Literature Review of Research on the Technology of Wire Rope Nondestructive Inspection in China and Abroad

Jingzhuo

2020

Journal of Sensors

The lifetime of wire rope is crucial in industry manufacturing, mining, and so on. The damage can be detected by using appropriate nondestructive testing techniques or destructive tests by cutting the part. For broken wires classification problems, this work is aimed at improving the recognition accuracy. Facing the defects at the exterior of the rope, a novel method for recognition of broken wires is firstly developed based on magnetic and infrared information fusion. A denoising method, which is adopted for magnetic signal, is proposed for eliminating baseline signal and wave strand. An image segmentation method is employed for parting the defects of infrared images. Characteristic vectors are extracted from magnetic images and infrared images, then kernel extreme learning machine network is applied to implement recognition of broken wires. Experimental results show that the denoising method and image segmentation are effective and the information fusion can improve the classification accuracy, which can provide useful information for estimating the residual lifetime of wire rope.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Magnetic and Infrared Information

Jingzhuo

2020

Journal of Sensors

“…MFL testing relies on the MFL that occurs when the rope is magnetized. Distortions form in the magnetic field on the wire rope surface as a result of internal and surface defects, thus revealing defects by measuring the magnetic leakage of the field [2]. e two primary ways to magnetize the rope for MFL testing are coil magnetization [3,4] and permanent magnet magnetization [5,6].…”

Section: Introductionmentioning

confidence: 99%

Application of Variational Mode Decomposition and k‐Nearest Neighbor Algorithm in the Quantitative Nondestructive Testing of Wire Ropes

Zheng

2019

Shock and Vibration

In this paper, we present a nondestructive testing device for wire rope by unsaturated magnetic excitation as an alternative to existing magnetic flux leakage (MFL) detection devices. The existing devices are heavy and inconvenient and offer somewhat lower accuracy and low signal-to-noise ratios (SNRs). Our design implements variational mode decomposition (VMD) and a wavelet transformation to remove noise from the raw MFL signals. Grayscale images representing the denoised MFL data simplify visual interpretation of the results and location of defects in both axial and circumferential directions. Quantification of defects is enabled using a k-nearest neighbor (KNN) algorithm to classify broken wires. Experimental results show that our design offers lighter weight, better convenience, and high sensitivity along with better removal of noise and more accurate classification of defects.

“…e principle of MFL detection is based on the distribution of leakage magnetic field generated by the magnetized wire rope. e damage of the wire rope will affect the magnetic flux leakage distribution on the surface, and the damage detection can be realized by measuring the leakage magnetic field [1].…”

Section: Introductionmentioning

confidence: 99%

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

2019

Shock and Vibration

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