阅读说明：本技术 考虑传感器感度的声发射事件震级获取方法、系统及可读存储介质 (Acoustic emission event magnitude acquisition method and system considering sensor sensitivity and readable storage medium ) 是由 彭康 尚雪义 郭宏扬 王云强 卢友昀 于 2019-10-11 设计创作，主要内容包括：本发明公开了一种考虑传感器感度的声发射事件震级获取方法、系统及可读存储介质,包括如下步骤：首先利用加窗互相关法计算每个声发射信号相对第一个到达的声发射信号P波走时差；再采用变网格搜索的方式计算虚拟场势值,确定声发射事件发生位置；计算每个传感器的感度系数；计算每个声发射信号的最大振幅,借助传感器的感度系数得到修正后的最大振幅；计算每个传感器修正后最大振幅对应的震级,再取所有震级的中位数作为该声发射事件震级。该方法能快速拾取P波走时差、具有较强的抗噪性,同时能降低大拾取误差的影响,较快的得到全局最优定位结果。此外,该方法得到的声发射事件震级较传统单一传感器得到的震级更为可靠。(The invention discloses a method and a system for acquiring the magnitude of an acoustic emission event by considering the sensitivity of a sensor and a readable storage medium, wherein the method comprises the following steps: firstly, calculating the P wave travel time difference of each acoustic emission signal relative to the first arrival acoustic emission signal by using a windowing cross-correlation method; calculating a virtual field potential value by adopting a variable grid searching mode, and determining the occurrence position of an acoustic emission event; calculating a sensitivity coefficient of each sensor; calculating the maximum amplitude of each acoustic emission signal, and obtaining the corrected maximum amplitude by means of the sensitivity coefficient of the sensor; and calculating the magnitude corresponding to the maximum amplitude after the correction of each sensor, and taking the median of all magnitudes as the magnitude of the acoustic emission event. The method can quickly pick up the P wave travel time difference, has strong noise resistance, can reduce the influence of large pick-up errors, and can quickly obtain the global optimal positioning result. In addition, the acoustic emission event magnitude obtained by the method is more reliable than that obtained by a traditional single sensor.)

1. An acoustic emission event magnitude acquisition method taking sensor sensitivity into consideration, comprising the steps of:

step 1: dividing the acquired signal set of the acoustic emission events into signal sets of different acoustic emission events according to the signal time difference, sequencing all the acoustic emission signals of each acoustic emission event according to triggering sequence, and performing windowing and lengthening on the sequenced acoustic emission signals to a point with the amplitude of 0 to obtain a windowed acoustic emission signal set of each acoustic emission event;

step 2: calculating the concentration of windowed acoustic emission signals of each acoustic emission event based on a cross-correlation method, the P-wave travel time difference (t) of each windowed acoustic emission signal relative to the first windowed acoustic emission signal₁,t₂,...,t_n) N is the number of acoustic emission signals;

and step 3: based on P-wave travel time difference (t)₁,t₂,...,t_n) Calculating virtual field potential values of grid points of a space where the emission event is located by adopting a virtual field method, taking the grid point with the maximum virtual field potential value as the position of the acoustic emission event, and further calculating the sensitivity coefficient of the sensor by utilizing the distance between the position of the acoustic emission event and the sensor;

and 4, step 4: correcting the maximum amplitude of all original acoustic emission signals of the acoustic emission event by using the sensitivity coefficient of the sensor;

and 5: determining the magnitude of each acoustic emission signal according to the maximum amplitude of the corrected acoustic emission signals, and taking the median of the magnitudes of all the acoustic emission signals as the magnitude of the acoustic emission event;

wherein M is_iRepresenting the magnitude of the ith acoustic emission signal,

2. The method of claim 1, wherein the windowing and growing of the signal of the acoustic emission event is performed as follows:

firstly, pick up the time T before the first arrival of a P wave₁And then pick up the time T after the first P wave peak₂Then automatically extending to the next time T with amplitude of 0₃Obtaining a windowed signal in the interval [ T ]₃-max(T₃-T₁),T₃]Finishing windowing and lengthening;

wherein, T₃-max(T₃-T₁) For unifying the length of the windowed signal, max (T)₃-T₁) For T in all windowed signals₃-T₁Is measured.

3. The method of claim 1, wherein the virtual field potential values of each grid point in the space of the emission event are calculated by a variable grid search method, first using L x L mm³The grid of (2) searching the whole rock sample space range, calculating to obtain the initial position of the acoustic emission event, and generating an L multiplied by L mm by taking the initial position as the center³Using a cube of l x l mm³Searching the fine grid of L x L mm³Cube, obtaining the accurate position of the acoustic emission event;

wherein, the value range of L is [3,8] mm, and the value range of L is [0.3,0.8] mm.

4. The method of claim 3, wherein the virtual field potential value is calculated using an exponential decay function:

wherein, σ is a constant, the value range is [10,200], d is a parameter related to the acoustic emission time error: the larger the acoustic emission time error, the larger the value of d.

5. Root of herbaceous plantThe method of claim 1,

6. The method of claim 1, wherein the sensitivity coefficient of each sensor is calculated using a lead-breaking experiment.

7. The method according to claim 4, wherein two sensors are arranged on each of the front, rear, left and right four faces of a 10cm X10 cm square rock sample, with the bottom point of the line segment where the left and rear two faces intersect as (0,0,0), the horizontal east as the positive X-axis, the horizontal south as the positive Y-axis, and the vertical upward as the Z-axis, and the number 1 to 8 sensors have coordinates of (5,10,8.5), (0,5,8.5), (5,0,8.5), (10,5,8.5), (5,10,1.5), (0,5,1.5), (5,0,1.5), (10,5,1.5) cm; dividing a grid of 1cm multiplied by 1cm on the top surface to carry out lead breaking experiments, calculating the sensitivity coefficient of each sensor in each lead breaking experiment, and taking the average value of the sensitivity coefficients of the sensors after lead breaking for multiple times as the sensitivity coefficient (Sc) of each sensor₁,Sc₂,...,Sc_n)。

8. The method of claim 7, wherein the sensitivity factor of each sensor is calculated as Sc-A_P·D/f(n,d)；

Wherein A is_PIs the first peak amplitude after the first arrival of the P wave, D is the distance between the acoustic emission event and the sensor, D is the unit vector of the connecting line direction of the acoustic emission event and the sensor, n is the unit vector of the receiving signal direction of the sensor,

9. An acoustic emission event magnitude acquisition system that accounts for sensor sensitivity, comprising:

signal windowing and length-compensating unit: dividing the acquired signal set of the acoustic emission events into signal sets of different acoustic emission events according to the signal time difference, sequencing all the acoustic emission signals of each acoustic emission event according to triggering sequence, and performing windowing and lengthening on the sequenced acoustic emission signals to a point with the amplitude of 0 to obtain a windowed acoustic emission signal set of each acoustic emission event;

a signal P wave travel time difference calculation unit: calculating the concentration of windowed acoustic emission signals of each acoustic emission event based on a cross-correlation method, the P-wave travel time difference (t) of each windowed acoustic emission signal relative to the first windowed acoustic emission signal₁,t₂,...,t_n) N is the number of acoustic emission signals;

an acoustic emission event localization unit: based on P-wave travel time difference (t)₁,t₂,...,t_n) Calculating virtual field potential values of grid points of a space where the acoustic emission event is located by adopting a variable grid searching method, and taking the grid point with the maximum virtual field potential value as a positioning result of the acoustic emission event;

acoustic emission event magnitude determination unit: determining the magnitude of each acoustic emission signal according to the maximum amplitude of the corrected acoustic emission signals, and taking the median of the magnitudes of all the acoustic emission signals as the magnitude of the acoustic emission event;

wherein M is_iRepresenting the magnitude of the ith acoustic emission signal,the corrected maximum amplitude of the ith acoustic emission signal is expressed in microvolts, k is a magnitude adjustment parameter with a value range of [ -1,0 [ -1]。

10. A computer readable storage medium comprising computer program instructions characterized in that: the computer program instructions, when executed by a processing terminal, cause the processing terminal to perform the method of any of claims 1 to 9.

Technical Field

The invention belongs to the technical field of acoustic emission, and particularly relates to a method and a system for acquiring the magnitude of an acoustic emission event by considering sensor sensitivity and a readable storage medium.

Background

The acoustic emission technology is widely applied to the fields of rock compression, nondestructive testing and the like, and the acoustic emission magnitude is an important basis for researching the fracture process, can be used for quantitatively evaluating the size of an acoustic emission event and is an important parameter for calculating an acoustic emission b value and a fractal parameter. The acoustic emission magnitude calculation mainly refers to an empirical formula lgN a-bM of Gutenberg-Richter seismic magnitude_LWherein M is_LIs the earthquake magnitude, N is the frequency of the corresponding earthquake magnitude, a and b are regression parameters, and b is the value of b. Richter magnitude M_LWith a maximum amplitude A_maxProportional ratio, i.e. M_L∝lgA_max. For acoustic emission events, the Gutenberg-Richter magnitude formula may be written as lgN ═ a-bA_dB/20 wherein A_dB＝20lgA_max(Colombo et al, 2003). Thus, the acoustic emission magnitude can be written as M ═ lgA_max. It is known that for an acoustic emission event, each triggered sensor can get a magnitude. Experiments show that the magnitude difference calculated by directly adopting the maximum amplitude of each acoustic emission signal is large. On the other hand, when the sensor receives a signal, the degree of attenuation of the acoustic emission signal varies because the sensitivity of the sensor is not uniform due to the influence of vaseline, the degree of adhesion, and the like.

Acoustic emission positioning is usually based on P-wave arrival time differences, and at present, artificial P-wave first arrival pickup is influenced by signal-to-noise ratio, artificial emotion, experience and the like, and data processing is slow. And the picking stability and precision of a long-time window average value ratio method (STA/LTA method), a high-order statistics method (PAI-S/K method), a Chi criterion method (AIC method), a cross-correlation method, a fractal dimension method, an artificial neural network method and the like are lower than those of manual picking. The method only utilizes a single sensor, and the full-waveform cross-correlation method is widely applied to the field of seismic waves by taking the maximum value of the full-waveform cross-correlation of a plurality of acoustic emission signals as the relative arrival time difference. However, the acoustic emission signal has a large difference in the wake and the cross-correlation-to-time difference calculated from the full waveform has a low reliability.

The common seismic source positioning method is mostly developed from a Geiger method, and the idea of the method is to establish a residual function between theoretical arrival time and observation time by using a ray travel time model and search the optimal coordinate in space to minimize the residual function. For example, Waldhauser and Ellsworth (2000) propose a dual residual localization method, which utilizes similar events to perform inversion simultaneously, eliminates errors caused by path differences, Aki and Lee (1976) and Aki and the like (1997) respectively utilize near-seismic data and far-seismic data to jointly invert seismic source parameters and velocity parameters of rock masses under a station, and thus the field of seismic imaging is developed. In the field of mine micro-seismic, the velocity-free method of the champion and the like (2011) considers the velocity as an unknown number, a process of measuring the velocity is omitted, the simplex method and the velocity-free method of the Lijian and the like are combined, the solving precision is improved, and the problems that a solving system is divergent, the positioning precision is low, the positioning is greatly influenced by a micro-seismic table network and the like are solved by the Lenan and the like (2014) based on L1 norm statistics and the simplex method. Recently, a virtual field localization method, which is a method different from the conventional localization idea, has been proposed by royal jersey et al (2016), and has a great practical application value to a signal source localization method which is insensitive to a pickup error. However, the method of Wangzeiwei et al uses Newton's iterative algorithm, which is not very stable at small scale such as acoustic emission.

In summary, the existing full-waveform cross-correlation P waves cannot be used for signals with large tail wave change when being relatively picked up, the problem of unstable calculation exists in the existing Newton iteration method adopted for virtual field positioning, and the existing acoustic emission magnitude calculation only considers the maximum amplitude of a single sensor and does not consider the influence of sensor sensitivity.

Disclosure of Invention

The invention aims to solve the technical problem of providing an acoustic emission event magnitude acquisition method, an acoustic emission event magnitude acquisition system and a readable storage medium, wherein the acoustic emission event magnitude acquisition method takes sensor sensitivity into consideration.

An acoustic emission event magnitude acquisition method taking sensor sensitivity into consideration comprises the following steps:

step 1: dividing the acquired signal set of the acoustic emission events into signal sets of different acoustic emission events according to the signal time difference, sequencing all the acoustic emission signals of each acoustic emission event according to triggering sequence, and performing windowing and lengthening on the sequenced acoustic emission signals to a point with the amplitude of 0 to obtain a windowed acoustic emission signal set of each acoustic emission event;

step 2: calculating the concentration of windowed acoustic emission signals of each acoustic emission event based on a cross-correlation method, the P-wave travel time difference (t) of each windowed acoustic emission signal relative to the first windowed acoustic emission signal₁,t₂,...,t_n) N is the number of acoustic emission signals;

and step 3: based on P-wave travel time difference (t)₁,t₂,...,t_n) Calculating virtual field potential values of grid points of a space where the emission event is located by adopting a virtual field method, taking the grid point with the maximum virtual field potential value as the position of the acoustic emission event, and further calculating the sensitivity coefficient of the sensor by utilizing the distance between the position of the acoustic emission event and the sensor;

the virtual field method adopts a signal source positioning method for a uniform velocity field to determine;

and 4, step 4: correcting the maximum amplitude of all original acoustic emission signals of the acoustic emission event by using the sensitivity coefficient of the sensor;

when an acoustic emission event occurs, the acoustic emission event is transmitted to the sensor in the form of waves, and the sensor converts the acoustic emission event into a voltage signal, namely an acoustic emission signal;

and 5: determining the magnitude of each acoustic emission signal according to the maximum amplitude of the corrected acoustic emission signals, and taking the median of the magnitudes of all the acoustic emission signals as the magnitude of the acoustic emission event;

wherein M is_iRepresenting the magnitude of the ith acoustic emission signal,is the corrected maximum amplitude of the ith acoustic emission signal in units of microV, k is the magnitude adjustment parameter, and the value range is [ -1,0 [ ]]。

Further, the specific process of performing windowing and lengthening on the signal of the acoustic emission event is as follows:

firstly, pick up the time T before the first arrival of a P wave₁And then pick up the time T after the first P wave peak₂Then automatically extending to the next time T with amplitude of 0₃Obtaining a windowed signal in the interval [ T ]₃-max(T₃-T₁),T₃]Finishing windowing and lengthening;

wherein, T₃-max(T₃-T₁) For unifying the length of the windowed signal, max (T)₃-T₁) For T in all windowed signals₃-T₁Is measured.

Windowing processing is carried out before cross-correlation pickup, and acoustic emission signals after windowing are not influenced by tail signals, so that the pickup precision of the P wave relative arrival time difference is improved.

Windowing only needs to pick up the time before the first arrival of the P wave and the time after the first peak value and before the zero point, then automatically lengthening the signal according to the same method, without amplifying and carefully picking up the signal like manually picking up the first arrival of the P wave, and after windowing and lengthening processing, the P wave travel time difference can be quickly obtained by using a cross-correlation method; the acoustic emission signal is influenced by current, background noise and the like, the P wave first arrival is difficult to pick up manually, and the time before the P wave first arrival, the time after the first peak value of the P wave and before the zero point are only needed to be picked up approximately after the windowing processing. Therefore, after the processing, the data has stronger noise resistance;

further, a variable grid searching method is adopted to calculate the virtual field potential value of each grid point in the space where the emission event is located, and firstly, the virtual field potential value is L multiplied by L mm³The grid of (2) searching the whole rock sample space range, calculating to obtain the initial position of the acoustic emission event, and generating an L multiplied by L mm by taking the initial position as the center³Using a cube of l x l mm³Searching the fine grid of L x L mm³Cube, obtaining the accurate position of the acoustic emission event;

wherein, the value range of L is [3,8] mm, and the value range of L is [0.3,0.8] mm.

Further, the virtual field potential value is calculated by using an exponential decay function:

wherein, sigma is a constant, the value range is more than or equal to 10 and less than or equal to 200, and d is a parameter related to the acoustic emission time error: the larger the acoustic emission time error, the larger the value of d.

The contribution of large pick-up error is small, the influence of the large pick-up error can be reduced by adopting the potential value of the virtual field of the exponential decay function, and the calculation method of d adopts a signal source positioning method for a uniform velocity field in the prior patent.

Further, the air conditioner is provided with a fan,

Sc_iindicating the sensitivity coefficient of the i-th sensor.

The maximum amplitude of the acoustic emission signal is corrected based on the sensor sensitivity, and the corrected maximum amplitude is obviously more consistent than the uncorrected maximum amplitude;

further, the sensitivity coefficient of each sensor was calculated using a lead-breaking experiment.

Further, two sensors are respectively arranged on the front, the rear, the left and the right of a square rock sample of 10cm multiplied by 10cm, the bottom point of the line segment of the intersection of the left surface and the rear surface is taken as (0,0,0), the horizontal east is taken as a positive X axis, the horizontal south is taken as a positive Y axis, the vertical upward direction is taken as a Z axis, and the coordinates of sensors No. 1 to No. 8 are (5,10,8.5), (0,5,8.5), (5,0,8.5), (10,5,8.5), (5,10,1.5), (0, 1.5), (10,5,1.5) cm; dividing a grid of 1cm multiplied by 1cm on the top surface to carry out lead breaking experiments, calculating the sensitivity coefficient of each sensor in each lead breaking experiment, and taking the average value of the sensitivity coefficients of the sensors after lead breaking for multiple times as the sensitivity coefficient (Sc) of each sensor₁,Sc₂,...,Sc_n)。

Further, the sensitivity coefficient of each sensor is calculated by using Sc ═ A_P·D/f(n,d)；

Wherein A is_PIs the first peak amplitude after the first arrival of the P wave, D is the distance between the acoustic emission event and the sensor, D is the unit vector of the connecting line direction of the acoustic emission event and the sensor, n is the unit vector of the receiving signal direction of the sensor,

r is the velocity ratio of P, S waves, and s is n d.

An acoustic emission event magnitude acquisition system that accounts for sensor sensitivity, comprising:

signal windowing and length-compensating unit: dividing the acquired signal set of the acoustic emission events into signal sets of different acoustic emission events according to the signal time difference, sequencing all the acoustic emission signals of each acoustic emission event according to triggering sequence, and performing windowing and lengthening on the sequenced acoustic emission signals to a point with the amplitude of 0 to obtain a windowed acoustic emission signal set of each acoustic emission event;

a signal P wave travel time difference calculation unit: calculating the concentration of windowed acoustic emission signals of each acoustic emission event based on a cross-correlation method, the P-wave travel time difference (t) of each windowed acoustic emission signal relative to the first windowed acoustic emission signal₁,t₂,...,t_n) N is the number of acoustic emission signals;

an acoustic emission event localization unit: based on P-wave travel time difference (t)₁,t₂,...,t_n) Calculating virtual field potential values of grid points of a space where the acoustic emission event is located by adopting a variable grid searching method, and taking the grid point with the maximum virtual field potential value as a positioning result of the acoustic emission event;

the virtual field potential value is determined by adopting a signal source positioning method for a uniform velocity field in the published patent;

acoustic emission signal maximum amplitude correction unit: correcting the maximum amplitude of all original acoustic emission signals of the acoustic emission event by using the sensitivity coefficient of the sensor;

when an acoustic emission event occurs, the acoustic emission event is transmitted to the sensor in the form of waves, and the sensor converts the acoustic emission event into a voltage signal, namely an acoustic emission signal;

acoustic emission event magnitude determination unit: determining the magnitude of each acoustic emission signal according to the maximum amplitude of the corrected acoustic emission signals, and taking the median of the magnitudes of all the acoustic emission signals as the magnitude of the acoustic emission event;

wherein M is_iRepresenting the magnitude of the ith acoustic emission signal,

the corrected maximum amplitude of the ith acoustic emission signal is expressed in microvolts, k is a magnitude adjustment parameter with a value range of [ -1,0 [ -1]。

A computer readable storage medium comprising computer program instructions which, when executed by a processing terminal, cause the processing terminal to perform a method of acoustic emission seismic acquisition that takes into account sensor sensitivity.

A signal source localization method for uniform velocity fields is application number: 201510973875.4 application date: publication days 2015-12-22: 2016-05-04.

Advantageous effects

The invention provides an acoustic emission magnitude acquiring method, an acoustic emission magnitude acquiring system and a readable storage medium considering sensor sensitivity, and mainly solves the problems that when the existing cross-correlation P waves are relative, signals which can not be used for wake waves with large changes are picked up, the existing Newton iteration method adopted by virtual field positioning is unstable in calculation, the existing acoustic emission magnitude calculation only considers the maximum amplitude of a single sensor, and the influence of the sensor sensitivity is not considered. The method comprises the following steps: firstly, calculating the P wave travel time difference of each acoustic emission signal relative to the first arrival acoustic emission signal by using a windowing cross-correlation method; calculating a virtual field potential value by adopting a variable grid searching mode, and further determining the occurrence position of an acoustic emission event; carrying out a lead breaking experiment on the acoustic emission system, and calculating the sensitivity coefficient of each sensor; calculating the maximum amplitude of each acoustic emission signal, and obtaining the corrected maximum amplitude by means of the sensitivity coefficient of the sensor; and calculating the magnitude corresponding to the maximum amplitude after the correction of each sensor, and taking the median of all magnitudes as the magnitude of the acoustic emission event. The windowing method only needs to roughly pick up the time before the first arrival of the P wave and the time after the first peak value, and then automatically supplements the length by means of a program without amplifying and carefully picking up signals like manually picking up the first arrival of the P wave, so that the method can quickly obtain the P wave travel time difference; the acoustic emission signal is influenced by current, background noise and the like, the manual P wave first arrival pickup is difficult, and the method only needs to roughly pick the time before the P wave first arrival and the time after the first peak value of the P wave, so that the method has stronger noise resistance; the virtual field potential value function adopts an exponential decay mode, and the contribution of large pick-up errors is small, so that the method can reduce the influence of the large pick-up errors. In addition, the method corrects the maximum amplitude of the acoustic emission based on the sensor sensitivity, the corrected maximum amplitude is obviously more consistent than the maximum amplitude which is not corrected (figure 5), and the magnitude obtained by taking the number of digits as the magnitude of the acoustic emission is more reliable than that obtained by a traditional single sensor. The method has the characteristics of strong noise immunity, easy global optimization, stable seismic level and the like.

Drawings

FIG. 1 is a flow chart of the method of the present invention;

FIG. 2 is a diagram of a sensor arrangement during acoustic emission monitoring;

FIG. 3 is a diagram of an example of windowed cross-correlation picking where (a) is the original waveform of the signal, (b) is the waveform after windowed supplemental growth, and (c) is the time series after windowed cross-correlation;

FIG. 4 is a diagram of an example variable mesh virtual field location;

FIG. 5 is a graph of an example acoustic emission magnitude determination.

Detailed Description

The method for acquiring the magnitude of the acoustic emission event considering the sensor sensitivity provided by the invention will be further explained with reference to the accompanying drawings 1 to 5.

The algorithm idea of the invention is described as follows: the relative arrival time difference of the P wave is picked up by adopting windowed cross correlation, so that the influence of the tail wave of the acoustic emission signal is reduced; the variable grid method is adopted to realize virtual field positioning, so that the problem of unstable calculation of the Newton iteration method can be solved; and further provides an acoustic emission magnitude calculation method considering sensor sensitivity to obtain a stable acoustic emission magnitude.

As shown in fig. 1, an acoustic emission event magnitude acquisition method considering sensor sensitivity includes the following steps:

step 1: calculating the P wave travel time difference of the acoustic emission signal by a windowing cross-correlation method;

moving the acoustic emission signals with the triggering time difference smaller than 40us to the same folder to form a signal set (S) of the acoustic emission events₁,S₂,...,S_n) And n is the number of acoustic emission signals. Then sequencing according to the triggering time of the acoustic emission signals, and recording the sequenced signals as (X)₁,X₂,...,X_n). Then to (X)₁,X₂,...,X_n) And (U) expanding, windowing, length supplementing and the like to obtain a windowed signal set₁,U₂,...,U_n). Finally, the P wave travel time difference (t) of each windowed acoustic emission signal relative to the first windowed acoustic emission signal is calculated by adopting a cross-correlation method₁,t₂,...,t_n)。

Step 2: positioning a virtual field method acoustic emission event by variable grid searching;

with the P wave travel time difference (t) in step 1₁,t₂,...,t_n) And as positioning data, adopting variable grid search to calculate a virtual field potential value in a signal source positioning method for a uniform velocity field, and taking a grid point with the maximum potential value as a positioning result of the acoustic emission event.

And step 3: calculating the sensitivity coefficient of each sensor through a lead-breaking experiment;

two sensors are arranged on each of the front, rear, left, and right sides of a 10cm × 10cm square rock sample, and the sensor coordinates are (5,10,8.5), (0,5,8.5), (5,0,8.5), (10,5,8.5), (5,10,1.5), (0,5,1.5), (5,0,1.5), (10,5,1.5) cm. Dividing a grid of 1cm multiplied by 1cm on the top surface to carry out lead breaking experiments, calculating the sensitivity coefficient of each sensor in each lead breaking experiment, and taking the average value of the sensitivity coefficients of the sensors after lead breaking for multiple times as the sensitivity coefficient (Sc) of each sensor₁,Sc₂,...,Sc_n)。

And 4, step 4: calculating the maximum amplitude of the acoustic emission signal after correction;

calculating the maximum amplitude (A) of each acoustic emission signal₁,A₂,...,A_n) Dividing the maximum amplitude of each sensor by the sensitivity coefficient to obtain a corrected maximum amplitude

And 5: calculating an acoustic emission magnitude according to the corrected maximum amplitude;

using a formula

And calculating the magnitude of the ith acoustic emission signal, and taking the median of all magnitudes as the final magnitude M of the event. Wherein the content of the first and second substances,

the unit of (a) is microvolts, and k is a magnitude adjustment parameter.