Recognition of Broken Wire Rope Based on Remanence using EEMD and Wavelet Methods

Abstract

The magnetic flux leakage method is widely used for non-destructive testing in wire rope applications. A non-destructive testing device for wire rope based on remanence was designed to solve the problems of large volume, low accuracy, and complex operations seen in traditional devices. A wavelet denoising method based on ensemble empirical mode decomposition was proposed to reduce the system noise in broken wire rope testing. After extracting the defects image, the wavelet super-resolution reconstruction technique was adopted to improve the resolution of defect grayscale. A back propagation neural network was designed to classify defects by the feature vectors of area, rectangle, stretch length, and seven invariant moments. The experimental results show that the device was not only highly precise and sensitive, but also easy to operate; noise is effectively suppressed by the proposed filtering algorithm, and broken wires are classified by the network.

Quantitative Nondestructive Testing of Wire Rope Using Image Super‐Resolution Method and AdaBoost Classifier

“…Various image quality measurements and comparison experiments are performed to show the effectiveness of the proposed algorithm. Compared with the super-resolution algorithm in literature [17,26,27], the proposed algorithm has better image quality improvement effect. After obtaining high-resolution defect images with good quality, the Ada-Boost classifier was designed to identify these defect images so as to achieve quantitative recognition of broken wires.…”

“…e image preprocessing mainly includes the defect location and segmentation, the gray-scale normalization, and circumferential interpolation. e location and segmentation of defects were performed by the modulus maxima method [17]. e modulus maxima method is used to obtain the axial position information of the defect regions.…”

“…Image quality comparison experiment is to prove the feasibility of the proposed super-resolution algorithm for defect image enhancement. e performance of the proposed algorithm is compared with several algorithms: bicubic interpolation (BI), wavelet super-resolution reconstruction (WSR) [17], stationary wavelet transform super-resolution method (SWTSR) [26], and non-subsampled contourlet transform super-resolution reconstruction (NSCTSR) [27].…”

“…e four measurement indicators are as shown in equations (11)- (17). Various super-resolution measurement results are shown in Table 6.…”

“…Zhao et al [16] used circumferential interpolation to improve the circumferential resolution of the original MFL images and achieved good detection results. Zhang et al [17] proposed wavelet super-resolution technology to improve the resolution of the defect image, making the edge information of the defect more obvious and easier to identify. Tan and Zhang [18] proposed a superresolution reconstruction method based on Tikhonov regularization to enhance defect image and verified that image property was the best when super-resolution result was triple.…”