280 likes | 490 Views
Automated Rivet Inspection System for Aging Aircrafts. Unsang Park, Lalita Udpa, George C. Stockman Computer Science and Engineering Michigan State University. Contents. Nondestructive Inspection (NDI) Magneto-optic Imager in NDI Motion-based Filtering (MBF)
E N D
Automated Rivet Inspection System for Aging Aircrafts Unsang Park, Lalita Udpa, George C. Stockman Computer Science and Engineering Michigan State University
Contents • Nondestructive Inspection (NDI) • Magneto-optic Imager in NDI • Motion-based Filtering (MBF) • Real-time implementation of MBF • Automated rivet inspection system • Rivet detection • Rivet classification • Results and conclusions • Future work
Nondestructive Inspection for Aircrafts • Detect subsurface defects Seam Rivet Crack
Nondestructive Inspection for Aircrafts • Increase service life of airplane • Prevent disasters Aloha Airlines B-737-200 lost part of its front fuselage during a flight in Hawaii, 1985
CCD Camera Light Source Polarizer Analyzer MO Sensor Induction Sheet Bias Coil Sample Magneto-optic Imager (MOI) • Eddy current excitation • Magneto-optic sensing • Imaging
Magneto-optic Imager (cont.) • Produce real-time analog images of inspected part • Images both surface breaking and subsurface cracks • Easy to interpret with minimal training • Applicable both on conducting samples as well as composites by tagging with ferromagnetic particles
Sample MOI Images Crack along seam Crack between two rivets, Radial crack on a rivet Corrosion dome
Drawback of MOI images • MOI image contains serpentine pattern noises due to the magnetic domain walls in magneto-optic sensor Signals due to domain walls Rivet
Moving direction of objects Moving direction of MOI Motion-based Filtering (MBF) • Additive Frame Subtraction In-5 In-4 In-3 In-2 In-1 In D5 D4 D3 D2 D1 Sn
Input Image Preprocessing RGB to Gray Additive Frame Subtraction Threshold Post processing Median Filter Stretch Output Image Motion-based Filtering (cont.)
MB filtered images Original Filtered
MB filtered images (cont.) Original Filtered
Frame grabber Record to VHS Collects MOI image Data Web camera Real-time implementation of MBF • Experimental setup for proof of concept Play on a Video player Record to movie file noise Play on a PC monitor
Real-time MBF MOI Max Sensing RGB to Gray Subtract Imaging Max Displaying Stretch Median Filtering Threshold Real-time implementation of MBF (cont.) • Data transfer rate • 4.6 Mbytes /sec ( 320240 pixels 16 bit 30 fps ) • Data are down sampled as the input images are dropped while an image is processed • Diagram of real-time Motion-based Filtering
Image capture: 20 ms RGB to Gray: 20 ~ 23 ms Additive Frame Subtraction: 1~2 ms/image Threshold: 1 ~ 2 ms Median Filter: 200 ~ 250 ms Stretch: 1 ~ 2 ms Output Image Real-time implementation of MBF (cont.) • Optimizing MBF algorithm in C++
Real-time implementation of MBF (cont.) • RGB to Gray conversion • Additive frame subtraction • MAX(I1-I3,I2-I3) MAX(I1,I2) - I3 • Median filter
Real-time implementation of MBF (cont.) • Execution time of MBF algorithm in C++
Drawbacks of current MOI inspection • No measure for quantitative interpretation • Data interpretation is subjective • Manual inspection by human operator (more than 10 hours per airplane) • Expensive labor cost • Error due to fatigue
Automated MOI inspection system • PRI Research and Development Corporation (PRI) • Developing and improving magneto-optic imager (MOI) • Michigan State University, ECE department • Image processing algorithm for filtering and classification • Boeing Phantom Works • Self-guided, suction cup robot – crawls over airplane skin
Motion-based Filtering Rivet detection Rivet classification Automated MOI inspection (cont.) • Currently focusing on radial cracks on rivets • Quantification of defects in MOI images • Implementing real-time rivet inspection algorithm
Segment out each rivet Obtain center and radius, c1, r1 Erode rivet with a circle of radius r1 yes, r1 r1-1 Area(rivet) = 0 no Obtain center and radius, c2, r2 Rivet detection • Hough transformation-based method • Circular Hough transformation • Morphological operation-based method
Rivet detection (cont.) Hough transformation Original Morphological operation
Rivet classification • Two-pass Hough transformation • 1st pass – Rivet detection • 2nd pass – Blob detection good bad Original image Filtered image - After 1st pass After 2nd Pass
Rivet classification (cont.) • Bayesian classifier • Feature selection Original image Hough transformation Morphological op.
Off-line test • Training • 10 normal, 10 defective rivet images • Obtain mean and variance of feature f1 • Testing • 222 rivet images including 66 defective rivet images Two-pass Hough Hough - Bayes Morph. - Bayes
Experimental Results • Accuracies of three algorithms
Conclusions • MB filtered image is optimal in image processing for automated rivet inspection • Morphological operation-based rivet detection is superior to Hough-based rivet detection both for execution time and accuracy • Bayesian classifier is superior to Hough-based classifier • Radial crack detection on rivets showed 99% accuracy in off-line test
Future work • Implement MBF and rivet inspection algorithms on the Digital Signal Processing (DSP) board • Improve robustness of the algorithms with the feedback from field test • Develop MOI inspection algorithms for other types of defects in aircrafts