WO2018165972A1 - Online flaw detection monitoring system and method for steel wire rope, and multi-rope friction hoisting system for use in mining - Google Patents

Abstract

An online flaw detection monitoring system and method for a steel wire rope (60), and a multi-rope friction hoisting system for use in mining. The system comprises: a flaw detection sensor (10), a communication module (20) and a computing and processing apparatus (30), wherein the flaw detection sensor (10) is arranged around a steel wire rope (60) to be detected and is used for collecting a flaw signal of the steel wire rope (60) in real time; the communication module (20) is used for converting the flaw signal of the steel wire rope (60) and transmitting same to the computing and processing apparatus (30); and the computing and processing apparatus (30) is used for extracting a fault characteristic value from the converted flaw signal and searching a pre-set fault characteristic library for a steel wire rope (60) fault category corresponding to the fault characteristic value. By means of converting a detected flaw signal of the steel wire rope (60) and extracting a fault characteristic value, and searching a pre-set fault characteristic library for a steel wire rope (60) fault category corresponding to the fault characteristic value, the type of damage to the steel wire rope (60) can be accurately determined at the same time as flaw detection is carried out on the steel wire rope (60), thereby facilitating operating personnel in carrying out a timely check and maintenance on a fault.

Description

Wire rope online flaw detection monitoring system, method and mine multi-rope friction lifting system Technical field

The invention relates to the field of flaw detection, in particular to a wire rope online flaw detection monitoring system and method, and a mine multi-rope friction lifting system.

Background technique

For mine hoists, the wire rope suspension is an important device for the normal operation of the hoist. Wire ropes may be damaged during use. If the damage is not detected in time and the location of the damage occurs, it may cause a rope break.

The Chinese invention patent application with the publication number CN104569143A discloses an on-line monitoring system for mine wire rope flaw detection, the system comprising a pair of pulleys arranged on the bracket, a steel wire rope connected between the pair of pulleys, and the bracket below the pulley There is a sensor driven by the detection frame. The detection frame is mounted on the bracket and can be moved along the slide. The detection frame is connected to the system console to control the sensor to entangle the detected wire rope. The system can not solve the impact of the jitter of the wire rope during the operation on the test results, can not solve the major problems of the measurement blind zone, and can not monitor the data changes in real time.

In order to overcome the influence of wire rope jitter on measurement, the Chinese utility model patent published as CN203502379U discloses a wire rope real-time dynamic flaw detection system, in which the acquisition unit includes at least one set of magnetic conductive sensors for picking up damaged signals of the wire rope. And zoom in, using two sensors for balanced output, and anti-interference processing of the signal, so as to more accurately detect the damage caused by the wire rope. However, the solution cannot judge the type of damage of the wire rope, and the operator needs to know the damage of the wire rope according to experience or actuality after learning that the wire rope is damaged, and the utility model still needs to be improved. In this scheme, the traditional encoder is used for speed detection, and the wire rope is easy to be used when it is used. Slip, so the detected speed is less accurate.

Summary of the invention

The object of the present invention is to provide a wire rope online flaw detection monitoring system and method, and a mine multi-rope friction lifting system, which can accurately determine the damage type of the wire rope while detecting the wire rope.

In order to achieve the above object, the present invention provides a wire rope online flaw detection monitoring system, comprising: a flaw detection sensor, a communication module and a calculation processing device, wherein the flaw detection sensor communicates with the calculation processing device through the communication module;

The flaw detection sensor is disposed around the wire rope to be inspected for collecting the defect signal of the wire rope in real time;

The communication module is configured to convert a defect signal of the wire rope and transmit it to the calculation processing device;

The calculation processing device is configured to extract a fault feature value from the converted defect signal, and search for a wire rope fault category corresponding to the fault feature value in a preset fault feature library.

Further, the computing processing device specifically includes:

a fault feature extraction module, configured to extract a fault feature value from the converted defect signal;

a fault feature library preset in the computing processing device for storing various wire rope fault categories and corresponding fault feature values;

The fault category finding module is configured to search for a wire rope fault category corresponding to the fault feature value in a preset fault signature database.

Further, the calculation processing device further includes:

a time recording module for recording a sequence of time intervals between two locations of the same fault type preset on the wire rope;

a speed calculation module for inter-portion between the two identical fault types The preset spacing and the sequential occurrence time interval calculate the running speed of the wire rope.

Further, the fault feature extraction module further includes:

a wavelet denoising unit, configured to perform one-dimensional wavelet denoising processing on the converted defect signal to obtain a reconstructed signal curve;

The feature value extracting unit is configured to perform feature value extraction on the reconstructed signal curve.

Further, the wavelet denoising unit specifically includes:

a preprocessing subunit, configured to preprocess the converted defect signal to remove part of the noise;

The one-dimensional wavelet decomposition sub-unit is configured to adopt wavelet transform on the pre-processed defect signal to realize multi-scale decomposition;

a decomposition coefficient processing sub-unit for calculating coefficients of each scale, and performing denoising processing on coefficients of each scale;

The one-dimensional wavelet reconstruction sub-unit is used to reconstruct the one-dimensional wavelet according to the lowest-level low-frequency coefficients and the high-frequency coefficients of each layer in each scale of wavelet decomposition.

Further, the method further includes an image capturing camera for acquiring a fault image of the wire rope; the communication module is further configured to transmit a fault image of the wire rope to the computing processing device;

The calculation processing device is further configured to perform image enhancement processing on the fault image of the wire rope, and present the enhanced fault image.

Further, the computing processing device includes:

An image gradation transformation module for expressing a gradation value of the fault image of the wire rope as an incident light component occupying a low frequency portion of the frequency domain, an incident light constant, and a reflected light component occupying a high frequency portion of the frequency domain;

a light component separation module for separating the incident light component, the incident light constant, and the reflected light component by a logarithmic method;

a low pass filter processing module for performing low pass filtering on the separated formula;

The image high-frequency enhancement module is configured to subtract the low-pass filtered formula from the separated formula, and retain the incident light constant, and then perform an exponential operation to obtain a high-frequency enhanced image.

Further, the low pass filter processing module is a median filter.

Further, the flaw detection sensor comprises N magnetically conductive flaw detection modules and is evenly distributed on the circumference, and each of the magnetic permeability flaw detection modules can cover 360/N degrees of the wire rope.

Further, the magnetic permeability detecting module includes an induction coil and two excitation coils having the same magnitude and opposite magnetic flux, and the two excitation coils are connected to an excitation source capable of supplying alternating current, when the defective wire rope is opposite to the When the magnetic permeability detecting module moves, the electromotive force signal induced by the induction coil is transmitted to the communication module.

Further, the utility model further comprises a fixing frame, a height adjusting mechanism and an angle adjusting mechanism, wherein the flaw detecting sensor is mounted on the angle adjusting mechanism, wherein the angle adjusting mechanism is mounted on the height adjusting mechanism, and the detecting sensor can be adjusted The inclination angle, the height adjustment mechanism is mounted on the fixing frame, and the height of the flaw detection sensor can be adjusted.

Further, the invention relates to a mine explosion-proof and intrinsically safe substation, and the mine explosion-proof and intrinsically safe sub-station comprises: an explosion-proof enclosure, an intrinsically safe power module, a remote power-off control module and data processing a module, the intrinsically safe power module, a remote power-off control module, and a data acquisition module are all integrated in a movement in the flameproof enclosure, the intrinsically safe power module being configured to supply power to the flaw detection sensor and a servo microcontroller for controlling a driving power of the wire rope, wherein the data processing module is configured to receive a signal transmitted by the flaw detection sensor and transmit the signal to the communication module through a communication interface.

Further, the communication module is a general and intrinsically safe communication module for mine installation, and is installed in a ground monitoring center; the communication module has a communication signal conversion unit and light And an AC/DC conversion circuit, wherein the communication signal conversion unit is configured to convert a defect signal of the wire rope into a USB interface signal, and the optical coupling and the non-essential nature of the AC/DC conversion circuit to the calculation processing device The safety output is isolated from the intrinsically safe output of the communication interface.

Further, the calculation processing device further includes:

The wire rope fault display module is configured to send a control command to the image capturing camera to determine a fault image of the wire rope at the moment when determining the wire rope fault category.

To achieve the above object, the present invention provides a wire rope online flaw detection monitoring method based on the aforementioned wire rope online flaw detection monitoring system, comprising:

The flaw detection sensor collects the defect signal of the wire rope in real time, and converts the defect signal of the wire rope through the communication module, and transmits the defect signal to the calculation processing device;

The calculation processing device extracts a fault feature value from the converted defect signal, and searches for a wire rope fault category corresponding to the fault feature value in a preset fault feature library.

Further, the operation of the calculation processing device for extracting the fault feature value from the converted defect signal, and searching for the wire rope fault category corresponding to the fault feature value in the preset fault feature database includes:

The calculation processing device performs one-dimensional wavelet denoising processing on the converted defect signal to obtain a reconstructed signal curve;

The calculation processing device performs feature value extraction on the reconstructed signal curve, and performs a search in a fault feature library preset in the calculation processing device according to the extracted fault feature value to determine the fault The wire rope failure category corresponding to the feature value.

Further, the calculating processing device performs one-dimensional wavelet denoising processing on the converted defect signal, and the operation of obtaining the reconstructed signal curve specifically includes:

Pre-processing the converted defect signal to remove part of the noise;

Wavelet transform is applied to the pre-processed defect signal to achieve multi-scale decomposition;

Calculate the coefficients of each scale and denoise the coefficients of each scale;

The reconstruction of one-dimensional wavelet is performed according to the lowest-level low-frequency coefficients and the high-frequency coefficients of each layer in each scale of wavelet decomposition.

Further, the speed calculation step is also included:

The calculation processing device extracts a fault feature value from the converted defect signal, and records a sequential occurrence time interval between two portions of the same fault type set in advance on the wire rope;

The calculation processing device calculates the running speed of the wire rope according to a preset interval between the portions of the two identical fault types and the sequential occurrence time interval.

Further, the wire rope online flaw detection monitoring system further includes an image acquisition camera for collecting a fault image of the wire rope; the wire rope online flaw detection monitoring method further includes a fault image collection and presentation step:

The image capture camera transmits a fault image of the wire rope to the computing processing device through the communication module;

The calculation processing device performs image enhancement processing on the fault image of the wire rope, and presents the enhanced fault image.

Further, the operation of the image processing processing on the fault image of the wire rope by the calculation processing device specifically includes:

The calculation processing device expresses a gray value of the fault image of the wire rope as an incident light component occupying a low frequency portion of the frequency domain, an incident light constant, and a reflected light component occupying a high frequency portion of the frequency domain, and taking a logarithm Separating the incident light component, the incident light constant, and the reflected light component;

The calculation processing device performs low-pass filtering on the separated formula, subtracts the low-pass filtered formula from the separated formula, and retains the incident light constant, and then performs an exponential operation to obtain a high-frequency enhancement image.

Further, the operation of the calculation processing device performing low-pass filtering processing on the separated formula is specifically:

The calculation processing device uses a median filtering algorithm to separate the incident light component and the incident light constant in the separated equation.

Further, the method further includes: when determining the wire rope failure category, the calculation processing device sends a control instruction to the image acquisition camera to collect a fault image of the wire rope at the moment.

To achieve the above object, the present invention provides a mine multi-rope friction lifting system comprising the aforementioned wire rope online flaw detection monitoring system.

Based on the above technical solution, the invention searches for the defect signal of the detecting wire rope and the extraction of the fault characteristic value, and then searches for the wire rope fault category corresponding to the fault feature value through the preset fault feature library, thereby detecting the wire rope at the same time. It can also accurately judge the type of damage of the wire rope, and thus facilitate the operator to timely check and repair the fault.

DRAWINGS

The drawings described herein are intended to provide a further understanding of the invention, and are intended to be a part of the invention. In the drawing:

1 is a schematic structural view of an embodiment of a wire rope online flaw detection monitoring system of the present invention.

2 is a schematic structural view of another embodiment of the wire rope online flaw detection monitoring system of the present invention.

3 is a schematic structural view of still another embodiment of the wire rope online flaw detection monitoring system of the present invention.

4 and 5 are respectively schematic views of different angles of installation and adjustment structure of the flaw detection sensor in the embodiment of the wire rope online flaw detection monitoring system of the present invention.

6 is a schematic view showing the implementation principle of the flaw detection sensor in the embodiment of the wire rope online flaw detection monitoring system of the present invention.

FIG. 7 is a schematic flow chart of an embodiment of a wire rope online flaw detection monitoring method according to the present invention.

FIG. 8 is a schematic flow chart of another embodiment of a wire rope online flaw detection method according to the present invention.

FIG. 9 is a schematic flow chart of still another embodiment of a wire rope online flaw detection monitoring method according to the present invention.

detailed description

The technical solution of the present invention will be further described in detail below through the accompanying drawings and embodiments.

FIG. 1 is a schematic structural view of an embodiment of a wire rope online flaw detection monitoring system of the present invention. In this embodiment, the wire rope online flaw detection monitoring system includes: a flaw detection sensor 10, a communication module 20, and a calculation processing device 30. The flaw detection sensor 10 communicates with the calculation processing device 30 via the communication module 20.

The flaw detection sensor 10 is disposed around the wire rope to be inspected for collecting the defect signal of the wire rope in real time. The flaw detection sensor 10 may specifically include N magnetic conductive flaw detection modules, and are evenly distributed on the circumference, and each of the magnetic conductive flaw detection modules can cover 360/N degrees of the wire rope, taking N=3 as an example, each guide The magnetic flaw detection module covers 120 degrees of the wire rope. Through the comprehensive coverage of the wire rope, the measurement blind zone is eliminated, thereby achieving accurate detection of wire rope defects.

The magnetic permeability detecting module adopts magnetic permeability detecting technology, has high sensitivity, and does not need magnetized steel wire rope, and only the magnetic detecting type detecting module directly forms a magnetic circuit with the steel wire rope, and if the steel wire rope is damaged, the magnetic circuit changes. Find the balance point of the change and measure the defect of the wire rope. When the wire rope is damaged, since the magnetic permeability of the wire rope is greater than the air, the flaw detection module can quickly sense the fault signal. Thereby accurately determining the fault location. The non-damaged and continuous wire rope has good magnetic permeability, and the flaw detection sensor does not generate a signal or generate a distinct signal when the wire rope passes. When the wire cross-sectional area of the wire rope is reduced, the magnetic permeability of the wire rope is deteriorated, and after the flaw detection sensor detects a signal change, the flaw detection signal is converted into a sine wave by the signal processing circuit. The amplitude of the sine wave signal outputted by the magnetic permeability testing module is proportional to the reduction of the cross-sectional area of the steel wire. The larger the amplitude, the more severe the reduction of the cross-sectional area of the wire rope. At the same time, it is inversely proportional to the distance from the broken wire of the wire rope to the flaw detection module. Large, the larger the signal amplitude.

Magnetic Conductive Flaw Detection Module Referring to FIG. 6, the magnetic permeability detecting module includes an induction coil 13 and two excitation coils 11, 12 having equal and opposite magnetic fluxes, and the two excitation coils 11, 12 are connected to an excitation source capable of supplying alternating current. 14. When the defective wire rope 60 moves relative to the magnetic permeability detecting module, the electromotive force signal induced by the induction coil 13 is transmitted to the communication module 20. When the wire rope 60 has damage such as broken wire, broken strands, corrosion, joint twitching, the wire rope 60 first passes through the exciting coil 11, and the magnetic flux of the exciting coil 11 changes due to the damage defect, breaking the balance, and the induction coil 13 generates the induced electromotive force ε+ When the defect passes through the excitation coil 12, the magnetic flux of the excitation coil 12 is changed, so that the induction coil 13 induces the electromotive force ε-. Therefore, when the defective wire rope 60 passes, the electromotive force induced by the induction coil 13 is 2 ε, and the signal can be converted into an analog signal by an amplifying circuit for subsequent processing.

In another embodiment, the wire rope online flaw detection monitoring system further includes a fixing frame, a height adjusting mechanism and an angle adjusting mechanism, wherein the flaw detecting sensor 10 is mounted on the angle adjusting mechanism, and the angle adjusting mechanism is installed at the height adjusting In the mechanism, the inclination of the flaw detection sensor 10 can be adjusted, and the height adjustment mechanism is mounted on the holder, and the height of the flaw detection sensor 10 can be adjusted. 4 and 5 respectively show a specific structural example of the mounting and adjusting structure of the flaw detecting sensor in different angles of the embodiment.

In FIG. 4, the base 51 and the riser 53 are welded by the cross plate 52 to form a fixed The frame 53 can be provided with an upwardly extending sliding slot 54 along which the sliding seat 55 can be adjusted in the vertical direction, and the upper position of the sliding seat 55 is provided with a hinged seat 56 for detecting the detecting sensor. 10 is hinged to the carriage 55 by the hinge seat 56, and the inclination can be adjusted with respect to the carriage 55. Here, the carriage 55 and the chute 54 constitute a height adjustment mechanism, and the carriage 55 and the hinge seat 56 constitute an angle adjustment mechanism. In another embodiment, in order to reduce the erosion of the slime and sewage on the equipment, it is also possible to make a fixed large frame, suspend the flaw detection sensor on the fixed large frame, and adjust the height and pressure of the sensor support by using the threaded legs of the main machine. The height of the wheel is such that the wire rope is located right in the center of the through hole of the sensor to adjust the gap between the flaw detection sensor and the wire rope.

In view of the fact that multiple wire ropes are typically used for suspension under the well, it can be seen in Figure 5 that four flaw detection sensors 10 are hinged to the carriage 55 via hinged seats 56. A mine explosion-proof and intrinsically safe substation can be further disposed on the base 51. The mine explosion-proof and intrinsically safe substation specifically includes: an explosion-proof enclosure 57, an intrinsically safe power module, a remote power-off control module, and a data processing module. The data is collected and stored by the mine explosion-proof and intrinsically safe substation, and the data is transmitted to the ground center station computer. All components are arranged in a stainless steel casing, and then protected by a stainless steel shield. The double-layer stainless steel protection ensures that the main machine can still work normally under the complicated and harsh environment such as water spray, humidity, low temperature and strong magnetism.

The intrinsically safe power module, the remote power down control module, and the data acquisition module can all be integrated into the movement within the flameproof enclosure 57. The intrinsically safe power module is responsible for supplying power to the flaw detection sensor 10 and the servo microcontroller for controlling the driving power of the wire rope. The data processing module is configured to receive the signal transmitted by the flaw detection sensor 10 and transmit the signal to the communication module 20 through a communication interface. The sensor's flaw detection signal is transmitted to the host computer (ie, the calculation processing device) through the communication interface through the data processing unit of the mine explosion-proof and intrinsically safe substation, and the analysis software of the host computer automatically extracts the fault feature value for damage classification and identification. , the processing results are displayed intuitively on the host computer.

In the present embodiment, the communication module 20 is configured to convert the defect signal of the wire rope and transmit it to the calculation processing device 30. Among them, for the mine environment, the communication module 20 preferably adopts a mine general and intrinsically safe communication module 20, which is installed in the ground monitoring center. The communication module 20 can have a communication signal conversion unit, an optocoupler, and an AC/DC conversion circuit. Wherein, the communication signal conversion unit is configured to convert the defect signal of the wire rope into a USB interface signal, the non-intrinsically safe output of the optocoupler and the AC/DC conversion circuit to the calculation processing device 30, and the intrinsic safety of the communication interface The output is isolated to ensure the intrinsic safety of the communication lines leading downhole. The communication interface also has a power indication, a communication status indication, and a fault indicator.

After receiving the signal from the communication module 20, the calculation processing device 30 can extract the fault feature value from the converted defect signal, and find the wire rope corresponding to the fault feature value in the preset fault feature library 32. The fault category, in addition to the detection of the wire rope, also achieves an accurate judgment of the type of damage of the wire rope, which facilitates the operator to timely troubleshoot and repair the fault.

The calculation processing device 30 performs on-line real-time monitoring and confirmation of damage types such as wire breakage, abrasion, rust, and cross-sectional area reduction of the wire rope, and in other embodiments, the fault location can be accurately positioned. When a fault signal such as a broken wire occurs, the calculation processing device 30 can also present the damage condition on the wire rope more clearly and intuitively by processing the collected fault picture. When necessary, it is also convenient to call up the comparative analysis of historical data, which greatly facilitates the on-site operator to analyze and warn the changes of the wire rope. Further, the calculation processing device 30 can also set the detection result real-time printing function.

FIG. 2 is a schematic structural view of another embodiment of the wire rope online flaw detection monitoring system of the present invention. In the embodiment, the calculation processing device 30 specifically includes: a fault feature extraction module 31, a fault feature library 32, and a fault category search module 33. The fault feature extraction module 31 is configured to extract a fault characteristic from the converted defect signal. Value. A fault signature library 32 is pre-set in the computing processing device 30 for storing various wireline fault categories and their corresponding fault feature values. The fault category finding module 33 is configured to search for a wire rope fault category corresponding to the fault feature value in the preset fault feature library 32.

For different wire rope failures, such as broken wire, wear, rust, and reduced cross-sectional area, the reflection signal is reflected in the signal amplitude, frequency and other characteristics, but the signal characteristics of similar faults very similar. With this property, the fault feature extraction module needs to find out the fault feature value reflecting the defect from the converted defect signal, so as to determine which type of fault is based on the fault feature value.

The extraction of the fault feature values may employ various existing extraction algorithms, such as Fourier transform, wavelet transform, and the like. In the present embodiment, wavelet feature is preferably used for fault feature extraction. This extraction method can successfully preserve signal features by using wavelet filtering to denoise, and thus is superior to the conventional low-pass filter in this point. Compared with the Fourier transform, the wavelet transform is a local transform of space (time) and frequency, and thus can effectively extract information from the signal. The multi-scale refinement analysis of functions or signals through the operation functions such as scaling and translation solves many difficult problems that the Fourier transform cannot solve.

Based on this, the fault feature extraction module 31 may further include: a wavelet denoising unit and a feature value extracting unit. The wavelet denoising unit is configured to perform one-dimensional wavelet denoising processing on the converted defect signal to obtain a reconstructed signal curve. The feature value extracting unit is configured to perform feature value extraction on the reconstructed signal curve.

For the wavelet denoising unit, it may specifically include the following subunits: a preprocessing subunit, a one-dimensional wavelet decomposition subunit, a decomposition coefficient processing subunit, and a one-dimensional wavelet reconstruction subunit. The preprocessing subunit is configured to preprocess the converted defect signal to remove part of the noise. The one-dimensional wavelet decomposition sub-unit is used to transform the pre-processed defect signal by wavelet transform to achieve multi-scale decomposition. Decomposition factor The processing sub-unit is used to calculate the coefficients of each scale, and performs denoising processing on the coefficients of each scale. The one-dimensional wavelet reconstruction sub-unit is used to reconstruct the one-dimensional wavelet according to the lowest-level low-frequency coefficients and the high-frequency coefficients of each layer in each scale of wavelet decomposition.

On this basis, the following is a detailed analysis of the process of wavelet denoising in combination with the formula. In general, the noise signal is mostly included in the higher frequency details. Therefore, after wavelet decomposition of the signal in this embodiment, the decomposed wavelet coefficients are weighted by a threshold threshold and the like, and then the small signal is further processed. Reconstruction can achieve the purpose of signal denoising. The following are explained separately:

1. Wavelet decomposition of one-dimensional signals, selecting a wavelet and determining the level of decomposition, and then performing decomposition calculation. A noisy one-dimensional signal model can be expressed as follows:

x(t)=f(t)+ε*e(t)

Where f(t) is a useful signal, x(t) is a noisy signal, e(t) is noise, and ε is the standard deviation of the noise figure.

2. Threshold quantization of wavelet decomposition high-frequency coefficients, selecting a threshold for high-frequency coefficients at each decomposition scale for soft threshold quantization. In the wavelet transform, the threshold required for each layer coefficient is generally selected according to the signal-to-noise ratio of the original signal, that is, the standard deviation of the decomposition coefficients of each layer of the wavelet is obtained, and after the signal noise intensity is obtained, the layers can be determined. Threshold. We represent the signal in the space V _j =V _j-1 +W _j-1 , that is to say for each signal x(t) represented on V _j can be represented by a basis function in two spaces:

This process is to decompose the signal x(t) into the sum of the low frequency signal and the high frequency signal, and cA ₀ , cA ₁ , and cD ₁ are the weight coefficients. φ _j-1,k (t) and W _j-1,k (t) are predetermined wavelet basis functions set in advance, such as db1, db2, and the like.

We decompose the coefficient A ₀ (k) in the scale metric space j to obtain two coefficients A ₁ (k) and D ₁ (k) in the scale metric space j-1. Similarly, we can also obtain the coefficient A ₀ (k) from the two coefficients A ₁ (k) and D ₁ (k) by reconstruction.

When the wavelet and scale are orthogonal in space, we can calculate the coefficients cA ₁ (k) and cD ₁ (k) using the inner product formula:

The following is the specific formula of the inner product calculation method:

In the above derivation process, the two-scale equations satisfying s=2 ^j-1 tk and MRA theoretical mesoscale function and wavelet function are used:

The properties of wavelet basis orthogonality are also utilized, ie, not m when only m=n-2k.

The specific coefficient calculation process is as follows:

For the above wavelet decomposition process, the essence is to decompose the wavelet into several digital filters, where h ₀ and h ₁ are the coefficients of the filter. This can be achieved by designing a coefficient array of the high pass filter and the low pass filter separately.

3. One-dimensional wavelet reconstruction, one-dimensional wavelet reconstruction based on the lowest-level low-frequency coefficients of wavelet decomposition and high-frequency coefficients of each layer. The reconstruction belongs to the inverse process of decomposition, and the denoising algorithm can be reconstructed by using hard threshold and soft threshold. The waveform curve after the wavelet reconstruction signal is smooth and the features are obvious, which is very beneficial for extracting the feature values for fault identification. The feature values herein may specifically include parameters such as peak value, wave width, wavelet coefficient, or wavelet packet energy.

The determination of the above characteristic value can also be used for calculating the running speed of the wire rope, that is, the defects of the same fault type are respectively formed in two parts of one wire rope, and the fault feature values corresponding to the same fault type defect are the same, and the two parts are The spacing between the two is known, so that the operating speed of the wire rope can be calculated from the time interval occurring before and after the same fault characteristic value and the spacing.

In FIG. 2, the calculation processing device 30 may further include a time recording module 34 and a speed calculation module 35. The time recording module 34 is configured to record the sequential occurrence time interval between two portions of the same fault type preset on the wire rope. The speed calculation module 35 is configured to calculate the running speed of the wire rope according to the preset spacing between the parts of the two identical fault types and the sequential occurrence time interval. For example, the time interval in which the same feature values occur sequentially is t, and the spacing between the two defect sites is s, and the running speed of the wire rope is v=s/t. Compared with the existing encoder speed detection, the test accuracy of this embodiment is higher and is not easily affected by the wire rope slip.

As shown in FIG. 3, it is another implementation of the wire rope online flaw detection monitoring system of the present invention. Schematic diagram of the structure of the example. Compared with the previous embodiment, the embodiment further includes an image capturing camera 40 for acquiring a fault image of the wire rope. The communication module 20 is also operative to communicate a fault image of the wire rope to the computing processing device 30. The calculation processing device 30 is further configured to perform image enhancement processing on the fault image of the wire rope and present the enhanced fault image.

The image acquisition camera 40 can be used to collect the actual state of the wire rope, and then the image enhancement technology can make the fault display more clearly and intuitively on the host computer. In conjunction with the foregoing wire rope fault detection and type discriminating, when determining the wire rope fault category, a control command may be sent to the image capturing camera 40 to collect the fault image of the wire rope at the moment, and displayed on the host computer. In order to facilitate the observation and judgment of the damage by the staff, the dangerous situation can be alerted in time. Correspondingly, the calculation processing device 30 may further include: a wire rope failure display module, configured to send a control instruction to the image acquisition camera 40 to determine the failure of the wire rope at the moment when determining the wire rope failure category image.

The enhancement of the fault image may adopt various existing image enhancement techniques, and the present invention provides an example based on image homomorphic filtering, that is, the calculation processing device 30 includes: an image gradation transformation module 36, a light component separation module 37, and a low The filter processing module 38 and the image high frequency enhancement module 39 are passed through. The image gradation transformation module 36 is configured to represent the gradation value of the fault image of the wire rope as an incident light component occupying a low frequency portion of the frequency domain, an incident light constant, and a reflected light component occupying a high frequency portion of the frequency domain. The light component separation module 37 is configured to separate the incident light component, the incident light constant, and the reflected light component by a logarithm method. The low pass filter processing module 38 is configured to perform low pass filtering on the separated formula. The low pass filter processing module 38 is preferably a median filter. The image high frequency enhancement module 39 is configured to subtract the low pass filtered equation from the separated equation, and retain the incident light constant, and then perform an exponential operation to obtain a high frequency enhanced image.

On this basis, the image processing will be described below in conjunction with the formula. When a fault signal such as a broken wire occurs, the image capturing camera 40 immediately collects the fault picture, and the gray value of the picture can be regarded as the product of the incident light component and the reflected light component, wherein the incident light occupies the low frequency portion of the frequency domain, and the corresponding image Background, and the reflected light depends on the nature of the object itself, that is, the brightness characteristics of the scene mainly depend on the reflected light. Since the homomorphic filtering frequency domain algorithm requires two Fourier transforms, which occupies a large computation space, it is difficult to meet the real-time requirements. Therefore, the homomorphic filtering is usually put into the spatial domain to operate and implement. The general idea of the homomorphic filtering spatial domain algorithm is to first low-pass filter the image, and then reduce the pass-filtered image with the original image, and the obtained result can achieve the effects of suppressing low frequency and enhancing high frequency.

The gray scale function f(x, y) of the image is expressed by the following formula:

f(x,y)=i ₀ ·i(x,y)·r(x,y)

Where i(x, y) is the incident light component, r(x, y) is the reflected light component, and i ₀ is the incident light constant. In order to retain a certain low frequency component, a better display effect is obtained, so i ₀ is introduced. Separating the incident and reflected light by a logarithmic method:

g(x,y)=ln f(x,y)=ln i ₀ +ln i(x,y)+ln r(x,y)

Since the incident light component and the incident light constant correspond to the low frequency portion of the image, and the reflected light component corresponds to the high frequency portion of the image, after the low pass filtering of g(x, y), the incident light component and the incident light can be approximately approximated. The constant (that is, the low frequency part of the image) is separated as follows:

g'(x,y)=LPFg(x,y)≈ln i ₀ +ln i(x,y)

Among them, LPF is a low pass filter. The low-pass filter uses a median filtering algorithm to filter, and the median filtering algorithm not only removes the noise of the transmission process, but also protects the edge of the broken wire. The median filter is a non-linear filter that arranges the gray values of the pixel points of the area covered by the structural elements in ascending order, and removes the intermediate value as the area of the central pixel gray value covered by the structural elements. In general, and odd numbers Pixel structure Elements such as 3x3, 5x5. The median filter and the gray value of the surrounding pixel gray value differ greatly, rather than a simple average. Therefore, median filtering can not only eliminate isolated noise points, but also reduce the range of blurred images and preserve the edge features of the image.

The median filter is a non-linear operation. The principle of digital signal median filtering is as follows:

Let one-dimensional sequences f ₁ , f ₂ , f ₃ , ... f _n . Take the window length (the number of points) as m (m is an odd number), and perform median filtering on the one-dimensional sequence, that is, extract m numbers f _tv ,..., f _t-1 , f _t ,f in the input sequence. _T+1 ,...,f _t+v , where f _t is the gray value of the center point of the window,

Then m points are in the gray value of the sorted numerical size, the number is centered, and this number is the output filter. Expressed as a mathematical formula as:

y _i =Med{f _iv ...,f _i ...,f _i+v }

Two-dimensional median filtering is represented by:

Where: A represents a window; f _ij represents a two-dimensional data sequence.

After the image is low-pass filtered, the original image is reduced by the filtered image, and lni _{0 is} added to retain a certain low-frequency component to obtain a high-frequency enhanced image:

s(x,y)=ln i ₀ +g(x,y)-g'(x,y)≈ln i ₀ +ln r(x,y)

Perform an exponential operation on s(x, y) to get the final enhancement result:

s'(x,y)=e ^s(x,y) ≈i ₀ r(x,y)

The homomorphic filtering algorithm can enhance the high frequency information of the image while retaining part of the low frequency information, achieving the dynamic range of the compressed image gray level and enhancing the image contrast effect. Insufficient image brightness and blurred detail due to poor illumination.

The above-mentioned wire rope online flaw detection monitoring system embodiment can be applied to various devices, equipment or systems that need to use wire ropes for operation, and is particularly suitable for mining multi-rope friction lifting systems. Therefore, the present invention also provides a mine multi-rope friction lifting system, including the aforementioned wire rope online flaw detection monitoring system.

Based on the above embodiment of the wire rope online flaw detection monitoring system, the present invention provides a corresponding wire rope online flaw detection monitoring method. FIG. 7 is a schematic flow chart of an embodiment of a wire rope online flaw detection method according to the present invention. In this embodiment, the wire rope online flaw detection monitoring method comprises:

Step 100: The flaw detection sensor collects the defect signal of the wire rope in real time, and converts the defect signal of the wire rope through the communication module, and transmits the defect signal to the calculation processing device;

Step 200: The calculation processing device extracts a fault feature value from the converted defect signal, and searches for a wire rope fault category corresponding to the fault feature value in a preset fault feature library.

In FIG. 8, the operation of extracting the fault feature value and searching for the wire rope fault category in the above step 200 may specifically include:

Step 210: The calculation processing device performs one-dimensional wavelet denoising processing on the converted defect signal to obtain a reconstructed signal curve.

Step 220: The calculation processing device performs feature value extraction on the reconstructed signal curve, and performs a search in a fault feature library preset in the calculation processing device according to the extracted fault feature value to determine The wire rope failure category corresponding to the fault characteristic value.

In step 210, the converted defect signal is pre-processed to remove part of the noise, and the pre-processed defect signal is subjected to wavelet transform to realize multi-scale decomposition; the coefficients of each scale are calculated, and each The coefficients of the scale are denoised; finally, the reconstruction of the one-dimensional wavelet is performed according to the lowest-level low-frequency coefficients and the high-frequency coefficients of each layer in each scale of wavelet decomposition.

In another embodiment of the wire rope online flaw detection monitoring method, the speed calculation step may be further included, that is, the calculation processing device extracts the fault feature value from the converted defect signal, and records two preset fault type portions on the wire rope. The interval between the occurrences of the interval, and then according to the location of the two identical fault types The preset spacing between the intervals and the sequence of occurrence intervals are used to calculate the running speed of the wire rope.

For the wire rope online flaw detection monitoring system further including the image acquisition camera system embodiment, the wire rope online flaw detection monitoring method further includes a fault image acquisition presentation step: as shown in FIG. 9 , which is another embodiment of the wire rope online flaw detection monitoring method of the present invention. Schematic diagram of the process. Compared with the previous embodiment, the fault image acquisition presentation step includes:

Step 300, the image capturing camera transmits a fault image of the wire rope to the computing processing device through the communication module;

Step 400: The calculation processing device performs image enhancement processing on the fault image of the wire rope, and presents the enhanced fault image.

In step 300, the image acquisition camera may collect the wire rope image at regular intervals, or may be driven by an event. For example, when the calculation processing device determines that the wire rope is faulty or determines the wire rope failure category, the image is driven by sending a control command to the image acquisition camera. The acquisition camera captures a fault image of the wire rope at this moment.

In step 400, the calculation processing means expresses the gradation value of the fault image of the wire rope as an incident light component occupying a low frequency portion of the frequency domain, an incident light constant, and a reflected light component occupying a high frequency portion of the frequency domain, and The incident light component, the incident light constant, and the reflected light component are separated by a logarithmic method. Then, the calculation processing device performs low-pass filtering processing on the separated formula, and subtracts the low-pass filtered formula from the separated formula, and retains the incident light constant, and then performs an exponential operation to obtain high-frequency enhancement. image. The calculation processing device preferably uses a median filtering algorithm to separate the incident light component and the incident light constant in the separated equation to implement low-pass filtering processing of the separated equation.

The various embodiments in the present specification are described in a progressive manner, and the focus of each embodiment is different, and the same or similar parts between the various embodiments may be referred to each other. For the method embodiment, due to its entirety and the steps involved and in the system embodiment There is a correspondence between the contents, so the description is relatively simple, and the relevant parts can be referred to the description of the system embodiment.

It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to be limiting; although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that The invention is not limited to the spirit of the technical solutions of the present invention, and should be included in the scope of the technical solutions claimed in the present invention.

Claims (23)

1. A wire rope online flaw detection monitoring system, comprising: a flaw detection sensor, a communication module and a calculation processing device, wherein the flaw detection sensor communicates with the calculation processing device through the communication module; wherein

   The flaw detection sensor is disposed around the wire rope to be inspected for collecting the defect signal of the wire rope in real time;

   The communication module is configured to convert a defect signal of the wire rope and transmit it to the calculation processing device;

   The calculation processing device is configured to extract a fault feature value from the converted defect signal, and search for a wire rope fault category corresponding to the fault feature value in a preset fault feature library.
2. The wire rope online flaw detection monitoring system according to claim 1, wherein the calculation processing device specifically comprises:

   a fault feature extraction module, configured to extract a fault feature value from the converted defect signal;

   a fault feature library preset in the computing processing device for storing various wire rope fault categories and corresponding fault feature values;

   The fault category finding module is configured to search for a wire rope fault category corresponding to the fault feature value in a preset fault signature database.
3. The wire rope online flaw detection monitoring system of claim 2, wherein the calculation processing device further comprises:

   a time recording module for recording a sequence of time intervals between two locations of the same fault type preset on the wire rope;

   And a speed calculation module, configured to calculate a running speed of the wire rope according to a preset spacing between the locations of the two identical fault types and the sequential occurrence time interval.
4. A wire rope online flaw detection monitoring system according to claim 2 or 3, The fault feature extraction module further includes:

   a wavelet denoising unit, configured to perform one-dimensional wavelet denoising processing on the converted defect signal to obtain a reconstructed signal curve;

   The feature value extracting unit is configured to perform feature value extraction on the reconstructed signal curve.
5. The wire rope online flaw detection monitoring system of claim 4, wherein the wavelet denoising unit comprises:

   a preprocessing subunit, configured to preprocess the converted defect signal to remove part of the noise;

   The one-dimensional wavelet decomposition sub-unit is configured to adopt wavelet transform on the pre-processed defect signal to realize multi-scale decomposition;

   a decomposition coefficient processing sub-unit for calculating coefficients of each scale, and performing denoising processing on coefficients of each scale;

   The one-dimensional wavelet reconstruction sub-unit is used to reconstruct the one-dimensional wavelet according to the lowest-level low-frequency coefficients and the high-frequency coefficients of each layer in each scale of wavelet decomposition.
6. The wire rope online flaw detection monitoring system according to claim 1, further comprising an image acquisition camera for acquiring a fault image of the wire rope; the communication module is further configured to transmit the fault image of the wire rope to the calculation process Device

   The calculation processing device is further configured to perform image enhancement processing on the fault image of the wire rope, and present the enhanced fault image.
7. The wire rope online flaw detection monitoring system according to claim 6, wherein said calculation processing means comprises:

   An image gradation transformation module for expressing a gradation value of the fault image of the wire rope as an incident light component occupying a low frequency portion of the frequency domain, an incident light constant, and a reflected light component occupying a high frequency portion of the frequency domain;

   a light component separation module for separating the incident light component, the incident light constant, and the reflected light component by a logarithmic method;

   a low pass filter processing module for performing low pass filtering on the separated formula;

   The image high-frequency enhancement module is configured to subtract the low-pass filtered formula from the separated formula, and retain the incident light constant, and then perform an exponential operation to obtain a high-frequency enhanced image.
8. The wire rope online flaw detection monitoring system of claim 7, wherein the low pass filter processing module is a median filter.
9. The wire rope online flaw detection monitoring system according to claim 1, wherein the flaw detection sensor comprises N magnetically conductive flaw detection modules, and is evenly distributed on a circumference, and each of the magnetic permeability flaw detection modules can cover 360/ of the wire rope. N degrees.
10. The wire rope on-line flaw detection monitoring system according to claim 9, wherein the magnetically conductive flaw detection module comprises an induction coil and two excitation coils having the same magnitude and opposite magnetic flux, the two excitation coils being connected to an excitation capable of supplying alternating current The source, when the defective wire rope moves relative to the magnetic permeability detecting module, the electromotive force signal induced by the induction coil is transmitted to the communication module.
11. The wire rope online flaw detection monitoring system according to claim 1, further comprising a fixing frame, a height adjusting mechanism and an angle adjusting mechanism, wherein the flaw detecting sensor is mounted on the angle adjusting mechanism, and the angle adjusting mechanism is installed at the height The tilting angle of the flaw detection sensor can be adjusted on the adjusting mechanism, and the height adjusting mechanism is mounted on the fixing frame, and the height of the flaw detecting sensor can be adjusted.
12. The wire rope online flaw detection monitoring system according to claim 1, further comprising a mine explosion-proof and intrinsically safe type substation, wherein the mine explosion-proof and intrinsically safe sub-station comprises: an explosion-proof casing, an intrinsically safe power source a module, a remote power-off control module, and a data processing module, wherein the intrinsically safe power module, the remote power-off control module, and the data acquisition module are integrated in a movement in the flameproof enclosure, the intrinsically safe type The power module is responsible for supplying power to the flaw detection sensor and the servo microcontroller for controlling the driving power of the wire rope, and the data processing module is configured to receive the signal transmitted by the flaw detection sensor and transmit the signal to the communication module through a communication interface. Piece.
13. The wire rope online flaw detection monitoring system according to claim 12, wherein the communication module is a mine general and intrinsically safe communication module installed in a ground monitoring center; the communication module has a communication signal conversion unit, an optocoupler and an AC/ a DC conversion circuit for converting a defect signal of the wire rope into a USB interface signal, the optocoupler and the non-intrinsically safe output of the AC/DC conversion circuit to the computing processing device The intrinsically safe output of the communication interface is isolated.
14. The wire rope online flaw detection monitoring system according to claim 6, wherein the calculation processing device further comprises:

    The wire rope fault display module is configured to send a control command to the image capturing camera to determine a fault image of the wire rope at the moment when determining the wire rope fault category.
15. A wire rope online flaw detection monitoring method based on the wire rope online flaw detection monitoring system according to any one of claims 1 to 14, comprising:

    The flaw detection sensor collects the defect signal of the wire rope in real time, and converts the defect signal of the wire rope through the communication module, and transmits the defect signal to the calculation processing device;

    The calculation processing device extracts a fault feature value from the converted defect signal, and searches for a wire rope fault category corresponding to the fault feature value in a preset fault feature library.
16. The wire rope online flaw detection monitoring method according to claim 15, wherein the calculation processing device extracts a fault feature value from the converted defect signal, and searches for a wire rope fault corresponding to the fault feature value in a preset fault feature library. The operations of the category specifically include:

    The calculation processing device performs one-dimensional wavelet denoising processing on the converted defect signal to obtain a reconstructed signal curve;

    The calculation processing device performs feature value extraction on the reconstructed signal curve, and performs a search in a fault feature library preset in the calculation processing device according to the extracted fault feature value to determine the fault The wire rope failure category corresponding to the feature value.
17. The wire rope online flaw detection monitoring method according to claim 16, wherein the calculation processing device performs one-dimensional wavelet denoising processing on the converted defect signal, and the operation of obtaining the reconstructed signal curve specifically includes:

    Pre-processing the converted defect signal to remove part of the noise;

    Wavelet transform is applied to the pre-processed defect signal to achieve multi-scale decomposition;

    Calculate the coefficients of each scale and denoise the coefficients of each scale;

    The reconstruction of one-dimensional wavelet is performed according to the lowest-level low-frequency coefficients and the high-frequency coefficients of each layer in each scale of wavelet decomposition.
18. The wire rope online flaw detection monitoring method according to claim 15, further comprising a speed calculation step:

    The calculation processing device extracts a fault feature value from the converted defect signal, and records a sequential occurrence time interval between two portions of the same fault type set in advance on the wire rope;

    The calculation processing device calculates the running speed of the wire rope according to a preset interval between the portions of the two identical fault types and the sequential occurrence time interval.
19. The wire rope online flaw detection monitoring method according to claim 15, wherein the wire rope online flaw detection monitoring system further comprises an image acquisition camera for collecting a fault image of the wire rope; and the wire rope online flaw detection monitoring method further comprises a fault image collection and presentation. step:

    The image capture camera transmits a fault image of the wire rope to the computing processing device through the communication module;

    The calculation processing device performs image enhancement processing on the fault image of the wire rope, and presents the enhanced fault image.
20. The wire rope on-line flaw detection monitoring method according to claim 19, wherein the operation of the image processing processing on the fault image of the wire rope by the calculation processing device comprises:

    The calculation processing device expresses a gray value of the fault image of the wire rope as an incident light component occupying a low frequency portion of the frequency domain, an incident light constant, and a reflected light component occupying a high frequency portion of the frequency domain, and taking a logarithm Separating the incident light component, the incident light constant, and the reflected light component;

    The calculation processing device performs low-pass filtering on the separated formula, subtracts the low-pass filtered formula from the separated formula, and retains the incident light constant, and then performs an exponential operation to obtain a high-frequency enhancement image.
21. The wire rope online flaw detection monitoring method according to claim 20, wherein the operation of the calculation processing device for performing low-pass filter processing on the separated equation is specifically:

    The calculation processing device uses a median filtering algorithm to separate the incident light component and the incident light constant in the separated equation.
22. The wire rope online flaw detection monitoring method according to claim 19, further comprising: said calculating processing means, when determining said wire rope failure category, transmitting a control command to said image capturing camera to collect said wire rope at said moment Fault image.
23. A mine multi-rope friction lifting system comprising the wire rope online flaw detection monitoring system according to any one of claims 1 to 14.

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