSI MVI NA A090576 1 1 0 0

A09E 2046 52118 LODSCC L 1.21 112 OD 2036 2051 88 NA 12.7 NQI SAI NA A092046 1 1 0 0

A09E 2060 53681 LODSCC L 4.04 75 OD 2051 2071 88 NA 16.9 NQI SAI NA A092060 1 1 0 0

A09E 2080 54803 LODSCC L 4.9 75 OD 2071 2090 88 NA 16.1 NQI SAI NA A092080 1 1 0 0

A15D 2484 19999 DENT X 1.2 30 X 2474 2490 0 NA 13.5 DSI DNT NA A152484 0

A15A 3648 19270 LIDSCC L 24.93 26 ID 3640 3654 99 NA 11.9 NQI SCI NA A153648 1 1 0 0

A18G 1165 26254 DENT X 10.73 110 OD 1137 1179 0 NA 35.6 NQI DNT NA A181165 0

A18D 2395 19999 DENT X 4.21 186 X 2377 2412 0 NA 29.6 DNT DNT NA A182395 0

B11G 1250 19999 IGA X 0 0 OD 1229 1267 60 NA 32.2 NDD MMI NA B111250 1 0 0 0

B11D 2431 20163 LODSCC L 8.78 70 OD 2410 2447 90 NA 31.3 DSI SAI NA B112431 1 1 0 0

B18B 3348 13403 LODEDM L 1.84 102 OD 3320 3367 60 NA 21.0 NQI SAI NA B183348 1 1 0 0

D13I 0610 73523 LODEDM L 1.47 104 OD 593 626 66 NA 20.0 NQI SAI NA D130610 1 0 0 0

D13F 1614 88520 CODSCC C 5.03 94 OD 1608 1619 73 NA 7.0 NQI SCI NA D131614 1 1 0 0

D13A 3600

18124 - 

20034 CODSCC C 0 0 OD 3595 3605 98 NA 35.0 NDD MCI NA D133600 1 0 0 0

D23B 3310 50050 LODSCC L 0.59 133 OD 3297 3316 78 NA 16.1 NQI MAI NA D233310 1 0 0 0

E09B 3544 46160 LIDSCC L 3.47 8 ID 3503 3548 23 NA 38.1 DNI MAI NA E093544 1 0 0 0

F17D 2263 19712 LODSCC L 19.84 90 OD 2234 2282 97 NA 40.6 DSI MMI NA F172263 1 1 0 0

F17B 3049 50704 LODSCC L 12.19 60 OD 3028 3069 95 NA 34.7 NQI MAI NA F173049 1 1 0 0

F22D 2765 21416 LIDSCC L 1.72 19 ID 2735 2783 49 NA 40.6 DNI SAI NA F222765 1 0 0 0

G08A 4013

19550 - 

22749 LODSCC L 40.6 138 OD 3990 4031 88 NA 34.7 DTI MMI NA G084013 1 1 0 0

G13F 1968 79999 DNG X 2.01 190 X 1958 1981 0 NA 19.5 DNG NOF NA G131968 0

G14C 3163 79999 LODSCC L 0 0 OD 3150 3180 10 NA 22.0 NDD SAI NA G143163 1 1 0 0

G14B 3547 51974 CODSCC C 2.52 139 OD 3530 3560 55 NA 27.0 NQI SCI NA G143547 1 1 0 0

H11I 0967 86698 IGA X 4.36 132 OD 953 984 38 NA 26.2 NQI SVI NA H110967 1 1 0 0

J14I 0915 83174 LODSCC L 2.02 131 OD 874 930 55 NA 47.4 DSI SAI NA J140915 1 1 0 0

J22G 1665 23541 LIDSCC L 0.99 18 ID 1624 1675 83 NA 43.2 DSI SAI NA J221665 1 0 0 0

K13I 0966 78589 LODSCC L 2.21 140 OD 941 979 86 NA 32.2 DSI SAI NA K130966 1 1 0 0

K13G 1709 17285 FATIGUE C 0.42 146 OD 1508 1880 99 NA 20.0 DSI SCI NA K131709 1 0 0 0

M04A 3817

20019 - 

23097 CIDSCC C 3.51 24 ID 3803 3874 80 NA 60.1 NQI MMI NA M043817 1 1 0 0

N12F 2097 24532 LIDSCC L 4.38 13 ID 2084 2106 47 NA 18.6 NQI SAI NA N122097 1 1 0 0

N12B 3707 59999 NOF X 4.6 188 OD 3688 3718 0 NA 25.4 DNG NOF NA N123707 0

N18A 4052 20550 CODSCC C 54.47 13 OD 4032 4062 82 NA 27.0 DTI MCI NA N184052 1 1 0 0

27 19 0 0
76

(a)

(b)

Figure 4.46 Display of the CDS results for representative manually measured missed signals 

associated with (a) the dent part of the probe response detected by CDS in a TSP 

zone also containing shallow ID cracking and (b) a low amplitude OD flaw identified 

as fatigue cracking.
77

(a)

(b)

Figure 4.47 Display of the CDS results for manually measured missed signals, identified as 

shallow (a) ODSCC and (b) volumetric OD flaw at the TSP region.
78

(a)

(b)

Figure 4.48 Display of the CDS results for manually measured missed signals at TSP regions,

both identified as IGA.
79

(a)

(b)

Figure 4.49 Display of the CDS results for a signal at a dented TSP elevation of the SG mock-up.

Shown above are two separate detection calls made using the multiple sort 

configuration.
80

(a)

(b)

Figure 4.50 Display of the CDS results for a signal at a dented TSP elevation of the SG mock-up. 

Shown above are two separate detection calls made using the multiple sort 

configuration.
81

(a)

(b)

Figure 4.51 Display of the CDS results for a signal at a dented TSP elevation of the SG mock-up. 

Shown above are two separate detection calls made using the multiple sort 

configuration.
82

(a)

(b)

Figure 4.52 Display of the CDS results for two separate signals at a dented TSP elevation of the 

SG mock-up. Shown above are the detection calls made using the multiple sort 

configuration.
83

(a)

(b)

Figure 4.53 Display of the CDS results for two separate signals at dented TSP elevations of the 

SG mock-up. Shown above are the detection calls made using the multiple sort 

configuration.
84

(a)

(b)

Figure 4.54 Display of the CDS results for two separate low-amplitude signals at TSP elevations 

of the SG mock-up. Shown above are the detection calls made using the multiple 

sort configuration.
85

(a)

(b)

Figure 4.55 Display of the CDS results for two separate signals at TSP elevations of the SG 

mock-up. Shown above are the detection calls at (a) a non-dented and (b) a dented 

TSP elevation made using the multiple sort configuration.
86

Figure 4.56 Representative POD curves as a function of depth for flaws at the TSP elevations

identified as LIDSCC using the CDS configuration with multiple sorts.
87

(a)

(b)

Figure 4.57 POD curves for flaws at the TSP elevations identified as LIDSCC using the CDS 

configuration with multiple sorts (TSP2), CDS configuration with single sort (TSP1), 

composite round-robin manual analysis (RR), and expert analyst (expert) results.
88

(a)

(b)

Figure 4.58 POD curves for flaws at the TSP elevations identified as LODSCC using the CDS 

configuration with multiple sorts (TSP2), CDS configuration with single sort (TSP1), 

composite round-robin manual analysis (RR), and expert analyst (expert) results.
89

4.3.2 Analyses of Data for Free-span Elevations

The results of analyses associated with automated screening of bobbin probe inspection data 

collected from free-span elevations of the SG tube bundle mock-up is discussed next. 

Comparative tests were once again performed to determine the effect of different data 

screening setups on detection and classification of flaws. The POD results using multiple and 

single sort configurations are presented first. The effect on the POD for free-span flaw signals 

of adjusting some key setup parameters is also discussed. 

Bobbin probe inspection data from the free-span region of the SG mock-up was analyzed next 

by using the two complementary data screening configurations in Fig. 4.26 and displayed again 

in Fig. 4.59. The first data screening configuration consisted of two separate sort setups for 

detection and classification of flaw signals in the free-span. A description of the two data 

screening configurations, displayed under the Indications menu of the CDS Editor window, was 

provided earlier. In reference to Fig. 4.59(a) and (b), the primary sort employed a differential 

channel (200 kHz) while the secondary sort employed an absolute channel (100 kHz) for 

detection. Different detection algorithms were also employed in the two setups. The detection 

algorithm for the primary sort is better suited for detection of closely-spaced signals whereas the 

one used for the secondary sort is better suited for detection of longer signals. To permit 

adjustment of the measurement window size for the classification stage, a peak-to-peak 

measurement method was used in the secondary sort configuration. The inclusion of the 

secondary sort was done mainly to provide a more accurate measurement of the signal 

amplitude (voltage) for long axial indications. 

Bobbin probe data from the free-span regions was first analyzed by activating both sorts shown 

in Fig. 4.59. For both sorts, the location test logic was slightly modified to minimize the number 

of potential entries associated with detection of signals near the expansion transition region. 

This was done by defining the beginning of the free-span screening region to start at ~12.5 mm 

(0.5 in.) above the TTS. As in the past, the CDS analysis results using the two aforementioned

screening configurations were sorted and graded using the Excel spread-sheet Macros

described earlier. The detection results were then used to generate POD curves for flaws 

present in the free-span of the SG mock-up. The manual analysis results from an earlier RR

exercise and the results generated by an expert analyst were once again used to help better 

assess the POD results obtained using the automated data screening software.

Data analysis results for representative signals identified either as a missed indication or as a 

false call using the sort configurations displayed in Fig. 4.59 are presented next. The majority of 

the missed indications by CDS in the free-span region of the SG mock-up were associated with 

low-amplitude signals present either by themselves or in conjunction with tube deformation 

(ding). Nearly all missed indications exhibiting relatively large signal amplitudes were 

associated with IGA type flaws. As noted previously, this is attributed to the characteristically

unpredictable nature of the bobbin probe response from such indications. Figures 4.60–4.62

display a series of low-amplitude signals identified as free-span ODSCC, which were not 

reported by CDS. In all cases, the indications were missed because of their low signal 

amplitude which was below the voltage threshold defined in the characterization test logic. Data 

analysis results for another missed indication identified as ODSCC in the free-span region is 

displayed in Fig. 4.63. Figure 4.63(a) shows the probe response in the main EddynetTM analysis 

window. The probe response at different test frequencies is shown in Fig. 4.63(b) in multiple 

Lissajous display format. Although the flaw signal in this case is clearly detectable in the 
90

measurement channel, this indication was missed because of the out-of-range phase response 

in the test channel. It is worth noting that although the peak-to-peak voltages measured 

manually for some of the signals shown here are above the prescribed detection threshold, the 

measured values may not be identical to those made by the CDS algorithms. Any 

discrepancies between the automated and manually measured signal amplitudes are in general 

associated with the type of detection algorithm employed, which may use either a fixed or a 

user-defined measurement window size, and the background suppression method. Such 

measurement variability is more likely to occur for low amplitude signals and for complex flaws 

where the signal is not clearly discernible. 

Data analysis results for representative missed indications in conjunction with tube 

deformation (free-span dent/ding) are displayed in Figs. 4.64 and 4.65. In all those cases, the 

signal from the ODSCC flaw was masked by the larger probe response (change in fill-factor) 

associated with the tube deformation. Another example of a missed indication attributed in part 

to tube geometry change is shown in Fig. 4.66. The flaw present in that zone was identified as 

IGA (volumetric degradation). Figure 4.66(a) shows the probe response at the primary analysis 

channel in the main EddynetTM analysis window. The probe response at different test 

frequencies, in multiple Lissajous display format, is shown in Fig. 4.66(b). Improper EC phase 

response attributed to IGA is plausibly the main factor for missing or mischaracterizing this 

particular signal. 

Data analysis results for representative missed indications in the free-span region identified as 

IGA type flaws are shown in Figs. 4.67 and 4.68. In all cases, missing of the flaw signal by CDS 

was once again attributed to improper phase angle response at the primary test frequency. As 

noted earlier, no viable data classification screening configurations have been identified in this 

study for reliable detection or classification of IGA based on bobbin probe inspection data. The 

test logic of the classification stage may be bypassed (i.e., report on detection alone) or relaxed 

in order to report such signals. This approach for detection of flaws with unpredictable probe 

response, however, could result in unacceptably high overcall rates. 

An example of a long axial indication in the free-span with separate report entries for each lobe 

of the differential signal is shown in Fig. 4.69. The use of a larger window size by the secondary 

sort in this case resulted in reporting of an additional entry based on the total length of the 

signal. Two separate entries, Fig 4.69(a), (b) were made by the primary sort shown in Fig. 

4.59(a). An additional entry was reported by the secondary sort shown in Fig. 4.59(b) based on 

the total length of the signal. The true peak-to-peak amplitude of the signal, however, was not 

captured by the measurement window. Examples of false calls associated with ferromagnetic 

tube-end markers and with high level of baseline noise are presented in Figs. 4.70(a) and 

4.70(b), respectively. The signals were reported by CDS as flaws because they satisfy all the 

conditions set by the classification test logic (i.e., fall within prescribed amplitude threshold and 

phase range) for all the test channels. As discussed previously, elimination of such calls through 

incorporation of more exclusive criteria into the characterization test logic could reduce the 

conservatism in reporting of relevant signals.

The bobbin probe data from the free-span regions was next analyzed by activating only the 

primary CDS sort displayed in Fig. 4.59(a). As expected, this resulted in a small number of 

additional missed indications in comparison with the previous configuration employing both 

sorts. Examples of signals not reported as a result of deactivating the secondary sort are 

shown in Fig. 4.71. The signal associated with an ODSCC indication in the free-span of the SG 
91

mock-up is shown in Fig 4.71(a). The reason for missing the signal in this case was attributed 

to its close proximity to the end of the tube section. Although no flaws are expected to be 

present in the mockup within 25 mm (1 in.) of the tube-end, variations in probe speed and in 

turn the acquisition sampling rate could lead to small variations in axial scaling of data. This can 

consequently result in small variations in locating the landmarks using the automatic landmark 

detection algorithm. Data analysis result for another missed indication is shown in Fig. 4.71(b). 

In this case, missing of the signal was attributed to the size of the measurement window used 

by the primary sort. This signal was detected using the previous screening configuration 

because of the larger measurement window used by the secondary sort configuration. The use 

of a larger window size resulted in capturing the entire signal which allowed proper 

measurement of its peak-to-peak amplitude which was above the voltage threshold for reporting 

by the classification test logic. 

The POD results for the free-span of the SG mock-up using the two data screening 

configurations discussed above are presented in Fig. 4.72. Separate POD curves were 

generated for the automated analysis results using the two-sort configuration (denoted as FS2) 

and the single sort configuration (denoted as FS1). To help better assess the results, the POD 

curves for CDS are once again plotted along with the previously established composite POD 

curve from the manual analysis of the data by the RR team (denoted as RR) and that from an 

expert analyst (denoted as expert). As noted previously, unlike the CDS and the manual 

analysis of RR data, the analysis by an expert was not performed in a totally blind test fashion, 

which may have influenced the corresponding POD results. The analysis results were fitted 

using both linear-logistic and log-logistic curves. The linear-logistic is expected to produce more 

conservative estimates of the detection probability. The POD curves were generated only for 

flaws identified as LODSCC in the free-span region of the SG mock-up. Separate POD curves 

for other flaw types present in that region were not generated because the database did not 

include a statistically acceptable number of flaws. Once again a small false call rate value was 

used to generate the POD curves in these studies. The fixed value was chosen based on the 

results from manual analysis of the EC inspection data by the RR teams. 

The POD curves for free-span ODSCC flaws using a linear-logistic fit is shown in Fig. 4.72(a). 

The results based on a log-logistic fit are shown in Fig. 4.72(b). As expected, the deviation 

between the POD results for the two automated screening configurations was small. However, 

the number of false calls was significantly different for the two data screening configurations. 

The number of overcalls using the first (multiple sorts) and the second (single sort) configuration 

were 175 and 81, respectively. The small increase in POD as a result of incorporating a 

secondary sort resulted in a measurably larger overcall rate. In reference to Fig. 4.72, the 

detection probability with CDS is higher than that from the composite RR analysis results and 

closer to the POD curve for the expert analyst results. 

Bobbin probe inspection data collected from all the free-span regions of the SG mock-up was 

next used to further assess the influence of some key CDS setup parameters on the detection 

of flaw signals. The POD results were then compared to those obtained using the CDS 

configurations discussed previously. Inspection data from the free-span was chosen for this 

study because the data screening configurations for those regions employ a smaller number of 

setup parameters compared to those used for the TSP elevations. Modification of the setup 

parameters included increasing of the amplitude threshold (T), reduction of phase depth window

(D), and a combination of both (TD). Thus, for the free span region two-sort, modified amplitude 

threshold, a notation of FS2_T is used, and so on. The modifications were included in the 
92

classification test logic for reporting of signals. The modified sort configurations were then 

applied to the entire dataset. 

Figure 4.73 displays the CDS editor window for the first modified setup, which includes two sort 

configurations for screening of bobbin probe data from the free-span elevations. The setup 

shown in Fig. 4.73(a) incorporates a differential channel and the one in Fig. 4.73(b) incorporates 

an absolute channel in its detection logic line, respectively. In comparison with the original setup 

examined previously, the detection threshold in the classification test logic was increased by 

0.1v (from 0.5v to 0.6v) in the first configuration and by 0.2v (from 0.5v to 0.7v) in the second 

configuration. The small increase in the threshold values here is analogous to the modification 

of setup parameters for site-specific applications to compensate for higher level of noise present 

in the EC inspection data (Ref. 5). The second modified setup used for the comparative 

assessments here is shown in Fig. 4.74. The lower limit of the depth window for reporting of 

flaw signals in this case was changed to ten percent, from its original value of zero percent, in 

the classification logic line. It should be noted that with the depth and the phase angle being 

dependent parameters, increasing the lower limit of the depth window is equivalent to reducing 

of the phase window defined in the classification logic line. As such, the depth adjustment will 

affect the detection of both shallow OD and shallow ID indications. Based on the phase 

calibration curve for the classification test channel (P1:400|100 kHz mix), changing the lower 

limit of the depth parameter here to 10 percent translates to a reduction of approximately 7° in 

the upper bound of the phase window (affecting detection of OD originated signals) and 

approximately 7° in lower bound of the phase window (affecting detection of ID originated 

signals). 

The CDS configurations discussed above were sequentially applied to the screening of bobbin 

probe inspection data collected from free-span regions of the SG mock-up. The analysis results 

for representative signals, which were missed as a result of modifying one or more of the test 

logic parameters, is displayed in Figs. 4.75 to 4.77. Figure 4.75 shows two previously detected 

flaw signals which were not reported by the modified CDS setups displayed in Fig. 4.73. Per 

Fig. 4.73, as indicated by the results, slight changes in voltage threshold or phase limits may 

result in missing of indications. The flaw signals in Figs. 4.75(a) and (b), both identified as axial 

ODSCC, were missed as a result of using a higher detection threshold in the classification test 

logic. Figure 4.76 shows representative missed indications using the CDS setup displayed in 

Fig. 4.74. The flaw signals, identified as circumferential and axial ODSCC, were not reported 

because their phase angle lies outside the prescribed flaw reporting window. Two other 

examples of missed indications attributed to the modified setup parameters are shown in Fig. 

4.77. The signal displayed in Fig. 4.77(a) was not reported because of its phase response. The 

signal displayed in Fig. 4.77(b) on the other hand was not reported by either one of the modified 

sort configurations shown in Figs. 4.73 and 4.74. 

A set of POD curves was subsequently generated using the results of analyses on the entire 

free-span data set with the optimized and the modified CDS configurations discussed above.

Once again, the optimized configuration here is in reference to the CDS setup shown in Fig. 

4.59 and the modified configurations are in reference to those shown in Figs. 4.73 and 4.74. It 

is worth noting that incorporation of a larger number of samples in the available database, 

particularly shallow indications, is expected to improve the reliability of POD calculations. To 

assess the viability of combining data from different flaw types, POD curves were first generated 

based on the detection results with the optimized sort configurations. Figure 4.78 shows the 

POD curve generated using the detection results for all flaws identified as SCC (axial and 
93

circumferential SCC of both OD and ID origin) and that generated using only the detection 

results for axial SCC (LODSCC) flaws. Figures 4.78(a) and 4.78(b) display the POD curves 

using log-logistic and linear-logistic fits, respectively. The results here indicate a negligible shift 

in the detection probability using different combinations of SCC flaws. Subsequently, composite 

POD curves were generated using the detection results for all the SCC flaws available in the 

database. The POD curve using the optimized CDS setup parameters and those using the 

modified setup parameters are presented in Fig. 4.79. The three modified configurations 

included the setups shown in Figs. 4.73, the setups shown in Fig. 4.74 and a combination of 

both. While the POD curves for all modified setups exhibit a shift to the right (reduction in 

detection probability), the largest shift is associated with the CDS setup with combined 

amplitude threshold and depth window adjustment. The results of comparative tests here 

generally indicate that relatively small adjustments of the test logic parameters can have 

measurable effects on the detection probability. It is also worth noting that the statistical 

distribution of the flaw sizes in a database will influence the level of change in the POD results.
94

(a)

(b)

Figure 4.59 Display of the CDS editor dialog box for the setups used for screening free-span 

indications. Shown here are the two sorts under the Indications list and their 

associated setup parameters. The detection logic line displays (a) a differential 

detection channel used for the first and (b) an absolute detection channel used for 

the second setup, respectively.
95

(a)

(b)

Figure 4.60 Display of data analysis results for representative signals at level-B elevation of the 

SG mock-up which were not reported using the CDS setup displayed in Fig. 4.59.
96

(a)

(b)

Figure 4.61 Display of data analysis results for representative signals at level-H elevation of the 

SG mock-up which were not reported using the CDS setup displayed in Fig. 4.59.
97

(a)

(b)

Figure 4.62 Display of data analysis results for representative signals at (a) level-F and (b) levelH of the SG mock-up which were not reported using the CDS setup displayed in Fig.

4.59.
98

(a)

(b)

Figure 4.63 Display of data analysis results for a signal at the level-F elevation of the SG mockup which was not reported using the CDS setups displayed in Fig. 4.59. The probe 

response is shown in the (a) main analysis window and (b) in multiple Lissajous 

window at different frequencies.
99

(a)

(b)

Figure 4.64 Display of data analysis results for representative signals at the level-I elevation of 

the SG mock-up which were not reported using the setups displayed in Fig. 4.59.
100

(a)

(b)

Figure 4.65 Display of data analysis results for representative signals at (a) level-B and (b) levelH elevation of the SG mock-up which were not reported using the setups displayed 

in Fig. 4.59.
101

(a)

(b)

Figure 4.66 Display of data analysis results for a signal at the level-H elevation of the SG mockup which was not reported using the CDS setups displayed in Fig. 4.59. The probe 

response is displayed in the (a) main analysis window and (b) in multiple Lissajous 

window at different frequencies.
102

(a)

(b)

Figure 4.67 Display of data analysis results for representative signals at the level-F elevation of 

the SG mock-up which were not reported using the CDS setups displayed in Fig. 

4.59.
103

(a)

(b)

Figure 4.68 Display of data analysis results for representative signals at the level-H elevation of 

the SG mock-up which were not reported using the CDS setups displayed in Fig. 

4.59.
104

(a)

(b)

Figure 4.69 Display of representative CDS analysis results for a long axial OD flaw for two 

separate entries in the free-span of the SG mock-up.
105

 

(a)

(b)

Figure 4.70 Display of the CDS analysis results associated with representative false calls in the 

free-span of the SG mock-up using the sort configuration of Fig. 4.59(b).
106

(a)

(b)

Figure 4.71 Display of data analysis results for representative signals in the free-span of the SG 

mock-up which were not reported using the CDS setup displayed in Fig. 4.59(a).
107

(a)

(b)

Figure 4.72 POD curves for free-span (FS) flaws identified as LODSCC using the CDS sort 

configurations shown in Fig. 4.59.
108

(a)

(b)

Figure 4.73 Display of the CDS editor dialog box for the modified configuration used to screen 

the free-span region of the SG mock-up. Shown here are the setup parameters for 

the sort using (a) a differential channel and (b) an absolute channel for detection.
109

(a)

(b)

Figure 4.74 Display of the CDS editor dialog box for the modified configuration used to screen 

the free-span of the SG mock-up. Shown here are the setup parameters for the sort 

configuration using (a) a differential channel and (b) an absolute channel for 

detection.
110

(a)

(b)

Figure 4.75 Display of CDS results for representative free-span signals. Shown here are flaws 

identified as axial ODSCC at (a) level-B and (b) level-H.
111

(a)

(b)

Figure 4.76 Display of CDS results for representative free-span signals. Shown here are flaws 

identified as (a) circumferential ODSCC at level-B and (b) axial ODSCC at level-C.
112

(a)

(b)

Figure 4.77 Display of CDS results for representative free-span signals. Shown here are flaws 

identified as axial ODSCC at (a) level-H and (b) at level-F.
113

(a)

(b)

Figure 4.78 POD curves for free-span (FS) flaws identified as SCC (axial and circumferential SCC 

of both OD and ID origin) and LODSCC (axial ODSCC) using the optimized CDS 

configuration shown in Fig. 4.59.
114

(a)

(b)

Figure 4.79 POD curves for free-span (FS) level flaws identified as SCC (axial and circumferential 

SCC of both OD and ID origin) using the optimized configuration shown in Fig. 4.59

(FS2) and the modified configurations shown in Figs. 4.73 (FS2_T) and 4.74 (FS2_D)

for (a) linear-logistic and (b) log-logistic fits.
115

4.3.3 Analyses of Data for Tubesheet Elevations

Limited studies were carried out to assess the ability of automated data screening to identify 

flaw signals in the bobbin probe data from the tube-sheet elevation of the SG mock-up. Unlike 

the free-span and the TSP elevations, manual analysis of bobbin probe inspection data from TS 

sections was deemed unreliable due to excessive levels of noise associated with the 

mechanical expansion of the tubes. Therefore, data from the TS was not evaluated as part of 

the past round-robin exercise. Furthermore, bobbin probe techniques are not generally 

considered reliable for detecting and characterizing cracking in close proximity to the TS 

expansion transition region. Higher resolution inspection methods such as rotating probe 

techniques are used for more reliable examination of those regions. As such, the CDS results 

for the TS elevations of the SG mock-up were evaluated in a qualitative manner. The 

automated analysis results suggest that, in comparison with other elevations of the SG mockup, flaws near the TTS region may be detected with comparable probability but with a 

significantly higher number of false calls.

The lowest level of the tube sections in the SG mock-up simulates the tube-sheet geometry. 

The bottom portion of each 30.5-cm (12-in.) long Alloy 600 tube section is mechanically 

expanded (hard rolled) into individual ~15.2-cm (6-in.) carbon steel collars. The small region 

near the TTS where the tube transitions from the expanded diameter to the nominal diameter 

simulates the expansion transition region and is located roughly in the middle of the TS-level 

tube sections. The majority of expansions were done at Argonne and a small subset of TS level 

tubes was supplied by an outside vendor. Comparison of EC inspection data between the two 

types of expansions suggests that on average those manufactured at Argonne have measurably 

higher baseline noise. The higher level of noise is attributed to the mechanical rolling process 

employed by the machine shop at Argonne. The manufactured flaws present in the TS level, 

which include both axial and circumferential cracks of ID and OD origin, are located mostly at or 

near the expansion transition region. 

Based on expert opinion sought prior to conducting past round robin exercises, it was decided 

that the EC signals near the TTS (upper edge of the collar) and at the expansion transition 

areas of the SG mock-up are representative of those found in the field. However, based on the 

results from manual analysis of data with bobbin and rotating probe, it was later concluded that 

on average the level of noise present in the TS level of the SG mock-up was significant enough 

to hinder the ability of the analysts to reliably detect and characterize flaws in that region. The 

non-uniform geometry of the mechanically expanded portion and of the expansion transition 

region were determined to be the primary contributors to the relatively large number of overcalls 

in the TS level. Additionally, a fraction of false calls recorded by analysts were attributed to 

unintentional damage introduced by the mechanical expansion process and the final tube-collar 

assembly. 

Bobbin probe data from the TS level of the SG mock-up was analyzed using the CDS software 

while keeping in perspective the issues discussed above. The CDS setups generated earlier 

based on examining the subset of tubes in the training dataset were used to assess the ability of 

automated data screening algorithms to detect flaw signals at the TS region of the SG mock-up. 

The test section was divided into two overlapping regions denoted as the TS inner region—

covering the entire TS section except for a 25-mm (1-in.) zone at the top and a 25-mm (1-in.) 

zone at the bottom edge of the TS collar—and the TTS region—covering 25 mm (1 in.) above 

and 50 mm (2 in.) below the top edge of the TS collar including the expansion transition region. 
116

The CDS setups for the two sort configurations denoted as TS and TTS under the Regions and

Indications setup area of the CDS Editor window is shown in Figs. 4.80(a) and 4.80(b), 

respectively. In both setups, the detection was based on the three-frequency mix (“turbo mixTM”) 

process channel (P3) that is commonly used for simultaneous suppression of the bobbin probe 

response from the TS edge and the expansion transition geometry. For a 1.27-mm (0.05-in.) 

tube wall thickness the mix, denoted here as P3, combines the data from the 400 kHz, 200 kHz, 

and 100 kHz frequencies to suppress the composite probe response from the abrupt change in 

electrical properties (ferritic steel collar) and the tube geometry change both occurring at the 

upper edge of the TS. The same P3 was selected as the detection and as the measurement 

channel in the classification test logic line for both the TS and TTS sort configurations. In 

reference to Fig. 4.80(a), an additional line was included in the TS sort classification test logic 

by selecting P3 and P1 (400|100 kHz TSP suppression mix) as the measurement and test 

channels, respectively. The choice of channels for the detection and classification of potential 

flaw signals is supported by the fact that the residual signal from P3 is considered as the 

primary indicator of flaw signals present in close proximity to the expansion transition region 

while P1 is expected to provide confirmatory information about signals present within the TS 

and away from its edge. It is worth noting that, because P3 is used as both the measurement 

and the test channel in the classification logic of the TTS sort, the reported values are 

essentially an indicator of detection alone and may not be used for characterization purposes as 

no calibration curve was generated for that channel.

Representative bobbin coil data from selected tube sections in the TS level of the SG mock-up 

with varying levels of noise are shown next. Figures 4.81-4.89 display measurements made in 

test sections with different levels of noise including inside the TS, covered by the TS sort 

configuration, and at the expansion transition region, covered by the TTS sort configuration. In 

all cases the signal is shown for both P3 and P1, which are the channels used in the 

measurement and classification test logic of the sort setups discussed above. Figures 4.81-

4.83 display three tube sections, which are representative of the range of baseline noise present 

in the TS-level tubes. The measured peak-to-peak values from P3 in these cases vary between 

0.85v and 1.6v with the associated measurements from P1 varying between 0.1v and 5.7v. The 

range of variation in the baseline noise here is a good indicator of the challenge involved with 

selecting an appropriate amplitude threshold for detection of signals in that region. As noted 

before, optimization of the threshold always poses a tradeoff between the detection probability 

and the false call rate. Examples of measured bobbin probe signals from un-flawed expansion 

transitions with relatively smooth geometries are shown in Figs. 4.84-4.86. The largest value of 

the mix residual signal measured from P3 in those tubes is less than 2.5v. Representative 

expansion transitions, identified as un-flawed test sections, with significantly higher mix residual 

signal are shown in Figs. 4.87-4.89. These false call signals resulted from using the sort 

configuration as specified in Fig. 4.80(b). Once again, these examples demonstrate the 

challenge associated with setting of the test logic parameters for reporting of flaws at the 

expansion transition regions of the SG mock-up. 

The CDS sort configurations displayed in Fig. 4.80 were initially used to analyze the bobbin 

probe data from TS sections of the SG mock-up. As noted earlier, the two overlapping sorts 

jointly cover the region extending approximately from 25 mm (1 in.) above the bottom edge to 

25 mm (1 in.) above the top edge of the TS collar. Preliminary evaluation of the results in this 

case indicated that the selected data screening parameters for the TS sort configuration of Fig. 

4.80(a) led to an unacceptably large false call rate in that section. As it was discussed earlier, 

the high false call rate was attributed mainly to non-uniform geometry of the mechanically 
117

expanded portion of the tube. In view of the limitations of the bobbin probe technique and of the 

alternative characterization test logic, no further attempts were made to adjust the sort 

parameters that might help in reducing the number of overcalls in that region of the mock-up.

For the next test the TS sort shown in Fig. 4.80(a) was deactivated and the screening of data 

was limited to a short section of each tube covered by the sort configuration shown in Fig. 

4.80(b). As mentioned above, this region is denoted as TTS in the Indications column of the 

CDS Editor dialog box, extending from TTS-2.0 in. to TTS+1.0 in. The elimination of the region 

inside the TS section from screening by CDS resulted in exclusion of a number of shallow 

indications in the database. Therefore, because of the bias in the statistical distribution of the 

remaining flaws, development of a POD curve was deemed as unreliable. As such, the CDS 

results for that region of the SG mock-up were evaluated only in a qualitative manner. 

The analysis results based on the CDS sort parameters shown in Fig. 4.80(b) indicated that 

nearly all consequential indications near the TTS were detected. The false call rate—defined as 

the number of false calls divided by the number of unflawed test sections—of >30 percent, 

however, was significantly higher than those from the TSP and the free-span elevations of the 

SG mock-up. Figures 4.87-4.89 display representative entries for un-flawed test sections 

reported as flaws by CDS. The false calls in all cases were attributed to the large signal from the 

abrupt change in geometry at the expansion transition region. Figures 4.90-4.94 display the

indications at or near the TTS, which were not reported by CDS using the setup described in 

Fig. 4.80(b). In Figs. 4.90-4.92, 4.94 the flaws were identified as axial IDSCC. In Fig. 4.93, the 

flaw was identified as circumferential IDSCC. In all those cases the small amplitude of the signal 

was determined to be the cause for missing the flaw. It is worth noting that, although the test 

logic parameters may be adjusted to detect the majority of the missed signals shown here, this 

in turn is expected to result in a significant increase in the number of false calls. Also, the flaw 

signals above the TTS that are located away from the expansion transition region may further 

be detected by increasing the coverage of the free-span sorts. 
118

(a)

(b)

Figure 4.80 Display of the CDS editor dialog box for the setups used for screening of the tubesheet level for (a) TS Indications and (b) TTS Indications.
119

(a)

(b)

Figure 4.81 Display of bobbin coil inspection data (low baseline noise) peak-to-peak 

measurement of the baseline signal at an arbitrary TS location from (a) P3 in the 

main analysis window and (b) P1 and P3 in a multiple Lissajous display.
120

(a)

(b)

Figure 4.82 Display of bobbin coil inspection data (high baseline noise) peak-to-peak 

measurement of the baseline signal at an arbitrary TS location from (a) P3 in the 

main analysis window and (b) P1 and P3 in a multiple Lissajous display.
121

(a)

(b)

Figure 4.83 Display of bobbin coil inspection data (high baseline noise) peak-to-peak 

measurement of the baseline signal at an arbitrary TS location from (a) P3 in the 

main analysis window and (b) P1 and P3 in a multiple Lissajous display.
122

(a)

(b)

Figure 4.84 Display of bobbin coil inspection data (smooth expansion transition) peak-to-peak 

measurement of the residual expansion transition signal from (a) P3 in the main 

analysis window and (b) P1 and P3 in a multiple Lissajous display.
123

(a)

(b)

Figure 4.85 Display of bobbin coil inspection data (smooth expansion transition) peak-to-peak 

measurement of the residual expansion transition signal from (a) P3 in the main 

analysis window and (b) P1 and P3 in a multiple Lissajous display.
124

(a)

(b)

Figure 4.86 Display of bobbin coil inspection data (smooth expansion transition) peak-to-peak 

measurement of the residual expansion transition signal from (a) P3 in the main 

analysis window and (b) P1 and P3 in a multiple Lissajous display.
125

(a)

(b)

Figure 4.87 Display of CDS results from a representative TS level tube section (rough TTS 

expansion transition) from (a) P3 in the main analysis window and (b) P1 and P3 in a 

multiple Lissajous display.
126

(a)

(b)

Figure 4.88 Display of CDS results from a representative TS level tube section (rough TTS 

expansion transition) from (a) P3 in the main analysis window and (b) P1 and P3 in a 

multiple Lissajous display.
127

(a)

(b)

Figure 4.89 Display of CDS results from a representative TS level tube section (rough TTS 

expansion transition) from (a) P3 in the main analysis window and (b) P1 and P3 in a 

multiple Lissajous display.
128

(a)

(b)

Figure 4.90 Display of manual data analysis results for a TS level tube section with a flaw near 

the expansion transition region in the (a) main analysis window and (b) in multiple 

Lissajous window at P1 and P3.
129

(a)

(b)

Figure 4.91 Display of manual data analysis results for a TS level tube section with a flaw near 

the TTS region in the (a) main analysis window and (b) in multiple Lissajous window 

at P1 and P3.
130

(a)

(b)

Figure 4.92 Display of manual data analysis results for a TS level tube section with a flaw near 

the expansion transition region in the (a) main analysis window and (b) in multiple 

Lissajous window at P1 and P3.
131

(a)

(b)

Figure 4.93 Display of manual data analysis results for a TS level tube section with a flaw below 

the TTS in the (a) main analysis window and (b) in multiple Lissajous window at P1 

and P3.
132

(a)

(b)

Figure 4.94 Display of manual data analysis results for a TS level tube section with a flaw below 

the TTS in the (a) main analysis window and (b) in multiple Lissajous window at P1 

and P3.
133

SUMMARY AND CONCLUSIONS

Automated data analysis software may be employed during ISI of SG tubes as replacement for 

the primary, secondary, or both human analyst teams. The use of computerized data screening 

tools can improve the efficiency and consistency of data analysis. AA algorithms that employ 

threshold based detection and rule-based classification algorithms require access to a 

statistically significant database of representative signals to ensure reliable application. 

Optimization of setup parameters in general is a tradeoff between detection probability and false 

call rate. Better understanding of the test logic algorithms and the associated critical 

parameters as well as the intrinsic limitations of a particular inspection technique can help 

improve ISI reliability.

The results of evaluations performed at ANL on the application of industry-standard 

computerized data screening software to eddy current examination of SG tubes was presented. 

Following the initial review of the available literature, a prominent software-based tool, the 

Computer Data Screening (CDSTM) module of the eddy current data analysis software 

Eddynet®Suite, from Zetec Inc., was acquired and used to automatically analyze bobbin probe 

data collected previously from the ANL-NRC SG tube bundle mock-up. Initially, a subset of that 

data was used for site-specific performance evaluations. The results of those initial tests were 

used to iteratively adjust the data screening parameters to further evaluate their influence on the 

detection and classification of signals. Alternative configurations of the screening parameters 

for different regions of the SG mock-up were further examined. The detection probability and 

false call rate were evaluated to help determine the influence of selected data screening 

parameters. The AA results in all cases were sorted out using ExcelTM with special purpose 

macros generated at Argonne. The results of analyses performed on eddy current inspection 

data from different elevations of the SG mock-up was discussed. 

Bobbin probe inspection data collected previously from all elevations (free-span, TSP, TS) of 

the SG mock-up were analyzed using the CDS program. Data from all free-span and TSP 

elevations were processed by CDS and the analysis results were subsequently used for 

comparative POD studies. Comparisons were made between the detection probability results 

based on automated analysis with that based on manual analysis of data from a previous roundrobin exercise. Unlike the free-span and the TSP elevations, analysis of bobbin probe 

inspection data from TS sections was deemed unreliable due to excessive level of EC noise 

associated with the mechanical expansion of the tubes. It is worth noting that bobbin probe 

techniques are not generally considered reliable for detection and characterization of flaws in 

the TS region. As such, the CDS results for TS elevations of the SG mock-up were evaluated 

only in a qualitative manner. In general, the results of analyses based on bobbin probe 

inspection from the free-span and TSP elevations suggest that automated data screening tools 

can provide comparable or better detection capability than that achieved by manual analysis 

when comparatively higher false call rates are tolerated. The higher POD values by AA software 

were achieved through incorporation of multiple data screening setups for the same region of 

tubing. The results also suggest that, in comparison with other elevations of the SG mock-up, 

flaws near the TTS region may be detected with comparable probability to other elevations but 

with significantly higher false call rate. Some notable considerations regarding application of AA 

tools for SG tube examinations are summarized below.

A number of factors could affect the performance of conventional AA programs. More notable 

factors include inadequate or overly constrained algorithms, modification of previously qualified 
134

setup parameters, data quality issues, influence of noise (low S/N), incomplete tube coverage, 

incorrect identification of support structures (landmarks), and inherent limitations of a particular 

NDE technique. 

Intrinsic to the rule-based nature of expert system algorithms, the possibility of missing new or 

unexpected forms of degradation exists if representative signals are not included in the 

qualification database used for optimization of AA software. A comprehensive database of 

tubes that realistically represent the test conditions encountered during field examinations must 

be available to fully optimize data screening parameters. As such, direct application of 

screening parameters established based on a review of data in the AAPDD that are provided in 

generic ETSSs may not be appropriate for field implementation of AA tools. The qualification 

database for site-specific performance demonstrations should consist of representative tubes 

and the current level of EC noise present in a particular SG. The reliability of AA results could 

further be improved through inclusion of a larger number of potential flaw types from inactive 

damage mechanisms based on the operating history at plants with similar SG units. A more 

inclusive performance demonstration database would allow incorporation of complementary 

data screening algorithms to help increase the detection probability while allowing more robust 

optimization of data screening parameters to keep the overcall rate to a manageable number.

Automated data analysis algorithms for bobbin probe examinations are typically set to provide 

complete coverage of the entire tube length. Past experience with field application of AA 

software, however, suggests that coverage is one possible contributor to missing of relevant 

signals even when the detection algorithms are fully optimized. Flaw signals could be omitted if 

they are located within possible gaps between neighboring regions of a tube covered by 

separate data screening configurations. A conservative approach to ensure continuous 

coverage is to have some overlap between adjacent data screening regions. Additionally, the 

measurement window size (when applicable) should be adjusted to ensure that signals, 

particularly those located near the interface between two regions of interest, are properly 

captured. Unintentional errors associated with improper assignment of data acquisition 

direction (positive/negative) could potentially lead to incorrect setting of the coverage by a 

particular data screening configuration. It is worth noting that, availability of a visual display that 

would automatically provide information about the extent of coverage could help reduce the 

possibility of leaving gaps in the EC inspection data.

Conventional AA tools commonly employ location-dependent data analysis algorithms. Correct 

identification of landmarks (hot/cold leg tube ends, tube support structures, 

dimensional/geometry transitions, etc.) in a particular SG design is therefore critical to their 

proper operation. Identification and labeling of landmarks in a SG is typically performed in an 

automated manner. When performed automatically, the positional scaling is done based on

data sampling rate obtained using the probe speed and the acquisition digitization rate (Refs. 1, 

6). Variations in probe speed and in turn the sampling rate could lead to small variations in axial 

scaling of data. This could result in small variations in locating the landmarks using the 

automatic landmark detection algorithm. More elaborate error checking capability could help 

with detecting such problems and in turn with identifying the source of discrepancy when the 

calculated positional information does not precisely match with the information provided in the 

SG landmark table. The AA process may only be allowed to continue once the landmark labels 

are corrected either manually or by making necessary adjustments to automatically located SG 

landmarks. The possibility, however, exists for AA software to process the data from a SG tube 

with mislabeled or omitted landmarks. This can result in improper application of location-
135

dependent algorithms and consequently missing of reportable signals. The same problem could 

also affect screening of data from SG tubes with displaced landmarks. Finally, as noted above, 

unintentional errors associated with improper assignment of data acquisition direction could lead 

to incorrect labeling of landmarks. 

Although AA algorithms could be optimized to provide improved detection capability over 

manual analysis, the possibility exists for visually discernable signals to go undetected using 

automated data screening tools. Some leading factors that could result in unintended errors 

when using AA tools were discussed above. With stringent oversight, the automated data 

analysis process can help improve the reliability of ISI results. This is an important

consideration when AA tools are discussed as potential replacement for both the primary and 

the secondary analyst team. Inclusion of manual analysis as an integral part of the ISI data 

analysis process can both help resolve potential discrepancies between independent AA tools

and between reported redundant entries by alternative or overlapping data screening 

configurations of the same AA tool. Incorporation of adjustments to the measured signals by AA 

algorithms may also be necessary particularly when alternate repair criteria are implemented.

137

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DC (2009).

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

Eddy Current Reliability from Steam Generator Mock–Up Round Robin, NUREG/CR-6785, 

ANL–01/22, U.S. Nuclear Regulatory Commission, Washington, DC (2002).

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Parameters in Examination Technique Specification Sheets, Technical Letter Report, U.S. 

Nuclear Regulatory Commission, Washington, DC (2009).

6. S. Bakhtiari, D. S. Kupperman, and W. J. Shack, Assessment of Noise Level for Eddy 

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NUREG/CR-7217 Application of Automated Analysis Software to Eddy Current Inspection Data 

from Steam Generator Tube Bundle Mock-up