Principles of Nondestructive Evaluation

1. Principles of Nondestructive Evaluation Lecture 39/20/99 Shreekanth Mandayam Graduate / Senior Elective 0909-504-01/0909-413-01 Fall 1999 http://engineering.rowan.edu/~shreek/fall99/nde/

2. 9/27/99 Plan • Magnetic Flux Leakage (MFL) NDE • Principle • Governing equations • Practice • Class projects • Paper formats • Open discussion • Task assignment

3. Direct Current Magnetization Scanner Hall Probe Specimen Current Lead

4. Magnetic Flux Leakage Signals 600 400 300 500 200 400 100 0 300 -100 200 -200 100 -300 -400 0 Axial Component of Flux Density Radial Component of Flux Density

5. MFL Image from a Rectangular Slot

6. Magnetic Flux Leakage (MFL) Detection of Defects Specimens Magnetic Images

7. Maxwell’s Equations

8. Electromagnetic NDE Methods

9. Static Phenomena: Magnetic Flux Leakage

10. Static Phenomena: MFL (contd.) Elliptic partial differential equation

11. NDE Processes Inverse Problem Difficulty Forward Problem Difficulty Informational Entropy LOW HIGH HIGH Elliptic Processes Parabolic Processes Hyperbolic Processes LOW LOW HIGH

12. Gas Transmission Pipeline Inspection • 280,000 miles • 24 - 36 inch dia. SCC Weld Valve T-section Corrosion Sleeve

13. Gas Pipeline “Incidents” in the US Mechanical damage is the single largest source of gas pipeline related incidents.

14. Permanent Magnet Data Acquisition and Storage Hall-effect Sensors

15. Gas Pipeline Inspection Defect Pipewall sensor Magnetic Flux Leakage (MFL) The “Pig” Data Acquisition Drive Section Sensors Brushes

16. Defect Characterization • Artificial Neural Networks • Multidimensional mapping from • MFL signal to defect profile MFL Signal Defect Profile mapping PIPE [a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 ]T [ 0 0 0 50 50 50 40 40 50 0 0 0 ]T

17. Defect Characterization MFL Signals Defect Profiles

18. Typical Results MFL Signal Predicted Profile 1-D Scan of Predicted Profile

19. output layer hidden layer 1 1 N Radial Basis Function Neural Network input layer Input processed signal Output defect profile vector N

21.  A 1      A = J -  +  v    A    t  Operational Variables Influencing B Governing Equation Probe Velocity Permeability Remanent Magnetism Stress Sensor Location

22. 1200 t=1.00 1000 t=0.75 800 t=0.50 t=0.25 600 t=0.00 400 200 0 1 3 5 7 9 11 13 15 17 19 21 23 25 position 80% 70% 60% 50% 40% 30% 20% Effect of Pipe Grade Family of B-H Curves Fixed Defect Fixed B-H Curve 1400 Effect of Defect Depth 1200 1000 800 600 400 200 0 Depth in % of pipe-wall thickness 1 3 5 7 9 11 13 15 17 19 21 23 position

23. Pz h ( d, l, w) = • g1 Wavelet Basis Function Bz - Bzmin g1 , Pr ) (Pz h Bzmax- Bzmin Invariance Transformation • Identify at least two distinct test signals • Synergistically combine to isolate unique defect signature Features from Tangential Component of Flux Density ( Bz) Pz (d, l, w, t) Invariance Transformation Function h (d, l, w) Parameter-Invariant Defect Signature Features from Normal Component of Flux Density ( Br) Pr (d, l, w, t)

24. 0.3” deep defect 0.2” deep defect Typical Results: Pipe-wall Thickness Wall thickness 1/2” 3/8” 5/16” 1/2” 3/8” 5/16”

25. Wall thickness 1/2” 3/8” 5/16” 1/2” 3/8” 5/16” 0.2” deep defect 0.3” deep defect Compensation Results

26. Specimen Pipe section Hall probe Probe mount Current leads Clamp Current Lead Experimental Set-Up

27. MFL Scans Defect Depth Pipe Grade X-42 X-52 X-65 X-70 0.06” 0.17” 0.25” Line Scans

28. Pipe Grade X-42 X-52 X-65 X-70 Before After Defect Depth 0.25” 0.17” 0.06” Compensation Results