by PJ Kulick Graduate Advisor: Dr. Shreekanth Mandayam MS Final Oral Presentation

Multi-sensor data fusion using geometric transformations for the nondestructive evaluation

of gas transmission pipelines

byPJ Kulick

Graduate Advisor: Dr. Shreekanth Mandayam

MS Final Oral PresentationAugust 29, 2003, 3:00 PM

Outline

• Introduction

• Objectives and Scope of Thesis

• Background

• Approach

• Implementation Results

• Conclusions

Gas Transmission Pipelines

Sleeve

Weld

Corrosion

SCC

T-section

Valve

• 280,000 miles• 24 - 36 inch dia.

OUTLINE

Introduction

Objectives/ Scope

Background

Approach

Implementation Results

Conclusions

In-Line Inspection

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Nondestructive Evaluation (NDE)

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Gas Transmission Pipeline Indications

• Benign– T-sections

– Welds

– Valves

– Taps

– Straps

– Sleeves

– Transitions

• Anomalies– Stress

Corrosion Cracking

– Pitting

– Arching

– Mechanical Damage

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

NDE using Multiple Inspection Modalities

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Data Fusion

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Data Fusion

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Objectives of This Thesis

• Develop data fusion techniques for the extraction of redundant and complementary information

• Validate techniques using simulated canonical images

• Validate techniques using laboratory NDE signals

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Expected Contributions

• A data fusion algorithm with the ability to identify redundant and complementary information present in multiple combinations of pairs of NDE data sets.

i. e. (MFL-UT, MFL-Thermal, UT-Thermal)

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

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 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

Magnetic Imaging

Previous Work in Data Fusion

• Mathematical Theory– Probability Theory

• Bayes’ Theorum

– Possibility Theory• Fuzzy logic

– Belief Theory• Dempster Shafer

– “Improved” DS Theories• Transferable Belief Model

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Previous Work in Data Fusion

• Mathematical Transforms– Discrete Fourier Transform (DFT)– Discrete Cosine Transform (DCT)– Wavelet based transforms

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Geometric Transformations

• Spatial TransformationOUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

),( yxf yxg ˆ,ˆ

Geometric Transformations

• Gray-level InterpolationOUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Approach

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

GeometricTransformation

Feature x1

Feature x2

Redundant/ Complementary

Information

g2(x2) Θ g1-1(x1, x2) = h

homomorphic operator

OBJECT

Approach

• Redundant Data Extraction

Train RBF (homomorphic operator +)

g1(x1, x2) = g2(x2) – h1

RBFNeural Network

x1

x2

x2 – h1

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Approach

• Redundant Data Extraction Test RBF

h1 = x2 – g1(x1, x2)

RBFNeural Network

x1

x2

h1∑-

+

x2 – h1

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Canonical Image Results

Simulation 1OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

x1x2

Redundant Complementary

• 6 Images

• 4 Training

• 2 Test

• 20 x 20 pixels

• 20 x 20 DCT sent into network in vector form


Simulation 1: Training Data ResultsOUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions


Simulation 1: Test Data ResultsOUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions


Simulation 2OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

x1x2

Redundant Complementary

• 6 Images

• 4 Training

• 2 Test

• 20 x 20 pixels

• 20 x 20 DCT fed into network in vector form



Introduction

Objectives/ Scope

Background

Approach


Conclusions



Introduction

Objectives/ Scope

Background

Approach


Conclusions


Simulation 2: Test Data ResultsOUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Experimental Setup

• Test Specimen SuiteOUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Experimental Setup: MFL

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Clamp Pipesection

Hallprobe

Probemount

Currentleads

Experimental Setup:Tangential MFL Images

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Experimental Setup: UT

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Ultrasound transducers Concretetestspecimen

Immersiontank

Linearactuators forscanning

Scanner controller & stepper motors

PC fordata acquisition & processing

Experimental Setup:UT Time of Flight (TOF) Images

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Experimental Setup: Thermal

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Experimental Setup:Thermal Phase Images

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

What is Redundant and Complementary Information?

• We have defined this as follows:OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Defect Profile

Method 1 NDE Signature

Method 2 NDE Signature

Redundant Information

Complementary Information

Experimental Setup:Tangential MFL Images

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Experimental Setup:UT Time of Flight (TOF) Images

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Experimental Setup:Thermal Phase Images

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Data Fusion Trials

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Trial 1 UT-MFL UT-Thermal MFL-Thermal



Data Fusion Trials

• Trial #1OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

UT and MFL Data Fusion ResultsTrial 1:


Data Fusion Trials

• Trial #2OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions



Data Fusion Trials

• Trial #3OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions



Data Fusion Trials

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions




Data Fusion Trials

• Trial #1OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

UT and Thermal Data Fusion ResultsTrial 1:


Data Fusion Trials

• Trial #2OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions



Data Fusion Trials

• Trial #3OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions



Data Fusion Trials

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions




Data Fusion Trials

• Trial #1OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

MFL and Thermal Data Fusion ResultsTrial 1:


Data Fusion Trials

• Trial #2OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions



Data Fusion Trials

• Trial #3OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions



Accomplishments

• Development of a generalized technique for fusing data from two distinct observations of the same object

• Design of an algorithm that can extract redundant and complementary information from two distinct observations of the same object

• Validation using simulated canonical images• Validation using lab data representative of the

NDE of gas transmission pipelines

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Conclusions

• Algorithm is sufficiently general – does not specify which features are redundant or complementary

• Efficacy has been demonstrated by defining the redundancy and complementarity of two NDE images by correlating defect signature pixels with the location, size and shape of the defect

• Definition and approach are extremely accurate in all instances of training data and sufficiently accurate in all instances of test data

• Information presented to the neural network is distinct; the matrices manipulated are non-singular

• The errors that occur during certain instances of training and testing illustrate the need for a large, more diverse data set

• Data fusion of UT/MFL proved better then data fusion of UT/Thermal or MFL/Thermal

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Directions for Future Work

• Enhancement of training and test data• Explore variety of image preprocessing

techniques• Investigate various definitions of redundant

and complementary information• Test technique’s robustness with noisy real-

world NDE signals• Adapt algorithm for heterogenous datasets

OUTLINE

Introduction

Objectives/ Scope

Background

Approach


Conclusions

Acknowledgements

• U.S. Department of Energy, "A Data Fusion System for the NondestructiveEvaluation of Non-Piggable Pipes," DE-FC26-02NT41648

• ExxonMobil, "Development of an Acoustic Emission Test Platform with a Biaxial Stress Loading System," PERF 95-11

• Joseph Oagaro

by PJ Kulick Graduate Advisor: Dr. Shreekanth Mandayam MS Final Oral Presentation

Documents

nde data sets

multisensor data fusion

research work

extraction of redundant

magnetic imagingprevious

multiple combinations

dftdiscrete cosine

inch dia