首页|Automatic defect identification in magnetic particle testing using a digital model aided De-noising method
Automatic defect identification in magnetic particle testing using a digital model aided De-noising method
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NSTL
Elsevier
? 2022Magnetic particle testing (MPT) has high sensitivity, which is popular in ferromagnetic material detection. However, results of MPT always mix with fake magnetic particle indication (FMPI) and point noise caused by nonrelevant magnetic particles. It mainly depends on manual work to find defects in MPT. In this paper, Hough Transform, which is robust to the point noise, is introduced to automatically identify the linear features of magnetic particle indication (MPI) caused by defects. However, the algorithm cannot distinguish between the similarly shaped FMPI and MPI. To address the problem, we proposed a digital model aided de-noising Method. Specifically, the density of normal component of magnetic flux on specimen surface is calculated by finite element analysis to evaluate the possibilities of FMPI occurrence. Then detection results are compared with the calculation to eliminate FMPI. Experiments show that scratches on hub bearing can be successfully identified by the proposed method.
NSTL
Elsevier
