Multi-sensor data fusion using geometric transformations for the nondestructive evaluation of gas transmission pipelines by PJ Kulick Graduate Advisor:

Multi-sensor data fusion using geometric transformations for the nondestructive evaluation of gas transmission pipelines by PJ Kulick Graduate Advisor: Dr. Shreekanth Mandayam MS Final Oral Presentation August 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 Implementation Results Conclusions

Nondestructive Evaluation (NDE) OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results 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 Implementation Results Conclusions

NDE using Multiple Inspection Modalities OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Data Fusion OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results 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 Implementation Results 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 Implementation Results Conclusions

Ultrasonic Testing Thermal Imaging Acoustic Emission Test Platforms Digital Signal/Image Processing Data Fusion Advanced Visualization Virtual Reality This research work is sponsored by: US Department of Energy National Science Foundation ExxonMobil Nondestructive Evaluation of Gas Pipelines 0.0” 0.2” 0.4” 0.6” Artificial Neural Networks 1 1 1         x1x1 x2x2 x3x3 y1y1 y2y2   w ij w jk w kl Input Layer Hidden Layers Output Layer 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 Implementation Results 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 Implementation Results Conclusions

Geometric Transformations Spatial Transformation OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Geometric Transformations Gray-level Interpolation OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Approach OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions Geometric Transformation Feature x 1 Feature x 2 Redundant/ Complementary Information g 2 (x 2 ) Θ g 1 -1 (x 1, x 2 ) = h homomorphic operator OBJECT

Approach Redundant Data Extraction  Train RBF (homomorphic operator  +) g 1 (x 1, x 2 ) = g 2 (x 2 ) – h 1 RBF Neural Network x1x1 x2x2 x 2 – h 1 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Approach Redundant Data Extraction  Test RBF h 1 = x 2 – g 1 (x 1, x 2 ) RBF Neural Network x1x1 x2x2 h1h1 ∑ - + x 2 – h 1 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Canonical Image Results Simulation 1 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions x1x1 x2x2 Redundant Complementary 6 Images 4 Training 2 Test 20 x 20 pixels 20 x 20 DCT sent into network in vector form

Canonical Image Results Simulation 1: Training Data Results OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Canonical Image Results Simulation 1: Test Data Results OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Canonical Image Results Simulation 2 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions x1x1 x2x2 Redundant Complementary 6 Images 4 Training 2 Test 20 x 20 pixels 20 x 20 DCT fed into network in vector form

Canonical Image Results Simulation 2: Training Data Results OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Canonical Image Results Simulation 2: Test Data Results OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Experimental Setup Test Specimen Suite OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Experimental Setup: MFL OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions Clamp Pipe section Hall probe Probe mount Current leads

Experimental Setup: Tangential MFL Images OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Experimental Setup: UT OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Experimental Setup: UT Time of Flight (TOF) Images OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Experimental Setup: Thermal OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Experimental Setup: Thermal Phase Images OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

What is Redundant and Complementary Information? We have defined this as follows: OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results 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 Implementation Results Conclusions

Experimental Setup: UT Time of Flight (TOF) Images OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Experimental Setup: Thermal Phase Images OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

Data Fusion Trials OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions Trial 1UT-MFLUT-ThermalMFL-Thermal Trial 2UT-MFLUT-ThermalMFL-Thermal Trial 3UT-MFLUT-ThermalMFL-Thermal

Data Fusion Trials Trial #1 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

UT and MFL Data Fusion Results Trial 1:

UT and Thermal Data Fusion Results Trial 1:

MFL and Thermal Data Fusion Results Trial 1:

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 Implementation Results 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 Implementation Results 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 Implementation Results Conclusions

Acknowledgements U.S. Department of Energy, "A Data Fusion System for the Nondestructive Evaluation 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

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Multi-sensor data fusion using geometric transformations for the nondestructive evaluation of gas transmission pipelines by PJ Kulick Graduate Advisor:

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