3-D Vision Based Magnetic Particle Indication Measuring for Identification and Evaluation of Cracks in Hub Bearing Raceway

Surface cracks in the raceway of hub bearings pose a significant safety threat. Magnetic particle testing (MPT) can be used to locate these cracks regardless of the influences caused by raceway structure. However, crack identification and evaluation in conventional MPT are quite challenging due to the low correlation between magnetic particle indication (MPI) and inspection images. To address this problem, this article introduces a dynamical MPI measuring method through 3-D vision. Specifically, a scanning 3-D vision system, an approach to forming MPI, and a curvature feature-based algorithm for MPI identification are newly proposed. 3-D measurement directly captures the spatial aggregation state of the magnetic particles, thereby enabling the connection between MPI and the cause of it, which is the leakage magnetic field above cracks. Starting from the electromagnetic field, we analyze the potential features of the 3-D profile of MPI. Magnetic force serves as a link, providing a theoretical basis for the identification and evaluation. Experiments show that our method successfully detects artificial notches with depths of 0.5-2.5 mm and natural microcracks with a maximum width of 40 mu m and differentiates between variations in crack depths of 0.5 mm. The sensitivity, stability, and evaluation ability can be demonstrated.