Estimating Defect Sizes from Pigging Information

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aper No.44CORROSIONc)LThe NACE International Annual Conference and Exposition

DETERMINING CORROSION DEFECT GEOMETRY FROMMAGNETIC FLUX LEAKAGE PIG DATA

J. Bruce Nestleroth, Steven W. Rust, and David A. BurgeonBattelle

505 King AvenueColumbus, Ohio 43201

Harvey HainesGas Research Institute

8600 West Bryn Mawr AvenueChicago, Illinois 60631

ABSTRACTAccurate determination of the size of corrosion defects is important for determining defect severity. Many magnetic flux

leakage pigs employ strong magnetizers and a large number of sensors for improved defect geometry determination methods.Anal ysis of data from 128 metal loss defects examined repeatedly under different test conditions is used to illustrate pigperformance capabilities. An assessment of two inspection variables that can degrade performance is also presented.Keywords: gas transmission pipeline, in-line inspection, magnetic flux leakage, metal loss corrosion

INTRODUCTIONIn-line inspection, more commonly called intelligent pigging, has been successfully used by the pipeline industry for

detection and characterization of metal loss corrosion for over 30 years. The magnetic flux leakage (MFL) technique is the mostcommon inspection modality. The interpretation of flux leakage signals can be difficult, because there is not a simplerelationship between flux leakage signal shape and corrosion defect geometry. The problem is compounded by inspectionvariables associated with pipeline inspection including product flow velocity, pipeline material variations, and operating pressure.

This paper examines the accuracy of recently developed characterization algorithms for predicting the depth, length, and widthof metal loss corrosion defects. The algorithms were developed using flux leakage data from machined defects in the Pull RigDefect Set of the GRI Pipeline Simulation Facility. The MFL signals were recorded using the MFL Test Bed Vehicle, which isalso part of the facility, Test conditions including velocity and magnetization level were varied during the data acquisition process.While the accuracy of any characterization algorithm is dependent on pipe material, inspection tool configuration (includingmagnetizer and sensors), and inspection variables (such as velocity and he pressure), these results show the general accuraciesof this class of characterization algorithms.

These results are part of a continuing effort to improve existing defect detection and characterization capabilities conductedfor Gas Research Institute at the Pipeline Simulation Facility. The specific objectives of this effort to improve defect detection

Copyright01996 by NACE International. Requests for permission to publish this manuscript in any form, in part or in whole must be made in writing to NACEInternational, Conferences Division, P.O. Box 218340, Houston, Texas 77218-8340. The material presented and the views expressed in thispaper are solely those of the author(s) and are not necessarily endorsed by the Association. Printed in the U.S.A.

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and characterization capabilities are intended to benefit vendors, pipeline owners, and researchers. For vendors, the effort seeksto provide characterization and compensation functions to improve inspection capabilities. This entailed developing functionsof extracted features of the MFL signals that could then be fitted against the known geometry characteristics of the machineddefects to provide functions for estimating the defect characteristics. For pipeline owners, the objective is to provide informationon the capabilities of true inspections to improve inspection, maintenance, and repair decisions. Finally, for researchers, theobjective is to identifi the greatest obstacles to advancing inspection capabilities in an effort to improve technology developmentdecisions. Specifically, this entailed identifying the degree of uncertainty associated with estimating each defect geometrycharacteristic, as well as the sources of and their relative contribution to that uncertainty.

PROCEDURESData were collected using the components of the GRI Pipeline Simulation Facility (PSF).2 The PSF Pull Rig consists of four

300-foot (100-meters) long unpressured pipe runs with diameters of 12,24,30, and 36 inches (305, 610,762, and915 mm). In-line inspection tools and test bed vehicles can be pulled through the pipe sections at velocities up to 20 mph. A removable metal-10SSdefecl set 3has been installed in the 24-inch (610-mm) pipe length. The primary defect set consists of81 defects located onthe outside pipe surface. The objective of this defect set is to examine the relationship between a defects MFL signalcharacteristics and its length, width, and depth without the complexity of other defect shape parameters, such as surface angle andplan rouncness. The defects are of varying equivalent length and width (1,2,3, 4.5, or 6 inches [25, 51, 76, 114, and 152 mm]),and maximum percent depth (20, 35, 50, 65, or 80 percent). All 81 defects have a 45 degree surface angle, a Odegree bottomangle, and an elliptical plan view. The secondary defect set consists of 47 defects located on the outside pipe surface designedto allow examination of the effects of different surface angles (22.5, 45,90 degrees) and plan roundness (elliptical or rectangular).

The primary and secondary defects sets were inspected during 29 pull rig runs of the MFL Test Bed Vehicle~ built for use atthe Pipelir e Simulation Facility. The runs were conducted over the span of one yearMarch 3, 1994, to March 17, 1995atvehicle ve ocities ranging from 2.51 mph (4.04 km/h) to 14.98 mph (24.10 km/h). As the test bed vehicle was equipped duringthese runs with sufficient sensors to span only 165 degrees, specific sections of the defect sets were inspected during each run.In total, th:se runs provided the MFL signal for each of 1078 inspections of the 128 defects of known geometry. Early in theanalysis of the collected MFL signals, it became clear that rectangular defects produce different signals than do elliptical defects.As will be discussed later, for example, rectangular defects appear deeper than do elliptical defects of comparable depth. Inaddition, deep (> 65 percent) and much wider-than-long (width/length > 3) defects produced MFL signals that exceeded themeasurement range of the test bed vehicles digitizer, resulting in truncated signals. As a result, much of the algorithmdevelopment focused on a subset of the studied defects838 elliptical defects without truncated MFL signals. Unless otherwiseindicated, therefore, the figures and results referenced in this paper reflect only this subset,

RESULTSThe data were used to develop characterization algorithms that relate a defects MFL signal to basic geometric features. Signal

quantification algorithms were developed for identifying amplitude, width, and length features in the MFL signal. These featureswere then utilized in estimating functions for predicting the maximum depth, equivalent length, and equivalent width of the studieddefects. The uncertainties in the estimated geometry features were then analyzed to characterize the relative contribution of thesources of that uncertainty. The estimation accuracy of the procedures was considered for each metal 10SSgeometry feature. Asmaximum depth is usually the geometry feature of principal concern, depth estimation is discussed first. This is followed bydiscussion of length and width estimation, respectively.Depth Estimation

A number of different algorithms can be used to relate defect signal amplitude to defect depth. The depth estimationalgorithms evaluated in these results were of the form

AMP = ct. DEPTH + BDDEPTHZ

The weights u and 13can be more than simple constants. For the most robust algorithms, these weights include compensation forvariables t Iat affect amplitude such as defect width, length, magnetization level, and velocity. Box and whisker plots of theextracted amplitude feature versus the actual maximum percent depth, shown in Figure 1, illustrate many aspects of thecharacterization algorithm. The top and bottom of the rectangular box represent the 75th and 25th percentiles, respectively, ofthe observed amplitudes at the specified depth. The middle hash mark within the rectangle represents the 50th percentile. Theupper and lower whiskers represent the 95th and 5th percentiles, respectively. Superimposed on the data is the developed

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calibration function for maximum depth. Analysis showed that compensating for the width/length ratio was as sufficient ascompensating for these two variables individually. Then, the calibration function which includes compensation for estimatedwidth/length ratio, background magnetization, and vehicle velocity is plotted for width/length ratios of 1/3,1, and 3, (The vehiclevelocity is assumed to be 2.51 mph (4.04 km/h), and the background magnetization is fixed at 70 gauss.)

The effect of compensating for estimated width/length ratio, background magnetization, and vehicle velocity when predictingmaximum percent depth is presented in Figure 2. Bell-shaped distribution curves for the predicted percent depth without and withcompensation are plotted for actual percent depths of 20, 50, and 80 percent. (The distributions at actual depths of 35 and 65percent arc similar, but are excluded for the sake of figure clarity.) Each curve spans 95 percent of the distribution in predictedmaximum depth, from the 2.5th percentile to the 97.5th, thereby presenting the accuracy in predicting maximum depth. The meanpredicted depth is indicated by the center line of the distribution. The solid-line curves present the predicted percent depth basedsolely on the extracted amplitude feature (Method A 1where cxis a constant and ~ = O). The percent depth prediction that includescompensation for estimated width/length ratio, background magnetization, and vehicle velocity is plotted using dashed lines(Method C2). The compensation significantly improves the estimation accuracy, especially for deeper defects. The improvementis manifest in the amount of uncertainty (i.e., the width of the bell-shaped curve) and the estimation bias (i. e., the differencebetween the mean prediction and the actual percent depth).

Once the success of compensation had been established, the determination of which of the compensation factors is primarilyresponsible for the improved accuracy was performed. The additional accuracy achieved by including background magnetizationand vehick velocity in the compensation function is minor. Figure 3 presents the predicted percent depth versus actual percentdepth whe e the estimation function does (Method C2; dashed line) and does not (Method C 1; solid line) include compensationfor background magnetization and velocity. An improvement in accuracy is apparent for only 80 percent deep defects. It follows,therefore, that compensation for estimated width/length ratio produces most of the improvement in predicted percent depth.

Even more improvement is possible if the width/length ratio can be estimated more accurately. Specifically, Figure 4 plotsthe effect on predicted maximum percent depth of compensating for estimated (Method C2; dashed line) versus actual (MethodB2; solid line) width/length ratio. Improved prediction is exhibited at all three depths.

As an aside, Figure 5 compares the distribution in predicted depth for elliptical (dashed line) and rectangular defects (solidline), The need to account for plan roundness when estimating depth is evident from the portrayed comparison. If noaccommodation for rectangular defects is made in the percent depth estimation procedure, rectangular defects are predicted tobe signiticmtly deeper than elliptical defects even for defects of the same actual depth. The difference is especially dramatic for50 and 80 percent defects.

These performance plots prompt the question of what sources are responsible for the percent depth estimation uncertainty.The stacked bars in Figure 6 report the percentage of unexplained depth variation due to defect geometry, inspection condition,and random error. These percentages are reported for 20, 50, and 80 percent deep defects, and the estimation results with andwithout compensation for background magnetization and vehicle velocity are contrasted, Figure 6 indicates that the compensationfor background magnetization and vehicle velocity accounts for much of the uncertainty stemming from inspection conditions.Some unexplained variation due to inspection condition remains among 80 percent deep defects following compensation, but itis negligible or absent for shallower defects.

Figure 7 presents the effect on the percentage of unexplained depth variation of compensating for estimated width/length ratio,background magnetization, and vehicle velocity, As Figure 2 showed, the total variability is reduced dramatically. In Figure 7,the percentage of unexplained variation due to random error has been reduced, but is still at least 50 percent. Defect geometryaccounts for the majority of the remaining variability. Inspection condition is responsible for at most only a small percentage.Length Estimation

Figure 8 portrays predicted length distribution curves versus actual length, where the length estimation involves only a linearcalibration of the extracted length feature. As is evident in Figure 8, equivalent length estimation is more accurate than maximumpercent denth estimation. The results for two slope threshold criterions are presented. The reported analyses in this manuscriptare based on features extracted using a 350 gauss/inch (13.8 gauss/cm) criterion. This criterion was chosen in an effort to optimizethe trade-off between defect detection and length estimation accuracy. As Figure 8 suggests, a higher criterion (500 gauss/inch[19.6 gauss/cm]) results in more accurate prediction of length, especially among short defects. Since the criterion distinguishesdefect signals from random noise, a higher criterion is less susceptible to estimation errors of this sort. However, the highercriterion also misses some shallow (


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Figure 9 plots the percentage of unexplained length variation due to defect geometry, inspection condition, and random error.Again, inspection condition represents at most a negligible percentage of the unexplained variation. Defectgeometryrepresentsthemajori:y of the variability among defects 1 inch (25 mm) in length, but less than 20 percent among defects 6 inches ( 152 mm)in length.Width Estimation

R was noted earlier that the prediction of equivalent width requires compensation for the defects amplitude feature. Figure 10presents


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Width EstimationCompensation for amplitude significantly improves the accuracy of equivalent width estimation for wide defects (i.e., greater

than 3 inches [76 cm]). A less dramatic improvement is reported for narrow defects. Despite this improvement, however, thedeveloped width estimation capability is four times less precise than the length estimation capability.

As with depth estimation, errors in width estimation are due primarily to defect geometry (and/or permanent local pipeconditions) and somewhat to random error, almost no unexplained width variability is attributable to inspection conditions.

ACKNOWLEDGMENTSFrederick R. Todt, Brandon J. Wood, and Halsey J. Boyd contributed invaluable support to the preparation of the data, its

analysis, and the development of the feature extraction procedures. Richard Davis, Michael Kurre, Dale Shoemaker, and RobertGertler contributed support of the pull rig and data collection process.

Thiswork was conducted for Gas Research Institute (GRI) under contract 5088-271-1696 using the Pipeline SimulationFacility, which was established to advance the state of the art of gas pipeline inspection technology, The facility is located atBattelle in Columbus, Ohio.

This paper was prepared by Battelle as an account of work sponsored by the Gas Research Institute (GRI). Neither GRI,members of GRI, Battelle, employees of Battelle, nor any person acting on behalf of them:

a. Makes any warranty or representation, express or implied, with respect to the accuracy, completeness, orusefidness of the information contained in this paper, or that the use of any apparatus, method, or processdisclosed in this paper may not infringe privately owned rights; or

b. Assumes any liability with respect to the use of, or for darnages resulting from the use of any information,apparatus, method or process disclosed in this paper.

Reference to trade names or specflc commercial products, commodities or services in this paper does not represent or constitute anendorsement, recommendation or favoring by GRI or Battelle of the specific commercial product, commodity, or service,

REFERENCES1. Bubenik, T.A. et al., Magnetic Flux Leakage (h4FL) Technology for Natural Gas Pipeline Inspection, ReportGRI-91 /0367,

Gas Research Institute, Chicago, November 1992.2. Nestlero@ J,B., Bubenik, T.A., and Haines, H.H., The Pipeline Simulation Facilities for the Development of In-Line Inspection

Technologies for Gas Transmission Pipelines, Materials Evaluation, Vol. 53, April 1995.3. Gas Research Institute, GRI Pipeline Simulation Facility Metal Loss Defect Set. Topical ReportGRI-94/0381, 19954. Teitsma, A. et al., The Design of a Test Bed Vehicle for the Development and Evaluation of In-Line Inspection Technologies,

The Irtternational Conference on Pipeline Reliability, Calgary, Alberta, Canada, June 1992.

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t 00

0c.

1,.I

,1 10 20 30 40 50 60 70 80 90 100

FIGURE 1 - Calibration of depth feature (amplitude) with compensation for width/length ratioversus actual percent depth of defect

Method AlC2 i

o 10 20 30 40 50 60 70 80 90 100ACTUAL PERCENT DEPTH

FIGURE 2 - Predicted percent depth versus actual percent depth of defect~ffect of compensation forestimated width/length ratio, background magnetization, and velocity

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80

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FIGURE 3 - Predicted percent depth versus actual percent depth of defecteffect of compensationfor background magnetization and velocity

. m?.Iuuli Method1!30

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FIGURE4 - Predicted percent depth versus actual percent depth of defect~ffect of compensationfor estimated versus true width/length ratio

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FIGURE 5 - Predicted percent depth versus actual percent depth of defecteffect of plan roundness

Percent1 00

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FIGURE 6 - Percentage of unexplained depth variation~ffect of compensation forbackground magnetization and velocity

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FIGURE 7 - Percentage of unexplained depth variationeffect of compensation for estimatedwidth/length ratio, background magnetization, and velocity

Threshold 35050020.32

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FIGURE 8 - Predicted length versus actual lengtheffect of reduced slope threshold criterion44/9



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Percent 100

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FIGURE 9 - Percentage of unexplained length variation

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21GURE 10 - Calibration of width feature with compensation for amplitude versus actual width of defect44/1 o



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Percent1 009080706050403020100

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Width (inches)(2.:4)(5.:8)(7.:2) (centimeters)

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FIGURE 12 - Percentage of unexplained width variationeffect of compensation for amplitude44/1 1

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Estimating Defect Sizes from Pigging Information