AU2017403783B2

AU2017403783B2 - Online flaw detection monitoring system and method for steel wire rope, and multi-rope friction hoisting system for use in mining

Info

Publication number: AU2017403783B2
Application number: AU2017403783A
Authority: AU
Inventors: Xinyu Gao; Yanfei KOU; Ziming KOU; Tengyu LI; Zhigang Li; Juan Wu
Original assignee: Taiyuan University of Technology
Current assignee: Taiyuan University of Technology
Priority date: 2017-03-17
Filing date: 2017-03-17
Publication date: 2020-09-03
Anticipated expiration: 2037-03-17
Also published as: ZA201901822B; WO2018165972A1; AU2017403783A1

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

ONLINE FLAW DETECTION MONITORING SYSTEM AND METHOD FOR STEEL WIRE ROPE, AND MULTI-ROPE FRICTION HOISTING SYSTEM FOR USE IN MINING TECHNICAL FIELD

The disclosure relates to the field of flaw detection, and particularly to an online flaw detection monitoring system and method for a steel wire rope and to a mining multi-rope friction hoisting system.

BACKGROUND

For a mine hoist, a suspension device for a steel wire rope is an important device for normal running of the hoist. A steel wire rope may be damaged during use, and further a rope broken accident may happen if the damage cannot be timely found and located. In Chinese Patent Application NO. CN104569143A, a mining online flaw detection monitoring system for the steel wire rope is disclosed. The system includes a pair of pulleys arranged on a bracket. A steel wire rope is connected between the pair of pulleys, and a sensor driven by a detection rack is arranged on the bracket below the pulleys. The detection rack is erected on the bracket and may move along a slide path thereof. The detection rack is connected with a system console and is configured to control the sensor to wrap around the detected steel wire rope. In the system, an influence of vibration of the steel wire rope in a running process on a monitoring result may not be eliminated, and a vital problem of measurement blind area may not be solved, and a data change may not be monitored in real time. In order to eliminate the influence of vibration of a steel wire rope on monitoring, a real-time dynamic flaw detection system for a steel wire rope is disclosed in Chinese Utility Model Patent Application NO. CN203502379U. An acquisition unit in the system includes at least one group of magnetically conductive sensors, to pick up and amplify a steel wire rope damage signal. Two sensors are used for balanced output, and performing anti-interference processing on the signal, to detect a damage to a steel wire rope accurately. However, a type of the damage to the steel wire rope may not be determined in this solution. Therefore, an operator should determine the type of the damage according to experiences or by observing a damage location of the steel wire rope after the damage to the steel wire rope is detected, which results in low practicability. In this solution, a conventional coder is used for detecting a speed. Since the steel wire rope is easy to slip during use, a detected speed may have low accuracy.

SUMMARY

The objective of the disclosure is to provide an online flaw detection monitoring system and method for a steel wire rope and a mining multi-rope friction hoisting system, to accurately determine a flaw type of the steel wire rope while implementing flaw detection for a steel wire rope. According to an aspect, an online flaw detection monitoring system for a steel wire rope is provided, which include a flaw detection sensor, a communication module and a computing processing device. The flaw detection sensor communicates with the computing processing device through the communication module. The flaw detection sensor is arranged around a to-be detected steel wire rope, and is configured to acquire a flaw signal of the steel wire rope in real time. The communication module is configured to convert the flaw signal of the steel wire rope and transmit the converted flaw signal to the computing processing device. The computing processing device is configured to extract a flaw characteristic value from the converted flaw signal, and search a preset flaw characteristic library for a steel wire rope flaw type corresponding to the flaw characteristic value. Furthermore, the computing processing device may include a flaw characteristic extracting module, a flaw characteristic library and a flaw type searching module. The flaw characteristic extracting module may be configured to extract the flaw characteristic value from the converted flaw signal.

The flaw characteristic library may be preset in the computing processing device, and may be configured to store steel wire rope flaw types and flaw characteristic values corresponding to steel wire rope flaw types. The flaw type searching module may be configured to search the preset flaw characteristic library for the steel wire rope flaw type corresponding to the flaw characteristic value. Furthermore, the computing processing device may further include a time recording module and a speed calculating module. The time recording module may be configured to record an occurrence time interval between occurrences of two parts preset in the steel wire rope which present a same flaw type. The speed calculating module may be configured to calculate a running speed of the steel wire rope based on a preset distance and the occurrence time interval between the two parts which present the same flaw type. Furthermore, the flaw characteristic extracting module may further include a wavelet denoising unit and a characteristic value extracting unit. The wavelet denoising unit may be configured to perform one-dimensional wavelet denoising on the converted flaw signal to obtain a reconstructed signal curve. The characteristic value extracting unit may be configured to extract a characteristic value from the reconstructed signal curve. Furthermore, the wavelet denoising unit may include a preprocessing subunit, a one-dimensional wavelet decomposition subunit, a decomposition coefficient processing subunit and a one-dimensional wavelet reconstructing subunit. The preprocessing subunit may be configured to preprocess the converted flaw signal to remove a part of noise. The one-dimensional wavelet decomposition subunit may be configured to perform wavelet transform on the preprocessed flaw signal to implement multiscale decomposition. The decomposition coefficient processing subunit may be configured to calculate a coefficient of each of multiple scales and perform denoising processing on the coefficient of each of the multiple scales. The one-dimensional wavelet reconstructing subunit may be configured to reconstruct a one-dimensional wavelet based on a low-frequency coefficient of a lowest level and a high-frequency coefficient of each of levels in each of the multiple scales for wavelet decomposition. Furthermore, the system may further include an image acquiring camera configured to acquire a flaw image of the steel wire rope. The communication module may further be configured to transmit the flaw image of the steel wire rope to the computing processing device. The computing processing device may further be configured to enhance the flaw image of the steel wire rope and display the enhanced flaw image. Furthermore, the computing processing device may include an image grayscale transform module, a light component separating module, a low-pass filtering processing module and an image high-frequency enhancing module. The image grayscale transform module may be configured to represent a grayscale value of the flaw image of the steel wire rope as an incident light component and an incident light constant which occupy a low-frequency part in a frequency domain and a reflected light component which occupies a high-frequency part in the frequency domain. The light component separating module may be configured to separate the incident light component, the incident light constant and the reflected light component using a logarithm calculation method. The low-pass filtering processing module may be configured to perform low pass filtering on an expression obtained after the separation. The image high-frequency enhancing module may be configured to subtract an expression obtained after the low-pass filtering from the expression obtained after separation, add to the incident light constant and perform an exponential operation to obtain a high-frequency enhanced image. Furthermore, the low-pass filtering processing module may be a median filter. Furthermore, the flaw detection sensor may include N magnetically conductive flaw detection modules uniformly distributed in a circumferential direction, and each of the N magnetically conductive flaw detection modules may cover a range of 360/N degrees of the steel wire rope. Furthermore, the permeability flaw detection module may include an induction coil and two excitation coils having a same amount of magnetic flux and opposite directions. Each of the two excitation coils may be connected with an excitation source capable of supplying Alternating Current (AC). In a case that the steel wire rope with a flaw moves relative to the magnetically conductive flaw detection module, an electromotive force signal induced by the induction coil may be transmitted to the communication module. Furthermore, the system may further include a fixing frame, a height regulation mechanism and an angle regulation mechanism. The flaw detection sensor may be mounted on the angle regulation mechanism, the angle regulation mechanism may be mounted on the height regulation mechanism and may be capable of regulating an inclination angle of the flaw detection sensor, and the height regulation mechanism may be mounted on the fixing frame and may be capable of regulating a height of the flaw detection sensor. Furthermore, the system may further include a mining flame-proof and intrinsically safe substation. The mining flame-proof and intrinsically safe substation may include a flame-proof housing, an intrinsically safe power supply module, a remote power cutting-off and transmitting control module and a data processing module. The intrinsically safe power supply module, the remote power cutting-off and transmitting control module and the data processing module may be all integrated in a motor in the flame-proof housing. The intrinsically safe power supply module may be configured to supply power to the flaw detection sensor and a servo single-chip microcomputer which is configured to control a driving power source of the steel wire rope. The data processing module may be configured to receive a signal transmitted by the flaw detection sensor and transmit the signal to the communication module via a communication interface. Furthermore, the communication module may be a mining ordinary and intrinsically safe communication module, and may be mounted at a ground monitoring center. The communication module may include a communication signal conversion unit, an optocoupler and an AC/Direct Current (DC) conversion circuit. The communication signal conversion unit may be configured to convert the flaw signal of the steel wire rope into a Universal Serial Bus (USB) interface signal, and the optocoupler and the AC/DC conversion circuit may be configured to isolate a non intrinsically safe output of the computing processing device from an intrinsically safe output of the communication interface.

Furthermore, the computing processing device may further include a steel wire rope flaw displaying module. The steel wire rope flaw displaying module may be configured to, during determination of the steel wire rope flaw type, transmit a control instruction to the image acquiring camera to acquire a current flaw image of the steel wire rope. According to another aspect, an online flaw detection monitoring method for a steel wire rope based on the aforementioned online flaw detection monitoring system for the steel wire rope is provided in the disclosure, which includes operations as follows. The flaw detection sensor acquires a flaw signal of a steel wire rope in real time, converts the flaw signal of the steel wire rope and transmits the converted flaw signal to the computing processing device by the communication module. The computing processing device extracts a flaw characteristic value from the converted flaw signal, and searches a preset flaw characteristic library for a steel wire rope flaw type corresponding to the flaw characteristic value. Furthermore, the operation that the computing processing device extracts a flaw characteristic value from the converted flaw signal, and searches a preset flaw characteristic library for a steel wire rope flaw type corresponding to the flaw characteristic value may include the following operations. The computing processing device performs one-dimensional wavelet denoising processing on the converted flaw signal to obtain a reconstructed signal curve. The computing processing device extracts a characteristic value from the reconstructed signal curve, and searches the flaw characteristic library preset in the computing processing device for the steel wire rope flaw type corresponding to the flaw characteristic value based on the extracted flaw characteristic value. Furthermore, the operation that the computing processing device performs one-dimensional wavelet denoising processing on the converted flaw signal to obtain the reconstructed signal curve may include the following operations. The converted flaw signal is preprocessed to remove a part of noise. Wavelet transform is performed on the preprocessed flaw signal to implement multiscale decomposition.

A coefficient of each of multiple scales is calculated, and denoising processing may be performed on the coefficient of each of the multiple scales. A one-dimensional wavelet is constructed based on a low-frequency coefficient of a lowest level and a high-frequency coefficient of each of levels in each of the multiple scales for wavelet decomposition. Furthermore, the method may further include a speed calculating step as follows. The computing processing device extracts the flaw characteristic value from the converted flaw signal, and records an occurrence time interval between occurrences of two parts preset in the steel wire rope which present a same flaw type. The computing processing device calculates a running speed of the steel wire rope based on a preset distance and the occurrence time interval between the two parts which present the same flaw type. Furthermore, the online flaw detection monitoring system for the steel wire rope may further include an image acquiring camera configured to acquire a flaw image of the steel wire rope, and the online flaw detection monitoring method for the steel wire rope may further include a flaw image acquiring and displaying step as follows. The image acquiring camera transmits a flaw image of the steel wire rope to the computing processing device through the communication module. The computing processing device enhances the flaw image of the steel wire rope, and displays the enhanced flaw image. Furthermore, the operation that the computing processing device enhances the flaw image of the steel wire rope may include the following operations. The computing processing device represents a grayscale value of the flaw image of the steel wire rope as an incident light component and an incident light constant which occupy a low-frequency part in a frequency domain and a reflected light component which occupies a high-frequency part in the frequency domain, and separates the incident light component, the incident light constant and the reflected light component using a logarithm calculation method. The computing processing device performs low-pass filtering on an expression obtained after separation, subtracts an expression obtained after the low-pass filtering from the expression obtained after separation, adds to the incident light constant, and performs an exponential operation to obtain a high-frequency enhanced image. Furthermore, the operation that the computing processing device performs low-pass filtering on the expression obtained after separation may include the following operation. The computing processing device separates the incident light component and the incident light constant in the expression obtained after separation using a median filtering algorithm. Furthermore, the method may further include an operation that the computing processing device transmits a control instruction to the image acquiring camera during determination of the steel wire rope flaw type, to acquire a current flaw image of the steel wire rope. According to another aspect, a mining multi-rope friction hoisting system is provided in the disclosure, which may include the online flaw detection monitoring system for the steel wire rope described above. According to another aspect, an online flaw detection monitoring system for a steel wire rope is provided, which comprises a flaw detection sensor, a communication module and a computing processing device, the flaw detection sensor communicating with the computing processing device through the communication module. The flaw detection sensor is arranged around a to-be detected steel wire rope, and is configured to acquire a flaw signal of the steel wire rope in real time. The communication module is configured to convert the flaw signal of the steel wire rope and transmit the converted flaw signal to the computing processing device. The computing processing device is configured to extract a flaw characteristic value from the converted flaw signal, and search a preset flaw characteristic library for a steel wire rope flaw type corresponding to the flaw characteristic value, to determine the steel wire rope flaw type, wherein steel wire rope flaw types are stored in the preset flaw characteristic library, and different steel wire rope flaw types correspond to different flaw characteristic values. The flaw detection sensor comprises N magnetically conductive flaw detection modules uniformly distributed in a circumferential direction, and each of the N magnetically conductive flaw detection modules covers a range of 360/N degrees of the steel wire rope. The online flaw detection monitoring system comprises an image acquiring camera which is configured to acquire a flaw image of the steel wire rope. The computing processing device comprises a steel wire rope flaw displaying module which is configured to, during determination of the steel wire rope flaw type, transmit a control instruction to the image acquiring camera to acquire a current flaw image of the steel wire rope. According to another aspect, an online flaw detection monitoring method for a steel wire rope based on the online flaw detection monitoring system for the steel wire rope is provided, which comprises: acquiring, by the flaw detection sensor, a flaw signal of a steel wire rope in real time, converting, by the communication module, the flaw signal of the steel wire rope, and transmitting, by the communication module, the converted flaw signal to the computing processing device; and extracting, by the computing processing device, a flaw characteristic value from the converted flaw signal, and searching, by the computing processing device, a preset flaw characteristic library for a steel wire rope flaw type corresponding to the flaw characteristic value, to determine the steel wire rope flaw type, wherein steel wire rope flaw types are stored in the preset flaw characteristic library, and different steel wire rope flaw types correspond to different flaw characteristic values, wherein the flaw detection sensor comprises N magnetically conductive flaw detection modules uniformly distributed in a circumferential direction, and each of the N magnetically conductive flaw detection modules covers a range of 360/N degrees of the steel wire rope, wherein the online flaw detection monitoring method further comprises: during determination of the steel wire rope flaw type, transmitting, by the steel rope flaw displaying module, a control instruction to the image acquiring camera to acquire a current flaw image of the steel wire rope; and acquiring, by the image acquiring camera, a flaw image of the steel wire rope. On the basis of the technical solutions described above, in the disclosure, the flaw signal of the steel wire rope is detected and converted, the flaw characteristic value is extracted, and the preset flaw characteristic library is searched for the steel wire rope flaw type corresponding to the flaw characteristic value, thereby accurately

8a determining a steel wire rope flaw type while implementing flaw detection for the steel wire rope, and further facilitating timely troubleshooting and maintenance of an operator.

BRIEF DESCRIPTION OF DRAWINGS

The drawings described here are intended to provide a further understanding to the disclosure and form a part of the application. The schematic embodiments of the disclosure and descriptions thereof are intended to interpret the disclosure rather than improperly limiting the disclosure. In the drawings: FIG. 1 is a schematic structural diagram of an online flaw detection monitoring system for a steel wire rope according to an embodiment of the disclosure. FIG. 2 is a schematic structural diagram of an online flaw detection monitoring system for a steel wire rope according to another embodiment of the disclosure. FIG. 3 is a schematic structural diagram of an online flaw detection monitoring system for a steel wire rope according to another embodiment of the disclosure.

8b

FIG. 4 and FIG. 5 are schematic diagrams respectively showing a mounting and regulating structure of a flaw detection sensor in an online flaw detection monitoring system for a steel wire rope according to the embodiment of the disclosure from different viewing angles. FIG. 6 is a schematic diagram showing an implementation principle of a flaw detection sensor in an online flaw detection monitoring system for a steel wire rope according to the embodiment of the disclosure. FIG. 7 is a schematic flowchart of an online flaw detection monitoring method for a steel wire rope according to an embodiment of the disclosure. FIG. 8 is a schematic flowchart of an online flaw detection monitoring method for a steel wire rope according to another embodiment of the disclosure. FIG. 9 is a schematic flowchart of an online flaw detection monitoring method for a steel wire rope according to yet another embodiment of the disclosure.

DETAILED DESCRIPTION

The technical solutions of the disclosure are further described below with reference to the drawings and the embodiments in detail. As shown in FIG. 1, which is a schematic structural diagram of an online flaw detection monitoring system for a steel wire rope according to an embodiment of the disclosure, the online flaw detection monitoring system for the steel wire rope according to the embodiment includes a flaw detection sensor 10, a communication module 20 and a computing processing device 30. The flaw detection sensor 10 communicates with the computing processing device 30 through the communication module 20. The flaw detection sensor 10 is arranged around a to-be-detected steel wire rope, and is configured to acquire a flaw signal of the steel wire rope in real time. The flaw detection sensor 10 may include N magnetically conductive flaw detection modules uniformly distributed in a circumferential direction. Each magnetically conductive flaw detection module may cover a range of 360/N degrees of the steel wire rope. For example, with taking N=3 as an example, each magnetically conductive flaw detection module covers a range of 120 degrees of the steel wire rope. The steel wire rope is completely covered to eliminate a measurement blind area, thereby accurately detecting a flaw of the steel wire rope. The magnetically conductive detection technology with high sensitivity is used in the magnetically conductive flaw detection module. Without magnetizing the steel wire rope, the magnetically conductive flaw detection module directly forms a magnetic circuit with the steel wire rope. If the steel wire rope is damaged, a change may occur in the magnetic circuit. The flaw of the steel wire rope may be detected by finding out a balance point of the change. When the steel wire rope is damaged, since magnetic conductivity of the steel wire rope is higher than magnetic conductivity of the air, the flaw detection module may rapidly detect a flaw signal, and further accurately determine a flaw location. An undamaged and continuous steel wire rope has good magnetic conductivity, and the flaw detection sensor may not detect any signal or a strong signal when the steel wire rope passes through the flaw detection sensor. When a steel wire sectional area of the steel wire rope is decreased, a magnetic conductivity performance of the steel wire rope become poor, and a change in a signal is detected by the flaw detection sensor. A flaw detection signal is converted into a sine wave by a signal processing circuit. An amplitude of the sine wave signal outputted by the magnetically conductive flaw detection module is directly proportional to a decrease amount in the steel wire sectional area, and the decrease amount in the steel wire sectional area of the steel wire rope is increased with the increase in the amplitude. Also, the amplitude is inversely proportional to a distance between a broken wire of the steel wire rope and the flaw detection module, and the amplitude of the signal is increased with the increase in the distance. Reference may be made to FIG. 6 for the magnetically conductive flaw detection module. The magnetically conductive flaw detection module includes an induction coil 13 and two excitation coils 11 and 12 having a same amount of magnetic flux and opposite directions. Each of the two excitation coils 11 and 12 is connected with an excitation source 14 capable of supplying AC. When the steel wire rope 60 with a flaw moves relative to the magnetically conductive flaw detection module, an electromotive force signal induced by the induction coil 13 is transmitted to the communication module 20. In a case that a damage condition of wire breakage, strand breakage, corrosion, junction loosening and the like occurs to the steel wire rope 60, the steel wire rope 60 passes through the excitation coil 11, the damage flaw causes the magnetic flux of the excitation coil 11 to change and therefore make unbalanced, and further the induction coil 13 generates an induced electromotive force e +. Then the steel wire rope 60 with the flaw passes through the excitation coil 12, the magnetic flux of the excitation coil 12 also changes, and further the induction coil 13 generates an induced electromotive force c -. Therefore, when the steel wire rope 60 with the flaw passes through the magnetically conductive flow detection module, the induced electromotive force generated by the induction coil 13 is 28, and the signal may be converted into an analog signal through an amplifier circuit for subsequent processing. In another embodiment, the online flaw detection monitoring system for the steel wire rope further includes a fixing frame, a height regulation mechanism and an angle regulation mechanism. The flaw detection sensor 10 is mounted on the angle regulation mechanism, the angle regulation mechanism is mounted on the height regulation mechanism and may regulate an inclination angle of the flaw detection sensor 10, and the height regulation mechanism is mounted on the fixing frame and may regulate the height of the flaw detection sensor 10. FIG. 4 and 5 respectively show structure examples of a mounting and regulating structure of the flaw detection sensor in the embodiment from different viewing angles. In FIG. 4, a base 51 and a stand plate 53 are welded together through a cross plate 52 to form the fixing frame, a sliding chute 54 extending upwards may be formed in the stand plate 53, and a position of the sliding seat 55 may be regulated along the sliding chute 54 in a vertical direction. A hinging seat 56 is arranged at an upper position of the sliding seat 55. The flaw detection sensor 10 is hinged to the sliding seat 55 through the hinging seat 56, and the inclination angle may be regulated relative to the sliding seat 55. Herein, the sliding seat 55 and the sliding chute 54 form the height regulation mechanism, and the sliding seat 55 and the hinging seat 56 form the angle regulation mechanism. In another embodiment, in order for reducing corrosion of coal slime and sludge to a device, a large fixing frame may also be manufactured, and the flaw detection sensor is suspended in the large fixing frame. The height of a sensor bracket and the height of a pressing wheel are regulated by a threaded support leg of a host device, to locate the steel wire rope right in the center of a through hole of the sensor, so as to regulate a gap between the flaw detection sensor and the steel wire rope to be uniform. Since multiple steel wire ropes may generally be used for suspension in a mine, it can be seen from FIG. 5 that there are four flaw detection sensors 10 which are respectively hinged to the sliding seats 55 through the hinging seats 56. A mining flame-proof and intrinsically safe substation may be further arranged on the base 51. The mining flame-proof and intrinsically safe substation includes a flame-proof housing 57, an intrinsically safe power supply module, a remote power cutting-off and transmitting control module and a data processing module. The mining flame-proof and intrinsically safe substation acquires and stores data and transmits the data to a computer of a ground central station. All the components are arranged in a stainless steel shell and then protected by a stainless steel protecting cover. Double-layer stainless steel protection ensures normal operation of the host device in a complex and severe environment of water drenching, damp, low temperature, strong magnet and the like. The intrinsically safe power module, the remote power cutting-off and transmitting control module and the data processing module may be all integrated in a motor in the flame-proof housing 57. The intrinsically safe power supply module is configured for supplying power to the flaw detection sensor 10 and a servo single chip microcomputer configured to control a driving power source of the steel wire rope. The data processing module is configured to receive a signal transmitted by the flaw detection sensor 10 and transmit the signal to the communication module 20 via a communication interface. The data processing unit of the mining flame-proof and intrinsically safe substation transmits the flaw detection signal of the sensor to an upper computer (i.e., the computing processing device) via the communication interface, and a flaw characteristic value is extracted automatically using analysis software of the upper computer to recognize a damage type, and a processing result is directly displayed on the upper computer. In the embodiment, the communication module 20 is configured to convert the flaw signal of the steel wire rope and transmit the converted flaw signal to the computing processing device 30. In a mine environment, a mining ordinary and intrinsically safe communication module 20 is preferably used as the communication module 20, which is mounted at a ground monitoring center. The communication module 20 may include a communication signal conversion unit, an optocoupler and an AC/DC conversion circuit. The communication signal conversion unit is configured to convert the flaw signal of the steel wire rope into a USB interface signal.

The optocoupler and the AC/DC conversion unit are configured to isolate a non intrinsically safe output of the computing processing device 30 from an intrinsically safe output of the communication interface, to ensure intrinsic safety of a communication line leaded to the mine. The communication interface is further provided with a power indicator, a communication state indictor and a flaw indicator light. Upon receiving the signal from the communication module 20, the computing processing device 30, as the upper computer, may extract a flaw characteristic value from the converted flaw signal and search a preset flaw characteristic library 32 for a steel wire rope flaw type corresponding to the flaw characteristic value, thereby accurately determining the flaw type of the steel wire rope while implementing flaw detection for the steel wire rope, and facilitating timely troubleshooting and maintenance of an operator. Besides online monitoring and determining the flaw type of wire breakage, abrasion, rusting, sectional area decrease and the like of the steel wire rope in real time, the computing processing device 30 may also accurately locate a flaw part in another embodiment. In a case that a flaw signal of wire breakage and the like occurs, the computing processing device 30 may be further configured to process an acquired flaw picture to present a flaw situation of the steel wire rope more clearly and directly. If necessary, historical data may also be conveniently called for comparison and analysis, which facilitates analysis and early warning of the operator for a change of the steel wire rope. In addition, the computing processing device 30 may be further provided with a real-time detection result printing function. FIG. 2 is a schematic structural diagram of an online flaw detection monitoring system for a steel wire rope according to another embodiment of the disclosure. In the embodiment, the computing processing device 30 includes a flaw characteristic extracting module 31, a flaw characteristic library 32 and a flaw type searching module 33. The flaw characteristic extracting module 31 is configured to extract a flaw characteristic value from the converted flaw signal. The flaw characteristic library 32 is preset in the computing processing device 30, and is used to store steel wire rope flaw types and flaw characteristic values corresponding to the steel wire rope flaw types. The flaw type searching module 33 is configured to search the preset flaw characteristic library 32 for s steel wire rope flaw type corresponding to the flaw characteristic value. The flaw signals corresponding to different types of steel wire rope flaws, for example, wire breakage, abrasion, rusting, sectional area decrease and other situation, are reflected to have different amplitudes, different frequencies and other different characteristics, and signals corresponding to the same type of flaws have similar characteristics. Based on the above property, the flaw characteristic extracting module is configured to find out the flaw characteristic value reflecting the flaw from the converted flaw signal, to determine the flaw type based on the flaw characteristic value. Multiple existing extraction algorithms for example Fourier transform and wavelet transform may be used to extract the flaw characteristic value. In the embodiment, the wavelet technology is preferably used to extract the flaw characteristic. With the wavelet transform extraction method, a characteristic of the signal may be reserved using wavelet filtering denoising, and thus the wavelet filtering is superior to a conventional low-pass filter. As compared with Fourier transform, wavelet transform refers to local transform in space (time) and frequency, and thus information may be effectively extracted from the signal. Multiscale detailed analysis may be performed on a function or the signal using an operation function of scaling, translation and the like, to solve many problems which cannot be solved in Fourier transform. On such basis, the flaw characteristic extracting module 31 may further include a wavelet denoising unit and a characteristic value extracting unit. The wavelet denoising unit is configured to perform one-dimensional wavelet denoising processing on the converted flaw signal to obtain a reconstructed signal curve. The characteristic value extracting unit is configured to extract a characteristic value from the reconstructed signal curve. The wavelet denoising unit may include a preprocessing subunit, a one dimensional wavelet decomposition subunit, a decomposition coefficient processing subunit and a one-dimensional wavelet reconstructing subunit. The preprocessing subunit is configured to preprocess the converted flaw signal to remove a part of noise. The one-dimensional wavelet decomposition subunit is configured to perform wavelet transform on the preprocessed flaw signal to implement multi-scale decomposition. The decomposition coefficient processing subunit is configured to calculate a coefficient of each of the multiple scales and perform denoising processing on the coefficient of each of the multiple scales. The one-dimensional wavelet reconstructing subunit is configured to reconstruct a one-dimensional wavelet based on a low frequency coefficient of a lowest level and a high-frequency coefficient of each of levels in each of the multiple scales for wavelet decomposition. On such basis, a wavelet denoising process is analyzed below in combination with a formula. Generally, a noise signal is mostly distributed in a high-frequency range. Therefore, in the embodiment, after wavelet decomposition is performed on the signal, weight processing is performed on wavelet coefficients obtained by the decomposition using a threshold and the like, and then a small signal is reconstructed to implement signal denoising, which are described below respectively. 1. In one-dimensional signal wavelet decomposition, a wavelet is selected and decomposition levels are determined, and decomposition calculation is performed. A one-dimensional noisy signal model may be represented as: x(t)= f (t)+ c * e(t), where f(t) denotes a useful signal, x(t) denotes a noisy signal, e(t) denotes noise and e denotes a standard deviation of a noise coefficient. 2. In threshold quantification for a high-frequency coefficient obtained by wavelet decomposition, a threshold is selected for the high-frequency coefficient in each of the decomposition scales to perform soft threshold quantification. In wavelet transform, a threshold required by a coefficient of each level is generally selected based on a signal to noise ratio of an original signal that is, the threshold is calculated based on a standard difference of a wavelet decomposition coefficient of each level. After noise strength of the signal is obtained, a threshold of each level may be determined. The signal is represented in a space of Vj= Vj1+W , that is, each signal x (t) represented in V, may be represented by a basis function in two spaces: x(t)= cA (k)#jk(t)= cA(k)#T,k_(t)+ cD,(k) Wk(t) k kk

This process is used to decompose the signal x(t) as a sum of a low

frequency signal and a high-frequency signal, where cA, cAj and cD, are weight

coefficients. #,_1, (t) and 1 4 _1(t) are preset known wavelet basis functions, for example, dbl and db2. The coefficient A, (k) is decomposed in a scale metric space j to obtain two coefficients A, (k) and D, (k) in a scale metric space j-1. Similarly, the two coefficients A, (k) and D (k) may also be reconstructed to obtain the coefficient

AO(k).

When the wavelet and the scale are orthogonal in the space, coefficients cA (k) and cD, (k) may be calculated according to an inner product formula:

=< , x(t) ,_ 0, >-<' (I~y,,Wg >-E cn) < 0,(t), )_1, (t) cD ,(k) >

A formula for an inner product calculation method is represented as follows.

<i $ (t ), _Q) >= 42 (b$(21 -n)4 2 1-kd

= VT-'20$(2_ --n)$(2-fIt -k)dt

=JJ 2#(2s+2k - n)(s)ds

=J( 1F$0(2s+2k-n)E h,(im)/p(2s- m)ds

=Sh0(m) 0$(2s+2k-n)$(2s-m)2ds

=h,(n-2k)

= I 221-10(21 -- n)W(2*-'t - k)dt

= 00(2s+2k - n)W(s)ds

- v¶0(2s + 2k - n) h (m) AW(2s - m)ds

- hi(m) 0(2s + 2k - n) W(2s - m)2ds

- hi(n - 2k)

In the above deduction process, s=2j- t-k and a double-scale equation

#(t) 0 #(2s - m), Wt =2nhlW(2s -m)#b(t)= Fih, i5 cn#(2t -n) which meets both a scale function and a wavelet function in a Multi-Resolution Analysis (MRA) theory, are used, and an orthogonality property of wavelet bases is also utilized, that is, 0 is obtained only in a case that m is not equal to n-2k. A coefficient calculation process is represented as follows.

cA(k) h(n-2k cA0 (n)

cD(k)= / (n -2k)cAo(n)

The essence to the above wavelet decomposition process is to decompose a

wavelet into multiple digital filters, where ho and hi denote coefficients of the filters.

The wavelet decomposition can be implemented by designing a coefficient array of a high-pass filter and a low-pass filter. 3. One-dimensional wavelet is reconstructed. A one-dimensional wavelet is reconstructed based on the low-frequency coefficient of the lowest level and the high frequency coefficient of each of levels for wavelet decomposition. Reconstruction is an inverse process of decomposition, and a reconstruction denoising algorithm based on hard threshold, soft threshold and the like may be used. A waveform curve obtained after wavelet reconstruction of the signal is smooth and has apparent characteristics, which facilitates extraction of characteristic value and further flaw recognition. Herein, the characteristic value may include a peak value, a wave width, a wavelet coefficient, wavelet packet energy or other parameter. A running speed of the steel wire rope may also be calculated based on the determined characteristic value. That is, the same type of flaw is formed on two parts of the same steel wire rope. Since the same type of flaws corresponds to the same flaw characteristic value and a distance between the two parts is known, the running speed of the steel wire rope may be calculated based on an occurrence time interval between occurrences of the same flaw characteristic value and the distance. In FIG. 2, the computing processing device 30 may further include a time recording module 34 and a speed calculating module 35. The time recording module 34 is configured to record an occurrence time interval between occurrences of two parts preset in the steel wire rope which present a same flaw type. The speed calculating module 35 is configured to calculate a running speed of the steel wire rope based on a preset distance and the occurrence time interval between the two parts which present the same flaw type. For example, if the occurrence time interval between occurrences of the same characteristic value is t and the distance between the two flaw parts is s, the running speed of the steel wire rope is v=s/t. As compared with existing coder-based speed detection, measurement accuracy is high in the embodiment, and can resist an influence of slipping of the steel wire rope. FIG. 3 is a schematic structural diagram of an online flaw detection monitoring system for a steel wire rope according to yet another embodiment of the disclosure. As compared with the above embodiments, the embodiment further includes an image acquiring camera 40 configured to acquire a flaw image of the steel wire rope. The communication module 20 is further configured to transmit the flaw image of the steel wire rope to the computing processing device 30. The computing processing device 30 is further configured to enhance the flaw image of the steel wire rope and display the enhanced flaw image. The image acquiring camera 40 may be configured to acquire an actual state of the steel wire rope, and a flaw is displayed on the upper computer more clearly and directly using image enhancement technology. In conjunction with flaw detection and type determination for the steel wire rope, a control instruction may be transmitted to the image acquiring camera 40 upon determining the steel wire rope flaw type, to acquire a current flaw image of the steel wire rope. The current flaw image is displayed on the upper computer, and thus the operator can observe and determine a damage condition, thereby timely making an early warning about a dangerous situation. Correspondingly, the computing processing device 30 may further include a steel wire rope flaw displaying module configured to, in a case that the steel wire rope flaw type is determined, transmit a control instruction to the image acquiring camera 40 to acquire a current flaw image of the steel wire rope. Various existing image enhancement technologies may be used for enhancing the flaw image. The disclosure provides an image homomorphic filtering-based example. That is, the computing processing device 30 includes an image grayscale transform module 36, a light component separating module 37, a low-pass filtering processing module 38 and an image high-frequency enhancing module 39. The image grayscale transform module 36 is configured to represent a grayscale value of the flaw image of the steel wire rope as an incident light component and an incident light constant which occupy a low-frequency part in a frequency domain and a reflected light component which occupies a high-frequency part in the frequency domain. The light component separating module 37 is configured to separate the incident light component, the incident light constant and the reflected light component using a logarithm calculation method. The low-pass filtering processing module 38 is configured to perform low-pass filtering processing on an expression obtained after separation. The low-pass filtering processing module 38 is preferably a median filter. The image high-frequency enhancing module 39 is configured to subtract an expression obtained after low-pass filtering from the expression obtained after separation, add to the incident light constant, and perform an exponential operation, to obtain a high-frequency enhanced image. On such basis, image processing is described below in combination with a formula. In a case that a flaw signal of wire breakage and the like occurs, the image acquiring camera 40 immediately acquires a flaw image with an image grayscale value equal to a product of an incident light component and a reflected light component. Herein, the incident light component occupies the low-frequency part in the frequency domain and corresponds to an image background, and reflected light component depends on a property of an object, that is, a brightness feature of a scenery determines the reflected light. Since Fourier Transform is performed twice in homomorphic filtering algorithm in a frequency-domain, a large computation space is occupied and a real-time requirement is difficult to be met. Therefore, homomorphic filtering is generally performed and implemented in a space domain. A general idea of the homomorphic filtering algorithm in the space domain is to perform low-pass filtering on the image, and then subtract the image obtained after the low-pass filtering from an original image, and an obtained result may realize an effect of low frequency suppression and high-frequency enhancement.

A grayscale function f(x,y) of the image is represented as the following

formula:

f (x,y)= io -i(x,y)-r(x,y),

where i(x,y) denotes the incident light component, r(x,y) denotes the

reflected light component and io denotes the incident light constant. io is added to

reserve a certain low-frequency component and obtain a good display effect. The incident light is separated from the reflected light using the logarithm calculation method as follows: g (x, y)= in f (x, y)= In i +ln i(x, y)+ln r (x, y)

Since the incident light component and the incident light constant correspond to a low-frequency part of the image, and the reflected light component corresponds to a high-frequency part of the image, the incident light component and the incident light constant (that is, the low-frequency part of the image) can be separated approximately after low-pass filtering is performed on g(x, y), as expressed in the following formula.

g (x,y)= LPFg (x, y) ~ In i. + in i(x, y),

where LPF denotes a low-pass filter, and a median filtering algorithm is used in the low-pass filter for filtering. With the median filtering algorithm, not only noise in a transmission process can be removed, but also an edge for a broken wire can be protected. The median filtering, as a nonlinear filtering, arranges grayscale values of pixels in a coverage area of a structural element in an ascending order, and takes a median of the grayscale values as a grayscale value of a central pixel in the coverage area of the structural element. A structural element with an odd number of pixels is used, for example, 3x3 and 5x5. Median filtering refers to filtering out a grayscale value of a pixel which is greatly different from grayscale values of pixels surrounding the pixel, in addition to calculating a median. Therefore, the median filtering can not only eliminate an isolated noise point, but also reduce a range of a blurred image, to reserve an edge characteristic of the image. The median filter executes a nonlinear operation. A principle of median filtering in a digital signal is described as follows.

A one-dimensional sequence ff 2f 3 ,.--- is assumed, and the length (point

number) of a window is assumed to be m (m is an odd number). Median filtering is performed on the one-dimensional sequence. That is, m numbers

ff,f,..., are extracted from the input sequence, where f is a

grayscale value of a central point of the window and v = 2 . Then, a median is 2 taken from the grayscale values of the m points arranged in order, and the median is an output of the filter. The median filtering is represented as a mathematical formula as follows: yi = Med {f;_.. fi..., f }. Two-dimensional median filtering is represented as: yy = Ied (fy A where A denotes a window and fj denotes a two-dimensional data sequence.

After low-pass filtering is performed the image, the image obtained after the

low-pass filtering is subtracted from the original image, and then inio is added to

reserve a certain low-frequency component, so as to obtain a high-frequency enhanced image represented as follows:

s(x,y)=In i0 +g(x, y)-- g (x,y) IIn , +In r(x,y)

An exponential operation is performed on s(x,y) to obtain a final

enhancement result represented as follows:

s~~ ~ ~ (x, y)= y)etr~,y

With the homomorphic filtering algorithm, high-frequency information on the image may be enhanced while reserving a part of low-frequency information, thereby having an effect of compressing a dynamic range of a grayscale of the image and enhancing contrast of the image, particularly in a case of insufficient brightness and blurred details of the image caused by poor illumination. The above online flaw detection monitoring system for the steel wire rope according to the embodiments may be applied to various devices, equipment or systems in which steel wire ropes are used for operation, and particularly to a mining multi-rope friction hoisting system. Therefore, a mining multi-rope friction hoisting system is further provided in the disclosure, which includes the above online flaw detection monitoring system for the steel wire rope. Based on the online flaw detection monitoring system for the steel wire rope according to the embodiments, an online flaw detection monitoring method for a steel wire rope is provided in the disclosure. As shown in FIG. 7 which is a schematic flowchart of an online flaw detection monitoring method for a steel wire rope according to an embodiment of the disclosure, the online flaw detection monitoring method for the steel wire rope according to the embodiment includes steps 100 and 200.

At block 100, a flaw detection sensor acquires a flaw signal of a steel wire rope in real time, converts the flaw signal of the steel wire rope through a communication module and transmits the converted flaw signal to a computing processing device. At block 200, the computing processing device extracts a flaw characteristic value from the converted flaw signal and searches a preset flaw characteristic library for a steel wire rope flaw type corresponding to the flaw characteristic value. In FIG. 8, the operation in block 200 that the flaw characteristic value is extracted and the steel wire rope flaw type is searched for may include the following steps. At block 210, the computing processing device performs one-dimensional wavelet denoising on the converted flaw signal to obtain a reconstructed signal curve. At block 220, the computing processing device extracts a characteristic value from the reconstructed signal curve and searches the flaw characteristic library preset in the computing processing device for the steel wire rope flaw type corresponding to the flaw characteristic value based on the extracted flaw characteristic value. At block 210, the converted flaw signal is preprocessed to remove a part of noise, and wavelet transform is performed on the preprocessed flaw signal to implement multiscale decomposition. A coefficient of each of the multiple scales is calculated, and denoising processing is performed on the coefficient of each of the multiple scales. One-dimensional wavelet is reconstructed based on a low-frequency coefficient of a lowest level and a high-frequency coefficient of each of levels in each of multiple scales for wavelet decomposition. An online flaw detection monitoring method for a steel wire rope according to another embodiment may further include a speed calculating step. The speed calculating step includes: the computing processing device extracts the flaw characteristic value from the converted flaw signal, records an occurrence time interval between occurrences of two parts preset in the steel wire rope which present the same flaw type, and calculates a running speed of the steel wire rope based on a preset distance and the occurrence time interval between the two parts which present the same flaw type. For the online flaw detection monitoring system for the steel wire rope according to the embodiment which further includes an image acquiring camera, the online flaw detection monitoring method for the steel wire rope further includes a flaw image acquiring and displaying step. As shown in FIG. 9 which is a schematic flowchart of an online flaw detection monitoring method for a steel wire rope according to another embodiment of the disclosure, as compared with the above embodiment, the flaw image acquiring and displaying step includes steps 300 to 400. At block 300, the image acquiring camera transmits a flaw image of the steel wire rope to the computing processing device through the communication module. At block 400, the computing processing device enhances the flaw image of the steel wire rope and displays the enhanced flaw image. The operation at block 300 that the image acquiring camera acquires the image of the steel wire rope may be performed periodically and may also be driven by an event. For example, when the computing processing device determines that a flaw occurs in the steel wire rope or determines the steel wire rope flaw type, a control instruction is transmitted to the image acquiring camera to drive the image acquiring camera to acquire a current flaw image of the steel wire rope. At block 400, the computing processing device represents a grayscale value of the flaw image of the steel wire rope as an incident light component and an incident light constant which occupy a low-frequency part in a frequency domain and a reflected light component which occupies a high-frequency part in the frequency domain, and separates the incident light component, the incident light constant and the reflected light component using a logarithm calculation method. Then, the computing processing device performs low-pass filtering processing on an expression obtained after separation, subtracts an expression obtained after the low-pass filtering from the expression obtained after separation, adds to the incident light constant and then performs an exponential operation to obtain a high-frequency enhanced image. Preferably, median filtering algorithm is used in the computing processing device to separate the incident light component and the incident light constant in the expression obtained after separation, to implement low-pass filtering processing on the expression obtained after separation. Multiple embodiments in the specification are progressively described, and the embodiments focus on different parts respectively. For the same or similar parts of the embodiments, one embodiment refers to another embodiment. The method embodiments and the steps thereof correspond to the contents in the system embodiments, and therefore are described simply, and for related parts, reference is made to a part of description in the system embodiments. It should be noted that the above embodiments are intended to illustrate the technical solutions of the disclosure rather than limiting the technical solutions of the disclosure. Although the disclosure is described with reference to the preferred embodiments in detail, it should be understood by those skilled in the art that modifications may be made to the embodiments of the disclosure or equivalent substitution may be made to a part of technical features without departing from the spirit of the technical solutions of the disclosure, and the modifications and the equivalent substitution should all fall within the scope of the technical solutions claimed by the disclosure. Throughout this specification and the claims that follow, unless the context requires otherwise, the words 'comprise' and 'include', and variations such as 'comprising' and 'including', will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers. The reference to any prior art in this specification is not, and should not be taken as, an acknowledgment or any form of suggestion that such prior art forms part of the common general knowledge of the technical field.

Claims

1. An online flaw detection monitoring system for a steel wire rope, comprising a flaw detection sensor, a communication module and a computing processing device, the flaw detection sensor communicating with the computing processing device through the communication module, wherein the flaw detection sensor is arranged around a to-be detected steel wire rope, and is configured to acquire a flaw signal of the steel wire rope in real time; the communication module is configured to convert the flaw signal of the steel wire rope and transmit the converted flaw signal to the computing processing device; and the computing processing device is configured to extract a flaw characteristic value from the converted flaw signal, and search a preset flaw characteristic library for a steel wire rope flaw type corresponding to the flaw characteristic value, to determine the steel wire rope flaw type, wherein steel wire rope flaw types are stored in the preset flaw characteristic library, and different steel wire rope flaw types correspond to different flaw characteristic values, wherein the flaw detection sensor comprises N magnetically conductive flaw detection modules uniformly distributed in a circumferential direction, and each of the N magnetically conductive flaw detection modules covers a range of 360/N degrees of the steel wire rope, wherein the online flaw detection monitoring system comprises an image acquiring camera which is configured to acquire a flaw image of the steel wire rope, and the computing processing device comprises a steel wire rope flaw displaying module which is configured to, during determination of the steel wire rope flaw type, transmit a control instruction to the image acquiring camera to acquire a current flaw image of the steel wire rope.

2. The online flaw detection monitoring system for the steel wire rope of claim 1, wherein the computing processing device comprises: a flaw characteristic extracting module configured to extract the flaw characteristic value from the converted flaw signal; a flaw characteristic library, which is preset in the computing processing device and configured to store steel wire rope flaw types and flaw characteristic values corresponding to steel wire rope flaw types; and a flaw type searching module configured to search the preset flaw characteristic library for the steel wire rope flaw type corresponding to the flaw characteristic value.

3. The online flaw detection monitoring system for the steel wire rope of claim 2, wherein the computing processing device further comprises: a time recording module, configured to record an occurrence time interval between occurrences of two parts preset in the steel wire rope which present a same flaw type; and a speed calculating module, configured to calculate a running speed of the steel wire rope based on a preset distance and the occurrence time interval between the two parts which present the same flaw type.

4. The online flaw detection monitoring system for the steel wire rope of claim 2 or 3, wherein the flaw characteristic extracting module further comprises: a wavelet denoising unit, configured to perform one-dimensional wavelet denoising on the converted flaw signal to obtain a reconstructed signal curve; and a characteristic value extracting unit, configured to extract a characteristic value from the reconstructed signal curve.

5. The online flaw detection monitoring system for the steel wire rope of claim 4, wherein the wavelet denoising unit comprises: a preprocessing subunit, configured to preprocess the converted flaw signal to remove a part of noise; a one-dimensional wavelet decomposition subunit, configured to perform wavelet transform on the preprocessed flaw signal to implement multiscale decomposition; a decomposition coefficient processing subunit, configured to calculate a coefficient of each of a plurality of scales and perform denoising processing on the coefficient of each of the plurality of scales; and a one-dimensional wavelet reconstructing subunit, configured to reconstruct a one-dimensional wavelet based on a low-frequency coefficient of a lowest level and a high-frequency coefficient of each of levels in each of the plurality of scales for wavelet decomposition.

6. The online flaw detection monitoring system for the steel wire rope of claim 1, wherein the communication module is further configured to transmit the flaw image of the steel wire rope to the computing processing device; and the computing processing device is further configured to enhance the flaw image of the steel wire rope and display the enhanced flaw image.

7. The online flaw detection monitoring system for the steel wire rope of claim 6, wherein the computing processing device comprises: an image grayscale transform module, configured to represent a grayscale value of the flaw image of the steel wire rope as an incident light component and an incident light constant which occupy a low-frequency part in a frequency domain and a reflected light component which occupies a high-frequency part in the frequency domain; a light component separating module, configured to separate the incident light component, the incident light constant and the reflected light component using a logarithm calculation method; a low-pass filtering processing module, configured to perform low-pass filtering on an expression obtained after the separation; and an image high-frequency enhancing module, configured to subtract an expression obtained after the low-pass filtering from the expression obtained after separation, add to the incident light constant and perform an exponential operation to obtain a high-frequency enhanced image.

8. The online flaw detection monitoring system for the steel wire rope of claim 7, wherein the low-pass filtering processing module is a median filter.

9. The online flaw detection monitoring system for the steel wire rope of claim 1, wherein each of the N magnetically conductive flaw detection modules comprises an induction coil and two excitation coils having a same amount of magnetic flux and opposite directions, each of the two excitation coils is connected with an excitation source capable of supplying Alternating Current (AC), and in a case that the steel wire rope with a flaw moves relative to the magnetically conductive flaw detection module, an electromotive force signal induced by the induction coil is transmitted to the communication module.

10. The online flaw detection monitoring system for the steel wire rope of claim 1, further comprising a fixing frame, a height regulation mechanism and an angle regulation mechanism, wherein the flaw detection sensor is mounted on the angle regulation mechanism, the angle regulation mechanism is mounted on the height regulation mechanism and is capable of regulating an inclination angle of the flaw detection sensor, and the height regulation mechanism is mounted on the fixing frame and is capable of regulating a height of the flaw detection sensor.

11. The online flaw detection monitoring system for the steel wire rope of claim 1, further comprising a mining flame-proof and intrinsically safe substation, wherein the mining flame-proof and intrinsically safe substation comprises a flame-proof housing, an intrinsically safe power supply module, a remote power cutting-off and transmitting control module and a data processing module, wherein the intrinsically safe power supply module, the remote power cutting-off and transmitting control module and the data processing module are all integrated in a motor in the flame proof housing, and the intrinsically safe power supply module is configured to supply power to the flaw detection sensor and a servo single-chip microcomputer which is configured to control a driving power source of the steel wire rope, and the data processing module is configured to receive a signal transmitted by the flaw detection sensor and transmit the signal to the communication module via a communication interface.

12. The online flaw detection monitoring system for the steel wire rope of claim 11, wherein the communication module is a mining ordinary and intrinsically safe communication module, and is mounted at a ground monitoring center, and the communication module comprises a communication signal conversion unit, an optocoupler and an AC/Direct Current (DC) conversion circuit, wherein the communication signal conversion unit is configured to convert the flaw signal of the steel wire rope into a Universal Serial Bus (USB) interface signal, and the optocoupler and the AC/DC conversion circuit are configured to isolate a non-intrinsically safe output of the computing processing device from an intrinsically safe output of the communication interface.

13. An online flaw detection monitoring method for a steel wire rope based on the online flaw detection monitoring system for the steel wire rope of any one of claims 1 to 13, comprising: acquiring, by the flaw detection sensor, a flaw signal of a steel wire rope in real time, converting, by the communication module, the flaw signal of the steel wire rope, and transmitting, by the communication module, the converted flaw signal to the computing processing device; and extracting, by the computing processing device, a flaw characteristic value from the converted flaw signal, and searching, by the computing processing device, a preset flaw characteristic library for a steel wire rope flaw type corresponding to the flaw characteristic value, to determine the steel wire rope flaw type, wherein steel wire rope flaw types are stored in the preset flaw characteristic library, and different steel wire rope flaw types correspond to different flaw characteristic values, wherein the flaw detection sensor comprises N magnetically conductive flaw detection modules uniformly distributed in a circumferential direction, and each of the N magnetically conductive flaw detection modules covers a range of 360/N degrees of the steel wire rope, wherein the online flaw detection monitoring method further comprises: during determination of the steel wire rope flaw type, transmitting, by the steel rope flaw displaying module, a control instruction to the image acquiring camera to acquire a current flaw image of the steel wire rope; and acquiring, by the image acquiring camera, a flaw image of the steel wire rope.

14. The online flaw detection monitoring method for the steel wire rope of claim 13, wherein the operation that extracting, by the computing processing device, the flaw characteristic value from the converted flaw signal, and searching, by the computing processing device, the preset flaw characteristic library for the steel wire rope flaw type corresponding to the flaw characteristic value comprises: performing, by the computing processing device, one-dimensional wavelet denoising on the converted flaw signal to obtain a reconstructed signal curve; and extracting, by the computing processing device, a characteristic value from the reconstructed signal curve, and searching, by the computing processing device, the flaw characteristic library preset in the computing processing device for the steel wire rope flaw type corresponding to the flaw characteristic value based on the extracted flaw characteristic value.

15. The online flaw detection monitoring method for the steel wire rope of claim 14, wherein the operation that performing, by the computing processing device, the one dimensional wavelet denoising on the converted flaw signal to obtain the reconstructed signal curve comprises: preprocessing the converted flaw signal to remove a part of noise; performing wavelet transform on the preprocessed flaw signal to implement multiscale decomposition; calculating a coefficient of each of a plurality of scales, and performing denoising processing on the coefficient of each of the plurality of scales; and reconstructing a one-dimensional wavelet based on a low-frequency coefficient of a lowest level and a high-frequency coefficient of each of levels in each of the plurality of scales for wavelet decomposition.

16. The online flaw detection monitoring method for the steel wire rope of claim 13, further comprising a speed calculating step: extracting, by the computing processing device, the flaw characteristic value from the converted flaw signal, and recording, by the computing processing device, an occurrence time interval between occurrences of two parts preset in the steel wire rope which present a same flaw type; and calculating, by the computing processing device, a running speed of the steel wire rope based on a preset distance and the occurrence time interval between the two parts which present the same flaw type.

17. The online flaw detection monitoring method for the steel wire rope of claim 13, wherein the online flaw detection monitoring method for the steel wire rope further comprises a flaw image acquiring and displaying step: transmitting, by the image acquiring camera, a flaw image of the steel wire rope to the computing processing device through the communication module; and enhancing, by the computing processing device, the flaw image of the steel wire rope, and displaying, by the computing processing device, the enhanced flaw image.

18. A mining multi-rope friction hoisting system, comprising the online flaw detection monitoring system for the steel wire rope of any one of claims I to 12.