CN111024807A - Steel wire rope magnetic flux damage detection and quantification method - Google Patents
Steel wire rope magnetic flux damage detection and quantification method Download PDFInfo
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Abstract
The invention provides a method for detecting damage and quantifying magnetic flux of a steel wire rope, which comprises the following steps of: step S10, collecting a magnetic flux signal of the steel wire rope to be detected through a magnetic flux sensor; step S20, preprocessing the collected magnetic flux signal of the steel wire rope to be detected; step S30, extracting a defect magnetic flux signal from the magnetic flux signal after the preprocessing; step S40, analyzing and calculating the defect magnetic flux signal to obtain the characteristic input quantity of the defect magnetic flux signal, wherein the characteristic input quantity of the defect magnetic flux signal comprises the waveform peak-to-peak value and the waveform width value of the defect magnetic flux signal; step S50, inputting the characteristic input quantity of the defect magnetic flux signal into a mapping relation function; and step S60, calculating to obtain the accurate loss amount and width of the section of the defect metal. The invention can carry out nondestructive detection on the steel wire rope by detecting the magnetic flux, realizes the magnetic flux quantitative detection of the damage section loss and the width of the steel wire rope, has accurate, efficient and quick calculation and has great application significance.
Description
Technical Field
The invention relates to a steel wire rope magnetic flux detection method, in particular to a steel wire rope magnetic flux damage detection quantification method.
Background
The steel wire rope is widely applied to the fields of industry, civilian use, military use and the like. Over the service life, various damages to the steel wire rope are inevitable, which can significantly reduce the mechanical properties of the material, such as strength, toughness, plasticity and the like, and seriously affect the safe use of the material, so that the steel wire rope needs to be checked regularly. The parameters of the steel wire rope defect are the metal section loss amount and the width, and the two parameters are directly related to the steel wire rope bearing capacity, so that the metal section loss quantification and the width detection of the steel wire rope defect are the most important.
The steel wire rope magnetic flux detection is the most commonly used effective method, and the magnetic flux detection mainly detects the magnetic flux variation of a detected object. The method has the advantages that: the detected flux value is related to the cross-section loss area of the measured object; whether the defect is outside or inside, the magnetic flux nondestructive detection can be detected; the magnetic flux signal is hardly influenced by the lifting of the magnetic flux sensor and the running jitter of the steel wire rope, even if the detection environment of the steel wire rope is relatively severe. However, when the axial width of the defect is smaller than a certain value, the detected magnetic flux signal is not only related to the cross-sectional loss area of the detected defect, but also related to the axial width of the defect, and the complex nonlinear relation is presented. The current magnetic flux detection method is difficult to accurately detect the loss area and width of the metal section of the defect, a large number of standard defects are required to be manufactured to train specific artificial neural network classification and identification, if the type of the steel wire rope is changed, the standard defects are required to be manufactured again and the artificial neural network is required to be trained, and the detection difficulty and the complexity are increased.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for detecting and quantifying the damage of the steel wire rope by magnetic flux, which is accurate in calculation, high in efficiency and quick.
Therefore, the invention provides a steel wire rope magnetic flux damage detection quantitative method, which comprises the following steps:
step S10, collecting a magnetic flux signal of the steel wire rope to be detected through a magnetic flux sensor;
step S20, preprocessing the collected magnetic flux signal of the steel wire rope to be detected;
step S30, extracting a defect magnetic flux signal from the magnetic flux signal after the preprocessing;
step S40, analyzing and calculating the defect magnetic flux signal to obtain the characteristic input quantity of the defect magnetic flux signal, wherein the characteristic input quantity of the defect magnetic flux signal comprises the waveform peak-to-peak value and the waveform width value of the defect magnetic flux signal;
step S50, inputting the characteristic input quantity of the defect magnetic flux signal into a mapping relation function;
and step S60, calculating to obtain the accurate loss amount and width of the section of the defect metal.
A further refinement of the invention is that said step S20 comprises the following sub-steps:
step S201, discrete point elimination is carried out on the magnetic flux signal Y;
step S202, the signal after the discrete point elimination is processed by baseline elimination.
In a further improvement of the present invention, in the step S201, let Y (i) be the i-th magnetic flux collecting signal, and when the values of Y (i) and the magnetic flux signals before and after Y (i) are greater than the preset magnetic flux threshold, Y (i) -1) + Y (i +1)](i ═ 1,2, …, N), resulting in a signal Y after discrete point rejection1(i) And N is the total number of sampling points.
In a further development of the invention, in step S202, a signal data sequence Y is found1(i) Respectively fitting all the maximum value points and minimum value points into a signal data sequence Y by using a cubic spline function1(i) Upper and lower envelope lines of (a); the mean of the upper and lower envelope is m1, by equation Y2(i)＝Y1(i) M1 obtaining a new signal data sequence Y2(i) As a flux signal after preprocessing.
A further refinement of the invention is that said step S30 comprises the following sub-steps:
step S301, setting a defect preset threshold value mp of a detected steel wire rope;
step S302, a plurality of groups of continuous sampling points are obtained through sampling;
step S303, according to the position information of each group of acquisition points, searching points in the range of the defect preset threshold value mp forward, namely when Y is2When (before) is less than or equal to mp, recording Y2(before) value，Y2(before) is a magnetic flux signal searched forward at the current acquisition point position; backwards at a point within the range of the defect preset threshold mp, i.e. when Y2When backswards is less than or equal to 0, recording Y2Value of (backwards), Y2(backwards) forward-searched flux signal for current pick point location; then intercept Y2(before) to Y2(backwards) as a defect flux signal.
A further refinement of the invention is that said step S40 comprises the following sub-steps:
step S401, selecting corresponding wavelet basis and decomposition layer number for the defect magnetic flux signal according to waveform characteristics to obtain different hierarchical data of the defect magnetic flux signal;
step S402, selecting smooth waveforms in different hierarchical data in a defect magnetic flux signal to obtain a waveform peak value T and a waveform baseline value L of the defect magnetic flux signal, and calculating the waveform peak value VPP of the defect magnetic flux signal according to a formula VPP-T-L;
step S403, solving a differential result of the defect magnetic flux signal, and further obtaining a waveform width value lw of the defect magnetic flux signal.
In a further improvement of the present invention, in step S403, a differentiation result h (S) of the defect magnetic flux signal is obtained according to the formula h (S) ═ df (S)/ds (S) ═ 1,2, …, k), where k is the number of data of the defect magnetic flux signal and f (S) is the data of the defect magnetic flux signal; and then, according to the position of the waveform peak point of the defect magnetic flux signal, the position of the maximum value of h(s) is taken forward, the position of the minimum value of h(s) is taken backward, and the distance between the maximum value and the minimum value is calculated as the waveform width value lw of the defect magnetic flux signal.
A further refinement of the invention is that said step S50 comprises the following sub-steps:
step S501, designing x widths fw and y metal section loss areas fs, wherein the x is multiplied by y standard damages in total, and x and y are natural numbers;
step S502, calculating x y standard wounds from step S10 to step S40 to obtain corresponding standard wound waveform peak value fvpp and standard wound waveform width value flw;
step S503, taking the standard flaw waveform peak value fvpp and the standard flaw waveform width value flw as input independent variables, taking the width fw and the metal section loss area fs as output standard quantities, and designing a multiple-order equation set or a multilayer neural network as a novel mapping relation function.
In a further improvement of the present invention, in the step S503, a multiple equation set fw ═ f is designed1(fvpp,flw)、fs＝f2(fvpp, flw) (flw. ltoreq.W) and fs. f3(fvpp) (flw > W) as a function of a novel mapping relationship, wherein f1、f2And f3Is a preset coefficient of the multiple power equation set, and W is a preset waveform width threshold value.
In a further improvement of the present invention, in the step S60, the waveform peak-to-peak value VPP and the waveform width value lw of the defect magnetic flux signal obtained in the step S40 are input to the multi-power group or the multi-layer neural network in the step S50 as new input arguments instead of the standard flaw waveform peak-to-peak value fvpp and the standard flaw waveform width value flw, and an accurate defect metal cross-sectional loss amount and width are calculated, where the defect metal cross-sectional loss amount is a metal cross-sectional loss area.
Compared with the prior art, the invention has the beneficial effects that: the steel wire rope can be subjected to nondestructive detection by detecting magnetic flux, so that the magnetic flux quantitative detection of the damage section loss and the width of the steel wire rope is realized, all types of defects can be identified, and the quantitative accuracy of the metal section loss and the defect width is high; the method is accurate, efficient and quick in calculation, solves the problems of complex data calculation, long time and incapability of accurate quantification in the steel wire rope magnetic flux detection, lays a solid foundation for the quantitative detection application of the actual damage of the steel wire rope magnetic flux detection, and has great application significance.
Drawings
FIG. 1 is a schematic workflow diagram of one embodiment of the present invention;
FIG. 2 is a schematic diagram of a detection architecture according to an embodiment of the present invention;
FIG. 3 is a waveform of a defect flux signal according to one embodiment of the present invention;
FIG. 4 is a waveform illustrating different levels of data for a defective flux signal according to one embodiment of the present invention;
FIG. 5 is a waveform illustrating a characteristic input of a defect flux signal according to an embodiment of the present invention;
FIG. 6 is a diagram of a standard wound object in use according to an embodiment of the invention;
FIG. 7 is a waveform of a defect flux signal calculated by the present invention for the standard flaw of FIG. 6;
FIG. 8 is a diagram illustrating a mapping relationship of an equation in a multi-order equation set according to an embodiment of the present invention;
FIG. 9 is a diagram illustrating a mapping relationship of another equation in a multi-order equation set according to an embodiment of the present invention;
FIG. 10 is a diagram illustrating a mapping relationship of yet another equation in a multi-order equation set in accordance with an embodiment of the present invention;
FIG. 11 is a pictorial illustration of a cord defect in an application in accordance with an embodiment of the present invention;
FIG. 12 is a graph of a defect flux waveform at 20 calculated by the present invention for the cord defect of FIG. 11;
fig. 13 is a waveform diagram of a defect flux signal calculated for the wire rope defect of fig. 11 according to the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, the present embodiment provides a method for detecting and quantifying damage of a steel wire rope by magnetic flux, including the following steps:
step S10, collecting a magnetic flux signal of the steel wire rope to be detected through a magnetic flux sensor;
step S20, preprocessing the collected magnetic flux signal of the steel wire rope to be detected;
step S30, extracting a defect magnetic flux signal from the magnetic flux signal after the preprocessing;
step S40, analyzing and calculating the defect magnetic flux signal to obtain the characteristic input quantity of the defect magnetic flux signal, wherein the characteristic input quantity of the defect magnetic flux signal comprises the waveform peak-to-peak value and the waveform width value of the defect magnetic flux signal;
step S50, inputting the characteristic input quantity of the defect magnetic flux signal into a mapping relation function (also called a new mapping relation function);
and step S60, calculating to obtain the accurate loss amount and width of the section of the defect metal.
The method has the advantages that by detecting the magnetic flux of the damaged steel wire rope, all types of defects including internal defects can be identified, the defect metal section loss and the width quantitative accuracy are high, the calculation is quick and simple, only a small amount of standard samples are needed, the samples do not need to be made again for the steel wire ropes with different structures and made of the same materials, and the like.
The method comprises the steps of collecting and preprocessing magnetic flux signals of the steel wire rope, extracting defect magnetic signals, analyzing and calculating the defect magnetic signals to obtain characteristic input quantity of the defect magnetic signals, and obtaining accurate defect metal section loss quantity and width by using the calculation method designed by the embodiment.
This example is further illustrated below with reference to the accompanying drawings: fig. 2 is a schematic view of a steel wire rope magnetic flux nondestructive testing structure, and the steel wire rope magnetic flux damage detection quantitative method shown in fig. 1 is adopted.
Specifically, the method for acquiring the magnetic flux signal of the steel wire rope to be detected by the magnetic flux sensor in step S10 is as follows: the magnetic flux signal is influenced by the speed of the steel wire rope, and the speed of the steel wire rope cannot be accurately detected in real time, so that the influence of the speed of the steel wire rope needs to be eliminated. Integrating the steel wire rope S to be detected with the magnetic flux signal by an integrator, and then sampling and acquiring data in an equal space or performing equidistant integration processing and acquisition on the steel wire rope S to be detected with the magnetic flux signal by the integrator, wherein the formula is as follows:
or Y ═ Sdl, where dt represents the derivative over time, N is the number of total sample points, and dl represents the derivative over spatial distance.
Step S20 in this example includes the following substeps:
step S201, discrete point elimination is carried out on the magnetic flux signal Y;
step S202, the signal after the discrete point elimination is processed by baseline elimination.
In this embodiment, step S201 performs discrete point elimination on the magnetic flux signal Y, which is also called as performing outlier elimination on the magnetic flux signal Y, i.e. eliminates unnecessary individual discrete points, and if Y (i) is greater than the values of the front and rear magnetic flux signals, Y (i) ═ Y (i-1) + Y (i +1)](i is 1,2, …, N), and obtaining a signal Y after the outlier rejection processing1(i) N is the total number of sampling points; that is, in step S201 of this example, let Y (i) be the i-th magnetic flux collecting signal, and when the values of Y (i) and the front and rear magnetic flux signals are greater than the preset magnetic flux threshold, Y (i) ═ Y (i-1) + Y (i +1)](i ═ 1,2, …, N), resulting in a signal Y after discrete point rejection1(i) And N is the total number of sampling points.
In this example, step S202 is used to perform baseline elimination on the above signals, the baseline elimination is performed by using methods including, but not limited to, envelope spectrum extraction, wavelet decomposition, window averaging, empirical mode decomposition, and the like, and the following is a preferred baseline elimination method implemented by empirical mode decomposition in this example: find the signal data sequence Y1(i) Respectively fitting all the maximum value points and minimum value points into a signal data sequence Y by using a cubic spline function1(i) Upper and lower envelope lines of (a); the mean of the upper and lower envelope is m1, by equation Y2(i)＝Y1(i) M1 obtaining a new signal data sequence Y2(i) The new signal data sequence Y is used as the magnetic flux signal after preprocessing2(i) To subtract the new sequence of low frequencies.
In step S30, for the magnetic flux signal processed as above, the magnetic flux signal at the defect is extracted, so as to better calculate the characteristic input quantity of the defect magnetic flux signal; the method specifically comprises the following substeps:
step S301, setting a defect preset threshold value mp of a detected steel wire rope; extracting sampling points which are larger than the defect preset threshold value mp, wherein the defect preset threshold value mp can be obtained through the actual test of the minimum defect of the steel wire rope to be tested, specifically, the defect preset threshold value mp can be set to be an appropriate threshold value according to the peak value of the minimum defect magnetic flux waveform of the steel wire rope to be tested, and can also be set and modified in a user-defined manner according to the requirements of users;
step S302, a plurality of groups of continuous sampling points are obtained through sampling, and each group of sampling points is a partial magnetic flux signal of each defect;
step S303, according to the position information of each group of acquisition points, searching points in the range of the defect preset threshold value mp forward, namely when Y is2When (before) is less than or equal to mp, recording Y2Value of (before), Y2(before) is a magnetic flux signal searched forward at the current acquisition point position; backwards at a point within the range of the defect preset threshold mp, i.e. when Y2When backswards is less than or equal to 0, recording Y2Value of (backwards), Y2(backwards) forward-searched flux signal for current pick point location; then intercept Y2(before) to Y2(backwards) as a defect flux signal. The defect preset threshold value mp can be 0, or P points are respectively intercepted on the left and right sides of the axial direction of the waveform to obtain the waveform of each defect magnetic flux signal, wherein P is 5-10 times of the data quantity of the current set of acquisition points, and can also be obtained through the test of the maximum defect of the actual steel wire rope.
In this example, step S40 performs waveform decomposition processing on the extracted defect magnetic signal, extracts a smooth waveform, obtains a peak-to-peak value of the defect waveform by a peak-to-peak value calculation method, obtains a width value of the defect waveform by a derivative extreme point peak-to-peak width calculation method, where the peak-to-peak value and the width value of the defect waveform are characteristic input quantities, and specifically includes the following sub-steps:
step S401, as shown in FIG. 3, selecting corresponding wavelet basis and decomposition layer number for the defect magnetic flux signal according to waveform characteristics to obtain different hierarchical data of the defect magnetic flux signal, as shown in FIG. 4;
step S402, selecting smooth waveforms in different hierarchical data in the defect magnetic flux signal (only smooth waveforms can be selected according to actual conditions), obtaining a waveform peak value T and a waveform baseline value L of the defect magnetic flux signal, and calculating the waveform peak value VPP of the defect magnetic flux signal through a formula VPP-T-L;
step S403, solving a differential result of the defect magnetic flux signal, and further obtaining a waveform width value lw of the defect magnetic flux signal.
In step S403 in this example, it is preferable to first obtain the differentiation result h (S) of the defect magnetic flux signal by the formula h (S) ═ df (S)/ds (S) ═ 1,2, …, k, where k is the number of data of defect magnetic flux signals and f (S) is the data of defect magnetic flux signals; as shown in fig. 5, the position of the maximum value of h(s) is taken forward and the position of the minimum value of h(s) is taken backward according to the position of the peak point of the waveform of the defect magnetic flux signal, and the distance between the maximum value and the minimum value is calculated as the waveform width value lw of the defect magnetic flux signal.
The solving method of the novel mapping relation function mentioned in step S50 in this example is as follows: the novel mapping relation function is obtained by carrying out mapping network calculation on the characteristic input values of a plurality of designed standard wounds and the actual metal section loss area and width values of the defects, or carrying out mapping network calculation on the characteristic input values of a plurality of different standard wounds, the actual metal section loss area and width values of the defects, the characteristic input values of a small amount of actual standard wounds and the actual metal section loss area and width values of the defects through simulation. It should be noted that, in the present example, the standard damage calculation mapping network is designed, and the calculation values of different standard damages are simulated through software, so that only a small amount of actual standard damages need to be designed.
That is to say, the mapping relation function of this example is obtained by performing mapping network calculation on the feature input values of a plurality of designed standard wounds and the actual metal section loss areas and width values of the defects, or by performing mapping network calculation on the feature input values of a plurality of different standard wounds, the actual metal section loss areas and width values of the defects, the feature input values of a small number of actual standard wounds, and the actual metal section loss areas and width values of the defects through simulation; training without designing a large number of standard injuries.
In addition, the steel wire ropes with different structures and the same material can be subjected to accurate defect metal section loss and width quantitative detection by only once mapping network calculation without re-manufacturing samples.
Step S50 in this example preferably includes the following specific sub-steps:
step S501, designing x widths fw and y metal section loss areas fs, wherein the total x is multiplied by y standard injuries, x and y are natural numbers, x is determined according to the maximum width of the actual steel wire rope defect and the corresponding mapping network, y is determined according to the maximum section loss area of the actual steel wire rope defect and the corresponding mapping network, and x and y can be preset or adjusted according to actual conditions and requirements;
step S502, calculating x y standard wounds from step S10 to step S40 to obtain corresponding standard wound waveform peak value fvpp and standard wound waveform width value flw;
step S503, taking the standard flaw waveform peak value fvpp and the standard flaw waveform width value flw as input independent variables, taking the width fw and the metal section loss area fs as output standard quantities, and designing a multiple-order equation set or a multilayer neural network as a novel mapping relation function.
In step S503, a multiple equation set fw ═ f is designed1(fvpp,flw)、fs＝f2(fvpp, flw) (flw. ltoreq.W) and fs. f3(fvpp) (flw > W) as a function of a novel mapping relationship, wherein f1、f2And f3The preset coefficient f of the multiple equation set can be calculated according to the data of the peak value fvpp of the standard flaw waveform, the width value flw of the standard flaw waveform, the width fw, the loss area fs of the metal section and the like1，f2，f3Or the corresponding mapping neural network is the novel mapping relation function; w is a preset waveform width threshold value, and can be set or modified according to actual conditions.
In step S60 of this example, the waveform peak-to-peak value VPP and the waveform width value lw of the defect magnetic flux signal obtained in step S40 are input to the multi-order equation group or the multi-layer neural network in step S50 as new input arguments instead of the standard flaw waveform peak-to-peak value fvpp and the standard flaw waveform width value flw, and the accurate defect metal cross-sectional loss amount and width are calculated, where the defect metal cross-sectional loss amount is a metal cross-sectional loss area.
The embodiment also provides an application test entityFor example, 6 different widths are designed for steel bars made of the same material of the tested steel wire rope: 10mm, 20mm, 30mm, 60mm, 90mm and 120 mm; loss area of 5 different metal sections: 9.4248mm2、18.8496mm2、28.2743mm2、37.6991mm2And 47.1239mm2(ii) a A total of 30 standard wounds. One of the standard wounds is shown in fig. 6.
Fig. 7 shows the waveform of the magnetic flux calculated by the present invention for 30 standard flaws. Extracting the peak value fvpp and the width value flw of the characteristic input quantity of the signal, and calculating to obtain a mapping relation function graph, wherein fw is f1(fvpp, flw) As shown in FIG. 8, fs ═ f2(fvpp, flw) (flw. ltoreq. W) is shown in FIG. 9, fs ═ f3(fvpp) (flw ≧ W) As shown in FIG. 10, in which the preset waveform width threshold value W is 90 mm.
By adopting the calculation method and the mapping relation function, the actual steel wire rope is tested, and the application example has 20 defects, such as one defect of the steel wire rope shown in fig. 11. The calculated defect flux waveform at 20 is shown in fig. 12. From this, it can be seen that, according to this example, the defects at 20 points of the wire rope can be completely identified, and the metal cross-sectional loss area and the width value of each defect can be accurately calculated.
In conclusion, the steel wire rope can be subjected to nondestructive detection by detecting the magnetic flux, so that the magnetic flux quantitative detection of the damage section loss and the width of the steel wire rope is realized, not only can all types of defects be identified, but also the quantitative accuracy of the metal section loss and the defect width is high; the method is accurate, efficient and quick in calculation, solves the problems of complex data calculation, long time and incapability of accurate quantification in the steel wire rope magnetic flux detection, lays a solid foundation for the quantitative detection application of the actual damage of the steel wire rope magnetic flux detection, and has great application significance.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A steel wire rope magnetic flux damage detection quantitative method is characterized by comprising the following steps:
step S10, collecting a magnetic flux signal of the steel wire rope to be detected through a magnetic flux sensor;
step S20, preprocessing the collected magnetic flux signal of the steel wire rope to be detected;
step S30, extracting a defect magnetic flux signal from the magnetic flux signal after the preprocessing;
step S40, analyzing and calculating the defect magnetic flux signal to obtain the characteristic input quantity of the defect magnetic flux signal, wherein the characteristic input quantity of the defect magnetic flux signal comprises the waveform peak-to-peak value and the waveform width value of the defect magnetic flux signal;
step S50, inputting the characteristic input quantity of the defect magnetic flux signal into a mapping relation function;
and step S60, calculating to obtain the accurate loss amount and width of the section of the defect metal.
2. The method for detecting and quantifying the damage of the steel wire rope according to the claim 1, wherein the step S20 comprises the following substeps:
step S201, for the magnetic flux signalYRemoving discrete points;
step S202, the signal after the discrete point elimination is processed by baseline elimination.
3. The method for detecting and quantifying the damage of a wire rope according to claim 2, wherein in step S201, a magnetic flux is setY(i) Is as followsiA magnetic flux collecting signal whenY(i) When the front and back magnetic flux signal values are larger than the preset magnetic flux threshold value,Y(i)=[Y(i-1)+Y(i+1)]/2 (i=1, 2, …,N) Obtaining the signal after the discrete point eliminationY 1(i)，NThe total number of sampling points.
4. The wireline flux detection of claim 3The method for quantifying damage is characterized in that in step S202, a signal data sequence is foundY 1(i) Respectively fitting all the maximum value points and minimum value points into a signal data sequence by using a cubic spline functionY 1(i) Upper and lower envelope lines of (a); the mean of the upper and lower envelopes is m1, by the formulaY 2(i)=Y 1(i) M1 obtaining a new signal data sequenceY 2(i) As a flux signal after preprocessing.
5. The method for detecting and quantifying the damage of the steel wire rope according to the magnetic flux of the claim 4, wherein the step S30 comprises the following substeps:
step S301, setting a defect preset threshold value mp of a detected steel wire rope;
step S302, a plurality of groups of continuous sampling points are obtained through sampling;
step S303, according to the position information of each group of acquisition points, searching forward points within the range of the defect preset threshold value mp, namely when the points are detectedY 2(before) When mp is not more than mp, recordY 2(before) The value of (a) is,Y 2(before) Forward searching for a magnetic flux signal for a current acquisition point position; backward to a point within the range of the defect preset threshold mp, i.e. whenY 2(backwards) When the value is less than or equal to 0, recordingY 2(backwards) The value of (a) is,Y 2(backwards) Forward searching for a magnetic flux signal for a current acquisition point position; then interceptingY 2(before) ToY 2(backwards) As a defect flux signal.
6. The method for detecting and quantifying the damage of the steel wire rope according to any one of claims 1 to 5, wherein the step S40 comprises the following substeps:
step S401, selecting corresponding wavelet basis and decomposition layer number for the defect magnetic flux signal according to waveform characteristics to obtain different hierarchical data of the defect magnetic flux signal;
step S402, selecting smooth waveforms in different hierarchical data in a defect magnetic flux signal to obtain a waveform peak value T and a waveform baseline value L of the defect magnetic flux signal, and calculating a waveform peak value VPP of the defect magnetic flux signal through a formula VPP = T-L;
step S403, solving a differential result of the defect magnetic flux signal, and further obtaining a waveform width value lw of the defect magnetic flux signal.
7. The method for detecting and quantifying the damage of the steel wire rope according to the claim 6, wherein in the step S403, the formula is firstly usedh(s)=df(s)/ds(s=1, 2, …,k) Solving a differential result of the defect flux signalh(s) Wherein, in the step (A),kthe number of data of the defective magnetic flux signal,f(s) Data that is a defect flux signal; then according to the position of the waveform peak point of the defect magnetic flux signal, taking forwardh(s) Is taken backwards to the position of the maximum ofh(s) The distance between the maximum value and the minimum value is calculated as the waveform width value lw of the defect magnetic flux signal.
8. The method for detecting and quantifying the damage of the steel wire rope according to the claim 7, wherein the step S50 comprises the following substeps:
step S501, designing x widths fw and y metal section loss areas fs, wherein the x is multiplied by y standard damages in total, and x and y are natural numbers;
step S502, calculating x y standard wounds from step S10 to step S40 to obtain corresponding standard wound waveform peak value fvpp and standard wound waveform width value flw;
step S503, taking the standard flaw waveform peak value fvpp and the standard flaw waveform width value flw as input independent variables, taking the width fw and the metal section loss area fs as output standard quantities, and designing a multiple-order equation set or a multilayer neural network as a novel mapping relation function.
9. The method for detecting and quantifying the damage of a steel wire rope according to claim 8, wherein the method is characterized in thatCharacterized in that, in the step S503, a multiple equation system fw =isdesignedf 1(fvpp, flw)、fs=f 2(fvpp, flw) (flw. ltoreq.W) and fs =f 3(fvpp) (flw > W) as a function of the novel mapping relationship, wherein,f 1、f 2andf 3is a preset coefficient of the multiple power equation set, and W is a preset waveform width threshold value.
10. The method for quantifying the damage in the flux detection of the steel wire rope according to claim 8, wherein in step S60, the waveform peak-to-peak value VPP and the waveform width value lw of the defect flux signal obtained in step S40 are substituted for the standard flaw waveform peak-to-peak value fvpp and the standard flaw waveform width value flw as new input arguments, and are substituted into the multi-power group or the multi-layer neural network in step S50 to calculate the accurate cross-sectional loss amount and width of the defect metal, wherein the cross-sectional loss amount of the defect metal is the metal cross-sectional loss area.
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