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

1. 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

2. Outline • Introduction • Objectives and Scope of Thesis • Background • Approach • Implementation Results • Conclusions

3. SCC Weld Valve T-section Corrosion Sleeve Gas Transmission Pipelines OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions • 280,000 miles • 24 - 36 inch dia.

4. In-Line Inspection OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

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

6. Benign T-sections Welds Valves Taps Straps Sleeves Transitions Anomalies Stress Corrosion Cracking Pitting Arching Mechanical Damage Gas Transmission Pipeline Indications OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

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

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

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

10. 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

11. 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

12. j j Hidden Layers Input Layer Output Layer 1 x1 y1 x2 j j j y2 x3 wkl wij 1 wjk Artificial Neural Networks j j j 1 j j Nondestructive Evaluation of Gas Pipelines 0.2” 0.0” 0.6” 0.4” Ultrasonic Testing Magnetic Imaging Virtual Reality Data Fusion Advanced Visualization Acoustic Emission • This research work is sponsored by: • US Department of Energy • National Science Foundation • ExxonMobil Thermal Imaging Digital Signal/Image Processing Test Platforms

13. 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

14. 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

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

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

17. Approach OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions Feature x1 Geometric Transformation Redundant/ Complementary Information OBJECT Feature x2 g2(x2) Θ g1-1(x1, x2) = h homomorphic operator

18. RBF Neural Network x1 x2 – h1 x2 Approach • Redundant Data Extraction  Train RBF (homomorphic operator  +) g1(x1, x2) = g2(x2) – h1 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

19. RBF Neural Network x1 - x2 – h1 ∑ h1 + x2 Approach • Redundant Data Extraction  Test RBF h1 = x2 – g1(x1, x2) OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

20. Canonical Image Results Simulation 1 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions x2 x1 • 6 Images • 4 Training • 2 Test • 20 x 20 pixels • 20 x 20 DCT sent into network in vector form Complementary Redundant

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

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

23. Canonical Image Results Simulation 2 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions x2 x1 • 6 Images • 4 Training • 2 Test • 20 x 20 pixels • 20 x 20 DCT fed into network in vector form Complementary Redundant

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

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

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

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

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

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

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

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

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

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

34. 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

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

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

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

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

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

40. UT and MFL Data Fusion Results Trial 1:

41. UT and MFL Data Fusion Results Trial 1:

42. Data Fusion Trials • Trial #2 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

43. UT and MFL Data Fusion Results Trial 2:

44. UT and MFL Data Fusion Results Trial 2:

45. Data Fusion Trials • Trial #3 OUTLINE Introduction Objectives/ Scope Background Approach Implementation Results Conclusions

46. UT and MFL Data Fusion Results Trial 3:

47. UT and MFL Data Fusion Results Trial 3:

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

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

50. UT and Thermal Data Fusion Results Trial 1: