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EPRI Steam Generator Management P O i f R t NDEProgram – Overview of Recent NDE

Research ActivitiesJames Benson

Technical Executive

5th International CANDU In-Service Inspection Workshop in conjunction with the NDT in Canada 2014 Conferenceconjunction with the NDT in Canada 2014 Conference

June 16-18, 2014

Recent Steam Generator NDE Research ActivitiesActivities

1. Development of Simulation Software for SG Eddy Current InspectionsCurrent Inspections

2. Development of SG Eddy Current Automatic Data Analysis AlgorithmsAnalysis Algorithms

2© 2014 Electric Power Research Institute, Inc. All rights reserved.

D l t f Si l ti S ft fDevelopment of Simulation Software for SG Eddy Current Inspections

Benefits of SG Tube Simulation (SGTSIM) Model• Predict EC probe signals for different SG tube defect geometries• Determine effect of probe wobble, test frequency, sludge

characteristics on probe measurements• Visualization of field/flaw interaction• Optimization of sensor/system design• Optimization of sensor/system design• Test bed for generating defect signatures• Useful in Probability of Detection (POD) models at low costy ( )• Assist in determining root cause of complex signals

K Ad f Si l i M d lKey Advantages of Simulation Model:• Provides an inexpensive and fast method to simulate realistic

defect geometries in steam generator tubing


g g g

Practical Applications of Simulator SoftwareSoftware• Utility Engineers− Assist in complex signal interpretation & Tube Integrity Assessments− Assist in POD calculations

• Inspection vendors– Assist in signal interpretationg p– Assist in SSPD development

• Probe developers– Aid in probe design– Aid in probe design

• NDE instructors– Training tool

G t i l f t i i d t– Generate signals for training data• Researchers / Qualifying Institute

– Generate signals for probe technique qualification (ETSS’s)


– Generate signals for performance demonstration (QDA/AAPDD)

SG Tube Simulator (SGTSIM) Features -Summaryy

Probes

B bbi X Probe

Tube Geometry

Support Pl

TubeSh

Free SBobbin

Pancake

X-Probe Plate Sheet Span

Defect Geometry+ Point(.115) Defect Geometry

Location Freespan

Orientation Circ

ShapeOther features: Freespan TSP TTS ID/OD

Circ Axial

Circular Elliptical Rectangular Real Cracks

Calibration (Manual/ Auto)

Data Export in MIZ Format

B t h P i Real Cracks Batch Processing


Results are validated with experimental eddy current test data

Example of Simulated Signals Exported into Eddynet Environment

Plus Point® Signal from OD Axial Flaw 0.3”L x 40% TW

50 kHz

200 kHz

300100


300 kHz

00kHz

Example of Simulated Signals Exported into Eddynet Environment

Plus Point® Signal from OD Axial Flaw 0.3”L x 40% TW

50 kHz 100 kHz 200 kHz 300 kHz


50 kHz 100 kHz 200 kHz 300 kHz

Examples of Simulated Signal Exported and Displayed in Eddynetand Displayed in Eddynet


Simulated 100% Ax Notch +Point® 200kHz Experimental 100% Ax Notch +Point 200kHz

Example of Exporting Data From a Simulated Flaw SignalFlaw Signal • Simulated data from a single channel can be exported in

Eddynet format y• Simulated data from +Point® and pancake coil probes has

been successfully injected into field data


Lissajous & 1D strip plots 3D plot

Example Simulation of a Real Crack – Experimental ValidationExperimental Validation

Crack Profile from MET data

Experimental Signals(Field Data)

+Point ® Probe Data @ 300KHz

MET data

Simulated Signals


Vertical Channel Horizontal Channel

Example Simulation of Tube-to-Tube Wear Bobbin Probe simulation compared to actual ECT datap

380 kHz

Length = 10”

Max Depth 0.019” (50% TW)

Simulated Symmetric Wear Depth Profile

Dep

th

Dep

th (m

)

Max Width= 0.194”

∆ W

ear

from

Max

D


Wear Length (m)

Example Simulation of Tube-to-Tube Wear Bobbin Probe simulation compared to actual ECT datap

380 KHz

Length = 10”

Depth 0.019” (50% TW)

Maximum Width=0 194”

Simulated Non-Symmetric Wear Depth Profile

Dep

th

epth

(m)

Maximum Width=0.194”

Wear Length (m)

∆ W

ear D

from

Max

De


Wear Length (m)

Experimental ValidationArray Coil on X-Probe®: Axial Channel – Vertical – (190 kHz)

Tube OD: 0.628’’; ID: 0.552”; Tube-to-tube wear length 10.1’’; Depth 50%Axial(real) Axial(real)


Experimental signals Simulated signals

Development of SG Eddy CurrentDevelopment of SG Eddy Current Automatic Data Analysis Algorithms

Benefits of Automated Data Analysis System

• Faster analysis of large volumes of data compared to manual data analysis.manual data analysis.

• Increase in consistency of degradation detection.

• Increase in accuracy of degradation characterization.

• Addresses concerns about future workforce shortages by g ystoring expert knowledge.

• Eliminate concerns about operator fatigue.Eliminate concerns about operator fatigue.


Typical Data Analysis Modules

Preprocessing• Structure detection

Raw Data Calibration • Filtering

• Frequency Mixing• Adaptive Thresholding

ROI Detection

Feature Selection

Classification


Classification (Feature Selection, Rule-Base)

Data Analysis Algorithm Development and Validation ProcessValidation Process

Training Data* - w/ Analysis Results

Algorithm DevelopmentAlgorithm Development

Test Data* - Blind Results

* - Data is from EPRI Automatic Analysis Performance

Algorithm Optimization


- Data is from EPRI Automatic Analysis Performance Demonstration Database (AAPDD)

Recent Data Analysis Algorithm Development

• Loose part detection algorithms for carbon steel, stainless steel and copper materialsand copper materials

– Rotating Probes

• 115 Pancake Coil

• +Point Coil®

– Array Probes


Loose Part Detection ChallengesCase 1: As distance of the loose part from tube wall OD

increases, detection becomes more difficult,

Case 2: As distance of the loose part from TTS decreases,

tubesheet signal becomes dominant and loose part detectiontubesheet signal becomes dominant and loose part detection

becomes more difficult.

Loose Part

dist_tubeOD

Loose Partdist_TTS


Case 1 Case 2

D l t f Al ith tDevelopment of Algorithms to Automatically Detect Loose Parts

From X Probe® Array DataFrom X-Probe® Array Data

Carbon Steel Loose Parts


Loose Part Test Configurations

a: Distance from Tube Wall ODb: Distance above TTS

a: 0-5mm

b: 0-18mmFor laboratory mock-up:

Loose Part

Ste

elLoose parts used in laboratory experiments

Car

bon

S


Spacers used in laboratory experiments

Calibration & Phase Correction

File: LB00HCAL00702/DHR000C009I014 Circ. Channel 50 KHz, Vert. component, 18mm above TTS, in contact with tube

Before Calibration After CalibrationBefore Calibration After Calibration

20 2020

40

60

80

20

40

60

80

Tube

100

120

140

100

120

140Tube sheet

Loose Part

2 4 6 8 10 12 14 16 18

160

180

2005 10 15 20 25 30 35

160

180

200


Step 1-TTS Detection & Segmentation File: LB00HCAL00702/DHR000C009I007 50 KHz, Vert. component,

2mm above TTS, in contact with tube

Axial projection P(i)

Derivative of P, D(i) = P(i) - P(i-1)

Location of detected TTS by thresholding D(i)

0 0 20 40 60 80

100

120 0-10 -5 0 5 10 15 20

P(i) D(i) = P(i)2D data

20

40

60

Tube

2040

60

2040

60

Loose Part

80

100

120

80100

120

80100

120

140

160

180

Loose Part

Tubesheet0

140160

180

0140

160180


5 10 15 20 25 30 35200

0200

0200

Step 2-Preprocessing - Median RemovalFile: LB00HCAL00702/DHR000C009I014 50 KHz AF, Vert. component, 18mm above TTS, in contact with tube

Before median removal

Segmented data After median removal

10

20

10

20

Input for step 3

30

40

30

40

L P t50

60

70

50

60

70

Loose Part

70

80

90

70

80

90


5 10 15 20 25 30 35

5 10 15 20 25 30 35

Step 3- Adaptive Thresholding and ROI Detection File: LB00HCAL00702/DHR000C009I025 50 KHz AF, Vert. component,

S t d I Th h ldi d ROI d t ti

10

Segmented Image Thresholding and ROI detection

20

30

40 Loose Part L P t40

50

60

Loose Part Loose Part

5 10 15 20 25 30 35

70

Residue from TTS


Residue from TTS

18mm above TTS, in contact with tube

Step 4- Feature Extraction & Classification

•Features extracted from calibrated data inside ROI boxes:– Maximum and minimum signal amplitude g p

– The corresponding phase at maximum and minimum signal amplitudesignal amplitude

•Rule-based classification applied to the extracted featuresfeatures


Step 4- Feature Extraction & ClassificationFile: LB00HCAL00702/DHR000C009I025 50 KHz AF, Vert. component,

18 b TTS i i h b18mm above TTS, in contact with tube File: LB00HCAL00702/DHR000C009I025 50 KHz AF, Vert. component,

10

Segmented Image Thresholding and ROI detectionClassification

10

20

30

L P t40

50

60

Loose Part Loose Part Loose Part

5 10 15 20 25 30 35

70


False ROI: Residue from TTS

Training Data Distribution & Result Type/Total tubes

# of tubes in training set

LP#1 / 10 6

• 100 files were randomly chosen and used for training• The set contains both lab data (LP#1-10) and field data (LP#11 & 12)• AA was trained based on this 100 flies

LP#2 / 10 2

LP#3 / 10 3

LP#4 / 50 14

LP#5 / 50 9

LP#6 / 10 1

LP#7 / 10 1

LP#8 / 10 1

LP#9 / 51 16

LP#1-LP#10: *LP#11: Carbon steel weld backing ring *LP#12: Carbon steel nail

LP#10 / 28 13

*LP#11 / 51 14

*LP#12 / 51 17

# tubes analyzed

Detection rate by human analyst

(PLP+WLP)

Detection rateby AA

(PLP+WLP)

NDD by human analyst

NDD by AA AA False Call Rate

/Tube

100 79% 85% 21% 15% 0.14(14/100)

"PLP" - Possible Loose Part (High confidence)"WLP" Weak Loose Part signal (Not likely to be reported in a field exam)


WLP - Weak Loose Part signal (Not likely to be reported in a field exam)"NDD" - No Loose Part signal - NDE technique limitation

Blind Test data

• After training was performed on the remaining 269 tubes

# tubes analyzed

Detection rate by human analyst

(PLP+WLP)

Detection rateby AA

(PLP+WLP)

NDD by human analyst

NDD by AA AA False Call Rate

/Tube

• "PLP" Possible Loose Part (High confidence)

269 71% 76% 29% 24% 0.21(58/269)

• "PLP" - Possible Loose Part (High confidence)• "WLP" - Weak Loose Part signal (Not likely to be reported in a field exam)• "NDD" - No Loose Part signal - NDE technique limitation

• Analysis of other loose part materials (e.g. stainless steel and copper) is in progress


and copper) is in progress

Summary

• EPRI SGMP conducts research to improve SG NDE capabilities, including:

detection sizing and characterization of tube degradation– detection, sizing and characterization of tube degradation– detection of foreign objects

• Results from SG NDE are used as input to decisions on:– tube repairs, inspection intervals, repair methods, deposit

removal processes and the need for foreign object retrievalretrieval

• Important aspects of the SGMP NDE research include:– development of eddy current simulation modeling tools– development of efficiency improvements of data analysis

through automation – qualification of data analysis processes and systems


qualification of data analysis processes and systems

Together…Shaping the Future of Electricity

epri steam generator management program – o i f r t ......epri steam generator management program...

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