The magnetic flux leakage method, used for nondestructive evaluation of ferromagnetic objects, generates greyscale images that are representative of the integrity of the specimen. Defective areas typically appear as bright regions in the image. Unfortunately, the task of defect characterization becomes more challenging due to the effects of variations in the test parameters associated with the experiment. One such test parameter is the permeability of the test object. Conventional invariant pattern recognition algorithms are not capable of performing invariance transformations to compensate for such variations. This paper describes a novel technique that uses wavelet basis functions to provide selective invariant features and eliminate image intensity variations from undesirable changes in operational variables. The performance of the invariance transformation is demonstrated by applying the method to magnetic flux leakage images obtained using a finite element simulation of in-line inspection of natural gas transmission pipelines.
All Science Journal Classification (ASJC) codes
- Condensed Matter Physics
- Mechanical Engineering
- Materials Science(all)