A method and system for flaw detection of steel wire rope embedded in conveyor belt

Abstract

The present invention discloses a method and system for flaw detection of embedded steel wire ropes in conveyor belts, and relates to the technical field of non-destructive flaw detection. The method comprises: obtaining M-channel current frame detection data of the target conveyor belt; determining the peak-to-peak value of the M-channel current frame detection data respectively, and judging whether there is connection part interference of the embedded steel wire rope of the target conveying section according to the size and number of the peak-to-peak value of the target channel data; if there is connection part interference, the connection part state of the embedded steel wire rope of the target conveying section is determined by detecting the jump point; if there is no connection part interference, the signal-to-noise ratio of the target channel data is determined, and judging whether there is a possibility of defect of the embedded steel wire rope of the target conveying section according to the size of the signal-to-noise ratio of the target channel data; if there is a possibility of defect, the defect information of the embedded steel wire rope of the target conveying section is determined by the continuous wavelet transform method. The present invention can realize automatic and accurate flaw detection of embedded steel wire ropes in conveyor belts.

Description

Technical Field

[0001] The invention relates to the technical field of nondestructive flaw detection, and in particular to a flaw detection method and system for a conveyor belt embedded steel wire rope. Background Art

[0002] Belt conveyors are often used in coal mining and coal production. They are known as the "throat" of coal production. Their safe operation is of great significance to improving production efficiency. Because when the conveyor belt is in operation, the steel wire rope in the belt is prone to various faults due to fatigue load, tensile pressure, aging and wear. After long-term use, the steel wire rope conveyor belt is prone to various faults, most of which will cause the strength of the belt body to decrease. In severe cases, there will be casualties, causing great losses to the company. On the one hand, the price of the conveyor belt is expensive, which can easily cost hundreds of thousands or even millions of yuan; on the other hand, downtime for maintenance will also lead to stagnation of coal mine production, seriously affecting economic benefits. In extreme cases, it may also lead to production accidents, resulting in irreversible consequences. Therefore, it is of great significance to realize online monitoring of the operation of the conveyor belt and timely discover the initial fault. Among them, the steel wire rope core conveyor belt, as the traction and carrying component of the belt conveyor, has the characteristics of high tensile strength, good impact resistance, long life, small elongation in use, good troughing, and good bending resistance. It is suitable for long-distance, large-range, and high-speed material transportation. It is composed of core rubber, steel wire rope, covering layer and edge rubber. During use, the steel wire rope conveyor belt needs to be monitored by a flaw detection device to ensure the normal operation of the steel wire rope conveyor belt.

[0003] In the 1980s, error back propagation was discovered in the research of Geoffrey Hinton, David Parker and others, which is now the BP algorithm. It can solve the problem of learning the connection weights between hidden layers, and can give the calculation process through mathematical methods, making the research on error back propagation algorithms more in-depth. In the early 1980s, China had already started research on the operation monitoring of conveyor belt conveyors. Wang Duanyi and others summarized the research on foreign conveyor belts and predicted that conveyor belt monitoring would turn to non-contact development; in the early 21st century, Zhang Chunming and others used new laser technology in surface detection. They investigated the detection research in surface-related fields at home and abroad at that time, and creatively proposed the use of line laser for surface detection. This method greatly improved the efficiency of defect recognition, but the cost was relatively high at the time.

[0004] The existing conveyor belt flaw detection solutions are as follows:

[0005] 1. Utility model CN215768558U discloses an automatic monitoring and flaw detection device for a steel rope core conveyor belt, including two correspondingly arranged mounting mechanisms. The mounting mechanism includes a connecting column and a slide groove, one side of the top of the slide column is fixedly connected to a screw rod, and one end passes through the through groove. Its important feature is that one end of the screw rod is threadedly engaged with a wing nut, and by loosening the wing nut, the width of the mounting mechanism can be adjusted according to the width of the conveyor rack to adapt to conveyor racks of different widths. Although compared with the prior art, the utility model can adjust the width of the mounting mechanism according to the width of the conveyor rack by loosening the wing nut to adapt to conveyor racks of different widths, and has higher flexibility, which improves the practicality of the device, and adjusts the height of the flaw detection sensor according to the height of the top surface of the steel rope core conveyor belt, so that the flaw detection sensor and the top surface of the steel rope core conveyor belt reach the optimal distance, so as to improve the accuracy of detection and further improve the practicality of the device.

[0006] 2. Utility model CN210392666U discloses a real-time inspection system for conveyor belts, including a roller monitoring module, a foreign body monitoring module, a walking module, a marking module, an alarm module, a machine vision and thermal sensing module, a multi-point mobile image inspection and analysis submodule, a conveyor belt real-time simulation module, a video anti-shake module and a belt foreign body detection module; the conveyor belt real-time simulation module is electrically connected to the roller monitoring module, the foreign body monitoring module, the walking module, the marking module, the alarm module, the machine vision and thermal sensing module, the multi-point mobile image inspection and analysis submodule, and receives the information sent by each module for analysis, and simulates the position state of the belt rotation in real time. This solution aims at the defects and shortcomings of the existing technology and provides a solution to the initial monitoring of coal mine conveyor belt failures.

[0007] 3. Utility model CN211206363U provides a new type of ultrasonic flaw detector device, including a detection conveyor belt and an output conveyor belt. A guide plate is arranged between the detection conveyor belt and the output conveyor belt, both ends of the detection conveyor belt and the output conveyor belt are installed on supporting drive rollers, a transmission device is arranged between the supporting drive rollers of the detection conveyor belt and the output conveyor belt, the detection conveyor belt is installed on the detection bracket, the output conveyor belt is installed on the output bracket, the guide plate is installed between the detection bracket and the output bracket, an ultrasonic flaw detector is arranged outside the detection conveyor belt, a display device and a control switch are arranged on the ultrasonic flaw detector, the lower end of the ultrasonic flaw detector is installed on the flaw detector bracket, and the flaw detector bracket is installed on the lower side of the detection bracket, which can greatly improve the detection efficiency of parts and components and avoid errors caused by manual fatigue.

[0008] The above solutions have the following defects respectively:

[0009] Utility model CN215768558U relies too much on human intervention and thus has a low degree of automation. Utility model CN210392666U relies on video inspection of moving images, so its stability will be affected by quality defects in video capture. Utility model CN211206363U has certain limitations in conveyor belt defect detection accuracy because sound waves will bypass the defect and cannot be reflected when the defect size is smaller than the wavelength.

[0010] In addition, the existing flaw detection devices have a simple structure and poor flexibility, and can generally only be installed on the corresponding model of the conveyor rack, resulting in low practicality. At the same time, although most of the invented devices have certain technical breakthroughs, they cannot achieve passive autonomous, full-range, all-weather, real-time online flaw detection. Summary of the invention

[0011] The object of the present invention is to provide a method and system for flaw detection of steel wire ropes embedded in conveyor belts, so as to realize automatic and accurate flaw detection of steel wire ropes embedded in conveyor belts.

[0012] To achieve the above object, the present invention provides the following solutions:

[0013] A method for flaw detection of steel wire rope embedded in a conveyor belt, the method comprising:

[0014] Acquire M current frame detection data of the target conveyor belt; one current frame detection data is the magnetic field strength data collected by a flaw detection module at the current moment; M flaw detection modules are arranged side by side on the target conveyor belt according to a set direction; the set direction is a direction perpendicular to the conveying direction of the target conveyor belt; wherein M is a positive integer greater than 0;

[0015] Determine the peak-to-peak values ​​of the current frame detection data of M channels respectively, and judge whether there is connection part interference of the embedded steel wire rope of the target conveying section according to the size and number of the peak-to-peak values ​​of the target channel data; the target channel data is the current frame detection data of any channel; the target conveying section is the area on the target conveyor belt corresponding to the target channel data;

[0016] If there is a connection interference in the embedded steel wire rope of the target conveying section, a method of detecting a jump point is used to determine the connection state of the embedded steel wire rope of the target conveying section;

[0017] If the embedded steel wire rope of the target conveying section does not have any connection interference, then determine the signal-to-noise ratio of the target route data, and judge whether the embedded steel wire rope of the target conveying section has a possibility of defect according to the signal-to-noise ratio of the target route data;

[0018] If there is a possibility of defect in the embedded steel wire rope of the target conveying section, the defect information of the embedded steel wire rope of the target conveying section is determined by using a continuous wavelet transform method.

[0019] The present invention also provides a conveyor belt embedded wire rope flaw detection system, the system is implemented by the above method, and the system comprises:

[0020] M flaw detection modules are arranged side by side on the target conveyor belt according to a set direction, and are used to collect magnetic field strength data of the target conveyor belt at each moment to obtain M detection data; one detection data is the accumulation of one current frame detection data in time; the set direction is a direction perpendicular to the conveying direction of the target conveyor belt; wherein M is a positive integer greater than 0;

[0021] The magnetic signal analysis control box is respectively connected to the M flaw detection modules, and is used to determine whether there is connection part interference or defect possibility of the embedded steel wire rope of the target conveyor belt according to the M detection data, and determine the connection part state when there is connection part interference of the embedded steel wire rope of the target conveyor belt, and determine the defect information when there is defect possibility of the embedded steel wire rope of the target conveyor belt.

[0022] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0023] The present invention provides a method and system for flaw detection of steel wire ropes embedded in a conveyor belt. M flaw detection modules are arranged side by side on a target conveyor belt according to a set direction to automatically collect magnetic field strength data of the target conveyor belt at each moment as M-path detection data. The peak-to-peak value and signal-to-noise ratio of target path data in the M-path detection data are determined. Based on the size and number of the peak-to-peak values ​​and the size of the signal-to-noise ratio, it is judged whether a target conveying section corresponding to the target path data has connection part interference or defect possibility. When the embedded steel wire rope of the target conveying section has connection part interference, a method for detecting jump points is used to determine the connection part state of the target conveying section. When the embedded steel wire rope of the target conveying section has defect possibility, a continuous wavelet transform method is used to determine the defect information of the target conveying section, thereby realizing automatic and accurate flaw detection of steel wire ropes embedded in conveyor belts. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0025] Figure 1 A flow chart of a method for flaw detection of steel wire ropes embedded in conveyor belts provided by the present invention;

[0026] Figure 2 This is a schematic diagram of the overall process of the method for flaw detection of embedded steel wire ropes in conveyor belts provided by the present invention;

[0027] Figure 3 It is a waveform diagram of a frame of detection data of a certain channel;

[0028] Figure 4 is the base noise waveform of a certain road;

[0029] Figure 5 It is the differential waveform of a certain channel base noise waveform;

[0030] Figure 6 It is a defect interference waveform of a certain channel;

[0031] Figure 7 It is the differential waveform of a certain defect interference waveform;

[0032] Figure 8 This is the first type of intermediate connecting part waveform;

[0033] Fig. 9 This is the waveform of the second intermediate connection part;

[0034] Fig.10 is the original waveform of the first typical defect signal;

[0035] Fig.11 This is the waveform of the first typical defect signal after wavelet transformation;

[0036] Fig.12 is the original waveform of the second typical defect signal;

[0037] Fig.13 This is the waveform of the second typical defect signal after wavelet transformation;

[0038] Fig.14 It is the original waveform of the third typical defect signal;

[0039] Fig.15 This is the waveform of the third typical defect signal after wavelet transformation;

[0040] Fig.16 A structural diagram of a conveyor belt embedded steel wire rope flaw detection system provided by the present invention;

[0041] Fig.17 This is an installation and deployment diagram of the conveyor belt embedded wire rope flaw detection system provided by the present invention;

[0042] Fig.18 This is a schematic diagram of the installation of the conveyor belt embedded wire rope flaw detection system provided by the present invention;

[0043] Fig.19This is a physical schematic diagram of the flaw detection module;

[0044] Fig. 20 Schematic diagram of the custom-made bracket;

[0045] Fig.21 This is a schematic diagram of the actual mine installation;

[0046] Fig. 22 This is the waveform diagram of the conveyor belt wire rope detection in implementation case 1;

[0047] Fig.23 This is the wire rope defect waveform diagram of implementation case 1;

[0048] Fig.24 This is the waveform diagram of the conveyor belt connection joint of implementation case 1;

[0049] Fig.25 This is the waveform diagram of the conveyor belt wire rope detection in implementation case 2;

[0050] Fig.26 This is the wire rope defect waveform diagram for implementation case 2;

[0051] Fig. 27 This is the waveform diagram of the conveyor belt connection joint of implementation case 2;

[0052] Fig.28 This is the conveyor belt test waveform diagram under the switch iron remover condition of implementation case 3;

[0053] Fig.29 This is the switch waveform of the iron remover for implementation case 3;

[0054] Fig.30 This is the conveyor belt defect waveform diagram of implementation case 3.

[0055] Explanation of symbols:

[0056] Flaw detection module-1, magnetic signal analysis control box-2, encoder-3, alarm-4, server-5. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The object of the present invention is to provide a method and system for flaw detection of steel wire ropes embedded in conveyor belts, so as to realize automatic and accurate flaw detection of steel wire ropes embedded in conveyor belts.

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] The present invention utilizes the metal magnetic memory effect for flaw detection. Metal magnetic memory flaw detection belongs to the field of non-destructive flaw detection and is one of many physical flaw detection methods such as X-ray, ultrasonic, magnetic powder, eddy current, gamma ray, penetration (fluorescence, coloring), magnetic memory, magnetic leakage, etc. It is a rapid non-destructive detection method that utilizes the metal magnetic memory effect to detect stress concentration, cracks, scratches, and rust of metal material parts.

[0061] The so-called metal magnetic memory effect means that when ferromagnetic metal materials are processed and operated, due to the combined effects of load and geomagnetic field, magnetic domain organization orientation and irreversible reorientation with magnetostrictive properties will occur in the stress and deformation concentration area. This irreversible change of magnetic state will not only be retained after the working load is eliminated, but also related to the maximum applied stress. This magnetic state on the surface of the metal component "memorizes" the location of microscopic defects or stress concentration, which is the so-called magnetic memory effect. When the ferromagnetic component in the geomagnetic field environment is subjected to external load, magnetic domain organization orientation and irreversible reorientation with magnetostrictive properties will occur in the stress concentration area. Fixed nodes of magnetic domains will appear in this part, generating magnetic poles and forming a demagnetization field, thereby minimizing the magnetic permeability of the ferromagnetic metal in this part and forming a leakage magnetic field on the metal surface. The tangential component Hpx of the leakage magnetic field intensity has a maximum value, while the normal component Hpy changes its sign and has a zero value. This irreversible change of magnetic state is still retained and remembered after the working load is eliminated.

[0062] Figure 1 This is a flow chart of the method for flaw detection of embedded steel wire ropes in conveyor belts provided by the present invention. Figure 2 The overall process diagram of the method for flaw detection of embedded steel wire ropes in conveyor belts provided by the present invention is shown in FIG. Figure 1 and Figure 2 As shown, the method for flaw detection of embedded steel wire ropes in conveyor belts provided by the present invention comprises:

[0063] Step S1: Acquire M current frame detection data of the target conveyor belt; one current frame detection data is the magnetic field strength data collected by a flaw detection module at the current moment; M flaw detection modules are arranged side by side on the target conveyor belt according to a set direction; the set direction is a direction perpendicular to the conveying direction of the target conveyor belt; wherein M is a positive integer greater than 0.

[0064] Step S2: respectively determine the peak-to-peak values ​​of the current frame detection data of the M routes, and judge whether there is any connection part interference of the embedded steel wire rope in the target conveying section according to the size and number of the peak-to-peak values ​​of the target route data; the target route data is the current frame detection data of any route; the target conveying section is the area on the target conveyor belt corresponding to the target route data.

[0065] Step S3: If there is interference in the connection part of the embedded steel wire rope of the target conveying section, the state of the connection part of the embedded steel wire rope of the target conveying section is determined by detecting the jump point. Step S3 is equivalent to Figure 2 The connection part algorithm is used to determine the connection part.

[0066] Step S4: If the embedded steel wire rope of the target conveying section does not have any connection interference, determine the signal-to-noise ratio of the target route data, and determine whether the embedded steel wire rope of the target conveying section has a defect according to the signal-to-noise ratio of the target route data. Step S2 and Step S4 constitute Figure 2 The rough judgment algorithm in .

[0067] Step S5: If the embedded steel wire rope of the target conveying section may have defects, the continuous wavelet transform method is used to determine the defect information of the embedded steel wire rope of the target conveying section. Step S5 is equivalent to Figure 2 The precise judgment algorithm in .

[0068] In specific applications, the present invention proposes a conveyor belt embedded steel wire rope defect detection system based on GMI magnetic sensors. The conveyor belt (i.e. conveyor belt) defect detection uses a sensor array device (i.e. flaw detection module). Each sensor can accurately determine whether three steel wire ropes have defects, and the entire array can completely determine the conveyor belt cross-sectional defects. After algorithm processing, defect information including defect location and damage degree can be further obtained.

[0069] The algorithm processes data by frame, and each frame of data corresponds to a conveyor belt length of approximately 25 cm. It can detect up to 3 defects and provide the degree of damage and location of each defect. The array device can also be used to determine the middle connection of the conveyor belt. After algorithm processing, it can accurately provide the length of the connection measured by each sensor, so as to determine whether there is any abnormal connection.

[0070] The above steps are discussed in detail below.

[0071] 1. Rough judgment algorithm

[0072] Rough judgment is a preliminary inspection of a frame of data, giving three results: no defects, possible defects, and interference of connected parts.

[0073] 1.1 Data Import

[0074] Select the magnetic field amplitude (unit: nanot nT) data collected by M detection modules and analyze each frame of M data. The detection distance of one frame of the detection module is about 25cm. The waveform of one frame of detection data of a certain channel is as follows: Figure 3 As shown, the horizontal axis represents the number of points (ie, positions), and the vertical axis represents the magnetic field amplitude (ie, magnetic field strength) corresponding to each point.

[0075] 1.2 Calculate the maximum peak value to determine whether the connection is partially interfered

[0076] In this embodiment, judging whether there is connection part interference in the embedded steel wire rope of the target conveying section according to the size and number of the peak-to-peak value of the target route data specifically includes:

[0077] Step S21: Determine the number of peak-to-peak values ​​of the target path data that is greater than a set peak-to-peak value threshold.

[0078] Step S22: Determine whether the number is greater than a first set threshold.

[0079] Step S22: If the number is greater than a first set threshold, there is connection portion interference in the embedded steel wire rope of the target conveying section.

[0080] Step S23: If the number is less than or equal to the first set threshold, there is no connection part interference in the embedded steel wire rope of the target conveying section.

[0081] In specific applications, the peak-to-peak values ​​of single-frame data of M channels are calculated respectively and compared with the set threshold value. Since the magnetic anomaly caused by the connection part usually has a strong amplitude and the amplitude difference of each sensor is small, the number m of peak-to-peak values ​​of each sensor greater than a certain threshold (that is, the set peak-to-peak value threshold) is counted. If the number is greater than the set value thod, it is judged as connection part interference and enters step 2 connection part algorithm judgment, otherwise it enters step 1.3.

[0082] 1.3 Calculate the signal-to-noise ratio to determine whether there are defects

[0083] In this embodiment, judging whether there is a possibility of a defect in the embedded steel wire rope of the target conveying section according to the signal-to-noise ratio of the target road data specifically includes:

[0084] Step S41: determining whether the signal-to-noise ratio of the target path data is greater than a second set threshold.

[0085] Step S42: If the signal-to-noise ratio of the target route data is greater than a second set threshold, the embedded steel wire rope of the target conveying section may be defective.

[0086] Step S43: If the signal-to-noise ratio of the target route data is less than or equal to the second set threshold, there is no possibility of defects in the embedded steel wire rope of the target conveying section.

[0087] In a specific application, the signal-to-noise ratio is calculated after the original data (target data) is differentiated. The differential calculation is performed on each single-frame original data filtered out in step 1.2. The differential calculation formula is as follows:

[0088]

[0089] Where: x k Represents the horizontal coordinate of the original data of the kth frame, x k+1 represents the horizontal coordinate of the original data of the k+1th frame, f(x k ) represents the ordinate of the original data of the kth frame, f(x k+1 ) represents the ordinate of the original data of the k+1th frame, f'(x k ) represents the vertical coordinate of the kth frame data after differentiation.

[0090] For a new set of single-frame data obtained after differential processing, the signal-to-noise ratio calculation formula is: Where Ps and Pn represent the effective power of the signal and noise respectively. Take the maximum absolute value of the differential data and the mean of a group of data far from the maximum value as the basis. The two are compared as the calculation of the first signal-to-noise ratio snr. If it is greater than the second set threshold thod1, it is judged to be defective, otherwise it is not defective. For possible defects, enter step 3 precise judgment algorithm.

[0091] Figure 4 is the base noise waveform of a certain road, Figure 5 is the differential waveform of a certain base noise waveform, Figure 6 is a defect interference waveform of a certain channel, Figure 7 is the differential waveform of a certain defect interference waveform. Figure 4 and Figure 5 As shown in the figure, if the ratio of the maximum value to the base value after the difference is relatively small, it can be considered that there is no defect signal. Figure 6 and Figure 7 As shown in the figure, if a defective signal is selected, the maximum value of the defective signal after differentiation is much higher than the adjacent background value.

[0092] 2. Connection part algorithm judgment

[0093] The connection part determination algorithm adopts the method of detecting jump points.

[0094] In this embodiment, the method of detecting the jump point to determine the connection state of the embedded steel wire rope of the target conveying section specifically includes:

[0095] Step S31: respectively obtain multiple frames of detection data of a previous time period and multiple frames of detection data of a subsequent time period of the target road data, and form a target road array with the target road data; the end time of the previous time period is continuous with the current time; the current time is continuous with the start time of the subsequent time period.

[0096] Step S32: performing multi-point difference and amplitude comparison processing on the target path array to determine the positions of the front and rear jump points.

[0097] Step S33: determining the length of the connection portion of the embedded steel wire rope of the target conveying section according to the positions of the front and rear jump points.

[0098] Step S34: determining the connection state of the embedded steel wire rope of the target conveying section according to the length of the connection state; the connection state includes: normal connection state and abnormal connection state.

[0099] The following is a detailed discussion of the three parts of obtaining a new array, calculating the jump point position, and calculating the number of waveform points for each channel.

[0100] 2.1 Get a new array

[0101] If this frame data is judged as a connection part interference, N frames of data are appropriately cached (where N is a positive integer greater than 3) so that this set of data can completely include the waveform of the connection part interference. In order to make the curve smoother and reflect the overall change trend of the waveform, each channel of data is divided into smaller segments, accumulated and processed to obtain a new array.

[0102] 2.2 Jump point location calculation

[0103] The jump point judgment is performed on the front and rear ends of the new array respectively. The jump point detection method adopts multi-point difference and amplitude comparison judgment method to obtain the front and rear jump point positions of each sensor.

[0104] After appropriate conversion, the real front and rear jump point positions are obtained, and the difference between the two can be used to obtain the corresponding length of the connection part. Since each sensor needs to be judged, the jump point position will inevitably be wrong. The front and rear jump point positions of each sensor can be calculated, and the respective means of the front and rear jump points are taken. Then, the change rate of the front and rear jump point positions and their respective means of each sensor are calculated (the change rate is the jump point position minus the mean and divided by the mean). Generally, if the change rate exceeds 30%, it is considered an abnormal jump point. If the change rate is large, adjust the threshold set when calculating the jump point (for example, 0.2, when it is calculated as an abnormal jump point, the threshold can be appropriately lowered or increased), recalculate this sensor to obtain a new jump position, and calculate the jump point at most twice. Finally, the length of each sensor and the position of the connection part are output.

[0105] 2.3 Calculation of waveform points per channel

[0106] Through the calculation in step 2.2, the length pd (i.e., the number of waveform points) of each sensor is obtained. By comparing it with the set threshold thod2, if it exceeds the set threshold, it is considered that the connection part is abnormal, otherwise it is normal. Among them, the continuous length of the normal connection part, that is, the number of points, is measured in advance, that is, the set threshold range. If the calculated length is larger or smaller than the set threshold range, it is considered that the connection part is abnormal.

[0107] See the two waveform diagrams of the middle connection part. Figure 8 and Fig. 9 It can be clearly seen from the figure that the amplitude of the connection part is much stronger than the background noise, and the distribution of each sensor is relatively uniform.

[0108] 3. Precision judgment algorithm

[0109] After the judgment in step 1.3, the data that may be defective is judged by the precision judgment algorithm.

[0110] In this embodiment, the method of using continuous wavelet transform to determine the defect information of the embedded steel wire rope of the target conveying section specifically includes:

[0111] Step S51: performing continuous wavelet transform of different scales on the target road data to obtain high-frequency defect detection data and low-frequency defect detection data; the high-frequency defect detection data corresponds to the continuous wavelet transform of the high-frequency scale, and the low-frequency defect detection data corresponds to the continuous wavelet transform of the low-frequency scale.

[0112] Step S52: dividing the high-frequency defect detection data into a plurality of high-frequency band data, and dividing the low-frequency defect detection data into a plurality of low-frequency band data; the number of the high-frequency band data is equal to the number of the low-frequency band data.

[0113] Step S53: respectively determining the signal-to-noise ratio of each of the high-frequency band data and each of the low-frequency band data, and determining the maximum signal-to-noise ratio data.

[0114] Step S54: If the maximum signal-to-noise ratio data is high-frequency band data, the defect information on the target conveying section is determined according to the signal-to-noise ratio of each high-frequency band data.

[0115] Step S55: If the maximum signal-to-noise ratio data is low-frequency band data, the defect information on the target conveying section is determined according to the signal-to-noise ratio of each low-frequency band data; the defect information includes: defect location and damage degree.

[0116] In specific applications, the precise judgment algorithm adopts the judgment method of continuous wavelet transform. The algorithm intends to use a smooth Gaussian function with low-pass properties and use its first-order derivative as the wavelet basis function to perform mutation point analysis. The following is a detailed discussion of the three parts of wavelet transform, signal-to-noise ratio calculation and defect judgment.

[0117] 3.1 Wavelet transform

[0118] Perform continuous wavelet transform on the raw data of each sensor. Wavelet transform can transform a time signal into the time-frequency domain, which can better observe the local characteristics of the signal and simultaneously observe the time and frequency information of the signal. The wavelet transform formula is as follows:

[0119]

[0120] Where: WT(α,τ) represents the wavelet transform of f(t), ψ(·) represents the wavelet function, f(t) represents the time signal, the scale α controls the expansion and contraction of the wavelet function, and the translation τ controls the translation of the wavelet function. The scale corresponds to the frequency (inversely proportional), and the translation τ corresponds to the time. The original waveforms and the waveforms after wavelet transform of the three typical defects provided by the present invention are shown in Figure 10-15 .

[0121] 3.2 Calculation of signal-to-noise ratio

[0122] After the transformation in step 3.1, the data is evenly divided into three segments, and each segment is judged according to the signal-to-noise ratio.

[0123] The signal-to-noise ratio is calculated as follows: first, the maximum absolute value of each data segment is taken as the signal level. The base calculation is to take the segment with the smallest maximum value among the three data segments, and take the average of a group of data far away from the maximum value as the base level for this segment. The signal level of each segment is compared with this base level as the signal-to-noise ratio of each segment.

[0124] Since defect types may vary, the wavelet transform in step 3.1 will take two different scales, corresponding to low-frequency defects and high-frequency defects respectively. The specific scale to be selected is determined based on the final signal-to-noise ratio, and the scale with a larger signal-to-noise ratio is selected.

[0125] 3.3 Defect determination

[0126] After calculation according to step 3.2, the signal-to-noise ratio snr1 of each segment is obtained and compared with the set threshold thod3. If it is greater than the set threshold, it is considered to be defective and the defect position is output, otherwise there is no defect. At the same time, if there is a defect, it can be judged as a light, medium or severe defect according to the calculated signal-to-noise ratio. Finally, through calculation, it can be output whether there is a defect in each frame of data of each sensor divided into three segments, as well as the corresponding signal-to-noise ratio, defect position and damage degree.

[0127] The present invention also provides a conveyor belt embedded wire rope flaw detection system, the system is implemented by the above method, Fig.16 This is a structural diagram of the conveyor belt embedded wire rope flaw detection system provided by the present invention. Fig.16 As shown, the system comprises:

[0128] M flaw detection modules 1 are arranged side by side on the target conveyor belt according to a set direction, and are used to collect magnetic field strength data of the target conveyor belt at each moment to obtain M detection data; one detection data is the accumulation of one current frame detection data in time. The flaw detection module 1 specifically includes multiple GMI (Giant Magneto-Impedance, abbreviated as GMI, giant magneto-impedance) magnetic sensors.

[0129] The magnetic signal analysis control box 2 is respectively connected to the M flaw detection modules 1, and is used to determine whether the embedded steel wire rope of the target conveyor belt has connection part interference or defect possibility according to the M detection data, and determine the connection part state when the embedded steel wire rope of the target conveyor belt has connection part interference, and determine the defect information when the embedded steel wire rope of the target conveyor belt has defect possibility. The magnetic signal analysis control box 2 is preferably a mining intrinsically safe magnetic signal analysis control box.

[0130] Furthermore, the system further comprises: an encoder 3 connected to the magnetic signal analysis control box 2, for locating the defect position on the target conveyor belt according to the defect information. The encoder 3 is preferably a mining intrinsically safe encoder.

[0131] The system further comprises: an alarm 4 connected to the magnetic signal analysis control box 2, for sending an alarm signal when there is a defect in the embedded steel wire rope of the target conveyor belt. The alarm 4 is specifically an alarm light or a mining intrinsically safe voice alarm.

[0132] The system further comprises: a server 5 connected to the magnetic signal analysis control box 2 for storing the detection data and deploying software. The server 5 is connected to the magnetic signal analysis control box 2 via an underground ring network.

[0133] As a specific embodiment, the conveyor belt embedded wire rope flaw detection system provided by the present invention also includes a first power supply and a second power supply. The first power supply is connected to the magnetic signal analysis control box 2, and is used to power the magnetic signal analysis control box 2. The second power supply is respectively connected to the magnetic signal analysis control box 2 and the alarm 3, and is used to power the alarm 3 when the magnetic signal analysis control box 2 detects that the target conveyor belt has defects. The first power supply and the second power supply are both flameproof and intrinsically safe voltage-stabilized power supplies for mining.

[0134] The conveyor belt embedded wire rope flaw detection system provided by the present invention is discussed in detail below using a specific installation and deployment example.

[0135] Fig.17 This is the installation deployment diagram of the conveyor belt embedded wire rope flaw detection system provided by the present invention. Fig.18 This is a schematic diagram of the installation of the conveyor belt embedded wire rope flaw detection system provided by the present invention. Fig.17 and Fig.18 As shown in the figure, the system consists of multiple intrinsically safe wire rope flaw detection sensors for mining (each flaw detection module consists of 11 GMI magnetic sensor arrays), intrinsically safe encoders for mining, intrinsically safe magnetic signal analysis control boxes for mining, intrinsically safe voice alarms for mining, power supplies, network switches, servers, etc. Among them, the intrinsically safe encoders are used to locate the specific defect locations, and the servers are used to store data and deploy software.

[0136] The target conveyor belt specifically includes an upper conveyor belt, an upper roller, a conveyor belt frame, a lower conveyor belt and a lower roller. The upper roller contacts the lower surface of the upper conveyor belt, and the lower roller contacts the lower surface of the lower conveyor belt. The upper surface of the upper conveyor belt is used to place materials. The flaw detection module is specifically located below the lower conveyor belt and is set at a distance from the lower surface of the lower conveyor belt. That is, there is no contact between the flaw detection module and the target conveyor belt.

[0137] In general, the number of flaw detection modules can be selected according to the actual width of the conveyor belt to complete the non-destructive flaw detection monitoring tasks of conveyor belts of different widths: for example, a 1.2-meter-wide conveyor belt requires 3 flaw detection modules, a 1.6-meter-wide conveyor belt requires 4 flaw detection modules, and a 2-meter-wide conveyor belt requires 5 flaw detection modules. Fig.19 , multiple flaw detection modules together constitute a wire rope flaw detection sensor.

[0138] The wire rope flaw detection sensor is a rectangular structure with an external dimension of 382mm×82mm×45mm. There are fixing bolt holes on its side. It can be fixed on the customized bracket with screws and the height of the bracket can be adjusted up and down to ensure that the distance between the sensor measuring surface and the lower surface of the lower belt (i.e., the lower conveyor belt) is controlled at 1 to 3 mm. For a schematic diagram of the customized bracket and sensor installation structure, see Fig.18 .

[0139] As a specific implementation, the schematic diagram of the customized bracket is shown in Fig. 20 , see the actual mine installation diagram Fig.21In addition, the specific installation method can be based on the site conditions. The basic principles are: (1) The flaw detection sensor is easy to disassemble so that the coal slime and sewage can be removed regularly; (2) The distance between the flaw detection sensor measuring surface and the belt surface can be adjusted and fixed; (3) The belt measuring surface should be adjusted or controlled to be flat, the flaw detection sensor measuring surface should be parallel to the belt cross section, and the ground should be hardened as much as possible.

[0140] The flaw detection effect of the present invention is further discussed below with reference to several specific implementation cases.

[0141] The conveyor belt wire rope flaw detection system of the present invention uses a flaw detection module composed of 11 GMI magnetic sensor arrays, and the following preferred implementation case uses four flaw detection modules, and the output waveform is drawn by the detection data of one or more modules. The horizontal axis represents the number of points (positions), and the position accuracy can reach the millimeter (mm) level. The vertical axis is the magnetic field amplitude (the amplitude unit is nanosecond nT).

[0142] Implementation Case 1

[0143] The measured data obtained by using the conveyor belt wire rope flaw detection system of the present invention in the first coal mining area are as follows: Figure 22-Figure 24 As shown, the surface conveyor belt has defects and also includes the connecting joint waveform. The horizontal axis represents the number of points (positions), the position accuracy can reach the millimeter (mm) level, and the vertical axis is the magnetic field amplitude (the amplitude unit is nanosecond nT). The conveyor belt is 1.6 meters wide and runs at a speed of 4.5m / s.

[0144] Implementation Case 2

[0145] The measured data obtained by using the conveyor belt wire rope flaw detection system of the present invention in the second coal mining area are as follows: Figure 25-27 As shown, the surface conveyor belt has defects and also includes the connecting joint waveform. The horizontal axis represents the number of points (positions), and the vertical axis is the magnitude of the magnetic field amplitude. The conveyor belt is 1.6 meters wide and runs at a speed of 2m / s.

[0146] Implementation Case 3

[0147] The measured data obtained by using the conveyor belt wire rope flaw detection system of the present invention in the third coal mining area are as follows: Figure 28-Figure 30 As shown, the surface conveyor belt has defects and also includes the connection joint waveform. The horizontal axis represents the number of points (positions), and the vertical axis represents the magnetic field amplitude. The conveyor belt is 1.6 meters wide and runs at a speed of 2m / s.

[0148] The above tests show that the metal magnetic memory effect conveyor belt wire rope flaw detection device based on a multi-GMI magnetic sensor array can detect different degrees of magnetic anomalies caused by defects in conveyor belt wire ropes, conveyor belt joints, iron remover switches, etc., and the changes in magnetic characteristics of different defect types are also different. The test found that defects of different degrees also have different changes in magnetic characteristics; defects in different positions also show different changes in magnetic characteristics. On the basis of the present invention, the sensitivity and accuracy of detecting conveyor belt wire rope defects, joints and other defects can be further improved by increasing the density of the GMI magnetic sensor array; similarly, the non-destructive flaw detection function of conveyor belt wire ropes of different widths can be adapted by increasing or decreasing the number of detection sensors.

[0149] The present invention proposes a method and system for flaw detection of conveyor belt steel wire ropes based on GMI magnetic sensors. By utilizing the metal magnetic memory effect, a flaw detection monitoring system for conveyor belt steel wire ropes is creatively developed, which has the advantages of fully passive measurement, non-contact, maintenance-free, real-time online detection, etc., and can work normally in harsh environments such as dust, dense fog, etc. By recording the distribution of the magnetic field intensity component perpendicular to the surface of the metal component along a certain direction, the stress concentration degree of the component and whether there are microscopic defects can be evaluated, and the stress concentration area inside the ferromagnetic metal component, that is, microscopic defects and early failure and damage, can be diagnosed to prevent sudden fatigue damage. For steel wire ropes, not only surface and internal damage can be detected, but also its internal stress changes and the severity of rust can be detected. Not only can the presence or absence of defects be found, but also the type and level of defects can be accurately determined. Because there is no excitation device, its performance degradation due to the adsorption of metal powder and debris during long-term use is avoided.

[0150] The present invention has the following advantages:

[0151] 1) Passive measurement is adopted without applying any magnetic field, which truly reflects the induced magnetic field of metal foreign bodies; it is not afraid of any non-magnetic materials spreading in the space or adhering to the equipment.

[0152] 2) It is not affected by water mist, dust, etc., is maintenance-free, and can achieve real-time online, non-destructive, passive, non-contact, and radiation-free detection. It can also detect changes in internal stress and the severity of rust.

[0153] 3) The flaw detection sensor is designed according to standardization and adopts a modular structure. It can be combined and spliced ​​at will to adapt to belts of different widths.

[0154] 4) The system platform supports multiple sets of front-end flaw detection sensors and distributed flaw detection systems with B / S architecture.

[0155] 5) No special magnetization device is required; no surface cleaning is required; no coupling technology is required; stress concentration areas can be detected quickly and accurately. At the same time, because there is no excitation device, the performance degradation caused by the adsorption of metal powder and debris during long-term use is avoided.

[0156] 6) For all magnetic signals, local analysis, local processing and local decision-making are carried out underground. There is no need to upload the magnetic information to local analysis, so as to avoid the interruption of the underground and surface ring network causing the equipment to malfunction. At the same time, the real-time performance of data analysis is greatly improved, and the equipment and platform operate independently.

[0157] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0158] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for flaw detection of steel wire rope embedded in a conveyor belt, characterized in that: The method comprises: Acquire M current frame detection data of the target conveyor belt; one current frame detection data is the magnetic field strength data collected by a flaw detection module at the current moment; M flaw detection modules are arranged side by side on the target conveyor belt according to a set direction; the set direction is a direction perpendicular to the conveying direction of the target conveyor belt; wherein M is a positive integer greater than 0; Determine the peak-to-peak values ​​of the current frame detection data of M routes respectively, and judge whether there is connection part interference of the embedded steel wire rope of the target conveying section according to the size and number of the peak-to-peak values ​​of the target route data; the target route data is the current frame detection data of any route; the target conveying section is the area on the target conveyor belt corresponding to the target route data; If there is interference in the connection part of the embedded steel wire rope of the target conveying section, a method of detecting a jump point is adopted to determine the connection part state of the embedded steel wire rope of the target conveying section; If the embedded steel wire rope of the target conveying section does not have any connection interference, then determine the signal-to-noise ratio of the target route data, and judge whether the embedded steel wire rope of the target conveying section has a possibility of defect according to the signal-to-noise ratio of the target route data; If there is a possibility of defect in the embedded steel wire rope of the target conveying section, the defect information of the embedded steel wire rope of the target conveying section is determined by using a continuous wavelet transform method.

2. The method for flaw detection of steel wire rope embedded in conveyor belt according to claim 1, characterized in that: The method of using continuous wavelet transform to determine the defect information of the embedded steel wire rope of the target conveying section specifically includes: Performing continuous wavelet transforms of different scales on the target road data to obtain high-frequency defect detection data and low-frequency defect detection data; the high-frequency defect detection data corresponds to continuous wavelet transforms of high-frequency scale, and the low-frequency defect detection data corresponds to continuous wavelet transforms of low-frequency scale; The high-frequency defect detection data is divided into a plurality of high-frequency band data, and the low-frequency defect detection data is divided into a plurality of low-frequency band data; the number of the high-frequency band data is equal to the number of the low-frequency band data; Determine the signal-to-noise ratio of each of the high-frequency band data and each of the low-frequency band data respectively, and determine the maximum signal-to-noise ratio data; If the maximum signal-to-noise ratio data is high-frequency band data, the defect information on the target conveying section is determined according to the signal-to-noise ratio of each high-frequency band data; If the maximum signal-to-noise ratio data is low-frequency band data, the defect information on the target transmission section is determined according to the signal-to-noise ratio of each low-frequency band data; the defect information includes: defect location and damage degree.

3. The method for flaw detection of steel wire rope embedded in conveyor belt according to claim 1, characterized in that: The method of detecting the jump point to determine the connection state of the embedded steel wire rope of the target conveying section specifically includes: Respectively acquiring a plurality of frames of detection data of a previous time period and a plurality of frames of detection data of a subsequent time period of the target path data, and forming a target path array with the target path data; the end time of the previous time period is continuous with the current time; the current time period is continuous with the start time of the subsequent time period; Performing multi-point difference and amplitude comparison processing on the target path array to determine the positions of the front and rear jump points; Determine the length of the connection portion of the embedded steel wire rope of the target conveying section according to the positions of the front and rear jump points; The state of the connection portion of the embedded steel wire rope of the target conveying section is determined according to the length of the connection portion.

4. The method for flaw detection of steel wire rope embedded in conveyor belt according to claim 1, characterized in that: The step of judging whether there is connection interference in the embedded steel wire rope of the target conveying section according to the size and number of the peak-to-peak value of the target route data specifically includes: Determine the number of peak-to-peak values ​​of the target path data that is greater than a set peak-to-peak value threshold; Determining whether the number is greater than a first set threshold; If the number is greater than a first set threshold, then there is a connection portion interference in the embedded steel wire rope of the target conveying section; If the number is less than or equal to the first set threshold, there is no connection part interference in the embedded steel wire rope of the target conveying section.

5. The method for flaw detection of embedded steel wire rope in conveyor belt according to claim 1, characterized in that: The step of judging whether the embedded steel wire rope of the target conveying section has a defect possibility according to the signal-to-noise ratio of the target road data specifically includes: Determining whether the signal-to-noise ratio of the target path data is greater than a second set threshold; If the signal-to-noise ratio of the target route data is greater than a second set threshold, then the embedded steel wire rope of the target conveying section may have defects; If the signal-to-noise ratio of the target route data is less than or equal to the second set threshold, there is no possibility of defects in the embedded steel wire rope of the target conveying section.

6. A conveyor belt embedded wire rope flaw detection system, characterized in that: The system is implemented by the method according to any one of claims 1 to 5, and the system comprises: M flaw detection modules are arranged side by side on the target conveyor belt according to a set direction, and are used to collect magnetic field strength data of the target conveyor belt at each moment to obtain M detection data; one detection data is the accumulation of one current frame detection data in time; the set direction is a direction perpendicular to the conveying direction of the target conveyor belt; wherein M is a positive integer greater than 0; The magnetic signal analysis control box is respectively connected to the M flaw detection modules, and is used to determine whether there is connection part interference or defect possibility of the embedded steel wire rope of the target conveyor belt according to the M detection data, and determine the connection part state when there is connection part interference of the embedded steel wire rope of the target conveyor belt, and determine the defect information when there is defect possibility of the embedded steel wire rope of the target conveyor belt.

7. The conveyor belt embedded wire rope flaw detection system according to claim 6, characterized in that: The system further comprises: An encoder is connected to the magnetic signal analysis control box and is used to locate the defect position on the target conveyor belt according to the defect information.

8. The conveyor belt embedded wire rope flaw detection system according to claim 6, characterized in that: The flaw detection module includes a plurality of giant magneto-impedance magnetic sensors.

9. The conveyor belt embedded wire rope flaw detection system according to claim 6, characterized in that: The system further comprises: An alarm is connected to the magnetic signal analysis control box and is used to send out an alarm signal when there is a defect in the embedded steel wire rope of the target conveyor belt.

10. The conveyor belt embedded wire rope flaw detection system according to claim 6, characterized in that: The system further comprises: The server is connected to the magnetic signal analysis control box and is used to store the detection data.

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