Magnetic flux leakage (MFL) detection is commonly employed to detect wire rope defects. However, nondestructive testing\n(NDT) of a wire rope still has problems. A wire rope nondestructive testing device based on a double detection board is designed to\nsolve the problems of large volume, complex operations, and limited circumferential resolution due to sensor size in traditional\ndevices. The device adopts two magnetic sensor arrays to form the double detection board and collects the MFL data of the\nmagnetized wire rope. These sensors on the double detection board are staggered and evenly arranged on the circumference of the\nwire rope. A super-resolution algorithm based on interpolation uses non-subsampled shearlet transform (NSST) combining\nprincipal component analysis (PCA) and Gaussian fuzzy logic (GFL) and fuses the data of double detection board to improve the\nresolution and quality of defect images. Image quality measurement and comparison experiments are designed to verify that defect\nimages are effectively enhanced. An AdaBoost classifier is designed to classify defects by texture features and invariant moments of\ndefect images. The experimental results show that the detection device not only improves the circumferential resolution, but also\nthe operation is simple; the resolution and quality of the defect images are improved by the proposed super-resolution algorithm,\nand defects are identified by using the AdaBoost classifier.
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