S. Mandayam/ NDE/ Fall 99 Principles of Nondestructive Evaluation Shreekanth Mandayam Graduate / Senior Elective / Fall Lecture 3 9/20/99

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

S. Mandayam/ NDE/ Fall 99 Direct Current Magnetization Hall Probe Specimen Current Lead Scanner

S. Mandayam/ NDE/ Fall 99 Magnetic Flux Leakage Signals Axial Component of Flux Density Radial Component of Flux Density

S. Mandayam/ NDE/ Fall 99 MFL Image from a Rectangular Slot

S. Mandayam/ NDE/ Fall 99 Magnetic Flux Leakage (MFL) Detection of Defects Specimens MagneticImages

S. Mandayam/ NDE/ Fall 99 Maxwell’s Equations

S. Mandayam/ NDE/ Fall 99 Electromagnetic NDE Methods

S. Mandayam/ NDE/ Fall 99 Static Phenomena: Magnetic Flux Leakage

S. Mandayam/ NDE/ Fall 99 Static Phenomena: MFL (contd.) Elliptic partial differential equation

S. Mandayam/ NDE/ Fall 99 NDE Processes Elliptic Processes Parabolic Processes Hyperbolic Processes HIGH LOW HIGH Forward Problem Difficulty Inverse Problem Difficulty Informational Entropy

S. Mandayam/ NDE/ Fall 99 Gas Transmission Pipeline Inspection Sleeve Weld Corrosion SCC T-section Valve 280,000 miles inch dia.

S. Mandayam/ NDE/ Fall 99 Mechanical damage is the single largest source of gas pipeline related incidents. Gas Pipeline “Incidents” in the US

S. Mandayam/ NDE/ Fall 99 Permanent Magnet Hall-effect Sensors Data Acquisition and Storage

S. Mandayam/ NDE/ Fall 99 Defect sensor Pipewall Magnetic Flux Leakage (MFL) Drive Section Brushes Sensors Data Acquisition Gas Pipeline Inspection The “Pig”

S. Mandayam/ NDE/ Fall 99 Artificial Neural Networks Multidimensional mapping from MFL signal to defect profile mapping [a 1 a 2 a 3 a 4 a 5 a 6 a 7 a 8 a 9 a 10 a 11 a 12 ] T [ ] T PIPE MFL SignalDefect Profile Defect Characterization

S. Mandayam/ NDE/ Fall 99 Defect Characterization MFL Signals Defect Profiles

S. Mandayam/ NDE/ Fall 99 Typical Results MFL Signal Predicted Profile 1-D Scan of Predicted Profile

S. Mandayam/ NDE/ Fall 99 Output defect profile vector input layer Input processed signal N hidden layer output layer 1 N 1 Radial Basis Function Neural Network

S. Mandayam/ NDE/ Fall 99

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

S. Mandayam/ NDE/ Fall 99 Effect of Pipe Grade Depth in % of pipe-wall thickness position t=1.00 t=0.75 t=0.50 t=0.25 t=0.00 Family of B-H Curves Effect of Defect Depth position % 70% 60% 50% 40% 30% 20% Fixed Defect Fixed B-H Curve

S. Mandayam/ NDE/ Fall 99 Invariance Transformation Identify at least two distinct test signals Synergistically combine to isolate unique defect signature Features from Tangential Component of Flux Density ( B z ) P z (d, l, w, t) Features from Tangential Component of Flux Density ( B z ) P z (d, l, w, t) Invariance Transformation Function h (d, l, w) Invariance Transformation Function h (d, l, w) Parameter-Invariant Defect Signature Parameter-Invariant Defect Signature B z - B z min B z max - B z min  h Features from Normal Component of Flux Density ( B r ) P r (d, l, w, t) Features from Normal Component of Flux Density ( B r ) P r (d, l, w, t) h ( d, l, w)= PzPz g1g1 (P z, P r g1 Wavelet Basis Function )

S. Mandayam/ NDE/ Fall ” deep defect 0.2” deep defect 1/2” 3/8” 5/16” Wall thickness Typical Results: Pipe-wall Thickness

S. Mandayam/ NDE/ Fall 99 Compensation Results 0.3” deep defect 0.2” deep defect 1/2” 3/8” 5/16” Wall thickness

S. Mandayam/ NDE/ Fall 99 Experimental Set-Up Specimen Current Lead Clamp Pipe section Hall probe Probe mount Current leads

S. Mandayam/ NDE/ Fall 99 MFL Scans X-42 X-52 X-65 X ” 0.17” 0.25” Pipe Grade Defect Depth Line Scans

S. Mandayam/ NDE/ Fall 99 Compensation Results Pipe Grade Defect Depth 0.25” 0.17” 0.06” X-42 X-52 X-65 X-70 Before After