Automated defect detection of corrosion or cracks using SAFT processed Lamb wave images

• US 7,783,433 B2
• Filed: 07/21/2007
• Issued: 08/24/2010
• Est. Priority Date: 05/22/2007
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Hedl, Radek, Gordon, Grant A.
• Primary Examiner(s): Barbee; Manuel L
• Application Number:
  US11/880,450
• Publication Number:
  US 20080289423A1
• Time in Patent Office: 1,130 Days
• Field of Search: 702/34, 702/35, 702/39, 702/56, 700/175, 700/176, 382/108, 382/152, 382/173
• US Class Current: 702/39
• CPC Class Codes: G01N 2291/0427 Flexural waves, plate waves, e.g. Lamb waves, tuning fork, cantilever
• CPC Class Codes: G01N 29/069 Defect imaging, localisation and sizing using, e.g. time of flight diffraction [TOFD], synthetic aperture focusing technique [SAFT], Amplituden-Laufzeit-Ortskurven [ALOK] technique
• CPC Class Codes: G01N 29/42 by frequency filtering or by tuning to resonant frequency
• CPC Class Codes: G01N 29/4463 Signal correction, e.g. distance amplitude correction [DAC], distance gain size [DGS], noise filtering
• CPC Class Codes: G01N 29/52 using inversion methods other that spectral analysis, e.g. conjugated gradient inversion
• CPC Class Codes: G01S 15/8977 using special techniques for image reconstruction, e.g. FFT, geometrical transformations, spatial deconvolution, time deconvolution
• CPC Class Codes: G01S 15/8997 using synthetic aperture techniques
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/20036 Morphological image processing
• CPC Class Codes: G06T 2207/30164 Workpiece; Machine component
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 5/73 Deblurring; Sharpening
• CPC Class Codes: G06T 7/10 Segmentation; Edge detection motion-based segmentation G06T7/215

Abstract

A system, method and computer program product is provided for automated defect detection of corrosion or cracks using synthetic aperture focusing technique (SAFT) processed Lamb wave images. The method comprises processing the first image using a synthetic aperture focusing technique (SAFT) to enhance a resolution and a signal to noise ratio of a first extracted ultrasonic image, applying a systemic background noise suppression algorithm to the first extracted ultrasonic image to render a second extracted ultrasonic image having reduced noise, and applying a deconvolution linear filtering process to the second extracted ultrasonic image to render a third extracted ultrasonic image.

Claims

1. An automated method for detecting and displaying corrosion or crack defects in an object by a computer on a display, comprising:

obtaining a first of a plurality of ultrasound images of the object acquired with guided Lamb waves generated by a transducer;

processing the first image of the object by the computer using a synthetic aperture focusing technique (SAFT) to enhance a resolution and a signal to noise ratio of a first extracted ultrasonic image of the object;

applying a systemic background noise suppression algorithm to the first extracted ultrasonic image of the object by the computer to generate a second extracted ultrasonic image of the object having reduced noise; and

applying a deconvolution linear filtering process by the computer to the second extracted ultrasonic image of the object to render a third extracted ultrasonic image of the object on the display.

2. The method of claim 1, wherein the plurality of ultrasound images is acquired using distinct ultrasonic excitation or filtering a frequency content of a return ultrasonic signal to employ a plurality of Lamb wave modes.

3. The method of claim 2, further including combining the first image with an additional image of the plurality of ultrasound images to render a composite image.

4. The method of claim 3, further including processing the composite image thorough an image segmentation algorithm.

5. The method of claim 4, wherein processing the composite image through an image segmentation algorithm further includes applying a voltage threshold algorithm determined from a statistical noise model for image data.

6. The method of claim 4, wherein processing the composite image through an image segmentation algorithm further includes applying a voltage threshold algorithm according to

$V_t={\eta }^{}$

⁡

( - ln ⁢

⁢

P fa ) 1 β

,

where V_t is a voltage threshold, η

is a scale parameter or characteristic life, β

is a shape parameter or slope of the Weibull distribution, and P_fa is a probability of false calls.

7. The method of claim 4, wherein processing the composite image thorough an image segmentation algorithm incorporates a mathematical model representing random noise.

8. The method of claim 4, wherein processing the composite image thorough an image segmentation algorithm incorporates a mathematical model representing the probability of detecting a defect.

9. The method of claim 3, wherein combining the first image with the additional image further includes employing a Bayesian method, fuzzy logic inference, weighting and voting scheme, Dempster-Shafer method, or a neural network technique.

10. The method of claim 1, wherein the first image is processed thorough an image segmentation algorithm.

11. The method of claim 10, wherein processing the first image through an image segmentation algorithm further includes applying a voltage threshold algorithm determined from a statistical noise model for image data.

12. The method of claim 11, wherein processing the first image through an image segmentation algorithm further includes applying a voltage threshold algorithm according to

$V_t={\eta }^{}$

⁡

( - ln ⁢

⁢

P fa ) 1 β

,

where V_t is a voltage threshold, η

is a scale parameter or characteristic life, β

is a shape parameter or slope of the Weibull distribution, and P_fa is a probability of false calls.

13. The method of claim 1, wherein applying the systemic background noise suppression algorithm to the first extracted ultrasonic image further incorporates a mathematical model representing systematic noise.

14. The method of claim 1, wherein applying the deconvolution linear filtering process incorporates a point spread function (PSF) estimated from a plurality of defect-free SAFT images.

15. An automated defect detection system, comprising:

a transducer configured for obtaining a first of a plurality of ultrasound images acquired with guided Lamb waves; and

a controller coupled to the transducer configured to processes the first image using a synthetic aperture focusing technique (SAFT) to render a first extracted ultrasonic image, apply a systemic background noise suppression algorithm to the first extracted ultrasonic image to render a second extracted ultrasonic image, and apply a deconvolution linear filtering process to the second extracted ultrasonic image to render a third extracted ultrasonic image.

16. The system of claim 15, wherein the plurality of ultrasound images is acquired using distinct ultrasonic excitation or filtering a frequency content of a return ultrasonic signal to employ a plurality of Lamb wave modes.

17. The system of claim 16, wherein the controller:

combines the first image with an additional image of the plurality of ultrasound images to render a composite image, and processes the composite image thorough an image segmentation algorithm.

18. The system of claim 17, wherein the controller employs a Bayesian method, fuzzy logic inference, weighting and voting scheme, Dempster-Shafer method, or a neural network technique to combine the first image with the additional image.

19. The system of claim 17, wherein the controller applies a voltage threshold determined from a statistical noise model for image data.

20. The system of claim 17, wherein the controller applies a voltage threshold algorithm according to

$V_t={\eta }^{}$

⁡

( - ln ⁢

⁢

P fa ) 1 β

,

where V_t is a voltage threshold, η

is a scale parameter or characteristic life, β

is a shape parameter or slope of the distribution, and P_fa is a probability of false calls.

21. The system of claim 16, wherein the controller incorporates a mathematical model representing systematic noise to apply the systemic background noise suppression algorithm to the first image.

22. The system of claim 16, wherein the controller incorporates a point spread function (PSF) estimated from a plurality of defect-free SAFT images to apply the deconvolution linear filtering process.

23. The system of claim 16, wherein the controller incorporates a mathematical model representing random noise to process the composite image thorough an image segmentation algorithm.

24. The system of claim 16, wherein the controller incorporates a mathematical model representing the probability of detecting a defect.

25. A computer program product for performing automated defect detection of an object, the computer program product comprising a computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

a first executable portion for obtaining a first of a plurality of ultrasound images of the object acquired with guided Lamb waves;

a second executable portion for processing the first image using a synthetic aperture focusing technique (SAFT) to generate a first extracted ultrasonic image of the object;

a third executable portion for applying a systemic background noise suppression algorithm to the first extracted ultrasonic image to generate a second extracted ultrasonic image of the object; and

a fourth executable portion for applying a deconvolution linear filtering process to the second extracted ultrasonic image to render a third extracted ultrasonic image of the object on a display.

26. The computer program product of claim 25, further including:

a fifth executable portion for combining the first image with an additional image of the plurality of ultrasound images to render a composite image; and

a sixth executable portion for processing the composite image thorough an image segmentation algorithm.