wavy wire rope free sample

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.

Wire ropes play an important role in many fields such as cranes, oil drilling rigs, elevators, and mine hoist. The safety of wire ropes is closely related to people’s life and resources loss as well as the normal operation of industry. Because of the complex structure of wire ropes and the diversity of application environment, it is difficult to evaluate the health of wire ropes in service [1, 2]. Thus, it is necessary to effectively and accurately perform the quantitative nondestructive testing (NDT) of wire rope by adopting proper methods.

At present, the NDT methods of wire rope include electromagnetic [3, 4], X-ray [5], acoustic [6–9], and optical [10] method [1]. X-ray apparatus has radioactive contamination; acoustic method detects wire rope by striking, which is simple but one-sided; CCD camera optical testing method can directly show the real defects through imaging, but it is susceptible to oil pollution; because of high sensitivity, high speed, and low cost, electromagnetic NDT method is widely used [11–14]. However, no single nondestructive testing technique can identify all kinds of defects. Infrared nondestructive testing does not contain dangerous radiation and has characteristic of noncontact; thus, it has widely applied in solving real problems in numerous areas [15].In addition, its popular application areas contain building sector [16, 17], aeronautics and astronautics [18], chemical industry [19], food [20], cultural heritage [21], and so on. Munoz et al. [22, 23] determined heat source dissipation from infrared thermographic measurements based on the heat diffusion equation provided by thermodynamics principles and identified damage evolution in carbon fibre reinforced composites combing acoustic emission and infrared thermography.

Magnetic flux leakage (MFL) detection of wire rope mainly includes the forward calculation model of MFL detection, pretreatment of MFL signal, and inversion of defect [24]. For example, Yan et al. [25] employed a three-dimensional finite element method (FEM) to analyze MFL signals. This method provided theoretical guidance for detection signal analysis and hardware design. Based on the magnetic dipole model, Yang [2] created the leakage magnetic field analysis models of single wire fracture, surface broken wire, and internal broken wire of wire rope, which provided the theoretical basis for the quantitative analysis of wire rope. Zhao and Zhang [11, 12] made FEM on the distribution of magnetic flux leakage of typical broken wire defects in steel cables, and obtained the relationship between MFL and detection distance, damage size, and internal broken wire. In [13, 14], a magnetic dipole model was established to design the prototype, which provided a theoretical basis for the quantification of defects. Through the FEM model of wire rope and the FEM simulation under different broken wires, DU et al. [26] studied the influence of different broken wires on the safety coefficient of wire rope.

Because actual MFL detection signals are polluted by many noise sources, it is necessary to preprocess the signals in order to reconstruct the defects. Zhang et al. [27, 28] utilized wavelet based on compressed sensing to denoise the strand wave, but it restored a lot of noise; then, they combined the Hilbert-Huang Transform (HHT) and Compressed Sensing Wavelet Filtering (CSWF) to reduce various background noises. Zheng and Zhang [29] exploited wavelet soft threshold to inhibit the noise; nevertheless, the denoising effect is poor. Then Zheng and Zhang [30] implemented Variational Mode Decomposition (VMD) and a wavelet transformation to remove noise from the raw MFL signals, which can effectively eliminate noise. Hong et al. [31] proposed an adaptive wavelet threshold denoising method based on a new threshold function, which achieved good denoising effect on the MFL signal of wire rope. To realize the visualization of defects, Zhao [13] utilized an adaptive notch filtering algorithm for suppressing wave noise.

To visualize and quantify defects and realize quantitative detection of broken wires, researchers need to implement defect inversion. In order to perform defect inversion, numerous scholars have used various methods. Through adopting the wavelet super-resolution reconstruction technique, the resolution of defect grayscale was improved in [32]. Zhang and Tan [33] proposed a super-resolution (SR) reconstruction method based on Tikhonov regular multiframe, which can effectively remain image features of defects while the axial resolution was reduced and circumferential resolution was increased. In [28, 32], researchers implemented classification of defects by adopting back propagation (BP) neural networks. However, BP was easy to fall into local minimum, which can lead to problems such as network underfitting and insufficient generalization ability. Wan et al. [34] investigated the theory on optimal wavelet packet with the Least Squares Support Vector Machine (LS-SVM) to diagnose elevator faults, which was then validated by the experiment. Zheng and Zhang and Qin et al. [29, 35] took the Support Vector Machine (SVM) with a radial basis function classified to conduct the fault pattern recognition, whereas this method was not very effective.

The researchers [15, 36] investigated the failures of steel ropes and defect of ferromagnetic specimens by means of thermovision. In [15], since the measurements required extremely sensitive thermovision technology, the method can detect the tight of ropes at certain conditions. In [34], the researchers developed a new active thermography technique, which can detect the defect in ferromagnetic steel specimens. The fusion of infrared and other information is effective and widely used. Kee and Oh et al. [16] combined air-coupled impact-echo and infrared thermography. It can improve effectiveness of the individual test data. Data fusion of ground-penetrating radar and infrared thermography improved the accuracy of detecting defects [37]. The researchers [38] combined finite element analysis with experimental data from infrared thermography, which provided accurate means to assess quantitatively the size and position of thermal imperfections. According to these, it is demonstrated that data fusion is effective. In this paper, fused data based on infrared thermography and magnetic is utilized to detect the number of broken wires.

Electromagnetic NDT for wire rope is susceptible to hardware design and magnetic signal processing. In [13, 14], the location and number of sensors can affect the quality of acquisition signal. Insufficient quantity will lead to the serious loss of MFL signal, while dense placement of sensors can lead to serious signal interference, resulting in difficulty of noise reduction. Meanwhile, the small broken wire defect information may be drowned out by noise. However, thermal infrared is a visualization method, which can intuitively grasp the surface damage state of wire rope and be closer to the actual damage pattern than magnetic data. Meanwhile, it is without the shortcomings of magnetic detection method and it can make up for the loss of small defects in magnetic information. Thus, the combination of the two methods supply more information for the damage and can avoid the loss of defect information.

To improve classification accuracy of broken wires and provide a reference for evaluating the service life of wire rope, the combination of infrared information and magnetic information is put forward for the first time to perform quantitative identification of wire rope. To processing magnetic signal, an algorithm based on Wavelet Total Variation (WATV) is proposed to remove noise from the raw MFL signals. The noise from high-frequency magnetic leakage, baseline drift, and strand waves can be suppressed by the proposed algorithm. To separate defects from infrared images, an image processing method based on distance is presented. After extracting statistical texture, invariant moment characteristics, and color moment, a fusion method based on kernel extreme learning machine (KELM) of decision level fusion is proposed to combine magnetic and infrared information. Experiment results show that the information fusion based on magnetic and infrared can improve the recognition rate of broken wires.

In the next sections, the platform to get data, the processing for magnetic data, steps for extracting infrared information, and recognition for broken wires after information fusion will be introduced in turn. In this paper, major innovations are as follows: (1) the proposed denoising algorithm based on WATV can eliminate noise generated by channel imbalance, the structure of wire ropes, and so on; (2) an infrared image segmentation algorithm based on distance is presented; and (3) information fusion combined magnetic with infrared to perform classification is firstly adopted.

In this part, through processing and fusing magnetic signal and infrared image, the classification for six kinds of broken wires is implemented. In this experiment, the number of broken wires is one, two, three, four, five, and seven. Many wires are wound into a strand, then it is wounded into a wire rope. The damage of the wire rope is related to the geometry and winding mode of the wire rope [1, 2, 13]. As shown in Figure 1, the structure of the wire rope is with a diameter of 28 mm. The length of the wire ropes is 6.5 m. The specimens used are 185, where the number of training samples is 139 and testing number is 46. The number of broken wires is from 1 to 5 and 7 wires, where the number of every samples set of broken wires is 30, 30, 32, 34, 35, and 34. The width of samples contains 2 mm, 5 mm, and 1.5 cm. The depth of defects is 1 mm. The type of defect is shown in Figure 2.

When there is no defect on wire rope and materials of the wire rope are uniform and identical, the magnetic flux through the cross-section of the wire rope should be equal in the axial direction. If there is a defect, the permeability at the defect becomes smaller, the magnetic field only passes through the air field and then returns to the inside of the wire rope; thus, magnetic leakage on surface is formed [12–14]. According to this principle, a magnetic flux leakage detection device is designed. Data collection contains magnetic signal acquisition and thermal infrared image acquisition. The specific devices and collecting procedures are as follows: the magnetic data acquisition device adopted contains Unsaturated Magnetic Excitation (UME) source, an array of 18 Giant Magnetoresistance (GMR) sensors, data acquisition unit, data storage, and control system [33].

As shown in Figure 4, data collection steps are as follows: After loading unsaturated magnetic field on wire rope, the weak MFL signal can be obtained through equal-space sampling. As the acquisition system moves along the axial direction of the wire rope, the photoelectric encoder produces the pulses. Then, the control system collects the defect information from 18 channels according to pulses. And the final magnetic data is stored in the SD card.

Because the rate of infrared radiation from defect location is different from that from nondefect location, the damage of wire rope can be detected. Infrared information acquisition system, as shown in Figure 5(a), includes heating unit and data collection. The heating unit is composed of the metal tube and tight wires. The metal tube is 40 mm in diameter and 20 cm in length. Wire is adopted to heat the metal tube. Infrared thermography is adopted to capture the images of defect information. The angle of camera should be adjusted according to the location of the defects to maintain the distance between the defect and the camera lens constant. The camera we adopted is thermal imager FLUKE TIX 660. The thermal resolution of the infrared camera is -20°C-1200°C. The distance between wire rope and camera is cm. The specific processes are as follows: after the wire is energized, the wire rope temperature rises by heating the metal tube. When the temperature of fault is maintained at about (°C), the defect images are taken by the infrared camera. Single images are acquired through the device shown in Figure 5(a). After installing the thermal infrared camera on the tripod, the defective part is heated, and the images of wire rope surface defect are obtained by panning the tripod. The focus of the image is formed by centering the defect and fixing the distance between the defect and the camera. The captured raw infrared picture is shown in Figure 5(b). (The defect is marked by a box.)

Infrared image acquisition: (a) schematic of infrared data acquisition device; (b) the raw infrared picture of defect; (c) thermal infrared image capture system; (d) testing platform for wire rope.

Using the system mentioned in Figure 4, raw UME signals can be obtained. As shown in Figure 6. Raw UME signals including incoherent baseline caused by channel imbalance, system noise, and strand wave noise produced by structure of wire rope should be filtered out to obtain pure defects information.

To eliminate the effect of uneven excitation on wire ropes and convert all the data with a uniform standard, normalization is necessary. Normalization is the basis of data visualization; hence, equation (18) is adopted to stretch the defects between 0–255.

Because circumferential data is acquired from 18 sensor channels, circumferential resolution is much lower than the axial one. The pixel count in circumferential is 18; however, the pixel count in axial is more than ten thousand. Three spline interpolations is employed to enhance the circumferential resolution, which increases the pixel count from 18 to 300. In addition, the procedure contributes to realize the visualization of defect images. The schematic of data after interpolations is shown in Figure 10. Then, we obtain gray image of leakage magnetic by converting the double data to unit 8. Figure 11 shows the grayscale image of a wire rope’s leakage magnetic field.

The image after texture filtering also exists strand wave, which makes trouble for feature extraction. The distances between strand waves are fixed according to the structure of wire rope, and the defects are located between strand waves. Therefore, an algorithm based on distance is proposed to part the damage. The algorithm can be described as follows:

(1)After binarization of image , locate the maximum and minimum values of the row and column with pixel value of 1 in the image, respectively. Then the image , as shown in Figure 13, is obtained: ( and are the maximum and minimum of line; and are the maximum and minimum of column).(2)For each line of image, find and :(3)Compute the distances for blocks whose pixel value is 1 by(4)For each line of the image , if the distance is between 10 and 70 and the block is larger than 12 (which can avoid the effect of oil pollution), maintain the line or set the line to zero. (The distance of two strands in wire rope is consist and strand wave shown in the image is also consist. Meanwhile, in order to reduce the effect of oil pollution on the segmentation defect, we choose the distance between 10 and 70 and the block larger than 12.)(5)Extract the defects of infrared images by finding the locations from that meet (4).

Image of broken wires (infrared image, magnetic image, and photo of the tested wire part from left to right): (a) one broken wire; (b) two broken wires; (c) three broken wires; (d) four broken wires; (e) five broken wires; (f) seven broken wires.

The defect images from UME and infrared have high dimension, which will reduce the speed of classification. Redundancy between features can also be disastrous for networks. Thus, it is necessary to employ proper features to implement recognition. Tan and Zhang [33] had proven that average contrast, third-order moment, conformance, and entropy were more sensitive than other texture features and odd order invariant moments were more sensitive than other moments. Thus, in this experiment, a part of statistical texture features and odd order invariant moments from the magnetic images and the color moments and areas from the infrared images are selected.

When completing classification via magnetic features only, a part of statistical texture features and odd order invariant moments is adopted. If the magnetic and infrared information are combined to classify the broken wires, we added the color moments and area of infrared images as features.3.3.2. Fusion Based on KELM

Infrared data is closer to the actual damage pattern than magnetic data and provides more color information; however, different sizes of same broken wires may lead to low accuracy. Magnetic data with the same broken wires has similar visual image. Thus, the combination of the two methods can supply more information for the damage and improve the classification accuracy.

In this part, the classification results are presented using different recognition algorithms. KELM has advantages of high running speed and good generalization, we adopted it to implement the recognition of 6 classes of broken wires. In this section, the defects by magnetic information, infrared data, and combination of the magnetic and infrared information are classified, respectively, which proves that the information fusion is more effective. For KELM, the penalty coefficient C and kernel parameter are adjusted from the set and . The KELM network is trained by a set of 139 randomly selected specimens, and the others are the testing samples.

Figure 17 shows the absolute error distribution of one group testing result when and . The training accuracy of two methods are all higher than 90%. When the magnetic information only exists in the network, the maximum error is 5. When the infrared information only exists in the network, the maximum error is also 5. And the most errors are concentrated in one and two broken wires. However, when the fusion features contain in the network, the maximum error is 2, and the recognition accuracy is higher. It is obvious that there are fewer errors using the fused features than that adopting magnetic features only and infrared only. Therefore, these testing results demonstrate that the fusion of magnetic and infrared not only is feasible but also can improve the recognition accuracy of broken wires.

Several recognition algorithms are applied to the MFL data: BP neural network [28, 29, 32], RBF algorithm [27], and KNN algorithm [30]. The data for recognition is the same as that used in the KELM network. Tables 4–6 show the recognition results for each method when the limiting error is 2 wires.

The research promotes recognition rate of broken wires and makes contributions to estimating the residual lifetime of wire rope. The two information can overcome the loss of small defects in magnetic signal noise reduction. The system we utilized have good performance facing the defects at the exterior of the rope. However, the thermal infrared acquisition system needs to be perfected to realize the image information acquisition of the whole wire rope. Furthermore, we have not been able to create defects inside of the wire rope. We will simulate the situation when the defect is inside the wire rope through analysis in future work. Meanwhile, efficient noise reduction algorithm is also one of the focuses of future research.

The best time to troubleshoot wire rope problems is while it is operating on the equipment. Unfortunately, due to the importance of keeping equipment running, the wire rope may be taken out of service before a wire rope engineer has the opportunity to examine it. The sample is then sent back to the wire rope manufacturer for cause determination. This article outlines the approach that one manufacturer of wire rope takes with these returned samples as well as troubleshooting on site. The lessons learned from these examinations should help prevent recurring problems as well as provide the answer to some puzzling wire rope reactions.

Much like a medical examiner or coroner, the director of engineering is called upon to perform an autopsy on a wire rope returned from the field. When a sample is sent in, the sender usually believes that there was some kind of “birth defect”. Oddly enough, it is the director of engineering that issued the birth certificate (test certificate) and is now responsible for determining the cause of death.

The most important thing to receive with the rope sample is proper documentation. Rope samples are often received with no paperwork, which delays the examination. Along with the rope sample, the following information should be supplied:

• changes – this would include changes in type of work; change in rope manufacturer or construction; equipment modifications, operator, lubrication etc.

As a rule, examinations are not performed on ropes from other manufacturers. Yet many ropes are received that were made elsewhere. Generally, this is not because of misrepresentation during the supply phase, but because of poor record keeping. It is recommended to use a wire rope inspection log where the user can enter the appropriate wire rope information along with the installation date. The wire rope manufacturer’s test certificate should also be stored with the inspection log. The inspection information that is recorded on the log is extremely valuable as the user can also monitor the rate of deterioration.

In one incident a user had experienced a total boom hoist rope failure. Samples of the rope were sent back and it was quickly determined that it was made by another manufacturer. The user was surprised because it had not bought a rope from that manufacturer for over three years. The company policy was to change boom hoist ropes at least every two years. Failure occurred because the rope was way beyond normal retirement criteria. This could have been picked up from an inspection form and the accident thus prevented. Of course, proper inspection techniques would have also prevented the failure.

The first part of the examination is to verify the rope construction. The manufacturer is not verified until the rope is disassembled so the marker can be obtained. The diameter is taken and compared to the diameter recorded at manufacture. The rope’s lay length is also measured and compared to the original measurement. The rope’s exterior surface is examined for wear and corrosion. If broken wires are present, they are counted and the type and location of fracture is noted and recorded.

Sometimes there may be deformation in the rope structure. This could be localised kinks, severe wire displacement or possibly a corkscrew or wave. If the latter condition is noted, the amplitude of the deformation and length of affected area is recorded. The rope is also examined for thermal damage. The rope is then disassembled and the internal rope is inspected much like the rope’s exterior, except that the degree of wire indentations (notching) is also examined.

An example of this is shown in Figure 1 which shows a rope that has operated around an extremely small radius, perhaps from jumping a sheave. Operating around such a small radius has caused this coil like condition.

Figure 2 is an example of a rotation resistant rope from a pedestal crane that has experienced a shock load, resulting in the inner rope popping out suddenly.

Sometimes, a wire rope failure has to be investigated at the scene. Figure 3 shows a wire rope that has completely failed. On closer examination, the majority of the wire ends were cut in shear with some broken in tension. The fact that most of the wire fractures were in shear indicates that a sharp object had severed the rope.

Upon closer investigation, the sheave shown in Figure 4 appears to be the murder weapon. The rope evidently looped out of the sheaves somehow and when put back under tension straddled the sheave and was cut by the sharp edge.

If the rope is still operating on the equipment there is an opportunity to see the rope problem and how it relates to its environment. When troubleshooting in-situ, no assumptions are made. Essentially, some basic steps are followed that should provide all the information needed to make a cause determination.

• check the condition of the rope related equipment: drum – general condition drum grooves – radius and pitch kicker plates or wear plates – condition and position sheave grooving – correct shape and size sheaves – free to rotate and in good condition rope guards – correctly fitted and in good condition wear plates or rollers – condition

A simple trick is to paint the damaged area and see where the affected area is positioned during the operating cycle. Figure 5 is an example of a rope that was at the flange in the layer change transition point. The rope should have been retired before it made it to this condition. A simple drum end rope cut earlier in the rope life would have moved the rope before it became a problem. Slip and cut programmes extend rope life and should be considered for all drum applications (especially smooth face drums). Information gathered should determine the cause of the problem. The most common problems are:

Before contacting the manufacturer, review publications like the Wire Rope Users Manual published by the USA’s Wire Rope Technical Board. Samples of various problems are illustrated which can help the user make an immediate determination. If the cause cannot be identified, take down all the information as covered earlier and ask the original manufacturer for assistance. No matter what has happened to the wire rope, it leaves behind undeniable clues that when matched with site information provides the investigator with the answer to the problem. Wire rope always tells the story.

Wire ropes are widely employed components in diverse areas, such as in industrial production, tourist cable cars, mining, metallurgy, shipbuilding, and elevators. Wire rope is a heavily loaded component, and long-term continuous operation eventually result in corrosion, wear, broken wires, loose wires, and fatigue, which decrease the loading strength of the rope, and can cause accidents, resulting in property damage and injury [1]. The traditional damage detection method is artificial visual inspection, which is a low efficiency, time-consuming, and unreliable method [1]. The development of a fast, non-destructive, and automatic detection technology is therefore necessary.

Wire rope defects include three main types: the loss of metallic area (LMA), local faults (FLs), and structural faults (SFs). The main non-destructive testing (NDT) methods employed for wire rope inspection include electromagnetic detection, ultrasonic guided wave (UGW) evaluation, radiation testing, eddy current inspection, and optical detection [1]. However, designing a precise detection device that can quantitatively determine the characteristics of defects, such as the number of broken wires, remains problematic, particularly when operating in severe environments [2].

The UGW method has been shown to provide a detection speed that is faster than other methods, but the method demonstrates a low anti-interference ability and suffers from strong background noise [3,4,5,6,7]. Treyssède and Laguerre’s [3] applied the transmission characteristics of UGW for wire rope testing. The researchers developed a semi-analytical finite element method, and calculated the optimal excitation and receiving sites. This approach provided a wave dispersion curve for spiral steel rope. Vanniamparambil [4] proposed a novel detection method that combined three technologies: UGW, acoustic emission techniques, and digital image processing. Xu [7] evaluated the detection precision of the UGW method for wire rope defects obtained at different frequencies, showing that wire ropes at higher frequencies had longer recovery lengths for their elastic waves. Raisuitis [5] investigated the propagation of UGWs along composite multi-wire ropes with various types of acoustic contacts between neighboring wires and the plastic core. Tse and Rostami [6] investigated the efficiency of employing the magnetostriction of ferromagnetic materials in conjunction with the UGW method for wire rope defect inspection, and the location and severity of defects were approximately identified and characterized using the short-time Fourier transform and wavelet analysis. Other detection methods, such as radiation testing [8] and eddy current inspection [9], have not been applied to wire rope inspection to a large extent.

Electromagnetic detection methods are commonly employed for the NDT of wire rope [2]. The basic principle behind wire rope electromagnetic detection is illustrated in Figure 1. The lower permeability of the air leads to magnetic field leakage (MFL) from the rope defect, and the strength of the MFL can be obtained from an appropriately designed magnetic detection device. In terms of the type of excitation source employed, electromagnetic detection can be divided according to the use of a coil [10,11] or a permanent magnet [12,13,14,15,16,17] for generating a magnetic field. Modified main-flux equipment has been developed for wire rope inspection, which induced changes in the electromagnetic field strength owing to the leakage field derived from defects in various large-diameter wire ropes [10]. Other researchers [11] employed a pair of saddle coils for the magnetization of a steel track rope, and this system was applied to detect small, inner flaws in the rope. Permanent magnets have been employed in a saddle structure to saturate wire rope in a uniform magnetic field [14,15,16,17]. Wang et al. [12] investigated the effect of excitation distance and the lift-off distance between the sensors and the wire rope surface on the detection precision. The researchers accordingly modified the magnetic circuit of the detector to restrain the impact of fluctuations in the sensor lift-off distance. Xu et al. [18] developed a magnetic excitation model. Based on this model, the researchers established design criteria for the size of the excitation structure, proposed a theoretical framework for the excitation structure size based on numerical analysis, and adjusted the theoretical design using finite element analysis (FEA).

Obtaining a precise MFL signal is the most important aspect for the accurate electromagnetic NDT of wire rope. For MFL signal acquisition, a commonly employed in-service NDT method utilizes an induction coil [10,17], Hall effect sensor [14,18,19,20,21], giant magnetoresistive (GMR) sensor [11,22], and tunnel magnetoresistive (TMR) sensor [23]. Jomdecha and Prateepasen [10] modified a conventional induction coil into a coil array that densely covered the wire rope to acquire the MFL signal. Wang and Tian [14] utilized FEA to address the problems associated with the weak MFL signals derived from small defects, and they investigated the gathered magnetism of the magnetization rope. They designed a detector with an annular pole polymagnet on one side using Hall elements as inductors. This detection system was able to capture weak MFL signals within the strong magnetic field. Xu and Wang [18] developed an online modular-detector NDT system using a Hall effect sensor that successfully detected inner broken wires. The researchers also presented three filtering algorithms. Detectors based on Hall effect sensor arrays have been widely applied for NDT under strong magnetic field conditions [19,20,21]. Cao [19] created an image from the defect data which was obtained by Hall sensors array, and applied digital image processing to extract and detect defect characteristics. Zhang et al. [20] employed signal processing to suppress the effect of lift-off distance, and applied statistical processing to distinguish different types of defects and to obtain binary image data describing the spatial extent of defects. Zhang et al. [21] applied a space filter to suppress the texture of strand waves after obtaining MFL gray-level images of wire rope defects, and the image spectrum texture was extracted as the characteristic vector used for recognition. GMR sensors have been employed for MFL signal acquisition because of their high sensitivity, high precision, and small size. GMR sensors were placed into a sensor array and densely distributed on the wire rope surface in a manner similar to that employed in a Hall effect sensor application [11]. Zhang and Tan [22] utilized the high sensitivity of a GMR sensor to develop a detection technique based on remanence magnetization, which combined the benefits of a simple structure and high detection speed with high precision. Wu et al. [23] demonstrated that TMR sensors can be applied to detect small discontinuities on a wire rope surface.

MFL signals contain a variety of distinct noise signals, which makes the development of an efficient de-noising algorithm challenging work. Currently, a number of noise reduction algorithms are commonly employed, including wavelet analysis de-noising, low-pass filter, notch filter [21], adaptive filter [20], morphological filter [24], and a de-noising algorithm based on compressed sensing (CS) [22]. Zhang et al. [20] applied digital image processing to develop a space filter for smoothing the defects in an MFL signal image. Zhang et al. [21] proposed a baseline estimation algorithm to suppress the effect of undulations in the lift-off distance and an adaptive notch filtering algorithm to filter the strand wave for increasing the signal-to-noise ratio. Zhang and Tan [22] utilized wavelet multi-resolution analysis to eliminate the baseline of the signal. Their work was based on the CS wavelet de-noising algorithm, and they calculated the best sparse transform expression to completely filter out the noise. Tian et al. combined the wavelet transform and the morphological transform to create a morphological filter algorithm that suppressed the interference associated with the baseline drift in the wire rope signal. Artificial neural networks have been widely applied to realize the quantitative detection of wire rope defects. These networks operate much like back propagation (BP) neural networks employed by a number of researchers [20,21,22]. However, BP neural networks suffer from some limitations and shortcomings, such as poor generalization and slow convergence.

To overcome the disadvantages of existing detection devices, we developed a prototype device based on the RMF of a wire rope. This inspection method utilizes GMR sensors for excitation signal acquisition. After magnetizing the wire rope with permanent magnets, the GMR sensor array was utilized to obtain the RMF strength of the rope surface. This detection system is non-contact and non-invasive which prolongs the service life of test equipment. A novel filter algorithm based on the Hilbert-Huang transform (HHT) and compressed sensing wavelet filtering (CSWF) was developed to suppress the various system noises. The HHT was employed to remove the DC component of the signal and balance the sensor channels. CSWF was employed to suppress high-frequency noises and strand waves. Then, we applied digital image processing to create a binary image using a filter based on corrosion and expansion. Subsequently, defects were located and segmented within the gray-level image. Because an 18 GMR sensor array was employed, the resulting gray-level image included only 18 pixels in its circumference. Three spline interpolations were performed to improve the circumferential resolution of the gray-level image. Thirteen image characteristics comprising 6 image textures and seven invariant moments were extracted as defect feature vectors. A radial basis function (RBF) neural network, which is a fast-learning classification network that provides a global optimum, was adopted to quantitatively detect the number of broken wires in the rope. Experimental results demonstrate that, when the absolute limiting error for the detected number of broken wires is 2, the recognition rate is as high as 93.75% with an average recognition error of 0.7813 wires.

Wire rope is widely used in mining operations due to its high strength, light weight, and good elasticity [1,2]. However, the degree of damage sustained by the wire rope increases considerably with the increase in the usage time and due to the increase in the long-term impact of factors such as tensile bending, alternating loads, and the environment. Furthermore, this damage is inevitable if it is not addressed in time, and it can adversely affect the productivity of mining operations and threaten the safety of both the personnel and the equipment. Coal mine safety regulations have been established to ensure the productivity of mining operations; according to these regulations, mining hoist ropes must be tested every day and their scrap period is two years. If the degree of damage does not exceed the relevant provisions, their usage can be extended by no more than one year.

Various methods have been proposed for the non-destructive testing of wire ropes. Most of the current studies are focused on methods such as ultrasonic detection [3], electromagnetic detection [4], X-ray detection, and magnetostriction [5], as well as eddy current, current, and vibration detection [6,7]. The electromagnetic detection method is the most widely implemented method, owing to its demonstrated reliability and practicality. The basic principle of the electromagnetic-based leakage detection method used in this study is shown in Figure 1. The permanent magnet magnetizes the wire rope to saturation, forming a closed magnetic circuit among the wire rope, magnet, and yoke. In the presence of a damage, the original magnetic induction lines through the wire rope form a closed magnetic circuit in the air and generate a leakage magnetic field.

When using the electromagnetic detection method to detect leakage, the wire rope detection signal is mixed with a variety of sources of interference noise, including the spiral structure of the wire rope, which produces periodic changes in the strand noise; the detection of the magnetic field in an environment of complex and variable high-frequency low-amplitude noise; the shaking of the wire rope during the operation process, producing low -frequency random noise; electromagnetic interference issuing from the electromagnetic detection circuit; detection line voltage jitter; drift; and other sources of noise, all of which affect the accurate judgment of the leakage signal. To address the aforementioned challenges, Peng, F. et al. [8] applied a multi-stage filtering method based on EEMD and optimal wavelets in three-dimensional UME signal processing to effectively suppress noise interference. Zhang, J. et al. [9] proposed a new filtering system consisting of the Hilbert yellow transform and compressive-aware wavelet filtering to denoise strand and high-frequency noises. Furthermore, Chun et al. [10] designed a filter based on the multi-stage wavelet analysis of a time-domain-reflection method. Moreover, they effectively eliminated the wild-point noise and industrial frequency interference noise. The abovementioned wire rope damage signal has been studied extensively. However, because the effect of wavelet packet decomposition depends on the choice of the wavelet basis function and the number of decomposition layers, it is not an adaptive signal decomposition method. In recent years, EMD has been widely used in mechanical fault diagnosis. However, owing to the existence of endpoint effects and modal confusion, this algorithm needs to be further studied. To address the limitations of EMD and WT, Dragomiretskiy et al. [11] proposed a new adaptive time-frequency analysis method called VMD in 2014. Compared with EMD and AWT, VMD can suppress interference signals, prevent the loss of useful information, and provide a high-quality data source for subsequent feature extraction. Moreover, it has high decomposition accuracy and operational efficiency and can effectively suppress the overlap mode in a signal decomposition process.

Wire rope detection is challenging because of signal noise reduction, as well as the difficulties involved in achieving a quantitative detection process following noise reduction, owing to the complex structure, shape, and location of the wire rope, which itself produces different types of defects. To solve this problem, some scholars have conducted representative studies. Li, J. et al. designed a nondestructive wire rope inspection device which used double detection plates to collect MFL data, improved the image resolution based on a super-resolution algorithm, and finally used the AdaBoost classifier to classify the defect images [12]. Zhang, J. designed a device based on a residual magnetic field device, proposing a novel filtering system to improve the signal-to-noise ratio, and at the same time used digital image processing techniques to achieve the quantitative recognition of defect images [13,14]. Tan, X. proposed a novel test structure with a huge array of magnetoresistive sensors to effectively identify multiple types of damage and finally applied radial basis neural networks for the quantitative recognition of magnetic images [15]. W Sharatchandra Singh et al. designed an ultrasonic sensor to detect wire rope damage signals by means of ultrasonic detection method and conducted quantitative recognition research using a BP neural network [16]. Artificial neural networks and related algorithms have contributed significantly to the field of pattern recognition. However, their recognition performance is significantly influenced by several parameters and can easily fall into a local minima in the optimization process. However, SVMs have few adjustable parameters and stable operation [17]. Thus, with fewer training samples, higher diagnostic accuracy can be achieved. Therefore, in this study we used SVMs based on PSO for the identification of internal and external wire rope damages.

In summary, it is difficult to detect the internal damage of wire ropes using the existing flaw detection equipment. Therefore, we have designed a wire rope detection device based on leakage magnetism. The detection device is implemented using permanent magnets to magnetize the wire rope, axial, and radial magnetization sensors in order to obtain the wire rope defect information. At the same time, the mapping relationship between internal damage and external damage was analyzed using the finite element method to prepare for the experiment. The VMD-AWT noise reduction method is used to reduce the noise of the original signal and calculate the wavelet information entropy based on the reconstructed signal to construct a multidimensional feature vector. Finally, the PSO-SVM algorithm is proposed to effectively and quantitatively classify and identify the internal and external defects of the wire rope using a multi-dimensional feature vector dataset.

THERE are four varieties of rope in the United States naval service: that made of the fibres of the hemp plant; the Manilla rope, made of the fibres of a species of the wild banana; hide rope, made of strips of green hide, and wire rope.

In some countries, ropes made of horse hair, of the fibrous husk of the cocoanut, called coir-rope, and of tough grasses, are quite common. In our own country, rope has been made from the flax and cotton plants. The metals have also been put in requisition, copper-wire rope being used for particular purposes, principally for lightning conductors, and iron and steel wire are in general use for standing rigging; steel wire being some fifty per cent. stronger than iron wire of the same size.

Of the many vegetable substances that are adapted to rope-making, the best is hemp-hemp-rope possessing in a remarkable degree the essential qualities of flexibility and tenacity.

Hemp in its transit from its native fields to the ropewalk passes through the operations of dew-rotting, scotching and hackling. In the first process water dissolves the glutinous matter that binds the fibrous portion to the woody core, thus partly setting the fibres free; scotching breaks the stalk and separates it still further from the fibre, and hackling consists in combing out the hemp to separate the long and superior fibres from the short and indifferent ones or tow.

The hemp of commerce is put up in bundles of about 200 lbs. each. If good, it will be found to possess a long, thin fibre, smooth and glossy on the surface, and of a yellowish green color; free from "spills" or small pieces of the woody substance; possessing the requisite properties of strength and toughness, and inodorous.

Russian and Italian hemp are considered the best, for the generality of purposes. Rope made from the best quality of Russian hemp, is more extensively used in the navy than any other kind.

The size of Rope is denoted by its circumference, and the length is measured by the fathom. The cordage allowed in the equipment of a man-of-war ranges from 1 1/4 (15-thread) to 10 inches inclusive.

Varieties of Rope. In rope-making the general rule is to spin the yarn from right over to left. All rope yarns are therefore right-handed. The strand, or ready, formed by a combination of such yarns, becomes left-handed. Three of these strands being twisted together form a right-handed rope, known as plain-laid rope. Fig. 14, Plate 7.

White Rope. Hemp rope, when plain-laid and not tarred in laying-up, is called white rope, and is the strongest hemp cordage. It should not be confounded with Manilla. It is used for log-lines and signal halliards. The latter are also made of yarns of untarred hemp, plaited by machinery to avoid the kinking common to new rope of the ordinary make. This is called "plaited stuff," or "signal halliard stuff."

The tarred plain-laid ranks next in point of strength, and is in more general use than any other. The lighter kinds of standing rigging, much of the running rigging, and many purchase falls are made of this kind of rope.

Cable-laid or Hawser-laid Rope, Fig. 15, is left-handed rope of nine strands, and is so made to render it impervious to water, but the additional twist necessary to lay it up seems to detract from the strength of the fibre, the strength of plain-laid being to that of cable-laid as 8.7 to 6; besides this, it stretches considerably under strain.

Shroud-laid. Rope, Fig. 16, Plate 7, is formed by adding another strand to the plain-laid rope. But the four spirals of strands leave a hollow in the centre, which, if unfilled, would, on the application of strain, permit the strands to sink in, and detract greatly from the rope"s strength, by an unequal distribution of strain. The four strands are, therefore, laid up around a heart, a small rope, made soft and elastic, and about one-third the size of the strands.

Experiments show that four-stranded rope, when under 5 inches, is weaker than three-stranded of the same size; but from 5 to 8 inches, the difference in strength of the two kinds is trifling, while all above 8 inches is considered to be equal to plain-laid when the rope is well made.

Tapered Rope is used where much strain is brought on only one end. That part which bears the strain is full-sized, tapering off to the hauling part, which is light and pliable. Fore and main tacks and sheets are made of tapered rope.

Twice-laid Rope is made from second-hand yarns. This rope may be readily known by the different shades of color of the yarns, but it is often difficult to determine, by mere inspection, whether it is relaid from what was good rope, and, consequently, still good, or made up from junk or condemned rigging, and worthless. Twice-laid rope is only met with on board ship when necessity has compelled its purchase on foreign stations.

Manilla Rope seems to be better adapted to certain purposes on board ship than hemp, being more pliable, buoyant, causing less friction, and not so easily affected by moisture. It is used for hawsers, tow-lines, and for light-running rigging and gun-tackle falls. Manilla is now less used in the navy than formerly. The Book of Allowances states that the cheap first cost of Manilla as compared with hemp is more than compensated by the greater market value of the hemp when worn-out. This statement is not correct if applied to the current relative values of hemp and Manilla junk in this country.

Hide Rope is made of strips cut by machinery from green hides. Formerly used for topsail tyes, and for tailing on to such ropes as are exposed to much chafe in some particular part, as topsail sheets, etc., it is now allowed only for wheel ropes. Its strength is about one-third that of hemp.

Hide rope requires care to keep it in good order, and should not be exposed to the weather unnecessarily. It should be given a lick of thin tar (Swedish preferred)

Avoid serving the splices of hide rope. When spare wheel ropes are stowed away they should be well oiled and headed up in a barrel to preserve them from rats and mice.

Wire Rope for general use in the navy is made from one quarter to seven inches, inclusive, in circumference, those being the maximum and minimum sizes likely to be needed.

When first introduced, it was thought that great difficulty would be found in manipulating wire rigging, but our best riggers cut, fit and splice it as readily as they do hemp rigging.

In its less bulk and cost, wire rope has decided advantages over hemp for the standing rigging. of ships, and now all vessels of the navy are provided with standing rigging of wire.

Besides the great advantage that wire rigging possesses of not being affected by the heat and sparks from the smokestack, its durability is at least three or four times that of common rope, and, when once completely set, does not require further pulling up.

In Appendix A will be found a table of comparative dimensions of chain cables, hemp, iron and steel rope, with breaking strains and weights per fathom.

Small Stuff is the general term applied to small rope. It is particularized by the number of threads or yarns which it contains, and is further known either as ratline stuff or seizing stuff.

Rogue"s Yarn is a single untarred thread, sometimes placed in the centre of the rope, or in the centre of each strand, denoting government manufacture.

Junk is supplied for the purpose of working up into various uses-such as for swabs, spun-yarn, nettle-stuff, lacings, seizings, earings, gaskets, &c.-of all of which the supply, in proper kind, is generally inadequate. Good junk is got out of such material as condemned hawsers-they having been necessarily made of the best stuff, and condemned before being much injured. Old rigging makes bad junk, not being condemned generally until much worn.

Shakings are odds and ends of yarns and small ropes, such as are found in the sweepings of the deck after work. They are collected, put in a bag kept for the purpose, and at certain times served out to the watch to be picked into Oakum, a good supply of which should always be on hand for any calking that may be required, for stuffing jackasses, boat"s fenders, &c.

Use of the Ropermaker"s Winch, Fig. 18, Plate 7. A ship"s winch, which will make very fair 2-inch rope, is about 15 inches in diameter. In the frame, which is double, are placed five hooks-the three upper ones for general use, the fourth for four-stranded rope, and the centre one for hardening up large rope after it has been laid up by the upper ones (the latter not being sufficiently strong for the purpose). The shanks of the hooks, between the two parts of the frame, are inserted in cogged barrels, which are turned by the wheel, one revolution of which gives nine to the hooks-any one of which can be thrown out of gear by hauling it back close to the after part of the frame.

The top, Fig. 17 (b), is a conical piece of wood, scored on the outside for the reception of the strands. Its use is to keep the strands separate between it and the winch, and to regulate the amount of twist in the rope behind it, by being moved along either slowly or rapidly. When four-stranded rope is required, a hole is bored through the centre, as a lead for the heart.

A length of junk being brought on deck, you proceed to unlay it by attaching the strands to separate hooks, and the loper to the other end-one hand holding back on it, and then heaving back-two hands following the rope down to separate the ends.

Spun-Yarn is made by hooking all the yarns that compose it (according to the size required) upon one hook. You then heave round, the reverse way to the lay of the yarns (which in ordinary rope are all right-handed) until there is plenty of back turn in them, holding on the ends by hand; then rub down and make it up.

reverse way; the yarns are thus hove up the contrary way to what they were originally, to soften them; for when drawn out of rope, they are usually hard and angular; and would not lie square, or bear an equal strain, if laid up in that condition. When thus relaid, the ends are knotted together, the loper hooked on-one hand holding on to it, the top put in, the winch hove round the same way as at first, and the top moved along towards the winch. When up to it, the top is taken out, the yarns unhooked, and hitched to a single hook, then the winch hove round the opposite way to what you have just been heaving it, to harden the stuff up; rub down and make up.

General Remarks on Rope. The strength of a rope-yarn of medium size is equal to 100 lbs., but the measure of strength of a given rope is not, as might naturally be supposed, 100 lbs. multiplied by the number of yarns contained in the rope. The twist given to the yarn, after certain limits, diminishes its strength, as already stated, and with the best machinery it is scarcely possible that each yarn of the tope should bear its proper proportion of strain. The difference in the average strength of a yarn differs with the size of the rope. Thus, in a 12-inch rope, the average strength of each yarn is equal to 76 lbs., whereas, in a rope of half an inch, it is 104 lbs.

Experiment has shown that by applying a constant, or even frequent, strain equal to half its strength, the rope will eventually break. This seems to be particularly the case with cable-laid rope, which is the weakest of all.

It has been ascertained that a good selvagee, carefully made with the same number and description of yarns, as the common three-stranded plain-laid rope, possesses about the same degree of strength.

It has been shown by experiment, that where a span is so placed as to form an angle less than 30 degrees, the strength of the two parts of the rope or chain of which it is composed, is less than the strength which one such part would have if placed in a direct line with the strain.

the direction pursued by the hands of a watch; the left-handed ropes, against the sun. An exception to this rule is in the hemp cables and hawsers, which are left-handed and are coiled away with the sun.

Rope contracts very considerably by wetting it. Advantage may be, and often is, taken of this, by wetting lashings, which are required to be very taut and solid, and are not permanent, as the lashing of a garland on a lower mast for taking it in or getting it out. For the same reason in rainy weather, braces, halliards, sheets, clew-lines, and other rigging requiring it, should be slacked up to save an unnecessary strain on the rope, and avoid the risk of springing a yard or carrying something away.

Running rigging has nothing to protect it from the effects of the weather, excepting, in hemp, the tar taken up in the process of manufacture, and after being wet the air should be allowed to circulate through it freely. Rope should never be stowed away until thoroughly dry.

Running rigging, when not in actual use, should be kept neatly coiled down near the pin to which it belays, taking care always to capsize the coil that the running part may be on top, so that it may run clear. In port, during good weather, the rigging may be coiled down in flemish coils, that is, perfectly flat, as soon as the decks are dry enough in the morning, and left so until the decks are cleared up at seven bells in the afternoon, when the ends should be run out, the rope coiled down snugly and triced up in readiness for washing decks in the morning.

One rope may be rove by another by putting the two ends together, and worming three yarns or pieces of spun-yarn in the lay for three or four inches on each side, and clove-hitching the ends around the rope, or opening the strands and laying them in. This is always done when reeving new braces by old ones, and with running rigging generally.

Practical Rule for ascertaining the Strength of Rope. The square of half the circumference gives the breaking strain of the weakest plain-laid rope in tons, and is therefore a safe rule.

For ascertaining the Weight of Rope. Three-strand, plain-laid, 25-thread yarn, tarred. Multiply the square of the circumference by the length in fathoms, and divide by 4.24 for the weight in lbs.

A Practical Rule for determining the relative Strength of Chain and Rope. Consider the proportionate strength of chain and rope to be ten to one-using the diameter of the chain and the circumference of the rope. Half-inch chain may, therefore, replace five-inch rope.

To find the size of Rope when rove as a Tackle to Lift a given Weight: Divide the weight to be raised by the number of parts at the movable block to get the strain on a single part, add one-third of this for the increased strain due to friction, and reeve the rope of the corresponding strength.

To find what Number of parts of a parts of a small Rope are equal to a large Rope: Divide the square of the circumference of the larger rope by the square of the circumference of the smaller, and the result will be the number of parts of the smaller equal to one part of the larger.

To Knot a Rope Yarn, Fig 19, Plate 8. Split in halves the two ends of a rope-yarn, scrape them down with a knife, crotch and tie the two opposite ends; jam the tie and trim off the ends.

A Bow-Line Knot, Fig. 26, Plate 8. Take the end of the rope (a), Fig. 24, in the right hand, and the standing part (b) in the left, laying the end over the standing part; with the left hand turn a bight of the standing part over it, Fig. 25; lead the end round the standing part, through the bight again, and it will appear like Fig. 26. The bight turned in the standing part is often called a Cuckold"s Neck.

A Running Bow-Line Knot, Fig. 28, Plate 8. Take the end of a rope, Fig. 27, round the standing part (b) and through the bight (c); make the single bow-line knot upon the part (d), and it is done.

A Bow-Line Knot upon the Bight of a Rope, Fig. 30, Plate 9. Take the bight (a) in one hand, Fig. 29, and the standing parts (b) in the other; throw a kink or Cuckold"s Neck over the bight (a) with the standing parts, the same as for the single knot; take the bight (a) over the large bights (c, c), bringing it up again: it will then be complete, Fig. 30. The best way to sling a man by a bow-line is to shorten up one of the lower bights, using the lower part as a seat and putting the arms through the part next above.

A Wall Knot. Unlay the end of a rope, Fig. 32, Plate 9, and with the strand (1) form a bight, holding it down on the side of the rope at (2); pass the end of the next (3) round the strand (1); the end of the strand (4) round the strand (3) and through the bight which was made at first by the strand (1); haul them rather taut, and the knot will then appear like Fig. 33.

To Double-Crown the same knot, Fig. 37, Plate 10. Lay the strands by the sides of those in the single crown, pushing them through the same bights in the single crown, and down through the double walling; it will then be like Fig. 37, viz. single walled, single crowned, double walled, and double crowned. The first walling must always be made against the lay of the rope: the parts will then lie fair for the double crown. The ends are scraped down, tapered, marled, and served with spun yarn. This knot is often used for the ends of man-ropes, and hence frequently called a Man-rope Knot.

Matthew Walker"s Knot, Fig. 39, Plate 10. This knot is made by separating the strands of a rope, Fig. 38, taking the end (1) round the rope, and through its own bight, the end (2) underneath through the bight of the first, and through its own bight, and the end (3) underneath, through the bights of the strands (1 and 2), and through its own bight. Haul them taut, and they form the knot, Fig. 39. The ends are cut off. This is a handsome knot for the end of a laniard, and is generally used for that purpose.

own part, Fig. 40. Render the parts through, jam taut, lay up and whip the end, Fig. 41. This knot is used for bucket ropes, &c. It should have a leather washer around its neck when exposed to chafe.

A Single Diamond Knot, Fig. 43, Plate 11. Unlay the end of a plain-laid rope for a considerable length, Fig. 42, and with the strands form three bights down its side, holding them fast. Put the end of strand (1) over strand (2), and through the bight of strand (3), as in the figure; then put the strand (2) over strand (3), and through the bight formed by the strand (1), and the end of (3) over (1), and through the bight of (2). Haul these taut, lay the rope up again, and the knot will appear like Fig. 43. This knot is used for the side ropes, jib guys, bell ropes, &c.

A Double Diamond Knot, for the same purpose, Fig. 44, Plate 11. With the strands opened out again, follow the lead of the single knot through two single bights, the ends coming out at the top of the knot, and lead the last strand through two double bights. Lay the rope up again as before, to where the next knot is to be made, and it will appear like Fig. 44.

A Sprit-Sail Sheet Knot, Fig. 47, Plate 11. Unlay two ends of a rope, and place the two parts which are unlaid, together, Fig. 45. Make a bight with the strand (1). Wall the six strands together, against the lay of the rope (which being plain-laid must be done from the right hand to the left), exactly in the same manner that the single walling was made with three; putting the second over the first, the third over the second, the fourth over the third, the fifth over the fourth, the sixth over the fifth, and through the bight which was made by the first; haul them rather taut, and the single walling will appear like Fig. 46; then haul taut. It must be then crowned, Fig. 47, by taking the two strands which lie most conveniently (5 and 2) across the top of the walling, passing the other strands (1, 3, 4, 6) alternately o