CN115266908A

CN115266908A - Flaw detection method and system for steel wire rope embedded in conveyor belt

Info

Publication number: CN115266908A
Application number: CN202210913826.1A
Authority: CN
Inventors: 李铮; 戴卫东; 费翔; 李函阳; 钱阳; 李定朋; 刘景毅; 苏光磊; 李燕南; 杨允峰; 高秀卫
Original assignee: Guochuang Intelligent Equipment Manufacturing Co ltd; Ningxia Guangtianxia Technology Co ltd
Current assignee: Guochuang Intelligent Equipment Manufacturing Co ltd; Ningxia Guangtianxia Technology Co ltd
Priority date: 2022-08-01
Filing date: 2022-08-01
Publication date: 2022-11-01

Abstract

The invention discloses a flaw detection method and a flaw detection system for an embedded steel wire rope of a conveying belt, and relates to the technical field of nondestructive flaw detection. The method comprises the following steps: acquiring M paths of current frame detection data of a target conveyor belt; respectively determining the peak value of M paths of current frame detection data, and judging whether the embedded steel wire rope of the target conveying section has interference of a connecting part according to the size and the number of the peak value of the target path data; if the interference of the connecting part exists, determining the state of the connecting part of the embedded steel wire rope of the target conveying section by adopting a method for detecting a trip point; if the interference of the connecting part does not exist, determining the signal to noise ratio of the target road data, and judging whether the embedded steel wire rope of the target conveying section has the possibility of defect according to the signal to noise ratio of the target road data; and if the defects exist, determining the defect information of the embedded steel wire rope of the target conveying section by adopting a continuous wavelet transform method. The invention can realize automatic and accurate flaw detection of the steel wire rope embedded in the conveying belt.

Description

Flaw detection method and system for steel wire rope embedded in conveyor belt

Technical Field

The invention relates to the technical field of nondestructive inspection, in particular to a method and a system for inspecting an embedded steel wire rope in a conveying belt.

Background

The belt conveyor is commonly used in coal mining and coal production work, is known as the throat key channel of coal mine production, and has great significance for improving the production efficiency in safe operation. When the conveying belt operates, the steel wire rope in the belt is worn due to fatigue load, tensile pressure and aging, various faults are easy to occur after the steel wire rope conveying belt is used for a long time, the strength of the belt body is mostly reduced, and casualties can occur seriously to cause great loss for enterprises; on the other hand, shutdown maintenance can also cause stagnation of coal mine production, seriously affect economic benefits, and possibly cause production accidents under extreme conditions, thereby causing irrecoverable consequences. Therefore, the online monitoring of the running condition of the conveyor belt is realized, and the early-stage fault can be found in time. The steel wire rope core conveying belt is used as a traction and carrying component of the belt conveyor, has the characteristics of high tensile strength, good impact resistance, long service life, small use elongation, good grooving performance and good bending resistance flexibility, and is suitable for conveying materials at long distance, long distance and high speed. The steel wire rope core conveying belt is composed of core rubber, a steel wire rope, a covering layer and edge rubber, and the steel wire rope core conveying belt is monitored by a flaw detection device in the using process so as to ensure normal operation of the steel wire rope core conveying belt.

In the eighties of the last century, error back-propagation was found in the research of Geoffrey Hinton, david Parker et al, respectively, that is, the current BP algorithm. The method can solve the problem of learning of the connection right between the hidden layers, and can give out a calculation process through a mathematical method, so that the study of an error back propagation algorithm is advanced layer by layer. The operation monitoring research of the conveyor belt conveyor in China in the early 80 s of the 20 th century has been developed, and Wangzhini et al summarized and concluded the research of foreign conveyor belts to predict that the monitoring of the conveyor belt will turn to the development of a non-contact type direction; in the beginning of the 21 st century, zhangchuming et al adopted a novel laser technology to be applied to surface detection, and they investigated the detection research in the related fields of internal and external surfaces at that time, and creatively proposed that the surface detection is performed by using line laser, and this method greatly improves the defect recognition efficiency, but the cost is relatively high at that time.

The existing flaw detection scheme for the conveying belt is as follows:

1. utility model CN215768558U discloses automatic monitoring flaw detection device for steel cord conveyor belt, including two installation mechanisms that correspond the setting. The mounting mechanism comprises a connecting column and a sliding chute, one side of the top end of the sliding column is fixedly connected with a screw, and one end of the screw penetrates through the through groove. The wing-shaped nut is screwed at one end of the screw, and the width of the mounting mechanism can be adjusted according to the width of the conveying frame by unscrewing the wing-shaped nut so as to adapt to the conveying frames with different widths. Although compared with the prior art, this utility model can be through unscrewing wing nut, installation mechanism's width can be adjusted according to the width of conveying frame to adapt to the conveying frame of different width, the flexibility is higher, improves the practicality of device, according to wire rope core conveyer belt top face altitude mixture control sensor's height of detecting a flaw, makes sensor and wire rope core conveyer belt top face of detecting a flaw reach the optimum distance, with the accuracy that promotes the detection, further improves the device practicality.

2. The utility model CN210392666U discloses a real-time inspection system for conveyor belts, which comprises a carrier roller monitoring module, a foreign matter monitoring module, a walking module, a marking module, an alarm module, a machine vision and heat sensing module, a multi-point movable image inspection and analysis submodule, a conveyor belt real-time simulation module, a video anti-shake module and a belt foreign matter detection and calculation module; the conveyor belt real-time simulation module is respectively and electrically connected with the carrier roller monitoring module, the foreign matter monitoring module, the walking module, the marking module, the alarm module, the machine vision and heat sensing module and the multi-point movable image inspection and analysis sub-module, receives information sent by the modules for analysis, and simulates the rotating position state of the belt in real time. The scheme aims at the defects and shortcomings of the prior art, and provides a scheme for monitoring the failure of the coal mine conveyor belt at the initial stage.

3. Utility model CN211206363U provides a novel ultrasonic flaw detector device, including detecting conveyer belt, output conveyer belt. Detect the conveyer belt, establish the stock guide between the output conveyer belt, detect the conveyer belt, install on supporting the drive roller at output conveyer belt both ends, detect the conveyer belt, establish transmission between output conveyer belt's the support drive roller, it installs on detecting the support to detect the conveyer belt, the output conveyer belt is installed on output support, the stock guide is installed between detecting support and output support, detect the conveyer belt outside and establish the ultrasonic flaw detector, establish display device on the ultrasonic flaw detector, and a control switch, the ultrasonic flaw detector lower extreme is installed on the flaw detector support, the flaw detector support mounting is at the detection support downside, can improve the detection efficiency of spare part greatly, avoid artifical error because of fatigue work production.

The above solutions have the following drawbacks:

utility model CN215768558U, too rely on the manual intervention operation, therefore degree of automation is lower. Utility model CN210392666U, because it relies on the video inspection of moving images, its stability can be influenced because of the quality flaw of video shooting. Utility model CN211206363U, because when the size of defect is less than wavelength, the sound wave will bypass the defect and can not reflect, therefore conveyer belt defect detection precision has certain limitation.

In addition, current flaw detection device simple structure, the flexibility is relatively poor, generally can only correspond to install on the conveying frame that corresponds the model, leads to the practicality lower. Meanwhile, although most of the devices have certain technical breakthroughs, the passive autonomous, full-range, all-weather and real-time online flaw detection cannot be realized.

Disclosure of Invention

The invention aims to provide a method and a system for detecting flaws of an embedded steel wire rope of a conveying belt, so that automatic and accurate flaw detection of the embedded steel wire rope of the conveying belt is realized.

In order to achieve the purpose, the invention provides the following scheme:

a flaw detection method for an embedded steel wire rope of a conveyor belt, comprising the following steps of:

acquiring M paths of current frame detection data of a target conveyor belt; one path of current frame detection data is magnetic field intensity data acquired by a flaw detection module at the current moment; m flaw detection modules are arranged on the target conveyor belt side by side according to a set direction; the set direction is a direction perpendicular to the conveying direction of the target conveying belt; wherein M is a positive integer greater than 0;

respectively determining the peak-to-peak values of M paths of the current frame detection data, and judging whether the embedded steel wire rope of the target conveying section has connection part interference or not according to the size and the number of the peak-to-peak values of the target path data; the target path data is any path of the current frame detection data; the target conveying section is an area on the target conveying belt corresponding to the target road data;

if the embedded steel wire rope of the target conveying section has connection part interference, determining the state of the connection part of the embedded steel wire rope of the target conveying section by adopting a method for detecting a trip point;

if the embedded steel wire rope of the target conveying section has no interference of a connecting part, determining the signal-to-noise ratio of the target road data, and judging whether the embedded steel wire rope of the target conveying section has possible defects according to the signal-to-noise ratio of the target road data;

and if the embedded steel wire rope of the target conveying section has the possibility of defects, determining the defect information of the embedded steel wire rope of the target conveying section by adopting a continuous wavelet transform method.

The invention also provides a flaw detection system for the steel wire rope embedded in the conveying belt, which is realized by adopting the method and comprises the following steps:

the M flaw detection modules are arranged on the target conveying belt side by side according to a set direction and used for acquiring magnetic field intensity data of the target conveying belt at each moment to obtain M-path detection data; one path of the detection data is the accumulation of one path of current frame detection data in time; the set direction is a direction perpendicular to the conveying direction of the target conveying belt; wherein M is a positive integer greater than 0;

and the magnetic signal analysis control box is respectively connected with the M flaw detection modules and is used for judging whether the embedded steel wire rope of the target conveyer belt has connection part interference or possible flaw according to the M paths of detection data, determining the state of the connection part when the embedded steel wire rope of the target conveyer belt has connection part interference, and determining the flaw information when the embedded steel wire rope of the target conveyer belt has possible flaw.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

the invention provides a method and a system for detecting flaws of an embedded steel wire rope of a conveyor belt, wherein M flaw detection modules are arranged on the target conveyor belt side by side according to a set direction to automatically acquire magnetic field intensity data of the target conveyor belt at each moment as M-path detection data, the peak value and the signal-to-noise ratio of the target path data in the M-path detection data are determined, whether connection part interference or possible flaws exist in a target conveying section corresponding to the target path data is judged based on the size and the number of the peak value and the signal-to-noise ratio, when the interference exists in the connection part in the embedded steel wire rope of the target conveying section, the state of the connection part of the target conveying section is determined by adopting a method for detecting a jump point, and when the possibility exists in the embedded steel wire rope of the target conveying section, the flaw information of the target conveying section is determined by adopting a continuous wavelet transform method, so that automatic and accurate flaw detection of the embedded steel wire rope of the conveyor belt is realized.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

FIG. 1 is a flow chart of a flaw detection method for a steel wire rope embedded in a conveyor belt provided by the invention;

FIG. 2 is a schematic overall flow chart of a flaw detection method for a steel wire rope embedded in a conveyor belt according to the invention;

FIG. 3 is a waveform diagram of a frame of test data;

FIG. 4 is a graph of a roadbed noise waveform;

FIG. 5 is a differentiated waveform of a roadbed noise waveform;

FIG. 6 is a diagram of a path defect interference waveform;

FIG. 7 is a differentiated waveform of a defect interference waveform;

FIG. 8 is a first intermediate connection portion waveform;

FIG. 9 is a second intermediate connection portion waveform;

FIG. 10 is a diagram illustrating an original waveform of a first exemplary defect signal;

FIG. 11 is a waveform of a first exemplary defect signal after wavelet transform;

FIG. 12 is an original waveform of a second exemplary defect signal;

FIG. 13 is a waveform of a second exemplary defect signal after wavelet transform;

FIG. 14 is an original waveform of a third exemplary defect signal;

FIG. 15 is a waveform of a third exemplary defect signal after wavelet transform;

FIG. 16 is a block diagram of a conveyor belt embedded wire rope flaw detection system provided by the present invention;

fig. 17 is an installation and deployment diagram of the conveyor belt embedded wire rope flaw detection system provided by the present invention;

FIG. 18 is a schematic view of the installation of a conveyor belt embedded wire rope flaw detection system provided by the present invention;

FIG. 19 is a schematic view of a product object of the inspection module;

FIG. 20 is a schematic view of a custom bracket;

FIG. 21 is a schematic view of an actual mine installation;

FIG. 22 is a waveform diagram of the conveyor belt wire rope detection of embodiment 1;

fig. 23 is a waveform diagram of a defect in the steel cord of embodiment 1;

FIG. 24 is a waveform diagram of a belt connecting joint according to embodiment 1;

fig. 25 is a waveform diagram of the conveyor belt wire rope detection of embodiment 2;

fig. 26 is a waveform diagram of a defect in the steel cord of embodiment 2;

FIG. 27 is a waveform diagram of a belt connecting joint according to embodiment 2;

FIG. 28 is a waveform of a belt test under the condition of the switch iron remover of embodiment 3;

fig. 29 is a waveform diagram of the iron remover switch of embodiment 3;

fig. 30 is a waveform diagram of a belt defect in embodiment 3.

Description of the symbols:

the system comprises a flaw detection module-1, a magnetic signal analysis control box-2, an encoder-3, an alarm-4 and a server-5.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

The invention utilizes the metal magnetic memory effect to detect the flaw. The metal magnetic memory flaw detection belongs to the field of nondestructive flaw detection, is one of various physical flaw detection methods such as X-ray, ultrasonic wave, magnetic powder, eddy current, gamma-ray, permeation (fluorescence and coloring), magnetic memory, magnetic flux leakage and the like, and is a rapid nondestructive detection method for detecting stress concentration, cracks, scratches and corrosion of metal material parts by utilizing the metal magnetic memory effect.

The metal magnetic memory effect refers to that: when the ferromagnetic metal material is processed and operated, due to the combined action of a load and a geomagnetic field, magnetic domain organization orientation with magnetostriction property and irreversible reorientation can occur in a stress and deformation concentration area, and the irreversible change of the magnetic state can be not only retained after the working load is eliminated, but also is related to the maximum acting stress. This magnetic state of the surface of the metal component "remembers" the location of the microscopic defect or stress concentration, a so-called magnetic memory effect. When a ferromagnetic component in the geomagnetic field environment is subjected to external load, magnetic domain organization orientation and irreversible reorientation with magnetostriction property can be generated in a stress concentration area, a fixed node of a magnetic domain can appear at the position, a magnetic pole is generated, a demagnetizing field is formed, and therefore the magnetic permeability of ferromagnetic metal at the position is minimum, and a leakage magnetic field is formed on the surface of the metal. The tangential component Hpx of the leakage field strength has a maximum value, while the normal component Hpy changes sign and has a zero value. This irreversible change in magnetic state remains memorized after the workload is removed.

Fig. 1 is a flowchart of a flaw detection method for an embedded steel wire rope of a conveyor belt according to the present invention, and fig. 2 is a schematic overall flowchart of the flaw detection method for an embedded steel wire rope of a conveyor belt according to the present invention. As shown in fig. 1 and 2, the flaw detection method for the steel wire rope embedded in the conveyor belt provided by the invention comprises the following steps:

step S1: acquiring M paths of current frame detection data of a target conveyor belt; one path of current frame detection data is magnetic field intensity data acquired by a flaw detection module at the current moment; m flaw detection modules are arranged on the target conveyor belt side by side according to a set direction; the set direction is a direction perpendicular to the conveying direction of the target conveying belt; wherein M is a positive integer greater than 0.

Step S2: respectively determining the peak-to-peak values of M paths of the current frame detection data, and judging whether the embedded steel wire rope of the target conveying section has connection part interference or not according to the size and the number of the peak-to-peak values of the target path data; the target path data is any path of the current frame detection data; the target conveying section is an area on the target conveying belt corresponding to the target road data.

And step S3: and if the embedded steel wire rope of the target conveying section has the interference of the connecting part, determining the state of the connecting part of the embedded steel wire rope of the target conveying section by adopting a method for detecting a trip point. Step S3 corresponds to the connected portion algorithm judgment in fig. 2.

And step S4: and if the embedded steel wire rope of the target conveying section has no interference of a connecting part, determining the signal-to-noise ratio of the target road data, and judging whether the embedded steel wire rope of the target conveying section has the possibility of defects according to the signal-to-noise ratio of the target road data. Step S2 and step S4 constitute the rough judgment algorithm in fig. 2.

Step S5: and if the embedded steel wire rope of the target conveying section has the possibility of defects, determining the defect information of the embedded steel wire rope of the target conveying section by adopting a continuous wavelet transform method. Step S5 corresponds to the precise judgment algorithm in fig. 2.

In the specific application, the invention provides a GMI magnetic sensor-based conveyor belt embedded steel wire rope defect detection system, a sensor array device (namely a flaw detection module) is adopted for detecting the defects of the conveyor belt (namely the conveyor belt), each sensor can accurately detect whether 3 steel wire ropes have defects, and the whole array can completely detect the defects of the section of the conveyor belt. After the algorithm processing, further defect information including defect position, damage degree and the like can be obtained.

The algorithm processes data according to frames, the length of a transmission belt corresponding to each frame of data is about 25cm, at most 3 defects are detected, and the damage degree and the defect positions of each defect are given. The array device can also be used for judging the middle connecting part of the conveyor belt, and the length of the connecting part measured by each sensor can be accurately given after algorithm processing, so that whether the connection is abnormal or not can be judged.

The above steps are discussed in detail below.

1. Rough judgment algorithm

The rough judgment is to carry out preliminary detection on one frame of data and give three results of no defect, possible defect and connection part interference.

1.1 data import

And (3) selecting magnetic field amplitude (unit: nano-T) data collected by the M paths of detection modules, and analyzing each frame data of the M paths of detection modules. The detection distance of a frame of the detection module is about 25cm, a waveform diagram of a certain path of detection data of a frame is shown in fig. 3, the abscissa represents the point number (namely the position), and the ordinate represents the magnetic field amplitude (namely the magnetic field intensity) corresponding to each point.

1.2 calculating the maximum peak value and judging whether the interference of the connection part exists

In this embodiment, the determining, according to the size and the number of the peak-to-peak value of the target road data, whether there is interference of the connection portion in the embedded steel wire rope of the target conveying section specifically includes:

step S21: and determining the number of peak-to-peak values of the target road data which are larger than a set peak-to-peak value threshold value.

Step S22: and judging whether the number is larger than a first set threshold value.

Step S22: and if the number is larger than a first set threshold value, the embedded steel wire ropes of the target conveying section have interference of a connecting part.

Step S23: and if the number is less than or equal to a first set threshold value, the embedded steel wire ropes of the target conveying section are not interfered by the connecting part.

In the specific application, peak-to-peak values of single-frame data of M paths are respectively calculated and compared with a set threshold, the peak-to-peak value of each path of sensor is counted as M which is larger than a certain threshold (namely, the set peak-to-peak value threshold) because the amplitude of the magnetic anomaly caused by a connecting part is generally stronger and the difference of the amplitudes of each path of sensor is smaller, if the number of the sensor is larger than a set value hood, the interference of the connecting part is judged, the algorithm judgment of the connecting part is carried out in step 2, and if not, the algorithm judgment of the connecting part is carried out in step 1.3.

1.3 calculating the signal-to-noise ratio and judging whether the defect exists or not

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

step S41: and judging whether the signal-to-noise ratio of the target path data is greater than a second set threshold value.

Step S42: and if the signal-to-noise ratio of the target road data is greater than a second set threshold value, the embedded steel wire rope of the target conveying section has the possibility of defects.

Step S43: and if the signal-to-noise ratio of the target road data is less than or equal to a second set threshold, the embedded steel wire rope of the target conveying section has no possibility of defects.

In a specific application, the signal-to-noise ratio is calculated after the difference is carried out on the original data (target path data). And (3) carrying out differential calculation on each path of single-frame original data screened out in the step 1.2, wherein the differential calculation formula is as follows:

in the formula: x is the number of_kAbscissa, x, representing raw data of the k-th frame_k+1Abscissa, f (x), representing raw data of the (k + 1) th frame_k) Ordinate, f (x), representing raw data of the k-th frame_k+1) Denotes the ordinate, f' (x), of the raw data of the (k + 1) th frame_k) And the ordinate of the k frame data after the difference is shown.

Aiming at a group of new single frame data obtained after differential processing, the signal-to-noise ratio calculation formula is as follows:

where Ps and Pn represent the effective power of the signal and noise, respectively. And taking the maximum value of the absolute value of the difference data, simultaneously taking the average value of a group of data far away from the maximum value as a base, comparing the two values to be used as the calculation of the first signal-to-noise ratio snr, judging that the defect is possible if the difference is larger than a second set threshold value thod1, and otherwise, judging that the defect is not defective. And (4) for the possibility of defects, entering a step 3 of judging the algorithm accurately.

Fig. 4 is a waveform after a difference of a certain road-bottom noise waveform, fig. 5 is a waveform after a difference of a certain road-bottom noise waveform, fig. 6 is a waveform after a difference of a certain road-bottom noise waveform, and fig. 7 is a waveform after a difference of a certain road-bottom noise waveform. As shown in fig. 4 and 5, if the maximum value and the base ratio after the difference are small, a defect-free signal can be considered. As shown in fig. 6 and 7, if the defect signal is selected, the maximum value after differentiation of the defect signal is much higher than the value near the substrate.

2. Connection portion algorithmic determination

The connection portion decision algorithm adopts a method of detecting a trip point.

In this embodiment, the determining the state of the connection portion of the embedded steel wire rope of the target conveying section by using the method for detecting the trip point specifically includes:

step S31: respectively acquiring multi-frame detection data of a previous time period and multi-frame detection data of a next time period of the target road data, and forming a target road array with the target road data; the ending time of the previous time interval is continuous with the current time; the current time is continuous with the starting time of the next period.

Step S32: and carrying out multi-point difference and comparison amplitude processing on the target road array, and determining the positions of front and rear jumping points.

Step S33: and determining the length of the connecting part of the embedded steel wire rope of the target conveying section according to the positions of the front and rear jumping points.

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

The following is a detailed discussion of the new array acquisition, trip point position calculation, and per-path waveform point number calculation, respectively.

2.1 obtaining a New array

If the frame data is judged to be the connecting part interference, N frame data (wherein N is a positive integer larger than 3) are buffered appropriately, so that the group of data can completely comprise the waveform of the connecting part interference. In order to make the curve smoother and reflect the overall variation trend of the waveform, each path of data is divided into smaller small segments, and the small segments are subjected to accumulation processing to obtain a new array.

2.2 hop Point location calculation

And respectively judging the jumping points of the front end and the rear end of the new array, wherein the jumping point detection method adopts a multipoint difference and amplitude comparison judgment method, so that the positions of the front jumping point and the rear jumping point of each sensor are obtained.

After proper conversion, the real front and back jumping point positions are obtained, and the difference between the two positions can obtain the corresponding length of the connecting part. Because each sensor needs to judge, the position of the jump point inevitably makes mistakes, the positions of the front and rear jump points of each sensor can be calculated, the respective mean values of the front and rear jump points are taken, then the change rates of the positions of the front and rear jump points of each sensor and the respective mean values are calculated respectively (the change rates are obtained by subtracting the mean value from the position of the jump point and dividing the mean value), the abnormal jump point is considered when the change rate exceeds 30 percent, the threshold value set when the jump point is calculated is adjusted with large change rate (for example, 0.2, when the abnormal jump point is calculated, the threshold value can be properly reduced or improved), the new jump position is obtained by recalculating the sensor, and the jump points are calculated at most twice. And finally outputting the length and the connection part position of each sensor.

2.3 Per Path waveform points count

The length pd (i.e. the number of waveform points) of each sensor is obtained through the calculation of step 2.2, and the connected part exceeding the set threshold is determined to be abnormal by comparing with the set threshold value thod2, otherwise, the connected part is normal. The continuous length of the normal connection part, namely the point number, is measured in advance, namely a threshold value range is set, and if the calculated length is larger or smaller than the set threshold value range, the connection part is considered to be abnormal.

Referring to fig. 8 and fig. 9, it is apparent that the amplitude of the connecting portion is much stronger than the substrate noise, and the distribution of each sensor is relatively uniform.

3. Precise judgment algorithm

And (4) after the judgment of the step 1.3, carrying out precise judgment algorithm judgment on the data which is possibly defective.

In this embodiment, the determining the defect information of the embedded steel wire rope in the target transportation segment by using the continuous wavelet transform method specifically includes:

step S51: performing continuous wavelet transformation 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 correspond to continuous wavelet transform of a high-frequency scale, and the low-frequency defect detection data correspond to continuous wavelet transform of a low-frequency scale.

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

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

Step S54: and if the maximum signal-to-noise ratio data is high-frequency data, determining defect information on a target conveying section according to the signal-to-noise ratio of each high-frequency data.

Step S55: if the maximum signal-to-noise ratio data is low-frequency band data, determining defect information on a target conveying band according to the signal-to-noise ratio of each low-frequency band data; the defect information includes: defect location and extent of damage.

In the specific application, the precise judgment algorithm adopts a continuous wavelet transform judgment method, the algorithm is supposed to adopt a smooth Gaussian function with low-pass property, and mutation point analysis is carried out by taking a first-order derivative of the smooth Gaussian function as a wavelet basis function. The details of wavelet transformation, signal-to-noise ratio calculation and defect determination are discussed in detail below.

3.1 wavelet transform

And performing continuous wavelet transformation on the raw data of each sensor. The wavelet transform can transform a time signal into a time frequency domain, can better observe the local characteristics of the signal, and can simultaneously observe the time and frequency information of the signal. The wavelet transform formula is as follows:

in the formula: WT (α, τ) represents the wavelet transform of f (t), ψ (-) represents the wavelet function, f (t) represents the time signal, scale α controls the stretching of the wavelet function, and translation τ controls the translation of the wavelet function. The scale corresponds to frequency (inverse ratio) and the amount of translation τ corresponds to time. The original waveform and the waveform after wavelet transformation of three typical defects provided by the invention are shown in fig. 10-15.

3.2 calculating the Signal-to-noise ratio

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

The signal-to-noise ratio calculation is carried out according to the following mode, firstly, the maximum value of the absolute value of each section of data is taken as a signal level, the base calculation is carried out by taking one section of data with the minimum maximum value in three sections of data, a group of data mean values far away from the maximum value are taken as a base level for the section of data, and each section of signal level is compared with the base level to be taken as each section of signal-to-noise ratio.

Because the defect types sometimes have differences, the wavelet transform of step 3.1 can take two different scales, which is specifically selected corresponding to the low-frequency defect and the high-frequency defect, and the scale with larger signal-to-noise ratio is selected according to the judgment of the final signal-to-noise ratio.

3.3 Defect determination

And (3) obtaining the signal-to-noise ratio snr1 of each section after calculation according to the step 3.2, comparing the signal-to-noise ratio snr1 with a set threshold value thod3, if the signal-to-noise ratio is larger than the set threshold value, outputting a defect position, and if the signal-to-noise ratio is not larger than the set threshold value, outputting a defect position. Meanwhile, if the defect exists, the defect can be judged to be light, medium or heavy according to the calculated signal-to-noise ratio. Finally, through calculation, the defect-free data of each path of sensor in the three equal sections can be output, and the corresponding signal-to-noise ratio, defect position and damage degree can be output.

The invention also provides a flaw detection system for the steel wire rope embedded in the conveying belt, which is realized by adopting the method, and FIG. 16 is a structural diagram of the flaw detection system for the steel wire rope embedded in the conveying belt provided by the invention. As shown in fig. 16, the system includes:

the M flaw detection modules 1 are arranged on the target conveying belt side by side according to a set direction and used for acquiring magnetic field intensity data of the target conveying belt at each moment to obtain M-path detection data; and one path of the detection data is the accumulation of one path of the current frame detection data in time. The flaw detection module 1 specifically comprises a plurality of GMI (giant magneto-Impedance) magnetic sensors.

And the magnetic signal analysis control box 2 is respectively connected with the M flaw detection modules 1 and is used for judging whether the embedded steel wire rope of the target conveyer belt has connection part interference or possible flaw according to the M paths of detection data, determining the state of the connection part when the embedded steel wire rope of the target conveyer belt has connection part interference, and determining the flaw information when the embedded steel wire rope of the target conveyer belt has possible flaw. The magnetic signal analysis control box 2 is preferably a mining intrinsic safety type magnetic signal analysis control box.

Further, the system further comprises: and the encoder 3 is connected with the magnetic signal analysis control box 2 and used for positioning the defect position on the target conveying belt according to the defect information. The encoder 3 is preferably a mining intrinsically safe encoder.

The system further comprises: and the alarm 4 is connected with the magnetic signal analysis control box 2 and used for sending out an alarm signal when the embedded steel wire rope of the target conveying belt has defects. The alarm 4 is specifically an alarm lamp or a mining intrinsic safety type voice alarm.

The system further comprises: and the server 5 is connected with the magnetic signal analysis control box 2 and is used for storing the detection data and deploying software. The server 5 is connected with the magnetic signal analysis control box 2 through a downhole ring network.

As a specific implementation manner, the flaw detection system for the steel wire rope embedded in the conveyor belt provided by the invention further comprises a first power supply and a second power supply. The first power supply is connected with the magnetic signal analysis control box 2 and used for supplying power to the magnetic signal analysis control box 2. And the second power supply is respectively connected with the magnetic signal analysis control box 2 and the alarm 3 and used for supplying power to the alarm 3 when the magnetic signal analysis control box 2 detects that the target conveying belt has defects. The first power supply and the second power supply are both mine explosion-proof and intrinsic safety type voltage-stabilized power supplies.

The following discusses the conveyor belt embedded steel wire rope flaw detection system provided by the invention in detail by using a specific installation and deployment example.

Fig. 17 is an installation and deployment diagram of the conveyor belt embedded wire rope flaw detection system provided by the present invention, and fig. 18 is an installation schematic diagram of the conveyor belt embedded wire rope flaw detection system provided by the present invention. As shown in fig. 17 and 18, the system is composed of a plurality of mining intrinsic safety type wire rope flaw detection sensors (each flaw detection module is composed of 11 GMI magnetic sensor arrays), a mining intrinsic safety type encoder, a mining intrinsic safety type magnetic signal analysis control box, a mining intrinsic safety type voice alarm, a power supply, a network switch, a server and the like. The intrinsic safety type encoder is used for positioning the position of the specific defect, and the server is used for storing data and deploying software.

The target conveying belt specifically comprises an upper conveying belt, an upper supporting roller, a conveying belt rack, a lower conveying belt and a lower supporting roller. The upper supporting roller is in contact with the lower surface of the upper conveying belt, and the lower supporting roller is in contact with the lower surface of the lower conveying belt. The upper surface of the upper conveying belt is used for placing materials. The flaw detection module is specifically located below the lower conveying belt and is away from the lower surface of the lower conveying belt by a set distance. Namely, the flaw detection module and the target conveying belt are not in contact.

Under general conditions, can come the quantity of the matching detection die set according to the actual width of conveyer belt, accomplish the nondestructive inspection monitoring task of the conveyer belt of different widths: need 3 modules of detecting a flaw if 1.2 meters wide conveyer belt, 1.6 meters wide conveyer belt needs 4 modules of detecting a flaw, and 2 meters wide conveyer belt needs 5 modules of detecting a flaw. Product object schematic diagram of flaw detection module referring to fig. 19, a plurality of flaw detection modules together form a wire rope flaw detection sensor.

The wire rope flaw detection sensor is of a cuboid structure, and has the following external dimensions: 382mm is multiplied by 82mm is multiplied by 45mm, a bolt fixing hole is arranged on the side surface of the sensor, the sensor can be fixed on a customized bracket by using a screw, the height of the bracket is adjusted up and down, and the distance between the measuring surface of the sensor and the lower surface of a lower belt (namely a lower conveying belt) is controlled at 1-3 mm. A schematic view of the custom bracket and sensor mounting arrangement is shown in fig. 18.

As a specific embodiment, a schematic view of the custom rack is shown in fig. 20, and a schematic view of an actual mine installation is shown in fig. 21. In addition, the specific installation mode can be based on the field conditions, and the basic principle is as follows: (1) The flaw detection sensor is convenient to disassemble so as to remove coal slime and sewage regularly; (2) The distance between the measuring surface of the flaw detection sensor and the surface of the belt is adjustable and fixed; (3) The measuring surface of the belt is adjusted or controlled to be a plane, the measuring surface of the flaw detection sensor is parallel to the cross section of the belt, and the ground is hardened as far as possible.

The flaw detection effect of the present invention will be further described below with reference to several specific examples.

One flaw detection module used by the conveyor belt steel wire rope flaw detection system consists of 11 GMI magnetic sensor arrays, while the following preferred embodiment uses four flaw detection modules, and the output waveform is drawn by one or more modules to detect data. The abscissa represents the number of points (positions), the position accuracy can reach millimeter (mm) level, and the ordinate is the magnetic field amplitude (amplitude unit is nano-ntt).

Example 1

The measured data obtained by using the conveyor belt wire rope flaw detection system of the present invention in the first coal mine area is shown in fig. 22-24, wherein the surface conveyor belt has defects, and also includes a connection joint waveform, the abscissa represents the number of points (positions), the position accuracy can reach millimeter (mm) level, the ordinate is the magnetic field amplitude (amplitude unit is nano-ntt), the 1.6 m wide conveyor belt runs at a speed of 4.5 m/s.

Example 2

The measured data obtained by using the conveyor belt wire rope flaw detection system of the present invention in the second coal mine area are shown in fig. 25-27, in which the surface conveyor belt has defects, and also includes the waveform of the connecting joint, the abscissa represents the number of points (positions), the ordinate is the magnitude of the magnetic field, and the conveyor belt 1.6 meters wide operates at a speed of 2 m/s.

Example 3

The measured data obtained by using the conveyor belt wire rope flaw detection system of the present invention in the third coal mine area are shown in fig. 28 to fig. 30, in which the surface conveyor belt has defects, and also includes the waveform of the connecting joint, the abscissa represents the number of points (positions), the ordinate represents the magnetic field amplitude, the 1.6 m wide conveyor belt runs at a speed of 2 m/s.

The tests show that the metal magnetic memory effect conveyor belt steel wire rope flaw detection device based on the multi-GMI magnetic sensor array can detect the defects of the conveyor belt steel wire rope, magnetic anomalies of different degrees caused by a conveyor belt joint, an iron remover switch and the like, and the magnetic characteristic variation of different defect types is also different. Tests have found that defects of different degrees are also different in magnetic characteristic variation; the magnetic characteristic changes exhibited by defects at different positions are also different. On the basis of the invention, the sensitivity and the precision for detecting defects such as the defects of the steel wire rope of the conveying belt, the joints and the like can be further improved by increasing the density of the GMI magnetic sensor array; in the same way, the nondestructive inspection function of the steel wire rope of the conveying belt with different widths can be adapted by increasing or decreasing the number of the detection sensors.

The invention provides a GMI magnetic sensor-based flaw detection method and system for a steel wire rope of a conveying belt. A flaw detection monitoring system for a steel wire rope of a conveying belt is creatively developed by utilizing a metal magnetic memory effect, has the advantages of complete passive measurement, non-contact, maintenance-free, real-time online detection and the like, and can normally work under severe environments such as dust, dense fog and the like. 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 the existence of micro defects can be evaluated, and the stress concentration area in the ferromagnetic metal component, namely the micro defects, early failure, damage and the like can be diagnosed, so that sudden fatigue damage can be prevented. For the steel wire rope, the damage on the surface and the inside can be detected, the internal stress change and the corrosion severity can also be detected, the defect can be found, and the type and the grade of the defect can also be accurately judged.

The invention has the following advantages:

1) Passive measurement is adopted, no magnetic field is applied, and the metal foreign body induction magnetic field is truly reflected; it is not feared that any non-magnetic conductive material will diffuse in space or adhere to the device.

2) The device is not influenced by water mist, dust and the like, is maintenance-free, can realize real-time online nondestructive passive non-contact radiation-free detection, and can detect the internal stress change and the corrosion severity.

3) The flaw detection sensor is designed according to the standardization, adopts a modular structure, and can be randomly combined and spliced to adapt to belts with different widths.

4) The system platform supports a plurality of sets of front-end flaw detection sensors and a distributed flaw detection system with a B/S framework.

5) No special magnetizing device is needed; the surface does not need to be cleaned; coupling technology is not required; the stress concentration part can be quickly and accurately detected, and meanwhile, because no excitation device is arranged, the performance reduction caused by adsorption of metal powder and scraps in the long-time use process is avoided.

6) The method has the advantages that all magnetic signals are analyzed, processed and decided locally in the underground, magnetic information does not need to be uploaded to the underground for analysis, the situation that the equipment cannot work normally due to the fact that the underground looped network is interrupted in the underground is avoided, meanwhile, the real-time performance of data analysis is greatly improved, and the equipment and the platform operate independently.

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understand the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims

1. A flaw detection method for an embedded steel wire rope of a conveyor belt is characterized by comprising the following steps:

2. The flaw detection method for the embedded steel wire rope of the conveyor belt according to claim 1, wherein the determining the defect information of the embedded steel wire rope of the target conveyor segment by using the continuous wavelet transform specifically comprises:

performing continuous wavelet transformation 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 correspond to continuous wavelet transform of a high-frequency scale, and the low-frequency defect detection data correspond to continuous wavelet transform of a low-frequency scale;

equally dividing the high-frequency defect detection data into a plurality of high-frequency band data, and equally 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 that of the low-frequency band data;

respectively determining the signal-to-noise ratio of each high-frequency band data and each low-frequency band data, and determining the maximum signal-to-noise ratio data;

if the maximum signal-to-noise ratio data is high-frequency band data, determining defect information on a target conveying band according to the signal-to-noise ratio of each high-frequency band data;

if the maximum signal-to-noise ratio data is low-frequency data, determining defect information on a target conveying section according to the signal-to-noise ratio of each low-frequency data; the defect information includes: defect location and extent of damage.

3. The method for detecting flaws of an embedded steel wire rope of a conveyor belt according to claim 1, wherein the determining the state of the connection part of the embedded steel wire rope of the target conveyor section by using the method for detecting the trip point specifically comprises:

respectively acquiring multi-frame detection data of a previous time period and multi-frame detection data of a next time period of the target road data, and forming a target road array with the target road data; the ending time of the previous time interval is continuous with the current time; the current time is continuous with the starting time of the later period;

carrying out multi-point difference and comparative amplitude processing on the target road array, and determining the positions of front and rear jumping points;

determining the length of a connecting part of an embedded steel wire rope of the target conveying section according to the positions of the front and rear jumping points;

and determining the state of the connecting part of the embedded steel wire rope of the target conveying section according to the length of the connecting part.

4. The method for detecting flaws of an embedded steel wire rope of a conveyor belt according to claim 1, wherein the step of judging whether the embedded steel wire rope of the target conveying section has interference of a connecting part according to the size and the number of peak-to-peak values of target road data specifically comprises the following steps:

determining the number of peak-to-peak values of the target road data which are larger than a set peak-to-peak value threshold;

judging whether the number is larger than a first set threshold value;

if the number is larger than a first set threshold value, connecting part interference exists in the embedded steel wire rope of the target conveying section;

and if the number is less than or equal to a first set threshold value, the embedded steel wire ropes of the target conveying section are not interfered by the connecting part.

5. The method for detecting flaws of the embedded steel wire rope of the conveyor belt according to claim 1, wherein the step of judging whether the embedded steel wire rope of the target conveyor section has a possibility of a flaw according to the signal-to-noise ratio of the target road data specifically comprises:

judging whether the signal-to-noise ratio of the target road data is greater than a second set threshold value;

if the signal-to-noise ratio of the target road data is greater than a second set threshold, the embedded steel wire rope of the target conveying section has the possibility of defects;

and if the signal-to-noise ratio of the target road data is less than or equal to a second set threshold, the embedded steel wire rope of the target conveying section has no possibility of defects.

6. A conveyor belt embedded wire rope flaw detection system implemented using the method of any one of claims 1-5, the system comprising:

and the magnetic signal analysis control box is respectively connected with the M flaw detection modules and is used for judging whether the embedded steel wire rope of the target conveyer belt has connection part interference or possible defects according to the M paths of detection data, determining the state of the connection part when the embedded steel wire rope of the target conveyer belt has connection part interference, and determining the defect information when the embedded steel wire rope of the target conveyer belt has possible defects.

7. The conveyor belt embedded wire rope inspection system of claim 6, further comprising:

and the encoder is connected with the magnetic signal analysis control box and used for positioning the defect position on the target conveying belt according to the defect information.

8. The conveyor belt embedded wire rope inspection system of claim 6, wherein the inspection module comprises a plurality of giant magneto-resistive sensors.

9. The conveyor belt embedded wire rope inspection system of claim 6, further comprising:

and the alarm is connected with the magnetic signal analysis control box and used for sending out an alarm signal when the embedded steel wire rope of the target conveying belt has defects.

10. The conveyor belt embedded wire rope inspection system of claim 6, further comprising:

and the server is connected with the magnetic signal analysis control box and used for storing the detection data.