s. mandayam/ece dept./rowan university a data fusion system for the nondestructive evaluation of...

S. Mandayam/ECE Dept./Rowan University

A Data Fusion System for the A Data Fusion System for the Nondestructive Evaluation of Nondestructive Evaluation of

Non-Piggable Pipes Non-Piggable Pipes PI: Shreekanth Mandayam

Co-PIs: Robi Polikar, John Chen

Rowan UniversityCollege of Engineering201 Mullica Hill RoadGlassboro, NJ 08028

(856) 256-5330http://engineering.rowan.edu/

Natural Gas Delivery Reliability Kick-Off MeetingNational Energy Technology Laboratory

Morgantown, West VirgininaDecember 3, 2002


OutlineOutline• Rowan University

– College of Engineering

• Project Objectives• Scope of Work• Project Tasks

1. Research Management Plan2. Technology Status Assessment3. Data Fusion Algorithms

1. Measures2. Defect Identification3. Defect Sizing

4. Validation Test Platform5. Data Management

• Discussion


Rowan UniversityRowan University

www.rowan.edu/engineering


Rowan EngineeringRowan Engineering• The Gift • Henry M.Rowan Hall• Four Departments:

– Civil and Environmental Engineering– Chemical Engineering– Electrical and Computer Engineering– Mechanical Engineering

• BS and MS Programs 450 students, 32 faculty

History and Background


Project ObjectivesProject Objectives

• To design sensor data fusion algorithms that can synergistically combine defect related information from heterogeneous sensors used in gas pipeline inspection for reliably and accurately predicting the condition of the pipe-wall

• To develop efficient data management techniques for signals obtained during multisensor interrogation of a gas pipeline


Scope of WorkScope of Work• Design, develop and test multisensor data fusion algorithms for

– Effective identification of the presence of pipe-wall anomalies

– Accurate sizing of identified defects

• Develop a test platform for validating the data fusion algorithms– Multisensor nondestructive evaluation using a combination of the

following methods as needed – magnetic flux leakage, acoustics and thermal.

• Actively interface with local and national utilities, vendors and manufacturers of pipeline inspection systems for improving the state-of-the-art in the management of pipeline inspection data.


Project TasksProject Tasks

• Research Management Plan• Technology Status Assessment• Data Fusion Algorithms

– Measures– Defect Identification– Defect Sizing

• Validation Test Platform• Data Management


Research Management PlanResearch Management Plan

Rowan University, Glassboro, NJ

Physical Acoustics Corp., Princeton, NJ


Research Management PlanResearch Management Plan

• Personnel• Data Fusion Algorithms




PersonnelPersonnel

• Rowan University– Dr. Shreekanth Mandayam (PI), Dr. Robi Polikar

(Co-PI) and Dr. John Chen (Co-PI)– 2 Graduate Research Assistants– 2 Undergraduate Research Assistants– Additional Undergraduate Students from

Engineering Clinic course sequence (not compensated)

• Physical Acoustics Corporation– Dr. Richard Finlayson


Data Fusion AlgorithmsData Fusion Algorithms

Sensors

Magnetic Flux Leakage (MFL)

Thermal Imaging

Acoustic Emission (AE)

Ultrasonic Testing (UT)

Other

Identification

Defect

Benign

Valve

Weld

T-section

Sizing

Depth

Length

Width

Increases Confidence

Measures

Redundancy

Complementary

Increases Accuracy

Information Quality


Test Platform and Data Test Platform and Data ManagementManagement

• Validate data fusion algorithms

• Obtain pipeline inspection signals from local utilities– Develop procedure for analyzing data


Technology Status Technology Status AssessmentAssessment

Nondestructive Evaluation Laboratory

College of Engineering

Rowan University


ndelab@rowanndelab@rowan• Capabilities

• Imaging (Magnetic, Ultrasonic, Thermal, Microwave) • Virtual Reality • Adaptive Signal/Image Processing • Artificial Neural Networks • Pattern Recognition • Software Development • Instrumentation • Biomechanics• Wireless Communications (Remote Monitoring) • VLSI Testing • Rapid Prototyping

• Sponsors• National Science Foundation, US Department of Energy,

American Institute for Cancer Research, American Cancer Society, NJ DOT, NJ State Police, Water Environment Research Foundation, US Army TACOM, US Navy, Lindback Foundation, etc.


Ultrasonic Testing

Thermal Imaging

Acoustic Emission

Test PlatformsDigital Signal/Image

Processing

Data Fusion

AdvancedVisualization

Virtual Reality

This research work is sponsored by:• US Department of Energy• National Science Foundation• ExxonMobil

Nondestructive Evaluation of Gas PipelinesNondestructive Evaluation of Gas Pipelines0.0” 0.2”

0.4” 0.6”

Artificial Neural Networks

1

1

1

x1

x2

x3

y1

y2

wij

wjk

wkl

InputLayer

Hidden Layers

OutputLayer

ndelab@rowan

Magnetic Imaging


Previous WorkPrevious Work• Data Fusion

– Invariance Transformations• MFL• UT

– Incremental Learning: Learn++ Algorithm

• Thermal Imaging

• Local Area Monitor (PAC)


The Inverse ProblemThe Inverse Problem

Amplitude, aFrequency, f

Coupling

device, c

Specimen geometry, gSpecimen material distribution, p

Defect geometry, d

Measurement

system, m

Energy input, x = F1(a, f)

Signal output, y = F2(a, f, c, g, p, d, m)


Experimental SetupExperimental Setup

Specimen

Current LeadClamp Pipe

sectionHallprobe

Probemount

Currentleads


Specimen PreparationSpecimen Preparation

X-GradePipe Sections:X-42, X-52,X-65, X-70

0.8 mm wide key-cutter slots1.5, 4.4, 6.4 mmdeep

Direct Current MagnetizationVarying current amplitude 200 A


Magnetic Flux Leakage (MFL) Magnetic Flux Leakage (MFL) ScansScans

X-42 X-52 X-65 X-70

1.5 mm4.4 mm6.4 mm

Pipe GradeDefectDepth

Line Scans0

Nor

mal

ized

Am

plit

ude

44 88 132 176

Length (mm)

X-42 X-52 X-65 X-70

1.5 mm4.4 mm6.4 mm

Pipe GradeDefectDepth

Line Scans0

Nor

mal

ized

Am

plit

ude

44 88 132 176

Length (mm)


Invariance TransformationInvariance Transformation

InvarianceTransformation

Algorithm

NDE Signalsdepend on defect geometryandoperationalparameters

NDE Signaturesunique to

defectgeometry

alone


Mathematical ModelMathematical Model

),()},,(),,,({ 21 ldhtldxtldxf

),()()(),( 11

12221 ldhxgxgxxf

)},,({)},,({),( 2211 kjikjiii tldxgtldxgldh

}),,({

)},,({),(

11

22

tldxg

tldxgldh

)()(),( 2211 xgxgldh


Analogy: Monoscopic VisionAnalogy: Monoscopic Vision

Larger object, farther

Smaller object, nearer

Focal point of the eye

Same image formed on the retina !!!


Analogy: Stereoscopic VisionAnalogy: Stereoscopic Vision

Larger object, farther

Smaller object, nearer

Focal point of left eye

Image inleft eye

Focal point of right eye

Image inright eye

Slightly different images,to brain

x1 x2

g2(x1) g2(x2)

g1(x1,x2)


Invariance TransformationInvariance Transformation

• Identify at least two distinct test signals

• Synergistically combine to isolate unique defect signature

Test Signal x1 (d,t)

Test Signal x1 (d,t)

Invariance TransformationFunctionh (d)

Invariance TransformationFunctionh (d)

Parameter-InvariantDefect Signature

Parameter-InvariantDefect Signature

h ( d ) = g2{x1(d,t)}

g1{x1(d,t)} Test Signalx2 (d,t)

Test Signalx2 (d,t)


1

1

1

x1

x2

x3

y1

y2

1wij

InputLayer

Hidden Layer

OutputLayer

Inputs Outputs

2

2ji

2

cx

ij e

-5 5

0

0.5

1(t)

t

RBF NetworkRBF Network

m

jijij cxg

11 ||)(||


Compensation ResultsCompensation Results

X-42 X-52 X-65 X-70Pipe Grade

DefectDepth

1.5 mm

4.4 mm6.4 mm

Invariance Transformation





• Thermal Imaging



Experimental SetupExperimental Setup

Ultrasound transducers Concretetestspecimen

Immersiontank

Linearactuators forscanning

Scanner controller & stepper motors

PC fordata acquisition & processing


Specimen PreparationSpecimen PreparationRectangular Slots

• 25.4 x 25.4 mm

• Varying depth 0.0, 5.1, 10.2, 15.2 mm

• 50.8 mm (2”) radius x 50.8 mm (2”) depth discs

• Thickness is representative of wall thickness for a 24” internal diameter ASTM C14 Class 2 non-reinforced concrete pipe and 24” to 36” internal diameter ASTM C 76 reinforced concrete pipe

• Dense concrete mixes with a low water-cement (ASTM Type I Portland) ratio: 0.40 to 0.45 range

• Two aggregate mixes:

• 1:1 ratio sand, cement

• 2.93 : 1.51 : 1 ratio of coarse aggregate (¾” top size gray granite), sand, cement

• Non-air entrained with the only admixture being a water reducer


1 MHz C-Scan Images1 MHz C-Scan Images

Cement & Sand Aggregate

0.0mm 5.1mm

10.2mm 15.2mm

0.0mm 6.9mm

13.2mm 18.5mm


500 kHz C-Scan Images500 kHz C-Scan Images

Cement & Sand Aggregate

0.0mm 5.1mm

10.2mm 15.2mm

0.0mm 6.9mm

13.2mm 18.5mm


3-D Defect Profiles3-D Defect ProfilesDefect Depth

Cement and Sand Aggregate

0.0 mm

5.1 mm

0.0 mm

5.1 mm,6.9 mm


3-D Defect Profiles3-D Defect Profiles

10.2 mm

15.2 mm

10.2 mm,13.2 mm

15.2 mm,18.5 mm





• Thermal Imaging



Incremental LearningIncremental Learning

Definition: The ability of an automated classifier to learn new information from additional data that may later become available.

Incremental Learning for Data Fusion

– Without forgetting previously acquired knowledge

– When old data is no longer available

– Even when new data introduces new classes and/or new features.


Classifier 4

Classifier 8

Classifier 1

Combining an Ensemble of ClassifiersCombining an Ensemble of Classifiers

Classifier 2

Classifier 7

Classifier 3

Classifier 5Classifier 6

Complex decisionComplex decision boundary to be learnedboundary to be learned

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OOOO

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Learn++ DetailLearn++ Detail

Feature Inputs

Classifiers

Voting Weights

Dataset 1Features f1-f6



w1w2

w3

w4w5

w6

w7w8

w9

Weighed Majority Voting

w1w2 w3 w4 w5 w6 w7

w8 w9

C2 C3 C4 C5 C6 C7 C8 C9C1


Results from Previous ResearchResults from Previous Research

Ultrasonic testing of welds


Learn ++ ResultsLearn ++ ResultsTypical A-Scans

Crack

Lack Of Fusion

Slag

Porosity


Training 1 Training 2 Training 3(8) (7) (12)

Data1 98.17% 84.67% 80.33%Data2 --- 97.83% 86.50%Data3 --- --- 97.01%Test 48.89% 66.81% 78.37%

Learn++ Results (New Classes Added)

Learn++ ResultsLearn++ ResultsResults on Test Data Set For Learn++ with New Classes Added in each training set

LOF Slag Crack PorosityData1 300 300 0 0Data2 200 200 200 0Data3 150 150 137 99Test 200 200 150 125

Distribution of Data


Learn ++ ResultsLearn ++ ResultsComparison of Results

0.052532

81.04%

Learn++ Results (No New Classes Added)Error Goal

Hidden Layer NodesNumber of Classifiers

Performance on Test Data

0.052527

78.37%

Learn++ Results (New Classes Added)Error Goal



0.001501

80.00%

Single Strong Classifier ResultsError Goal







• Thermal Imaging



Sample: 304 SS, 0.25 in. thick with two simulated defects: 2 in. x 2 in. x 0.13 in. deep and 2 in. x 2 in. x 0.063 in. deep

Laptop computer

Computer

Power supply

IR camera

Sample with simulated defects

Two 105 W halogen lamps,sinusoidally powered

Lock-In IR Thermography SchematicLock-In IR Thermography Schematic


Phase image for 32s period

Magnitude image for 32s period

Phase image for 100s periodPhase image for 16s period

Defect ImagesDefect Images


Thermosonics Defect DetectionThermosonics Defect Detection

Closed fatigue crack

Machine tool clamped in test rig

Ultrasonic energy from welder applied here





• Thermal Imaging



Local Area MonitorLocal Area Monitor

• Developed in conjunction with the U.S. Federal Highway Administration (FHWA)

• Modular (2 - 8 Channel) DSP-based AE system with 16-bit A/D • Battery/solar powered • Modem/cellular standard communications • 4 high-speed and 8 low-speed parametrics • Digital AE features and waveforms processed simultaneously • Software programmable filters • Conducive to harsh outside environments



Rowan University, Glassboro, NJ

Physical Acoustics Corp., Princeton, NJ



• Research Management Plan• Technology Status Assessment• Data Fusion Algorithms





Sensors


Thermal Imaging



Other

Identification

Defect

Benign

Valve

Weld

T-section

Sizing

Depth

Length

Width


Measures

Redundancy

Complementary

Increases Accuracy

Information Quality


Data Fusion MeasuresData Fusion MeasuresNDE Signal x1

NDE Signal x1

Invariance TransformationFunctionh1


Redundant DefectRelated Information

Redundant DefectRelated Information

h1 = g21{x1(d,t)}

g11{x1(d,t)} NDE Signalx2

NDE Signalx2

NDE Signal x1

NDE Signal x1



Complementary DefectRelated Information

Complementary DefectRelated Information

h2 = g22{x1(d,t)}

g12{x1(d,t)} NDE Signalx2

NDE Signalx2



Sensors


Thermal Imaging



Other

Identification

Defect

Benign

Valve

Weld

T-section

Sizing

Depth

Length

Width


Measures

Redundancy

Complementary

Increases Accuracy

Information Quality


Learn++ Algorithm for Incremental LearningLearn++ Algorithm for Incremental Learning

other

AE

UT

MFL

EMAT

Rule-BasedWeight Assigning

Preprocessed defect

signals from various sources

Ensemble Combination

FinalDecision

Learn++

One set of ensemble of classifiers, combined through weighted majority voting

A base classifier trained with signals / features of one imaging modality

E..

…



Sensors


Thermal Imaging



Other

Identification

Defect

Benign

Valve

Weld

T-section

Sizing

Depth

Length

Width


Measures

Redundancy

Complementary

Increases Accuracy

Information Quality


Validation Test PlatformValidation Test Platform

• Specimen Fabrication

• Test Platform Development

• Multi-sensor NDE signals


Conceptual Design: Conceptual Design: Test Platform Test Platform

DataAcquisition

SignalConditioning

Display/User Interface

Specimen

Load Cell

SimulatedDefect Double

ActingHydraulicRam

AESensors


Specimen FabricationSpecimen Fabrication

Rowan Waterjet Machining Center


Specimen FabricationSpecimen Fabrication

• Provided by Shell Oil Co.• 0.5” Thick SA-516 grade 70

Steel Coupons• Simulated Cracks of varying

depths– .08”, .16”, and .32” deep

• Two sets of 3 specimens each• Uni-axial and Biaxial Loading

– simulates axial and hoop stresses of a pressurized pipeline


Test Platform Design CriteriaTest Platform Design Criteria

• Design Challenges– Rigid Frame

– Perform Biaxial Loading of Specimen• 30,000 psi 1st Dimension • 15,000 psi 2nd Dimension

– Short Manufacturing Time– Low Cost


Test Platform Design ApproachTest Platform Design Approach

• Hydraulics– Enerpac RCH-Series

Hollow Plunger Cylinders• 30 Ton Capacity• Double Acting Cylinder

– PUJ-1401B Electric Pump• Flow Rate 20in3/min• Reach Full Load in a

minute or less

• Cost Estimation– Approx. $3000 to $4000 for

hydraulic setup


Current Test Platform DesignCurrent Test Platform DesignFrame

Load TransducerSpecimen

LoadingScrews

Specimen ClampingBracket


Data ManagementData Management

• Work with our industrial partner – Physical Acoustics Corp.

• Local utilities – PSEG and South Jersey Gas


DiscussionDiscussion

s. mandayam/ece dept./rowan university a data fusion system for the nondestructive evaluation of...

Documents

mandayamece dept

rowan hall

data fusion system

nj slide

background slide

gas pipeline slide

richard finlayson slide

gas pipeline inspection