Failure analysis of hoisting steel wire rope

Magnetic leakage distribution results from linear defects of oil–gas pipelines in a weak magnetic field, which is modeled by the magnetic dipole theory. The analysis is useful for the identification of defects located either inside or outside the pipelines. The results indicate that the radial signals of inside–outside defects can be clearly distinguished, and the axial signals are basically the same in a weak magnetic field. The theoretical and the experimental results are very consistent.

https://corpus.ulaval.ca/jspui/bitstream/20.500.11794/13132/1/NDTEINT_Weak%20magnetic.pdf

Weak magnetic flux leakage: A possible method for studying pipeline defects located either inside or outside the structures

Electromagnetic methods are commonly employed to detect wire rope discontinuities. However, determining the residual strength of wire rope based on the quantitative recognition of discontinuities remains problematic. We have designed a prototype device based on the residual magnetic field (RMF) of ferromagnetic materials, which overcomes the disadvantages associated with in-service inspections, such as large volume, inconvenient operation, low precision, and poor portability by providing a relatively small and lightweight device with improved detection precision. A novel filtering system consisting of the Hilbert-Huang transform and compressed sensing wavelet filtering is presented. Digital image processing was applied to achieve the localization and segmentation of defect RMF images. The statistical texture and invariant moment characteristics of the defect images were extracted as the input of a radial basis function neural network. Experimental results show that the RMF device can detect defects in various types of wire rope and prolong the service life of test equipment by reducing the friction between the detection device and the wire rope by accommodating a high lift-off distance.

Non-Destructive Detection of Wire Rope Discontinuities from Residual Magnetic Field Images Using the Hilbert-Huang Transform and Compressed Sensing

Wire rope inspection by nondestructive testing methods, sensors and signal processing techniques are mainly reviewed in this paper. Owing to the difference of physical mechanism and testing principles, magnetic flux leakage, eddy current, acoustic emission and ultrasonic guide wave testing as well as other inspection methods for steel wire rope are summarized. Then, the commonly and frequently used testing sensors of inductive coil, hall element, magnetoresistive sensors and others are compared in the perspective of their corresponding operating principles, development situation, advantages and disadvantages. Furthermore, signal processing techniques including the signal filtering techniques such as the time and frequency analysis methods, quantitative data processing methods such as the machine learning and defect classification are studied. Finally, the challenges and future developing trends of wire rope inspection in practical applications are discussed.

A Review of Wire Rope Detection Methods, Sensors and Signal Processing Techniques

Wire rope is a necessary tool in practical applications especially in crane, elevator and bridge construction, which plays an important role in the national economy and daily life, and safety inspection for wire rope is the key to ensure people’s life and property. However, detection signals are usually complicated due to the twining structures, which make the wire rope defect signal and strand signal mix together. What’s more, no reports and studies have appeared to solve this problem. In view of the situation and challenges above, this paper proposes a combined signal processing method based on notch filtering and wavelet denoising to process detected wire rope signals. Basic time domain, frequency domain and joint time–frequency analysis are first conducted, thereafter, conventional signal processing methods such as lowpass filtering and adaptive analysis are presented according to the signal characterizations. These comparisons and results demonstrate that a conventional single method is incapable of wire-rope-detection signal identification and differentiation. Nonetheless, after the notch filter design and calculation, the processing results for the typical wire rope inspection signals in the experiments indicate that the combined methods can not only distinguish steel wire rope defect signal and strand signal effectively but also with high detection accuracy, even for the inner defect. Finally, the feasibility and reliability are verified by a series of signal processing results and comparisons, which demonstrate that this new method has great application potential and is of vital significance to the development of wire rope safety inspection.

A New Signal Processing Method Based on Notch Filtering and Wavelet Denoising in Wire Rope Inspection

Magnetic flux leakage (MFL) techniques are used extensively for non-intrusively detecting and characterizing wire rope defects. Traditionally, MFL signals are captured with induction coil sensors. However, the output of coil sensors is related to the wire rope speed, and they can only provide the axial distribution along the wire rope. Hall sensors array are designed due to the limitation of coil sensors. In this paper, a Hall sensors array was designed to capture the MFL signals both axially and circumferentially. 30-channel data from Hall sensors are processed to compose a MFL image. A digital image process technique is introduced to preprocess the MFL image, the MFL images from different types of defects show different texture characteristics. Gray level co-occurrence matrix is utilized for feature extraction of the texture in the MFL image. Five typical eigenvalues (contrast, correlation, energy, homogeneity and entropy) are used as the inputs of back propagation (BP) networks. After training with typical samples, the BP networks show good performance in the quantitative recognition of different defects. The result of this work shows that texture analysis method for MFL image is suitable for feature extraction and quantitative detection of wire rope defects.

Min Zhao

Papers

Characterization of Wire Rope Defects with Gray Level Co-occurrence Matrix of Magnetic Flux Leakage Images