epri automated analysis of bobbin coilprobe eddy current data

EPRI Project ManagerJ. Benson

EPRI • 3412 Hillview Avenue, Palo Alto, California 94304 • PO Box 10412, Palo Alto, California 94303 • USA800.313.3774 • 650.855.2121 • [email protected] • www.epri.com

Automated Analysis of Bobbin CoilProbe Eddy Current Data

1002785

Final Report, December 2002

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ORGANIZATION(S) THAT PREPARED THIS DOCUMENT

Michigan State University

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iii

CITATIONS

This report was prepared by

Michigan State UniversityEngineering Building, Room 2120East Lansing, MI 48824City, State Zip

Principal InvestigatorS. Udpa

This report describes research sponsored by EPRI.

The report is a corporate document that should be cited in the literature in the following manner:

Automated Analysis of Bobbin Coil Probe Eddy Current Data, EPRI, Palo Alto, CA: 2002.1002785.


v

REPORT SUMMARY

This report provides a summary of ongoing research to develop algorithms for performingautomated analysis of eddy current test data. The research focus is on analysis of bobbin coildata for detecting degradation in steam generator tubes.

BackgroundAutomated eddy current data analysis systems have been available for more than a decade toprovide rapid detection of degradation in steam generator tubing. EPRI published an assessmentof commercial data analysis software in 2002: Assessment of Automated Eddy Current DataAnalysis Technology for Steam Generator Tubing, Bobbin Coil Probe Data (EPRI report1003140). That report stated none of the three systems evaluated were successful in detectingdegradation in all 21 damage mechanism categories and that overcall rates for the once throughsteam generator (OTSG) data sets were much higher than desired. Limitations in detectingdegradation or inefficiencies resulting from large numbers of overcalls have limited use ofautomated data analysis systems at some plants.

ObjectivesTo develop algorithms that will automatically analyze bobbin coil eddy current data and identifydegradation in steam generator tubes; to achieve values for probability of detection (POD)greater than 90% for all known degradation categories; and, to achieve degradation overcall rateslower than achieved by available commercial systems.

ApproachThe project team was provided with steam generator bobbin coil eddy current inspection data todevelop algorithms for automatically detecting and classifying degradation in steam generatortubes. Along with this set of “training” data, expert opinion analysis results from twoindependent qualified data analysts also were provided. This same set of training data had beenpreviously used to assess the capabilities of commercially available automated data analysissystems. Initial algorithm development will focus on OTSG data, which has traditionally beenthe most challenging for automated data analysis. Inclusion of data from Westinghouse andCombustion Engineering (CE) steam generators also is planned.

Once the algorithms produce superior results on the training data compared to results fromcommercial systems, a “test” data set will be processed by the newly developed algorithms,without the help of expert opinion results. Following a successful performance demonstration,the newly developed algorithms can either be incorporated into a stand-alone system or be addedas a separate data analysis option to existing commercial data analysis systems.

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ResultsPrevious EPRI work included an assessment of three commercial automated data analysissystems on a common set of field bobbin coil data. The assessment included training data from67 OTSG tubes. These same 67 tubes were included in the present EPRI algorithm developmentproject aimed at improving the capabilities of automated data analysis.

Development of a multistage classification algorithm has produced promising automated dataanalysis results. The automated data analysis system consists of two stages: signal preprocessing(to remove noise and low-frequency trends) and signal classification (uses rule-based algorithmsand statistical models that account for inherent variability in real-world systems).

The EPRI-developed algorithm detected 96% of the defects in the OTSG training data, which iscomparable to results achieved by the three commercial systems (range of detection: 93% to98%). The EPRI-developed algorithm defect overcall rate of 9 per tube represented a significantimprovement over the 13 to 34 overcalls per tube by the three commercial systems.

EPRI PerspectiveIn 2000, EPRI initiated a project to develop software algorithms to perform automatic analysis ofbobbin coil eddy current data. To date, the project has resulted in extremely promising results,and it is expected that the algorithms developed will provide improved capabilities (higher PODand lower false call rates) than are currently available from commercial systems.

Following a successful demonstration of the algorithms, they could be used either in a stand-alone system or as an analysis option from within another data analysis software product.Whether used alone or in parallel with another independent automatic analysis software product,utilities would benefit from EPRI-developed automatic analysis software. Benefits would includecost savings associated with a reduction in data analyst staffing levels, more consistent dataanalysis leading to increased POD, and a shorter inspection duration.

Currently, the newly developed automated data analysis software is being prepared to processfield data from over 600 OTSG tubes. Results of the software validation on this test data set willbe used to identify where algorithm improvements may be needed. Similar algorithm validationis planned in 2003 for field data from hundreds of tubes from Westinghouse- and CE-designedsteam generators.

KeywordsSteam generatorsAutomatic data analysisEddy currentBobbin probe


vii

ABSTRACT

Automated eddy current data analysis systems for detection of steam generator tube degradationcan provide significant benefits to utilities. Potential benefits may include:

1. Cost savings by reducing manpower and equipment requirement needs,

2. Schedule savings by:– reducing the impact of data analyst shortages during peak outage periods– providing analysis results at rates equal to or less than the rates for data acquisition

3. Reliability improvements by providing consistent, repeatable and accurate inspection results

This report provides a summary of the status of ongoing research to develop algorithms forperforming automated analysis of eddy current test data. The focus of the research is on analysisof bobbin coil data for detection of degradation in steam generator tubes.

Chapter 1 of the report begins with a discussion of the design of steam generators in nuclearpower plants and a summary of degradation mechanisms that have occurred in steam generatortubing. The principles of eddy current testing are briefly discussed.

The initial step of the data analysis process, signal preprocessing, is described in Chapter 2. Aseries of preprocessing routines are used to process the raw data and identify those data segmentsthat require further analysis. Various types of data segmentation, filtering, de-noising andthresholding are part of the signal preprocessing step.

Chapter 3 addresses the multistage approach of data processing and signal classification aimed atreducing the number of overcalls while maintaining a high degradation detection rate. Acombination of rule bases and statistical classifiers are used to eliminate the non-defectindications systematically while retaining the defect and dent indications at each stage. Inaddition, certain classes of defects are handled separately. Volumetric indications, like wear andimpingement, merit their own processing routines. Separate algorithms for wear andimpingement detection were also developed.

The results of various stages of the automated data analysis process are provided throughout thereport, and summarized in Chapter 4 following the completion of all data analysis stages. Thedata used to demonstrate the effectiveness of the various algorithms consists of a set of field datafrom once through steam generators. The results of the algorithm are compared to the “expertopinion” results that were verified by two independent Qualified Data Analysts.


ix

CONTENTS

1 INTRODUCTION ................................................................................................................. 1-1

Steam Generator in Nuclear Power Plant........................................................................... 1-1

Principles of Eddy Current Testing and its Application in Steam Generator TubeInspection........................................................................................................................... 1-2

Multifrequency Eddy Current Techniques ...................................................................... 1-4

Bobbin Coil Probe.......................................................................................................... 1-5

Research Objectives .......................................................................................................... 1-6

2 SIGNAL PREPROCESSING ............................................................................................... 2-1

Data Segmentation............................................................................................................. 2-2

Adaptive Filtering................................................................................................................ 2-3

Adaptive Filtering using the NLMS Algorithm ................................................................. 2-3

Wavelet Shrinkage De-Noising ...................................................................................... 2-5

Zero-phase High Pass Filter............................................................................................... 2-5

Dynamic Thresholding (Neyman-Pearson Detector)........................................................... 2-7

Neyman-Pearson Detector ............................................................................................ 2-7

Moving Average Filter........................................................................................................2-12

Distance Threshold ...........................................................................................................2-13

Results of the Preprocessing Module ................................................................................2-14

3 MULTISTAGE SIGNAL PROCESSING AND CLASSIFICATION........................................ 3-1

Magnitude Thresholds........................................................................................................ 3-1

Calibration Curve based Phase Thresholds........................................................................ 3-1

Rule Base I......................................................................................................................... 3-3

Rule Base II........................................................................................................................ 3-8

Hidden Markov Models......................................................................................................3-10

Eddy Current Classification...........................................................................................3-11

Impingement Classifier ......................................................................................................3-12

Wear Identification.............................................................................................................3-14

4 SUMMARY AND CONCLUSIONS....................................................................................... 4-1

5 REFERENCES .................................................................................................................... 5-1


x

LIST OF FIGURES


xi

Figure 1-1 Steam Generator in Nuclear Power Plant.............................................................. 1-1

Figure 1-2 Eddy Current Generation and Flow in a Conducting Specimen.............................. 1-3Figure 1-3 Impedance-plane Trajectory of a Coil over a Conducting Non-ferromagnetic

Test Specimen [3] ........................................................................................................... 1-4Figure 1-4 Differential Bobbin Probe, Comprising two Coaxial Air Core Probes in a Tube ...... 1-6

Figure 1-5 Schematic of a differential bobbin probe scanning a heat exchanger tube ............. 1-7Figure 1-6 Typical differential probe signals. The lower traces are strip chart displays of

the respective imaginary (left) and real (right) components of the eddy current probesignals............................................................................................................................. 1-8

Figure 1-7 Multifrequency Bobbin Coil Probe Eddy Current Signals........................................ 1-9Figure 1-8 Eddy Current Data Analysis System .....................................................................1-10

Figure 2-1 Overall Structure of the Preprocessing Algorithm................................................... 2-1Figure 2-2 Entry and Exit point signals in the tube data........................................................... 2-2Figure 2-3 Block diagram of the adaptive filtering algorithm. ................................................... 2-3Figure 2-4 Schematic of the NLMS adaptive noise rejection system used for minimizing

noise in eddy current data. .............................................................................................. 2-4

Figure 2-5 (a) Original raw data, (b) High pass filtered (DCT) data.......................................... 2-6Figure 2-6 Improved Dynamic Thresholding Algorithm........................................................... 2-9Figure 2-7 (a) Original raw data (Vertical, mix channel), (b) Output of Dynamic

Thresholding (possible defect locations).........................................................................2-10Figure 2-8 (a) Illustration of the output potential defect points of the Dynamic

Thresholding algorithm, (b) Zoomed in view of the defect region....................................2-11

Figure 2-9 (a) Input to Moving Average filter, (b) Output of Moving Average Filter .................2-12Figure 2-10 (a) Locating the minima in the vertical component, (b) Corresponding

minima represented in the impedance plane ..................................................................2-13Figure 3-1 Block diagram of the Multistage classification module............................................ 3-2

Figure 3-2 The ideal and practically used phase calibration curves......................................... 3-2Figure 3-3 Phase calibration curves for all four channels of one of the calibration tubes -

R999C999G003 .............................................................................................................. 3-3Figure 3-4 The IPT of a DENT indication in all four channels .................................................. 3-4Figure 3-5 The IPT of an OD defect in all four channels.......................................................... 3-5

Figure 3-6 The IPT of an ID indication in all four channels ...................................................... 3-6Figure 3-7 Overall approach of Rule Base I. ........................................................................... 3-7

Figure 3-8 Scatter plot of the variance of the 400 kHz channel for one plant........................... 3-9Figure 3-9 Cross correlation between the horizontal components vs. the cross

correlation between the vertical components (200 kHz & 400 kHz). ...............................3-10Figure 3-10 Flowchart for eddy current signal classification using HMMs (a) Training and

(b) Testing ......................................................................................................................3-11Figure 3-11 Vertical component and IPT plot of an impingement ...........................................3-13

Figure 3-12 MIX channel vertical and horizontal components of two support plate signalsith t 3 15


xii

without wear. ..................................................................................................................3-15

Figure 3-13 MIX channel vertical and horizontal components of two support plate signalswith wear. .......................................................................................................................3-16

LIST OF TABLES

Table 2-1 Data distribution in the EPRI OTSG training database 2-1405/01/2007http://energysearch.epri.com/highlight/index.html?url=http%3A//www.epriweb.com/backup/download...

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Table 2 1 Data distribution in the EPRI OTSG training database. ..........................................2 14

Table 2-2 Results of the Preprocessing Module.....................................................................2-14Table 2-3 Analysis of missed indications in the EPRI OTSG training database. .....................2-15Table 3-1 Summary of results after magnitude & phase thresholding, followed by Rule

Base I.............................................................................................................................. 3-7

Table 3-2 Summary of missed flaws/dents in Rule Base I....................................................... 3-8Table 3-3 Results of Applying Rule Base II. ...........................................................................3-10Table 3-4 Performance of the HMM on the EPRI OTSG training database. ...........................3-12Table 3-5 Results of the IMPINGEMENT CLASSIFIER on all four plants...............................3-14

Table 3-6 Summary of the wear classifier. .............................................................................3-17Table 4-1 Overall summary of the OTSG classification algorithm............................................ 4-2Table 4-2 Summary of missed flaws. ...................................................................................... 4-2


1INTRODUCTION

Steam Generator in Nuclear Power Plant

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1-1

Heat exchanger tubes are used in a variety of industries, including, power stations, petrochemicalplants, oil refineries, air conditioning and refrigeration units for transferring heat to the fluidcirculating outside the tube. A steam generator or heat exchanger unit used in nuclear powerplants, shown in Figure 1-1, transfers heat from the primary loop to the secondary to producesteam, which is used to run the turbines. Typically these tubes are made of Inconel and, for onesteam generator design, are approximately 7.5m high with an internal diameter of 15.5mm and1mm wall thickness. The tube bundle is supported by ferromagnetic support structures, whichare distributed along the length of the tubes.

Figure 1-1Steam Generator in Nuclear Power Plant

The steam generator tubes are continuously exposed to harsh environmental conditions includinghigh temperatures, pressures, fluid flow rates and material interactions resulting in various typesof degradation mechanisms such as mechanical wear between tube and tube supports, outer

Introduction

diameter stress corrosion cracking (ODSCC), pitting, thinning, primary water stress corrosioncracking (PWSCC), and inter granular attack (IGA). Tube degradation can progress completelythrough the tube wall, thereby contaminating the fluids on the secondary side of the steamgenerator. It is critical that the primary coolant, which is radioactive, does not leak into thesecondary side. Consequently the steam generator tubes in nuclear power plants need to beinspected periodically for degradation.

Historically, steam generator tube inspection has been a challenging issue. There have beennumerous cases of unscheduled plant shutdowns in the past, which typically cost $500,000 a day.Hence there is a strong economic incentive to develop reliable nondestructive evaluation (NDE)methods for steam generator tube inspection. Visual examination and ultrasonic techniques havelimited use as they are very slow and time consuming. These obstacles have led the way to awidespread use of eddy current techniques for the inspection of non-ferrous tubing, particularly


1-2

p y q p g p yin the nuclear power industry. Eddy current inspection has proven to be both fast and effective indetecting and sizing most of the degradation mechanisms that occurred early in the life of firstgeneration steam generators. However, as the nation’s steam generators have aged, newer andmuch more subtle forms of degradation have appeared that require more intelligent applicationof eddy current tests.

Conventionally, eddy current data analysis is carried out by human analysts. Normally, duringinspection, signals from multiple channels, frequencies and probe types are recorded. Dataanalysts use their experience as they review the shape of Lissajous patterns and phase of thesignal in each channel to make an assessment of the tube condition. Through the use of multi-frequency eddy current systems, modern equipment is now capable of acquiring the necessarydata to detect and correctly diagnose indications of tube degradation. Consistent and reliableanalysis techniques are required to achieve improved detection, classification andcharacterization results. Human analysis, apart from being slow, is often inconsistent. Inspectionresults are often not consistent with prior inspection and/or with other analysts. Thus, there is aneed for automated signal classification systems that can provide accurate and consistent signalinterpretation.

Principles of Eddy Current Testing and its Application in Steam GeneratorTube Inspection

The basic principle underlying eddy current testing can be illustrated with a simple arrangementshown in Figure 1-2 [1]. When a coil carrying an alternating current is brought in closeproximity to an electrically conducting, non-ferromagnetic test specimen, and an alternatingmagnetic field is established, the alternating magnetic field causes currents, called eddy currents,to be induced in the conducting test specimen in accordance with Faraday’s law ofelectromagnetic induction. The alternating eddy current, in turn, establishes a field whosedirection is opposite to that of the original or primary field. Consequently, the net flux linkageassociated with the coil decreases. Since the inductance of a coil is defined as the flux linkageper ampere, the effective inductance of the coil decreases relative to its value in air. The presenceof eddy currents in the test specimen also results in a resistive power loss. The effect of thispower loss manifests in the form of a small increase in the effective resistance of the coil. Anexaggerated view of the changes in the terminal characteristics of the coil is shown in Figure 1-3,where the variation in resistance and inductance is plotted in the impedance plane [2]. When a

Introduction

flaw or inhomogeneity whose conductivity differs from that of the host specimen is present, thecurrent distribution is altered. Consequently, the impedance of the coil changes relative to itsvalue obtained with an unflawed specimen. Systems that are capable of monitoring the changesin impedance can, therefore, be used to detect flaws in a specimen that is scanned by a coil.

The eddy currents exhibit a unique phenomenon known as the “skin effect” which causes thecurrent density at a particular depth to decrease with an increase in the frequency of excitation.Skin depth (δ), also called standard depth of penetration, is defined as the depth at which eddycurrent density has decreased to 1/e of the surface value. The skin depth can be computed asfollows:

π∝ σδ

f=

1(1-1)


1-3

where f is the excitation frequency of the circuit, μ is the magnetic permeability of the targetmaterial, and σ is the electrical conductivity of the target material. The skin depth is often usedas a guideline to select the excitation frequency for a given test specimen.

The variations in coil impedance caused by discontinuities in the test specimen are often verysmall in comparison with the background value of the coil impedance. The detection andmeasurement of the small changes is often accomplished using bridge circuits [2].

Conducting Specimen

AlternatingCurrent

Eddy Current

Figure 1-2Eddy Current Generation and Flow in a Conducting Specimen

Introduction

Figure 1 3


1-4

Figure 1-3Impedance-plane Trajectory of a Coil over a Conducting Non-ferromagnetic Test Specimen[3]

Factors that influence the eddy current field, and therefore the coil impedance, are as follows:

The separation between the coil and specimen surface, called lift-off

The electrical conductivity of the specimen

The magnetic permeability of the specimen

The frequency of the AC inducing the eddy current field

The design of the eddy current probe

Geometric factors

Discontinuities, such as cracks, corrosion, pitting

Successful detection and characterization of flaws requires a careful design of signal processingprocedures to compensate for these effects. The elimination of undesired response and extractionand interpretation of relevant information forms the basis of considerable research activity ineddy current inspection.

Multifrequency Eddy Current Techniques

Single frequency eddy current tests offer excellent sensitivity to a number of different types ofsteam generator tubing under normal conditions. However conditions are often complicated by anumber of factors and consequently inspection needs cannot be effectively solved by singlefrequency examinations.

State of the art multifrequency eddy current testing overcomes most of the single frequencyinspection limitations. The multifrequency technique consists of collecting data simultaneouslyusing several excitation frequencies from just one probe pull. This provides data that are

Introduction

analyzed using multifrequency mixing or multiparameter techniques. The technique not onlyallows the effect of extraneous discontinuities to be nullified but also improves the classificationand characterization results.

As mentioned earlier, each frequency is sensitive to a certain depth of the test sample. Lowfrequencies have large skin depths and hence generate strong indications of support structuresthat are located outside the tube. Thus, they are often used to determine location of support platesand other support structures along the tube. They can also be used to detect depositions ofcorrosion products on the outside of the tubes. Higher frequencies have a much smaller skindepth. There exists a well-defined relationship between the phase angle of the eddy currentsignal and the depth of defects, incorporated into the phase calibration curve, which is exploitedto detect, classify and characterize the signals obtained from the high frequency channels. Due tothe different skin depths at different frequencies (skin effect), signals from defects and supportfeatures change with frequency. In effect, this means that multifrequency response signals haveadditional information that can be analyzed to extract relevant features. The various advantagesof multifrequency techniques can be summarized as follows [4]:


1-5

Collects data at several test frequencies simultaneously. This decreases the in-serviceinspection time and human exposure time to radiation.

Allows separation of discontinuities that give dissimilar signals at different frequencies.

Improves sensitivity to different types of discontinuities.

Improves the detection, interpretation and sizing of defects even in the presence of artifactsthat complicate the analysis procedure.

Two types of multifrequency probes have been predominantly used in recent years. The first isthe bobbin coil probe, which consists of two identical coils connected in a differential mode andexcited at multiple frequencies. The second type of probe is the rotating probe [5]. One benefitprovided by this probe is to increase the resolution of tube degradation measurements.

The bobbin coil probe is mainly used for the initial detection of possible degradation to quicklydetermine those areas of the tube requiring additional inspection with other types of probe thathave improved ability to size and characterize degradation, such as rotating probes. Although thebobbin coil probe is the most widely used probe, it has limitations in its ability to detectdegradation in all regions of the tube (e.g., expansion transitions), and hence these tube regionsare further investigated by rotating probes. Other limitations of the bobbin coil probe include theability to accurately size and characterize degradation.

Bobbin Coil Probe

A simple air core probe, when oriented coaxially in a tube, is called a bobbin coil. Figure 1-4shows a differential bobbin arrangement, in which the signals from two identical bobbin coils aresubtracted, in an attempt to provide a flaw signal that is more distinguishable from a relativelyconstant background signal [6].

When eddy current probes were first used for inspecting heat exchanger tubes, they weregenerally composed of two identical bobbin coils mounted closely together, operating in the

Introduction

differential mode. A schematic of such a probe is shown in Figure 1-5. The recorded signalswere the induced voltage in one coil subtracted from the voltage in the other coil. The advantageof this probe is that its signal is resistant to various anomalous effects, such as probe wobble,temperature variations, and gradual variations in the inspected tube’s electrical conductivity anddiameter. This probe is very sensitive to abrupt anomalies, such as pitting corrosion and frettingwear [7]. Figure 1-6 shows a typical differential probe defect signal, measured in a laboratory,from a differential probe. The lower traces are strip chart displays of the respective imaginary(left) and real (right) components of the eddy current probe signals.

Typical signals generated by a multifrequency-bobbin probe testing system are shown in Figure1-7. The 35kHz low frequency channel is usually designed to locate the structure signal, such astube support plate (TSP), top of tube sheets (TTS), etc.

Research Objectives

Although it is relatively easy to understand the basic eddy current probe-flaw interaction, real-world analysis of steam generator tube eddy current data is difficult largely due to noise and


1-6

many unwanted indications that cause significant distortion in the flaw signal. Automatic flawdetection systems for bobbin coil eddy current data have been well studied by many researchers[3,4]. These studies mainly focus on the relationship between flaw characteristics and the shapeand orientation of the corresponding Lissajous pattern of the eddy current signal and attempt tomimic the decision process of a human expert.

Figure 1-4Differential Bobbin Probe, Comprising two Coaxial Air Core Probes in a Tube

Introduction


1-7

Figure 1-5Schematic of a differential bobbin probe scanning a heat exchanger tube

Introduction


1-8

Figure 1-6Typical differential probe signals. The lower traces are strip chart displays of therespective imaginary (left) and real (right) components of the eddy current probe signals.

Introduction


1-9

Figure 1-7Multifrequency Bobbin Coil Probe Eddy Current Signals

The objective of developing an automatic flaw detection system for analyzing steam generatoreddy current data is to provide utilities with significant cost savings associated with reducedanalyst requirements and faster inspections. Additionally, the added consistency and accuracythat automated data analysis potentially affords may allow utilities to demonstrate higher tubedegradation detection probability and improved sizing accuracy. These capabilities could providethe basis for longer inspection intervals and the use of alternate repair criteria. Figure 1-8 showsthe overall schematic of an automatic multifrequency bobbin coil probe eddy current dataanalysis system. The different modules of the system are described in detail in the next section.

Introduction

Eddy currentsignals

Classification

Degradation No Degradation

Preprocessing

DetermineDegradation Type

DefectCharacterization


1-10

Figure 1-8Eddy Current Data Analysis System

2SIGNAL PREPROCESSING

The objective of signal preprocessing is to extract “meaningful” information from the data to beused subsequently for analysis. The raw signal is passed through a series of preprocessingroutines that reduce the amount of data to be analyzed and classified by the classificationalgorithms. Figure 2-1 shows a schematic diagram of the overall preprocessing module.

Ad ti Filt i

Raw Signal(from Multiview – AAPACK)

Data Segmentation


2-1

Dynamic Thresholding(Neyman-Pearson Detector)

Moving Average Filter

Adaptive Filtering

Distance Threshold

Potential Indications

Figure 2-1Overall Structure of the Preprocessing Algorithm

Signal Preprocessing

The various filtering operations carried out in the preprocessing routines as shown in Figure 2-2are explained in detail in this chapter.

Data Segmentation

The objective of this step is to determine, in an automated fashion, the range of data points thatneed to be analyzed and those that need to be discarded (for instance, data points at the beginning– ‘air to tube entry point’ and end of the tube – ‘tube exit point to air’). Figure 2-2 shows theentry and exit points in the entire length of the tube data. The automated procedure records themaximum (near saturation) voltage reached at the exit point of the probe (transition from tube toair) and then scans for a voltage magnitude equal to 80% of the peak in the first half of themeasurement data. If the algorithm does not detect any sample in the 80% range, then it picks the1

stdata point as “ the starting point within the tube ”, else the data point with voltage magnitude in

the 80% range is chosen as “ the starting point within the tube ”. The exit point is chosen as thedata point with maximum voltage peak at the tube end.


2-2

Figure 2-2Entry and Exit point signals in the tube data


Adaptive Filtering

Adaptive filters are commonly used for minimizing time varying noise. Examples of itsapplications to nondestructive evaluation problems can be found in [8, 9]. In the case of steamgenerator inspection, such noise may be generated due to variations in liftoff due to probewobble. An adaptive filter is capable of adjusting its impulse response appropriately using analgorithm that minimizes the error between the filter output and a reference input. We utilize afinite impulse response (FIR) filter whose coefficients are estimated using the least mean square(LMS) algorithm to implement the adaptive system.

The overall adaptive filtering algorithm is implemented in two steps as depicted in Figure 2-3.The data is first passed through a normalized least mean square (NLMS) adaptive filter toremove time-varying noise from the data. This is followed by a wavelet based de-noisingtechnique to remove any remaining random system noise. The reason for using a two-stepprocedure is due to the characteristics of the noise. Time varying noise requires an adaptive noisecancellation scheme. Such an adaptive procedure typically requires a stochastic gradient descentalgorithm that tracks the stochastic properties of the noise in order to minimize it. The LMS andNLMS procedures are important members of this family of algorithms.

The system noise, on the other hand, is not time varying. Popular digital filtering algorithmstypically used to minimize non-time varying noise, such as low pass / high pass filtering usuallydesigned on the basis of a trade-off between the sharpness of the filter characteristics and thewindow length. Shorter windows are required to minimize the computational effort, whereaswider windows are required for sharper filter characteristics [10]. Wavelet shrinkage denoisingtechniques provide an intelligent scheme that identifies the noise components and literally shrinkthem to reduce their effect on the signal. Such techniques do not use windowing schemes, andh f d ff f h i l i i f i l fil [11]


2-3

therefore do not suffer from the implementation issues of conventional filters [11].

The following sub-sections describe the processing steps associated with the proposed algorithm.

NLMS AdaptiveFiltering

Wavelet ShrinkageDenoising

Raw EddyCurrent Signal

Filtered ECSignal

Figure 2-3Block diagram of the adaptive filtering algorithm.

Adaptive Filtering using the NLMS Algorithm

Figure 2-4 shows the schematic of the adaptive LMS algorithm for noise cancellation. The ideaunderlying the approach is to exploit the correlation properties of noise in eddy current signalsand the signals due to defects and other artifacts in the tubes. The reference input, u k , and theprimary input, d

k , to the adaptive system ideally are signals obtained from the eddy currentprobe containing only noise and from a probe passing over a defect respectively. In bobbin coilinspection, we have a choice of data at four different frequencies. Data from the higherfrequencies, while containing correlated time varying noise, also contain flaw signals that can be


correlated. Thus, one high-frequency signal (the 400 kHz signal is used as the reference in thisstudy) is used as the reference signal, and the low frequency locator channel signal (35 kHz) isused as the signal containing only noise. We assume that the signal statistics change slowly.

Adaptive FilterHk (z)

Signal PlusNoise

Noise

εk

Outputyk =? k

Error Signal

+–

dk

uk Adaptive FilterHk (z)

Adaptive FilterHk (z)

Signal PlusNoise

Noise

εk

Outputyk =? k

Error Signal

+–

dk

uk

Figure 2-4Schematic of the NLMS adaptive noise rejection system used for minimizing noise in eddycurrent data.

In order to understand the function of the adaptive filter, consider Figure 2-4, along with thefollowing equations:

kk

kkk

u

ds

?

?

=∋

=+(2-1)


2-4

where d k is the defect signal corrupted by noise, u k is the reference time-varying noise signal, s k

is the defect signal itself, and ? k , ? k∋ represent the noise from the two channels. Let yk denote thepredicted signal at the output of the adaptive filter. The underlying assumption is that the noisecontained in the primary and the reference inputs, ? k and ? k∋, are highly correlated with eachother, and uncorrelated with the signal component, s k . When the adaptive filter coefficients areoptimized using the LMS algorithm, the algorithm converges to the minimum mean square error(MMSE) solution, y k . This solution provides the best estimate, ? k , of the noise contained in theprimary input d

k in the least square sense, that is, y k ? k . Since ε k = s k +? k − y k , the filter outputis the target signal with a smaller noise component. The above argument shows that the adaptivefilter allows cancellation of correlated components between d k and u k , which in this case is thetime-varying noise component. Consequently, the error signal at the output of the noise rejectionsystem provides an estimate of the desired defect signal component in the primary input signal.

Since we have both horizontal and vertical components of both channels, we apply the adaptivefiltering twice, once with the horizontal components, and once with the vertical components ofthe two channels.


Wavelet Shrinkage De-Noising

In the last processing step of the proposed algorithm, any residual system noise in the adaptivenoise-cancellation system output is removed from the filtered eddy current data. This noise istreated as additive white Gaussian noise (AWGN), and a wavelet-based thresholding approach isutilized. The technique is known as adaptive wavelet shrinkage de-noising or soft thresholding[8]. In this method, the wavelet coefficients, w , of the eddy current data are "shrunk" towardszero using the relation,

(w, ) sgn(w)[ wΓ?= ]−?+

(2-2)

The threshold, ? , depends upon the noise characteristics of the data and is estimated from thefinest resolution level of wavelet transform of the data. Since the noise characteristics vary fromprobe to probe and from one tube to another, the threshold is computed adaptively for each tube.

Zero-phase High Pass Filter

The raw data usually contains unwanted low frequency components that can be removed using azero phase high pass filter. In order to ensure a zero-phase, a Discrete Cosine Transform (DCT)is used for this purpose.

The 1D discrete cosine transform is defined as

(2-3)


2-5

for

The inverse DCT is defined as

(2-4)

for

where in both (2-1) and (2-2)


The DCT filter is applied to all the channels in the data. Figure 2-5 shows the effect of the DCTzero-phase filter on one of the channels.

(a)


2-6

(b)

Figure 2-5(a) Original raw data, (b) High pass filtered (DCT) data


Dynamic Thresholding (Neyman-Pearson Detector)

This dynamic threshold algorithm makes use of the Neyman – Pearson detector that reduces thenumber of data points to be analyzed significantly. The adaptive (dynamic) thresholdingalgorithm calculates a variable threshold for consecutive segments of data and marks all signalpoints above the threshold as possible defect locations. The details of the Neyman – Pearsondetector are provided in this section.

The dynamic thresholding algorithm decomposes the mix-channel signal into smaller segmentsthrough a finite-length sliding window, and computes a threshold for each segment of data. TheNeyman – Pearson (NP) detector is used to compute the variance-based optimal threshold thatmaximizes the probability of detection of an actual defect signal for a given probability of falsealarm (PFA). Based on the thresholds, small segments of data points are marked as potentialdefect indications.

Neyman-Pearson Detector

This section explains the various steps of the NP detector algorithm with reference to the presentapplication.

1. Background

A Neyman-Pearson (NP) Detector maximizes the probability of detecting a signal (in presence ofnoise) for a given probability of false alarms (PFA). Basically, the detector is implemented bythresholding the output of a sliding window based test statistic.

2. Modeling Assumptions

Let y(n), 0 = n = N-1, denote the adaptive filtered data samples, M denote the length of thesliding window, and X be the data vector comprising samples contained within the sliding

i d05/01/2007http://energysearch.epri.com/highlight/index.html?url=http%3A//www.epriweb.com/backup/download...

2-7

window.

We assume that each sample of the high pass filtered data can be modeled as:

X = W (Hypothesis H 0)

or, X = S + W (Hypothesis H 1),

where W is a non-defect signal (noise) and S is a defect signal.

An NP detector defines the likelihood ratio,

[| ]()

[| ]

0

1

PX HL x = PX H

(2-5)


Let us assume that both S and W can be modeled as multivariate Gaussian signals, and inparticular, suppose

W = N (0,C W) (2-6)

S = N (μ S, C S) (2-7)

where μS is the mean of S, and CW and CS denote the covariance matrices of W and S,respectively. We further assume that noise is white, i.e., CW is a diagonal matrix,

CW = σW

2I (2-8)

where σσσσW

2denotes the noise variance.

3. Neyman-Pearson Detector

Under the above assumptions, taking the logarithm of the likelihood function, L(X), andsimplifying the resulting expression, we can show that the NP detector becomes:

Decide H 1 to be true if T (X) > δ, and

Decide H 0 to be true if T (X) = δ ,

where T(X), known as the Test Statistic, is given by:

Tx X (C I ) X[C s

(C s w I )] XT

wsw s

T 212

21

2( )= +σ

−

∝ +1σ

+σ−

(2-9)

and the threshold, δ, is determined from the desired probability of false alarms (PFA), i.e.,


2-8

PFA = Probability { T (X) > δ | H 0 } (2-10)

4. Implementation Details

Step 1: Approximate separation of signal and noise components

A histogram of |y(n)| is used to approximately separate the two components. We compute a 15-20 bin histogram of |y (n)| with outliers removed and identify the bins that dominate thehistogram to be consisting mostly of noise points, with the rest being predominantly signalpoints. Call them w (n) and s (n), respectively.

Step 2: Estimation of μS, CS, σ W

2


Compute the variance of w (n) to get σ W2. Subdivide s(n) into L segments, each of length M.

Find the mean and covariance matrix of the “L” vector samples to get μS and CS, respectively.

(Note: If signal is assumed to be white, then simply compute the mean and variance of s (n) toget μS and σS

2, respectively)

Step 3: Computation of the test statistic, T (X), for all data points

Extend y (n) by padding (M-1)/2 zeros (assuming M to be an odd number) at the two ends. Use asliding window of length M to compute T(X) at each point.

Step 4: Determine PFA and choose a threshold, δ

Suppose T W (X) denotes the vector comprising test statistic values of {w (n)} only. If PFAdenotes the desired probability of false alarms, then compute the threshold, δ, as

δ = (100 - PFA)th

percentile of T W (X).

(Note: This means P {T W (X) > δ} = PFA)

Step 5: Selecting probable defect or non-defect indications

Select all points where T (X)>δ as probable defect points, the rest being non-defects.

The dynamic thresholding algorithm was applied to both the vertical and horizontal componentsof the mix channel to identify potential defect locations. The two sets of outputs (potential defectlocations in the horizontal and vertical components) are combined to give the potential defectlocations that need to be analyzed further. Figure 2-6 illustrates this process.

NPDetector

HorizontalComponent

MIX05/01/2007http://energysearch.epri.com/highlight/index.html?url=http%3A//www.epriweb.com/backup/download...

2-9

NPDetector

MIX

VerticalComponent

MIX

Potential DefectLocations

Figure 2-6Improved Dynamic Thresholding Algorithm

Note that this algorithm may miss defects if the signal magnitudes are very small. These defectscan be detected by appropriately adjusting the threshold parameters. However, this will result inan increased number of potential defect indications. Hence, the choice of the thresholds is acritical element of this algorithm.


Figure 2-7 (a-b) and Figure 2-8 present the results of implementing this scheme.

0 500 1000 1500 2000 2500 3000 3500-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15Original Signal

(a)

0 500 1000 1500 2000 2500 3000 3500-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06Possible defect locations marked by NP detection

(b)

Figure 2-7(a) Original raw data (Vertical, mix channel), (b) Output of Dynamic Thresholding (possibledefect locations)

Figure 2-8(a) shows a typical preprocessed signal and Figure 2-8(b) shows the zoomed in versionof the specific defect.


2-10


(a)


2-11

(b)

Figure 2-8(a) Illustration of the output potential defect points of the Dynamic Thresholding algorithm, (b)Zoomed in view of the defect region

A fact to be emphasized is that any defect not marked by this preprocessing algorithm is notprocessed further and is hence missed. It is important to pick all potential defects at this initial


stage, even though some of the potential defects may later be dismissed as a non-defectcondition.

Moving Average Filter

The moving average filter is used to remove the high frequency noise riding on the potentialdefect signals in the vertical channel. Thus, this stage essentially acts as a low pass filter.

The filter uses a 3-point window for smoothening purposes. If X(k) represents a vertical channeldata point in the original data, and Y(k) represents the corresponding data point in the smoothedsignal, then we have

Y(k) = [X(k-1) + X(k) + X(k+1) ] / 3 (2-11)

Figure 2-9 shows the effect of the moving average filter on one of the channels.

(a)


2-12

(b)

Figure 2-9(a) Input to Moving Average filter, (b) Output of Moving Average Filter


Distance Threshold

For each signal section identified by the dynamic thresholding, the local minima in all channelsis detected followed by the corresponding local maxima. Figure 2-10 illustrates how localminima are identified (from 200kHz channel) using the vertical channel information and verifiedusing the impedance plane trajectory. The asterisks represent the actual defects and the circlesrepresent the minima identified by the algorithm.

(a)


2-13

(b)

Figure 2-10(a) Locating the minima in the vertical component, (b) Corresponding minima representedin the impedance plane


Further analysis of the minima-maxima pairs in the data allows us to establish a rule todistinguish between defects and non-defects as follows:

Distance Rule: “If the distance between the local minimum and maximum in the verticalcomponent (termed as the “ Min-max distance ) of the mix channel is greater than 3 data points,then this indication is treated as a potential defect, else it is considered as a non-defect signal.”

This rule indicates that for defects and dents, the number of data points between the localminimum and the corresponding local maximum is more than 3 points.

Results of the Preprocessing Module

The performance of the adaptive filtering followed by the Neyman-Pearson Detector wasanalyzed on a tube-by-tube basis. The data distribution for each of the four plants in the EPRIOTSG bobbin database is shown in Table 2-1. The NP detector used a Probability of FalseAlarm (PFA) of 40%. Table 2-2 shows the final results of the preprocessing module.

Table 2-1Data distribution in the EPRI OTSG training database.

Plant # Defects(Expert Opinion)

# Dents(Expert Opinion)

# Tubes

ANO 42 4 22

CRYS 49 - 19

OCN 30 5 17

TMI 34 - 9

Table 2-2Results of the Preprocessing Module

Plant # Defects + dents beforepreprocessing

# Defects + dentsafter preprocessing

# Overcalls afterpreprocessing

ANO 42+4 42+4 11736

CRYS 49 49 18913

OCN 30 5 28 3 1412505/01/2007http://energysearch.epri.com/highlight/index.html?url=http%3A//www.epriweb.com/backup/download...

2-14

OCN 30+5 28+3 14125

TMI 34 34 8427

Total 155+9 153+7 53201


Table 2-2 indicates that, at the end of the preprocessing stage, we are left with 53361 potentialindications. (Note: By “potential indication”, we mean the set of data points that constitute asingle defect or dent). Thus, in all 53361 potential indications need to be further processed andclassified. Out of the 155 defect indications, 153 defects were detected by the preprocessingstage. Two of the dents were missed at this stage. Table 2-3 shows the details of the missedindications after preprocessing. In addition to the two dents, the algorithm misses one MBM andone wear indication, all in the same plant.

Table 2-3Analysis of missed indications in the EPRI OTSG training database.

Plant Cal. Group Row Tube Indication Category

OCN 27 70 78 8469 MBM

OCN 56 114 4 7316 Wear

OCN 27 9 58 27281 Dent

OCN 27 60 126 15166 Dent

This classification algorithm is described in the next chapter in detail.


2-15


3MULTISTAGE SIGNAL PROCESSING ANDCLASSIFICATION

A multistage approach of processing and classification was employed with the aim of reducingthe number of overcalls in each stage while maintaining a high detection rate. Some of the keyfeatures that are used in these algorithms are the magnitude of signals and the calibration curvebased phase-thresholds.

The enormous number of potential indications obtained after the preprocessing stage is reducedusing a multistage signal-processing algorithm as shown in Figure 3-1. A combination of rulebases and statistical classifiers are used to eliminate the non-defect indications systematicallywhile retaining the defect and dent indications at each stage. In addition, certain classes ofdefects are handled separately. Volumetric indications, like wear and impingement, merit theirown processing routines. These algorithms are applied to selected segments of data (usually atsupport plate locations) and any indications that are retained by these algorithms are merged withthe indications remaining at the end of the main branch of the algorithm. The algorithms for wearand impingement detection are described at the end of this chapter.

Magnitude Thresholds

The magnitude thresholds are chosen based on a statistical analysis of the magnitudes of thepotential defect indications in all the four channels. It is observed that most defects have a highermagnitude as compared to the non-defects. Thus, a relatively high threshold removes a largenumber of overcalls and yet picks up most of the defects. The magnitude thresholds aredetermined by using the statistical distributions of all the flaw and non-flaw indications in eachplant. All the potential indications are then passed on to the “Calibration-curve based PhaseThresholding” stage.

Calibration Curve based Phase Thresholds

The phase calibration curves of the tubes can be used to adaptively compute the phase thresholdsFigure 3-2 compares an ideal phase calibration curve with an experimental curve. Ideally, alldefects have a phase angle in the range of 0 to 180 degrees. However, most of the shallowdefects are generally too small for the eddy currents to generate a significant vertical componentsignal. Thus, in practice the calibration curves (especially, for the 200kHz and 400kHz channel


3-1

g , p ( p y,or low frequency channels) tend to have a range much less than 180 degrees. Figure 3-3illustrates the calibration curves generated for one of the calibration tubes – R999C999G003.The phase thresholds computed for the four channels are indicated above each of the calibrationcurves.

Multistage Signal Processing and Classification

Potential IndicationsPotential Indications

Magnitude and Phase ThresholdsMagnitude and Phase Thresholds

NonNon --DefectsDefects DefectsDefects

Rule Base IRule Base I

Rule Base IIRule Base II

Hidden Markov ModelsHidden Markov Models

CombineCombine

Wear RulesWear Rules

DentsDents

ImpingementImpingementRulesRules

DentsDents

Defects/Defects/ NDDsNDDs

Figure 3-1Block diagram of the Multistage classification module

Ideal Calibration CurvePhase Angle (ASME degrees)

% Depth

ID OD

Calibration Curve used in practicePhase Angle (ASME degrees)

% Depth

ID OD


3-2

Figure 3-2The ideal and practically used phase calibration curves


A few degrees are added to the phase thresholds computed from these calibration curves and thethresholds are applied to all the potential indications. The output of the phase thresholded data isthen applied to the next set of rules to further reduce the number of potential indications. Theserules are divided into two sections, and are designated as Rule Base I and Rule Base II in thisreport for simplicity.

R999C999:Cal3

200kHz - [3.4 91.6] MIX – [16.5 96.7]

400kHz – [17.8, 110.5] 600kHz - [4.0, 134.7]

Figure 3-3Phase calibration curves for all four channels of one of the calibration tubes -R999C999G003

Rule Base I

This stage makes use of the phase-trend feature of the defect indications. Phase trend refers tothe variation of the phase angles of the indication with frequency. Defects are observed to have a


3-3

monotonically increasing or decreasing change in phase in the impedance plane trajectory (IPT)with frequency. This can be attributed to the skin effect phenomenon, described earlier.


Inner diameter (ID) defects tend to have a monotonic decreasing trend with an increase infrequency while outer diameter (OD) defect phase angles increase monotonically with frequency.This difference allows a classification of defects broadly into ID and OD flaw categories.

In addition to the phase trend, this stage performs a second level of phase thresholding on theindications, where the thresholds are chosen for each frequency channel (200kHz, 400kHz,600kHz and MIX) based on the available statistics of phase variation in the IPT. Thesethresholds differ for ID and OD indications due to the difference in the phase ranges of these twotypes of indications.

In addition to defects, tubes in steam generators can contain dent indications that do not followthe phase trend. This stage also incorporates rules that screen out dents before performing ID-ODclassification. The dent indications typically have a large peak-to-peak value in the horizontalcomponent. Also the phase angles of these indications vary within a very small range close to thehorizontal axis (i.e. ~0 o or ~180 o). Both of these features are used to classify the dent indications.

Figure 3-4 shows the IPT of a typical dent indication in all the four channels.

Figure 3-4The IPT of a DENT indication in all four channels


3-4


The phase angle of the indication is indicated by the lighter color line (red, if viewed in color)and is also indicated below the figure. The number adjacent to it, underlined by the dotted line,corresponds to the magnitude of the indication.

Since ID indications also exist in the same phase range as dents, the rule-base checks for a phasetrend on all the indications that are classified as dents at this stage. If the indication follows atrend, then it is classified as a potential defect. If the indication does not follow a trend, then it isclassified as a dent.

The indications that fail the dent rule are now passed through the OD-rule which checks for amonotonic increasing phase trend within a phase range of around 30

oto 160

o. This range is

different for each frequency channel and is decided based on the phase ranges of the OD defectsavailable in the training database. Figure 3-5 shows the IPT plots of an OD defect in all the fourchannels. This figure clearly illustrates the phase trend rule for OD flaws.

Figure 3-5The IPT of an OD defect in all four channels

The indications that pass the OD-rule test are classified potential defects, and those that fail areapplied to the ID-rule. The ID-rule looks for a reverse phase trend in the indications within aphase range of around 40

oto -20

o. Again, this range differs for each frequency channel. [Note:

Phase angles in the 600 kHz channel for some IDs fall in the negative half of the impedanceplane. This requires a negative bound for the phase range for ID.] Figure 3-6 shows the IPT of an


3-5


3-6

ID indication in all four channels and illustrates the monotonic decrease in phase angles withincreasing frequency.

Figure 3-6The IPT of an ID indication in all four channels

The indications that pass through the ID-rule are classified as potential defects and go to the nextstage (Rule Base II), and those that fail the ID-rule are considered to be non-defect signals.

Figure 3-7 summarizes the overall approach of Rule Base I.



3-7

Dent

ID

OD

P otential Indication

Is it a D ent ?

I s it an ID ?

I s it an ID ?

I s it an O D ?

NDD P otential D efects D ents

yes

yes

yes

yes

nono

no

no

RULE BASE I

Figure 3-7Overall approach of Rule Base I.

Results of applying the magnitude and phase thresholds followed by the rules in Rule Base I onthe four plants in the EPRI OTSG training database are shown in Table 3-1. The “# Defects +dents before Rule Base I” refers to the results following the preprocessing step. The “# Defects+ dents after Rule Base I” refers to the results following the preprocessing step, the magnitudeand phase threshold step and the Rule Base I step.

Table 2-1 provides a summary of the distribution of defects and dents in the EPRI OTSG trainingdatabase. After the Rule Base I step, 93% of the defects were detected (144 of the 155), Table 3-1. This represents a 90% POD at a 90% confidence level. A comparison in the number of defectovercalls + dent overcalls, before and after the Rule Base I step, shows an 88% reduction in thenumber of overcalls (53,201 before vs. 6,119 after).

Table 3-2 summarizes the flaws missed in this stage. Note that a majority of the missed flaws arewear and impingement. A separate algorithm has been developed for detecting these flaws.

Table 3-1Summary of results after magnitude & phase thresholding, followed by Rule Base I.

Plant # Defects + dentsbefore Rule Base I

# Defects + dentsafter Rule Base I

# Defect overcallsafter Rule Base I

# Dent overcallsafter Rule Base I

ANO 42+4 40+2 1580 64

CRYS 49 48 1725 40

OCN 28+3 22+3 1605 23

TMI 34 34 1048 34



3-8

Table 3-2Summary of missed flaws/dents in Rule Base I.

PlantCal.Grp. Row Tube Indication Category Stage

ANO 3 44 48 24593 Dent Phase thrs.

ANO 3 44 47 24576 Dent Phase thrs.

ANO 17 150 11 3839 Groove IGA Phase trend

ANO 52 31 40 24552 SCC (SP) Phase trend

CRYS 36 46 37 1915 ODIGA 1st

span Phase trend

OCN 27 108 111 21570 Wear Magn. thrs

OCN 27 108 111 20064 Wear Magn. thrs

OCN 23 71 126 15666 Impingement Phase trend

OCN 27 108 111 15457 Wear Phase trend

OCN 27 137 71 15856 Wear Phase trend

OCN 25 135 11 12200 ODI TSP Phase trend

Rule Base II

All of the potential defect indications identified by Rule Base I are then processed through asecond rule base (Rule Base II). Note that any indications that are labeled “Dent” in Rule Base Iare not processed further.

Rule Base II classifies the potential defect indications, from Rule Base I, on the basis of physicalfeatures. The data is classified based on the variance of the indications. The variance is computedfor both the vertical and the horizontal component from all the channels.

An analysis of the data indicated that the variance of the horizontal component does not containany discriminatory information. However, a statistical analysis of the variance of the verticalcomponent indicates that, by using an appropriate threshold, a large number of Rule Base Iovercalls can be filtered out. This is indicated by the plot of the vertical variance for the 400 kHzchannel (Figure 3-8). The red dots correspond to defects, as determined by expert opinion, andthe blue dots correspond to Rule Base I overcalls.



3-9

Figure 3-8Scatter plot of the variance of the 400 kHz channel for one plant.

The threshold is selected on a plant-by-plant basis. The second physical feature is the crosscorrelation of the horizontal component and vertical component for each channel. In addition, thecross correlation across channels (for instance, the cross correlation between the horizontalcomponents of the 200 kHz and 400 kHz channels) was also computed.

Figure 3-9 plots the cross correlation between the horizontal components of 200 kHz and 400kHz channels versus the vertical components of the same channels for one of the plants. The bluedots represent Rule Base I overcalls and the red dots represent defects, as determined by expertopinion.



3-10

Figure 3-9Cross correlation between the horizontal components vs. the cross correlation betweenthe vertical components (200 kHz & 400 kHz).

The figure shows that defects lie in the extreme right hand top corner, suggesting a very highcorrelation. On the other hand, non-defects do not have a strong correlation. Additionalinformation may also be obtained by using the correlation between the other channels. Results ofapplying Rule Base II to data from each of the four plants are shown in Table 3-3.

Table 3-3Results of Applying Rule Base II.

# Flaws Input(Expert Opinion)

# Flaws Detectedby Rule Base II

# Overcallsby Rule Base II

ANO 40 38 370

CRYS 48 48 375

OCN 22 20 361

TMI 34 33 511

Hidden Markov Models

The Hidden Markov Model (HMM) [12] is a statistical model that has been successfully used tosimplify the design and use of pattern classifiers. Statistical models are useful because they canaccount for the inherent variability in real-world systems. This provides the classifier a measureof robustness to measurement noise. The theory of HMMs was first developed by Baum et al



3-11

[13] and since then they have been applied to a multitude of areas [14, 15, 16]. Each signal isrepresented by means of a sequence of symbols, and HMMs are used to derive probabilisticmodels from the given data. All HMMs can be parameterized by means of a set of probabilitydistributions that are derived from a training database. These distributions can be used todetermine the likelihood that a given sequence of symbols was generated from the model. Sincemost real world signals are analog in nature, a clustering stage (called vector quantization [17]) isperformed to convert the signal into a sequence of symbols.

A typical sequence of steps in using a HMM would be as follows. A set of training sequences isobtained and used to generate the model. In a multiclass classification problem, we generate onemodel for each class. Additional data can then be used to refine the models and improve theircapability. Finally, given an unknown sequence, we identify the model most likely to havegenerated the sequence.

Eddy Current Classification

The general scheme for the classification of eddy current signals is shown in Figure 3-10. A setof training signals is recorded and relevant features are extracted from each eddy current signal.Vector quantization of the data is carried out since discrete observation probability densities areused in the HMM. All four channels (200 kHz, 400 kHz, 600 kHz and Mix channels) are used,and the time sequence is replaced by a set of symbols. The set of vector quantized data are thenused to train a HMM. Two models are developed, one each for defects and non-defects.

During recognition, the unknown signal is vector quantized and applied to each of the models.The probability that the unknown signal was generated from each of the models is obtained, andthe unknown signal is assigned to the class with the maximum probability.

Results of applying the HMM algorithm to data from each of the four plants are presented inTable 3-4.

Eddy Current signal

Feature extractionFeature extraction

(Vector Quantization)(Vector Quantization)(VQ)(VQ)

Train HMMTrain HMM

Feature ExtractionFeature Extraction

Test against storedTest against storedmodelsmodels

Select max.Select max.probabilityprobability

Recognition result

Eddy Current signal

(a) (b)Figure 3-10Flowchart for eddy current signal classification using HMMs (a) Training and (b) Testing



3-12

Table 3-4Performance of the HMM on the EPRI OTSG training database.

# Flaws Input to HMM # Flaws Detected by HMM # Overcalls

ANO 38 38 94

CRYS 48 48 95

OCN 20 20 72

TMI 33 33 100

Impingement Classifier

A simple rule using only mix channel data was derived for classifying an indication in the TSPregion as an Impingement or Non-impingement. Since, impingements are usually found in theTSP region, the algorithm only considers the TSP regions for impingement detection.

The TSP region is divided into two halves, and each half is independently checked forimpingements. The various features that are computed are as follows:

Phase angle

MagnitudeV max −μιν , H max−min

V max −mean ,V max− mean

Figure 3-11 shows the vertical component and the IPT plot of the MIX channel of animpingement indication.




3-13

mean

max

min

Entire TSP extent

1st half 2nd half

Vmax-mean

Vmin-mean

Vmax-min

Figure 3-11Vertical component and IPT plot of an impingement

As shown in the figure, most impingement indications have a prominent vertical componentchange above the mean (first in the negative direction, and then in the positive direction) in theregion of the flaw. This feature is captured by computing the V max-mean and V min-mean values. Thesetwo are combined to form a normalized feature given by:

max min max min

max minmax min

−−

−−−− ⋅

=⋅VV

VVV norm mean mean

mean (3-1)

The other feature of interest is the ratio of the Vertical peak-to-peak value and the Horizontalpeak-to-peak value and is expressed as V max−min H max−min .

Since the mix channel is being considered, one would expect to have close to a zero residue inthe TSP region for non-defective indications. However, if an impingement exists in the region,the magnitude of the residue would be substantially higher. Thus, the magnitude was also used asa feature for the classification of the TSP indications. These 4 features were combined usingBoolean rules for classification purposes.

Table 3-5 shows the performance of this module on all the TSP regions of all four plants.



3-14

Table 3-5Results of the IMPINGEMENT CLASSIFIER on all four plants

# Impingements(Expert Opinion)

# ImpingementsDetected

(Auto Analysis)

# ImpingementOvercalls

(Auto Analysis)

All Plants 8 8 129

Wear Identification

Wear in steam generator tubes is a type of degradation that mostly appears at structure locations.Like impingements, these types of degradations are handled separately in the multistageclassification algorithm. An analysis of the training data revealed that the residue in the mixchannel at support plates has sufficient discriminatory information that can be used todifferentiate supports with wear from ones without wear. Figure 3-12 shows the vertical andhorizontal components of TSP signals without wear while Figure 3-13 shows the samecomponents for support signals with wear.

Comparison of the two figures reveals that the low frequency content in the residue of thevertical component is higher for support signals with wear. Thus, the energy in the lowfrequency coefficients of support signals can be used to discriminate between supports with andwithout wears. The energy is obtained by first computing the Fourier Transform of supportsignals and then computing the normalized energy in the low frequency coefficients (first 20coefficients). Support signals from calibration groups that had wear were used to extract thefeatures of interest (energy) and compute thresholds. This threshold was applied to all thesupport signals in a plant.



3-15

(a)

(b)

Figure 3-12MIX channel vertical and horizontal components of two support plate signals without wear.



3-16

(a)

(b)

Figure 3-13MIX channel vertical and horizontal components of two support plate signals with wear.

The results of applying the thresholds to support signals from the four plants are summarized inTable 3-6.


Table 3-6Summary of the wear classifier.

# S S # TSP With Wear # Wear Detected # Wear Overcalls05/01/2007http://energysearch.epri.com/highlight/index.html?url=http%3A//www.epriweb.com/backup/download...

3-17

Plant # TSP Signals # TSP With Wear(Expert Opinion)

# Wear Detected(Auto Analysis)

# Wear Overcalls(Auto Analysis)

ANO 330 2 0 29

CRYS 225 1 1 37

OCN 255 6 5 57

TMI 135 0 0 6

The results indicate that the wear classifier is capable of detecting most of the wear signals in theEPRI OTSG training database. However, the thresholds need to be optimally selected in order toimprove the performance further.


4SUMMARY AND CONCLUSIONS


4-1

A multistage automated classification algorithm for OTSG bobbin data has been described. Animportant point to note is that these algorithms are designed to be totally automated, withminimal operator input. Given a raw set of eddy current data, the algorithm undertakes amultitude of analysis procedures and identifies possible defect indications, including locations,without any intervention from an operator. The automatic data analysis system consists of twostages: signal preprocessing and degradation type classification. The algorithm can besummarized as follows:

Preprocessing : This phase includes two major steps: adaptive filtering and dynamicthresholding. The adaptive filtering step removes correlated noise (such as noise due to probewobble) by using tube-specific filters. The dynamic thresholding stage further removes lowfrequency trends and identifies potential flaw indications in the data.

Classification : A multistage classification algorithm is used to classify potential flawindications into different classes. The first stage consists of a rule base that classifies datainto degradation signals (wear, impingement and NQI) and benign signals. The next stage(Rule Base II) reduces the number of NQI overcalls by applying a second set of rules to acombination of physical features. Finally, Hidden Markov Models are used to further reducethe number of NQI overcalls.

The parameters for the rule bases and the HMM were modified to be plant-specific.

A summary of the overall performance of the algorithm on the EPRI OTSG training database isshown in Table 4-1. This table includes the results of the wear and impingement classifiers. Theresults of these two classifiers have been combined with the results of the main branch of theclassification algorithm, consisting of the rule base (Rule Base I and Rule Base II) and theHMM. As seen from the table, the newly developed algorithms detected 96% of the expertopinion defects (149 of 155) and achieved a 93% POD at a 90% confidence level, with 9.1overcalls per tube. The missed flaws are listed in Table 4-2. Future algorithm development willfocus on further reduction in the number of missed indications and a reduction in the number ofovercalls.

Summary and Conclusions

Table 4-1Overall summary of the OTSG classification algorithm.

Plant #Tubes

# Defects +Dents

(Expert Opinion)

# Defects + DentsDetected

(Auto Analysis)

# Defect Overcalls

(Auto Analysis)

# Defect Overcallsper Tube

(Auto Analysis)

ANO 22 42+4 39+2 151 6 905/01/2007http://energysearch.epri.com/highlight/index.html?url=http%3A//www.epriweb.com/backup/download...

4-2

ANO 22 42+4 39+2 151 6.9

CRYS 19 49 48 124 6.5

OCN 17 30+5 29+5 187 11.0

TMI 9 34 33 147 16.3

Total 67 155+9 149+7 609 9.1

Table 4-2Summary of missed flaws.

Plant Cal. Group Row Tube Indication Category

ANO 52 31 40 24545 SCC_SP

ANO 52 31 40 24553 SCC_SP

ANO 17 150 11 9627 Groove IGA

CRYS 36 46 37 1917 Wear

OCN 27 70 78 8469 MBM

TMI 35 45 16 7085 ODI_TSP

5REFERENCES


5-1

[1] W. Lord and R. Palanisamy, “Development of theoretical Models for NDT eddy currentPhenomena”, Symposium on Eddy Current Characterization of Materials and Structures ,NBS, Gaithersburg, Maryland, September 1979.

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[6] Kevin Kennedy Associates - http://www.kkai.com/mt12a.html

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[9] Y. Zhu and J.P. Weight, “Ultrasonic nondestructive evaluation of highly scatteringmaterial using adaptive filtering and detection,” IEEE Transactions on Ultrasonics,Ferroelectrics and Frequency Control, vol. 41, no. 1, pp. 26-33, 1994.

[10] A.V. Oppenheim and R.W. Schafer, Discrete Time Signal Processing , 2nd

ed., PrenticeHall: Englewood Cliffs, NJ, 1989.

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[12] L. R. Rabiner, “A tutorial in hidden Markov models and selected applications in speechrecognition,” Proc. IEEE , Vol. 77, No. 2, pp 257-286, 1989.

References

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[14] J. S. Bridle, “Stochastic models and template matching: Some important relationshipsbetween two apparently different techniques for automatic speech recognition,” Proc.Inst. Of Acoustics, Autumn Conf., pp. 1-8, 1984.

[15] R. P. Lippmann, E. A. Martin and D. B. Paul, “Multistyle training for robust isolatedd h i i


5-2

word speech recognition,” Proc. ICASSP’87, pp. 705-708, 1987.

[16] S. Zhong and J. Ghosh, “HMMs and coupled HMMs for multichannel EEGclassification,” Proc. IJCNN’02, pp. 1154-1159, 2002.

[17] J. Makhoul, S. Roucos and H. Gish, “Vector quantization in speech coding,’ Proc. IEEE,Vol. 73, No. 11, pp. 1551-1588, 1985.


epri automated analysis of bobbin coilprobe eddy current data

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