Coal compression mechanical test acoustic emission signal processing method and system

Abstract

The invention discloses a coal body compression mechanical test acoustic emission signal processing method and system, and relates to coal rock mass analysis. The method comprises the steps that under the supercritical water coupling effect condition, an acoustic emission original data set of the whole coal body uniaxial compression damage process is collected; respectively carrying out time domain processing, frequency domain conversion and spatial positioning calculation on the original data set, and outputting a time domain characteristic parameter, a frequency domain characteristic parameter set and a spatial position parameter set; and performing data fusion on the time domain characteristic parameter set, the frequency domain characteristic parameter set and the spatial position parameter set, establishing a multi-dimensional characteristic data matrix through time synchronization, and analyzing acoustic emission signal laws of different damage stages of the coal body under the supercritical water coupling effect. Aiming at the low recognition precision of the coal body damage stage under the supercritical water coupling effect, the recognition precision is improved by performing time domain processing, frequency domain conversion, spatial positioning calculation and the like on an acoustic emission original data set.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of coal rock mass analysis, and particularly relates to a coal body compression mechanics test acoustic emission signal processing method and system. BACKGROUND

[0002] With the continuous increase of the depth of coal resource mining in China, the safe and efficient mining of deep coal seams is facing more and more severe challenges. Supercritical water coupling fracturing technology as a new coal seam permeability improvement technology can effectively improve the permeability of coal seams and improve the gas extraction efficiency through the synergistic effect of supercritical water with high permeability and low viscosity. This technology has broad application prospects in the fields of coalbed methane development and gas disaster prevention. However, the failure mechanism of coal body under the action of supercritical water coupling is complex, and accurate identification of different stages in the coal body failure process is of great significance for optimizing fracturing parameters and preventing engineering accidents.

[0003] Acoustic emission technology as a real-time and non-destructive monitoring method has been widely used in the field of rock mechanics. By monitoring the elastic wave signals released during the generation and propagation of microcracks in materials, rich information about the damage evolution of the materials can be obtained. In the monitoring of the coal rock failure process, the acoustic emission signals contain information about the whole process from crack initiation, propagation, penetration to final failure. Accurate identification of different stages of coal body failure, such as initial compression stage, elastic deformation stage, stable crack propagation stage, unstable crack propagation stage and post-failure stage, plays a key role in understanding the failure mechanism and predicting the failure time.

[0004] However, under the action of supercritical water coupling, the identification of the failure stage of coal body faces many technical difficulties. First, the traditional acoustic emission signal processing method mainly uses single-dimensional feature parameter analysis, such as only analyzing the ring count, energy or amplitude in time domain, which is difficult to fully reflect the failure characteristics of coal body under complex fluid-structure coupling. Second, the supercritical water coupling changes the mechanical properties and failure mode of coal body, making the failure criterion established under conventional conditions no longer applicable, leading to misjudgment of the failure stage. Third, the existing methods lack comprehensive utilization of time domain, frequency domain and spatial information of acoustic emission signals, and cannot accurately capture the characteristic transition points of different failure stages. In addition, the mode transformation process from tensile failure to shear failure of coal body lacks quantitative characterization methods, which affects the in-depth understanding of the failure mechanism.

[0005] Therefore, there is an urgent need for an acoustic emission signal processing method for supercritical water coupling conditions to improve the identification accuracy of coal body failure stage. SUMMARY

[0006] The coal body compression mechanics test acoustic emission signal processing method provided by the present application can improve the identification accuracy. The coal body compression mechanics test acoustic emission signal processing method provided by the present application can improve the identification accuracy.

[0007] The coal body compression mechanics test acoustic emission signal processing method provided by the present application can improve the identification accuracy. The coal body compression mechanics test acoustic emission signal processing method provided by the present application can improve the identification accuracy. The coal body compression mechanics test acoustic emission signal processing method provided by the present application can improve the identification accuracy.

[0008] Further, S2, the time domain characteristic parameter set is output, including: extracting the ringing count parameter sequence from the original data set, where i=1, 2,..., n represents the time serial number; performing time sequence accumulation operation on the ringing count parameter sequence to generate the accumulated ringing count value sequence as the first time domain characteristic parameter; extracting the amplitude parameter sequence from the original data set, constructing a sliding window, generating the b value time sequence as the second time domain characteristic parameter through least square fitting; extracting the energy parameter sequence and the corresponding time sequence from the original data set, constructing a sliding window with a fixed number of events, calculating the energy release index to generate the value time sequence as the third time domain characteristic parameter; extracting the rising time , amplitude , ringing count and duration of each acoustic emission event from the original data set in parallel, calculating the rising angle matrix and the average frequency matrix to generate the RA-AF value pair data set , as the fourth time-domain characteristic parameter; the RA-AF value pair data set is calculated by using kernel density estimation, a density distribution matrix is generated, as the fifth time-domain characteristic parameter; the first time-domain characteristic parameter to the fifth time-domain characteristic parameter are taken as a time-domain characteristic parameter set.

[0009] In particular, the supercritical water coupling effect causes complex physical and chemical effects in the coal body, including the extraction effect of supercritical water, stress redistribution caused by the pore pressure gradient, and the coupling effect of fluid seepage and solid deformation, which together cause the coal body failure process to exhibit multi-scale and nonlinear characteristics. A single time-domain parameter is difficult to capture the overall picture of this complex failure process, and therefore five complementary time-domain characteristic parameters are constructed:

[0010] Cumulative ringing count value sequence As a macroscopic damage accumulation indicator, the slope change directly reflects the stage transition of crack propagation rate. Under the supercritical water coupling effect, the damage accumulation of the coal body presents obvious nonlinear characteristics, and the transition moment from stable failure to unstable failure can be accurately identified by tracking the inflection point of the cumulative curve.

[0011] b-value time sequence Based on the magnitude-frequency relationship in seismology, the dynamic evolution of the crack size distribution in the coal body is characterized. The permeation effect of supercritical water changes the heterogeneity of the coal body, causing the b-value to exhibit characteristic changes at different failure stages: the b-value is high at the initial stage, indicating that small-scale cracks dominate; the b-value sharply decreases near the failure, indicating the formation of large-scale cracks.

[0012] Energy release exponent value time sequence From the perspective of energy dissipation, the acceleration characteristics of the failure process are quantified. In the supercritical water coupling cracking process, the action of fluid pressure changes the energy release mode, and the time sequence evolution of the energy release exponent value can sensitively reflect the transition of this energy release mechanism.

[0013] RA-AF value pair data set Through the two-dimensional mapping of the rise angle and the average frequency, quantitative identification of the failure mode is achieved. Under the supercritical water coupling effect, the coal body undergoes a transition process from tensile failure to shear failure, and this evolution of the failure mode is clearly characterized by the RA-AF characteristic space, overcoming the limitations of traditional methods that cannot distinguish between failure types.

[0014] ​The density distribution matrix further reveals the clustering characteristics of the RA-AF feature space, identifying the dominant damage modes and their evolutionary trends through the spatial distribution of probability density. The location and shape of density peaks exhibit regular changes at different damage stages, providing a statistical basis for stage division.

[0015] These five time-domain characteristic parameters construct a complete characterization system for the coal body failure process from multiple dimensions, including damage accumulation, crack scale distribution, energy release, failure mode, and statistical distribution. Through multi-parameter synergistic analysis, not only can the characteristic boundaries of each failure stage be identified, but also precursory information of stage transitions can be captured, thereby significantly improving the performance of supercritical coal seams. Accuracy of identifying coal body failure stages under water coupling.

[0016] Furthermore, amplitude parameter sequences are extracted from the original dataset in chronological order. Where i = 1, 2, ..., n; construct a sliding window processor, set the computation window length W = 2000 data points, the sliding step size S = 500 data points, and generate the window sequence. Where j = 1, 2, ..., m, .

[0017] For each window Perform the following processing: Extract the amplitude subsequence within the window. Convert amplitude to magnitude parameters The magnitude parameters are divided into intervals, and statistics are compiled for each magnitude interval. Number of events within Construct a set of magnitude-frequency data pairs. The least squares method was used to perform linear fitting on the data pairs to establish... The regression equation; by minimizing the sum of squared errors Solve for parameters a and b.

[0018] Extract the fitted slope b as the b-value for that window; sort the b-values ​​of each window according to the center time of the window to generate a time series of b-values. , of which each Corresponding window The central moment Outlier detection and removal are performed on the time series of b-values. or Outliers are filled using linear interpolation; the output is a quality-controlled b-value time series. As the second time-domain feature parameter.

[0019] Furthermore, energy parameter sequences are extracted from the original dataset in chronological order. and the corresponding timestamp sequence where i = 1, 2, …, n, ensures that the energy value corresponds to the timestamp one by one; a fixed number of event sliding window processor is constructed, the number of acoustic emission events in the window N = 500 is set, the sliding step is 1 event, and a window sequence is generated where k = 1, 2, …, n-N+1.

[0020] For each window The following processing is performed: extracting the energy subsequence in the window and the time subsequence ; calculating the cumulative energy in the window where N represents the event sequence number in the window; constructing a cumulative energy-time data pair set where is the starting time of the window; performing logarithmic transformation on the data pair to obtain ; using the least square method to linearly fit the transformed data ; solving the parameters by minimizing the sum of squared errors ; extracting the fitting slope as the energy release index of the window; sorting the values of each window according to the time of the center event of the window to generate a value time sequence where each corresponds to the center event time of the window ; outputting the value time sequence as the third time domain feature parameter. In particular, the energy release index quantitatively represents the acceleration degree of energy release by constructing a power law relationship between the cumulative energy W and the time t

[0021] . Under the coupling effect of supercritical water, the damage evolution inside the coal body follows the principle of self-organized criticality: when , it indicates that the energy release shows a deceleration trend, corresponding to the stable crack propagation stage; when , it indicates that the energy release shows an acceleration trend, indicating that the system is close to a critical damage state. The physical nature of this power law relationship lies in the coupling effect of the supercritical permeation and the pore water pressure, which changes the stress field distribution inside the coal body, enhances the interaction between microcracks, forms a positive feedback mechanism, and leads to accelerated damage accumulation.

[0022] ​​​Firstly, the fixed event number N=500 sliding window design is adopted to ensure enough statistical samples for power-law fitting in each window, and the sliding step of 1 event ensures the time resolution, which can finely track the dynamic changes of The design overcomes the limitations of fixed time window when the acoustic emission event density changes greatly, and is particularly suitable for the non-uniform distribution characteristics of acoustic emission activity under the action of supercritical water coupling.

[0023] Secondly, the power-law relationship is linearized by logarithmic transformation, and the least squares method is used to solve the value of βt, which not only improves the calculation efficiency, but more importantly, obtains the statistically optimal estimate by minimizing the sum of squares of errors. This method is more stable than direct nonlinear fitting, reduces the numerical uncertainty, and improves the reliability of the value.

[0024] From the perspective of damage stage identification, the value time series provides a sensitive precursor indicator: in the initial compaction and elastic deformation stage, the value is usually less than 1 and relatively stable; after entering the stable crack propagation stage, the value starts to fluctuate and rise; when approaching the peak strength, the value increases sharply and exceeds 1, indicating the entry into the non-stable crack propagation stage. This stage feature is particularly evident under the action of supercritical water coupling, because the presence of fluid exacerbates the nonlinear characteristics of energy release.

[0025] Further, four parameters of each acoustic emission event i are extracted in parallel from the original data set: rise time , amplitude , ring count and duration , where i=1, 2,..., n.

[0026] For the characteristics of coal under the action of supercritical water coupling, the damage mode parameter calculation is performed: the rise angle is calculated, with the unit of ms / dB, representing the stress wave rise rate; the average frequency is calculated, with the unit of kHz, representing the signal frequency characteristic; the RA-AF two-dimensional feature space is constructed, and the value pair dataset is generated.

[0027] Based on the damage mechanism of coal under the action of supercritical water coupling, the damage mode classification threshold is set: when , it is determined as tensile failure mode; when shear failure mode; wherein a is supercritical characteristic threshold value under water coupling effect, the value range is -1.5 to -0.5.

[0028] The proportion of tensile failure and shear failure events in each time window is counted to generate a failure mode evolution sequence.

[0029] The RA-AF value pair data set and the failure mode evolution sequence are used as the fourth time domain feature parameter to represent the dynamic process of the transformation of coal from tensile failure to shear failure under the action of supercritical water coupling.

[0030] In particular, the RA (Rise Angle) and AF (Average Frequency) parameters represent the essential characteristics of acoustic emission signals from two dimensions of time and frequency domains. Reflects the steepness of the stress wave front, which corresponds to the crack propagation speed in physics: tensile cracks propagate rapidly and brittlely, producing steep wave fronts (low RA value); while shear cracks propagate slowly due to friction, forming a gentle wave front (high RA value). Characterizes the signal's dominant frequency characteristics: tensile failure releases elastic waves dominated by high-frequency components (high AF value); shear failure produces low-frequency components due to crack surface friction (low AF value). Therefore, the RA / AF ratio quantitatively distinguishes the two failure modes from a physical mechanism.

[0031] The supercritical water coupling fundamentally changes the failure mode evolution law of coal. The supercritical strong permeability and extraction weaken the strength of coal matrix, promoting the formation of initial tensile cracks; as the load increases, the coupling of pore water pressure and stress changes the stress field distribution, inducing the transformation of cracks to shear mode. This gradual transformation process of failure mode is accurately quantified by the RA-AF characteristic space: the initial stage is concentrated in the low RA-high AF area (tensile dominant), and then migrates to the high RA-low AF area (shear dominant).

[0032] Further, the kernel density estimation is used to calculate the two-dimensional probability density of the RA-AF value pair data set to generate a density distribution matrix as the fifth time domain feature parameter, including: obtaining the RA-AF value pair data set from the fourth time domain feature parameter, wherein i=1, 2, …, n; using a multivariate Gaussian function as the kernel function to calculate the density of the data set to obtain the density distribution matrix D as the fifth time domain feature parameter.

[0033] Furthermore, S3 performs a frequency domain transformation on the original dataset, outputting a set of frequency domain feature parameters, including: the time-domain waveform signal of each acoustic emission event in the original signal dataset. Perform a Fast Fourier Transform to generate the corresponding frequency domain signal. From each frequency domain signal The frequency corresponding to the maximum amplitude is extracted as the master frequency, and the amplitude value corresponding to the frequency is used to generate a master frequency-amplitude parameter sequence, which serves as a frequency domain feature parameter set to identify supercritical conditions. Characteristic frequency distribution of coal body destruction under water coupling.

[0034] Furthermore, S4 performs spatial positioning calculations on the original dataset, outputting a set of spatial location parameters, including: extracting the reception time data of each acoustic emission event from the original dataset at multiple sensors. Where i is the event number and j is the sensor number, a time difference matrix is ​​constructed. Based on the time difference matrix and preset sensor spatial coordinates A set of spatial positioning equations for acoustic emission sources was established; the average sound wave propagation velocity of the coal body was determined through wave velocity calibration tests. , and sensor coordinates The parameters are combined to form the set of parameters for positioning calculation; the spatial positioning equations of the acoustic emission source are transformed into an optimization problem; the objective function is constructed using the minimum absolute deviation criterion; and the simplex optimization algorithm is applied to iteratively minimize the objective function to obtain the optimal spatial coordinates for each acoustic emission event. and time of occurrence .

[0035] Wherein, the objective function is:

[0036] Where N represents the number of sensors involved in the positioning calculation; Let represent the time when the i-th sensor receives the acoustic emission signal; t represents the time when the acoustic emission event occurs (an unknown to be solved); x represents the three-dimensional spatial coordinate vector of the acoustic emission source. (Unknown quantities to be solved); Represents the three-dimensional spatial coordinate vector of the i-th sensor. (Known quantities); This represents the Euclidean distance from the acoustic emission source to the i-th sensor; This represents the average propagation speed of sound waves in the coal body (a known quantity obtained through calibration tests).

[0037] In particular, traditional acoustic emission localization often uses the least squares method; however, in supercritical environments... - Under water coupling conditions, complex multiphase fluid interactions occur within the coal body, leading to local anomalies in the sound wave propagation path: The density mutation caused by phase transition will cause local distortion of sound velocity; Water The reflection and refraction at the interface will cause some sensors to receive delayed signals. The square amplification effect of least squares on outliers will seriously affect the positioning accuracy.

[0038] The application adopts the minimum absolute deviation criterion , and the linear penalty characteristic limits the influence of abnormal time data within the linear range, and will not be amplified too much due to the square term, thereby ensuring the positioning stability in complex fluid environment.

[0039] Supercritical Water coupling makes the coal body present strong heterogeneity, and the sound velocity field distribution is complex, resulting in the target function presenting multi-peak and non-convex characteristics. The optimization algorithm based on gradient is easy to fall into local extremum. As a direct search method, the simplex algorithm does not depend on gradient information, and searches in the solution space through geometric deformation (reflection, expansion, contraction), which can effectively jump out of the local optimum, and is particularly suitable for processing non-smooth and non-convex optimization problems. Specifically, the simplex optimization algorithm in the application refers to the Nelder-Mead simplex algorithm, which is a direct search optimization method and does not need to calculate the gradient. The simplex is a geometric body composed of n+1 vertices in n-dimensional space. In the four-dimensional optimization problem in the application , the simplex is a four-dimensional geometric body composed of five vertices, and each vertex represents a candidate solution . The target function contains an absolute value term, which is not derivable at some points, and the simplex algorithm avoids the difficulty of gradient calculation. In addition, for the complex sound field in the supercritical -water coupling environment, the simplex algorithm searches through geometric transformation and is not easy to fall into the local extremum caused by sound velocity anomaly.

[0040] The application optimizes the occurrence time t as a to-be-solved variable together with the spatial coordinates x, instead of regarding it as a known quantity in the traditional method, which fully considers the clock synchronization error and trigger time uncertainty in the supercritical environment. Water coupling will produce continuous micro-fracture groups, making it difficult to accurately define the starting time of a single event, and the space-time coupled solution avoids the transmission of time error to spatial positioning.

[0041] Finally, the average wave speed obtained through the calibration test is used instead of a complex variable speed model, which is an engineering treatment based on the particularity of the supercritical -water environment. Although the sound velocity in the coal body changes in space, under the optimization framework, the average wave speed cooperates with the minimum absolute deviation criterion to adaptively reduce the influence of local sound velocity anomaly, and realizes the balance between calculation efficiency and positioning accuracy.

[0042] Furthermore, time synchronization processing is performed on the time-domain feature parameter set, frequency-domain feature parameter set, and spatial location parameter set to establish a unified time reference axis. Based on the unified time reference axis, data alignment and interpolation processing are performed on parameter sequences with different sampling frequencies and time scales. All time-aligned parameter sequences are combined column-wise to construct a multi-dimensional feature data matrix M. Based on the cumulative ringing count value sequence... The slope change points and b-value time series The abrupt change points are identified, and the stage transition index of the coal body failure process is calculated, dividing the entire loading process into multiple failure stages with different acoustic emission activity characteristics. Statistical indicators for each failure stage are calculated, including the proportion of failure modes, frequency distribution, spatial clustering characteristics, and temporal evolution characteristics. Based on the statistical indicators of each failure stage, a characterization of supercriticality is output. Multidimensional quantitative analysis results of the entire process of coal body from initial damage to final failure under water coupling.

[0043] Another aspect of this application provides a supercritical... A uniaxial compression acoustic emission signal processing system for coal under water coupling includes: a data acquisition module, in supercritical... Under water coupling conditions, raw acoustic emission data of the entire uniaxial compression failure process of coal body is collected; a time-domain processing module processes the raw data in the time domain and outputs a time-domain feature parameter set; a frequency-domain processing module processes the raw data in the frequency domain and outputs a frequency-domain feature parameter set; a spatial positioning module performs spatial positioning calculations on the raw data and outputs a spatial position parameter set; a data fusion module fuses the time-domain feature parameter set, frequency-domain feature parameter set, and spatial position parameter set, and establishes a multi-dimensional feature data matrix through time synchronization to generate supercritical data. Multidimensional quantitative analysis results of the entire process of coal body from initial damage to final failure under water coupling.

[0044] Compared to existing technologies, the advantages of this application are:

[0045] Addressing the lack of supercritical technologies in existing technologies This application addresses the issue of traditional single-dimensional analysis failing to comprehensively characterize the complex failure process of coal bodies, and provides a systematic method for processing acoustic emission signals from coal body compression mechanics tests. By performing time-domain processing, frequency-domain transformation, and spatial positioning calculations on the raw acoustic emission dataset, the cumulative ringing count, b-value, and energy release index are extracted. The system incorporates time-domain characteristic parameters such as RA-AF value pairs and their probability density distributions, frequency-domain characteristic parameters such as master frequency-amplitude, and spatial location parameters such as three-dimensional spatial coordinates. A multi-dimensional feature data matrix is ​​constructed using time synchronization and data fusion techniques, enabling the analysis of supercritical... A multi-dimensional quantitative characterization of the entire process of coal body failure from initial damage to final failure under water coupling can accurately identify the stage transition moments of coal body failure, quantitatively analyze the differences in acoustic emission signal characteristics at different failure stages, and dynamically track the transformation process of tension / shear failure modes, providing a basis for supercritical... - This study provides effective data processing and analysis methods for the research on the failure mechanism and safety early warning of water-coupled fracturing coal bodies. Attached Figure Description

[0046] This application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0047] Figure 1 This is an exemplary flowchart of an acoustic emission signal processing method for coal body compression mechanics testing, according to some embodiments of this application.

[0048] Figure 2 This is a schematic diagram of acoustic emission signal waveform characteristic parameters according to some embodiments of this application;

[0049] Figure 3 These are schematic diagrams of a sliding window algorithm according to some embodiments of this application;

[0050] Figure 4 This is a schematic diagram illustrating a damage mode classification method based on RA-AF values ​​according to some embodiments of this application;

[0051] Figure 5 This is a schematic diagram of RA-AF visualization cloud map processing according to some embodiments of this application;

[0052] Figure 6 This is a schematic diagram of the frequency domain transformation of the acoustic emission waveform according to some embodiments of this application;

[0053] Figure 7 This is a schematic diagram of acoustic emission spatial positioning results according to some embodiments of this application. Detailed Implementation

[0054] The methods and systems provided in the embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0055] like Figure 1 As shown, in supercritical Under the condition of water coupling effect, the acoustic emission raw data set of the whole process of uniaxial compression failure of coal body is collected, the raw data set includes the ring count, ring count rate, energy, amplitude, rise time, duration, average frequency, peak frequency and sensor receiving time data of each acoustic emission event; the raw data set is processed in time domain to output the time domain characteristic parameter set; the raw data set is converted in frequency domain to output the frequency domain characteristic parameter set; the raw data set is calculated for spatial positioning to output the spatial position parameter set; the time domain characteristic parameter set, the frequency domain characteristic parameter set and the spatial position parameter set are fused, a multi-dimensional characteristic data matrix is established through time synchronization, the supercritical Acoustic emission signal law of different failure stages of coal body under water coupling effect.

[0056] The acoustic emission sensor probe in the acoustic emission acquisition system can collect the acoustic emission waveform multi-parameter information of the coal body failure internal acoustic emission signal waveform event, including ring count, ring count rate, energy, amplitude, rise time, duration, average frequency (AF), peak frequency, etc., as shown in Figure 2 The acoustic emission spatial positioning information parameters of the coal body internal failure event can also be obtained through the sample acoustic velocity calibration method. Through the analysis and processing of the collected coal acoustic emission characteristic signal information parameters, more other parameter information such as rise angle (RA), main control frequency and amplitude can be obtained, and further more effective related inversion analysis of rock fracture damage mechanism can be carried out, and the acoustic emission information of coal fracture expansion can be interpreted and studied.

[0057] Acoustic emission ring count refers to the number of signal waveform oscillations of the acoustic emission event signal waveform collected in the coal body failure process that exceeds the set threshold value, so the ring count analysis can be used to further study the internal crack damage degree of the coal sample in the damage and deterioration process. The acoustic emission cumulative ring count is the cumulative value of the ring count.

[0058] In this paper, the amplitude of the acoustic emission signal waveform parameter is divided by ten to represent and replace the magnitude M, and then the related calculation of the acoustic emission b value is carried out, and the specific relationship is as follows: ; ; wherein, M represents the earthquake magnitude; E is the absolute energy in the acoustic emission parameter; N represents the earthquake frequency between ; N(E) is the number of hits of absolute energy greater than E; a and b are constants.

[0059] In the process of extracting the acoustic emission b value parameter information, the sliding window algorithm is adopted to count the acoustic emission signal parameter time of arrival, amplitude and other related parameter data, and the acoustic emission b value is calculated by the least square method. When extracting and calculating the acoustic emission b value parameter, the related parameters of the sliding window algorithm are set: the calculation window is 2000, the end time in the calculation window is taken as the occurrence time of the acoustic emission signal, the sliding window is 500, and the algorithm schematic diagram is shown in Figure 3 .

[0060] The dynamic distribution of the calculated acoustic emission b value changing with time is the related information that can reflect the scale of the damage event and has specific physical meaning. If small-scale damage events occur mainly in the coal sample at this time, the acoustic emission b value is relatively large; if large-scale damage events occur mainly in the sample at this time, the acoustic emission b value is relatively small.

[0061] Therefore, further considering the law characteristics shown by the acoustic emission b value variation characteristics of the uniaxial compression coal sample at different damage stages in this paper can be explained as follows: if the acoustic emission b value continuously increases during uniaxial loading, it shows that the proportion of small-scale damage events occurring in the coal body at this time continuously increases; otherwise, it shows that the large-scale damage events in the coal body at this time increase; if the acoustic emission b value is in a relatively stable state of fluctuation, it represents that the occurrence probability frequency of different scale damage events in the coal body at this time is similar. Similarly, when the acoustic emission b value continuously fluctuates and changes in a small time span range, the different scale damage events occur in a small amplitude and slow transition, and we define the internal crack development state of the coal body at this time as a gradual stable expansion state; if the acoustic emission b value changes and fluctuates in a large time span range, the different scale damage events have a relatively large and rapid damage amplitude, and we define this time as a sudden unstable expansion state.

[0062] The absolute energy W in the acoustic emission signal parameter and the time satisfy the following relationship: . ; wherein t represents the test time of the acoustic emission signal recording; is the test time; represents the energy release index; N represents the total number of acoustic emission events occurring in the test time; is the total number of acoustic emission events.

[0063] In the process of extracting and processing the acoustic emission value, the sliding window algorithm similar to the acoustic emission b value calculation method is adopted, and the fixed acoustic emission event N is set to 500 to calculate the acoustic emission value. Through the analysis and research of the calculated acoustic emission The value changes in different damage stages can show the stability of the internal structure of the coal and the damage degree of the crack, when , it means that the coal is in a relatively stable state (metastable state) at this stage, if , it means that the coal damage process at this stage is relatively slow, the energy dissipation decreases, and the sample damage tends to be stable, if , it means that the internal structure stability of the coal is poor at this time, the rate of fracture events increases rapidly, and the energy dissipation increases.

[0064] The relationship between the acoustic emission waveform rising angle RA and the average frequency AF can be used to analyze the damage form of the material. The value of RA is the ratio of the rising time RT to the amplitude A; the value of AF is the ratio of the ringing count C to the duration DT. The calculation formulas of acoustic emission RA and AF are as follows: ; The damage form classification based on RA-AF is shown in . Among them, when the tensile damage event occurs in the coal body, the waveform mainly propagates in the form of longitudinal wave, the energy is released quickly, the waveform reaches the maximum amplitude in a short time, and the waveform rising angle RA is relatively small; when the shear damage event occurs, the waveform mainly propagates in the form of shear wave, the energy is released relatively slowly, the waveform reaches the maximum amplitude in a long time, and the waveform rising angle RA is relatively large.

[0065] In the key density function estimation method, each element of the multi-parameter acoustic emission signal waveform collected in the uniaxial compression acoustic emission test corresponds to the probability density distribution. The real density estimation function of the multi-element data is: ; in the formula, is the estimated value of the real density, represents the number of data, is the data, is the smoothing parameter.

[0066] The key density function adopts a multivariate Gaussian function as an arbitrary local function that can meet certain constraint conditions: ; wherein, is the dimension of the data space.

[0067] The value of the smoothing parameter is verified and determined by the least square cross, and the square error minimization estimation between and the real density is carried out: Since the real density function cannot be directly obtained, a quadratic fitting Newton type method is adopted here to realize it, and the calculation of the optimal smoothing parameter is as follows: For a multivariate distribution, the parameters are determined according to the following formula: .

[0068] The kernel density estimation (KDE) is calculated by MATLAB programming language to realize the coal failure mode classification method based on the RA-AF value. Different RGB values are given to the RA-AF scatter plot according to different probability densities, so that the density distribution is displayed in different colors. Finally, the acoustic emission RA-AF value distribution density visualization processing is realized, and the coal failure acoustic emission RA-AF value cloud chart is obtained, as shown in Figure 5 .

[0069] Through the above extraction and processing of the acoustic emission time domain signal characteristic parameter information in the coal failure process, some crack characteristics and failure modes reflecting the coal failure events are obtained. In addition to extracting and analyzing the acoustic emission time domain signal characteristic parameter information, the acoustic emission signal event waveform can also be processed. The acoustic emission time domain characteristic signal waveform is converted into frequency domain characteristic waveform through the conversion of acoustic emission signal event waveform from time domain to frequency domain, and then more obvious acoustic emission signal evolution characteristics are obtained from another angle.

[0070] For the obtained acoustic emission signal waveform event in time domain, since any continuous waveform can be superimposed by an infinite number of sine / cosine waves, the Fourier transform (FT) method can be used to convert the signal waveform (time domain) parameter information obtained by acoustic emission monitoring at each time into corresponding frequency domain parameter information.

[0071] Firstly, the basic principle of Fourier transform (FT) is introduced. Let be the acoustic emission time domain waveform signal to be processed. The Fourier transform process is: ; the corresponding inverse Fourier transform is: ; wherein, contains the time domain signal frequency and other related characteristic parameters in , so is called the frequency spectrum of . And the relationship between the signal phase , frequency and amplitude is expressed as: . Fourier transform (FT) is expressed as discrete Fourier transform (DFT) and further calculated as fast Fourier transform (FFT) according to the processing process, wherein the fast Fourier transform is a transformation method based on the premise of discrete Fourier transform.

[0072] Firstly, for the discrete Fourier transform method, it is assumed that a group of data signals is composed of N discrete points, and the group of data signals is , and The expressions of the continuous Fourier transform and inverse transform are respectively: ; .

[0073] After the discrete Fourier transform, the continuous function has become To make it a finite discrete data, the continuous function is approximated as a finite number of discrete points, and these discrete points are respectively made discrete Fourier transform and inverse transform: ; . .

[0074] The above transformation process is expressed in matrix form as follows, let , then: .

[0075] Fast Fourier transform (FFT) is an effective and fast calculation algorithm of discrete Fourier transform, which has strong advantages in analyzing dynamic non-stationary acoustic emission signals such as the monitoring records of the uniaxial compression failure test of coal body in this paper. Its principle is to divide the acoustic emission signal data processing into a series of discrete data groups and make discrete Fourier transform on each group, and then integrate the calculation results to get the fast Fourier transform result of the original data group. Therefore, the existing acoustic emission signal event wavelet waveform data is transformed by using the fast Fourier transform (FFT) method, and then the frequency domain characteristic signal frequency parameter information characteristic law of coal body failure can be obtained more efficiently and conveniently.

[0076] Taking the acoustic emission monitoring result data set of A-0-3 coal body in A-0 test group as an example, and taking a certain event waveform in the acoustic emission signal waveform event in the whole failure process as an example, the frequency domain signal waveform spectrum diagram obtained by fast Fourier transform of the acoustic emission time domain signal waveform data is shown in .

[0077] Through the acquisition of spatial position information of acoustic emission events in the uniaxial failure process of coal body, i.e. spatial positioning, the position and energy information of acoustic emission events and crack development and evolution in coal body can be further analyzed and visualized.

[0078] The coal and rock acoustic emission positioning technology mainly uses the time difference of the same acoustic emission rupture event collected by the acoustic emission sensor probes fixed on the surface of coal and rock in different directions to calculate the specific position information of the acoustic emission signal event, including three-dimensional coordinates, energy, etc.

[0079] ​Because the distance between the source of the internal damage event signal and the surface sensor probe is much larger than the length of the internal damage micro-crack, we assume that the micro-crack event positioning point is a point source, and the acoustic emission positioning equation is: ; wherein X and Xi are the coordinate positions of the damage event signal source and the sensor i receiving the source, respectively; cp represents the wave speed of the coal body; t represents the damage event occurrence time; ti represents the receiving time of the signal sensed by the sensor i.

[0080] Through the test preparation stage of the test before the test, the acoustic emission probe of the sample is fixed and the coal body acoustic velocity calibration test is carried out, the coordinates of each sensor can be determined and the specific wave speed can be measured, and the micro-crack event positioning point coordinates and the occurrence time t of the acoustic emission event are unknown.

[0081] Among them, the occurrence time t of the acoustic emission event can be simply regarded as directly corresponding to the acquisition through acoustic emission signal monitoring, but in the actual acoustic emission monitoring and spatial positioning specific calculation, considering the influence of the wave speed in the model and the existence of individual large error abnormal values in the positioning accuracy, the acoustic emission event position and time need to be processed by a certain method.

[0082] The specific determination method of the related parameters of the acoustic emission spatial positioning event position and occurrence time in this paper adopts the least absolute deviation and simplex numerical optimization algorithm to further accurately position. The objective function f(x) is set as the absolute value of the difference between the damage event occurrence time and the receiving time of the signal sensed by the sensor, and the solution is the corresponding value when the objective function f(x) reaches the minimum. The specific formula is as follows: Through the above acoustic emission spatial positioning data calculation and processing analysis method, the acoustic emission spatial positioning characteristic information result graph as shown in can be finally obtained.

[0083] S5, time synchronization processing of each parameter set: extracting the cumulative ringing count value sequence , b value time sequence , value time sequence and the corresponding time stamp from the time domain characteristic parameter set; extracting the main control frequency-amplitude parameter sequence and the corresponding time stamp from the frequency domain characteristic parameter set; extracting the spatial coordinate sequence and the corresponding time stamp from the spatial position parameter set; establishing a unified time reference axis, mapping all parameter sequences to the same time coordinate system;

[0084] Multi-scale data alignment and interpolation processing: For parameter sequences with different sampling frequencies, linear interpolation or spline interpolation method is used to unify to the same time resolution; for parameter sequences generated by sliding window, the window center time is taken as the effective time point of the parameter; for event-level parameters, the original timestamp is kept unchanged, and null values are filled in non-event moments.

[0085] All time-synchronized parameters are combined by column to form a feature matrix , where T is the time vector, is the cumulative ring count value sequence, b is the b-value time sequence, is the energy release index sequence, RA and AF are the rise angle and average frequency sequences, D is the RA-AF probability density, and is the dominant frequency and corresponding amplitude, x, y, z are spatial coordinates; each row represents a multi-dimensional feature vector at a time point; the matrix is subjected to Z-score standardization processing to eliminate the influence of different parameter dimensions, so that the mean of each parameter is 0 and the standard deviation is 1.

[0086] Based on the multi-parameter coupling criterion, the coal body failure stage is divided: through the cumulative ring count value sequence , the first derivative and the second derivative are calculated to identify the acceleration point of acoustic emission activity; the rate of change of the b-value time sequence is monitored , when , it is marked as a stress concentration intensification point; the stage transition index STI is calculated , where represents the normalized value of the first derivative of the cumulative ring count; when STI exceeds the threshold value 0.6, it is determined as the stage transition point; based on the above criteria, the loading process is divided into: initial compaction stage (b-value stable at 2.0±0.2, ), elastic deformation stage (b-value slowly decreases to 1.5±0.2, ), plastic yield stage (b-value rapidly decreases to below 1.0, ) and unstable failure stage (b-value drops sharply to below 0.5, cumulative ring count increases exponentially).

[0087] Extract the characteristic parameter statistics of each failure stage: initial compaction stage: calculate , the proportion of tensile failure events is more than 80%; the mean of the dominant frequency is concentrated in 30-50 kHz, and the standard deviation is less than 10 kHz; the spatial distribution aggregation index SDI is less than 0.3, showing random distribution; the b-value remains at 2.0±0.2, , the b-value remains at 0.8±0.1.

[0088] Elastic deformation stage: tensile failure ratio decreased to 60-80%, shear failure events Start to increase; the main frequency shifts to 50 kHz to 100 kHz, the migration rate ; the spatial aggregation index SDI rises to 0.3-0.5; the b value decreases at a rate of 0.01-0.02 / min, The value increases to 1.0-1.5.

[0089] Plastic yield stage: shear failure ratio rises to 40-60%, RA-AF probability density shows bimodal characteristics, peak interval ΔP>0.5; the main frequency further evolves to 100-150 kHz, and the proportion of high-frequency events (>100 kHz) exceeds 50%; spatial positioning shows that acoustic emission sources are concentrated in 2-3 regions, SDI increases to 0.5-0.8; b value rapidly decreases to below 1.0, The value exceeds 1.5.

[0090] Unstable failure stage: shear failure ratio exceeds 60%, probability density D shows single-peak high-density aggregation, peak density>0.8; the main frequency-amplitude parameter shows a surge in high-frequency high-amplitude (>80 dB) events above 150 kHz; spatial positioning reveals the formation of macroscopic fracture surface, SDI>0.8; b value drops sharply to below 0.5, The value increases sharply.

[0091] Generate supercritical water coupling effect: establish characteristic parameter evolution model: construct b value-time, -time, main frequency barycenter-time fitting curve; determine the precursor characteristic combination of each stage transition, including b value decrease rate>0.05 / min, growth rate>0.1 / min, shear failure ratio growth rate>5% / min; establish the coupling relationship model of "parameter evolution-failure mode-spatial distribution", output discriminant criteria: when b<1.2, , SDI>0.5, coal body enters a dangerous state; form a complete set of identification rules for the gradual failure of coal bodies under supercritical water coupling effect, including the threshold range of characteristic parameters and state transition criteria in each stage.

[0092] The above description of the application and its embodiments is illustrative, and not restrictive. Many variations of the application incorporating modifications without departing from the spirit or essential characteristics of the application are possible. These embodiments should be considered in a descriptive sense only and not for purposes of limitation. The present application is well adapted to carry out the objects and attain the ends and advantages mentioned above as well as those that are inherent therein. While the application has been depicted, described, and are illustrated in the best mode and / or all possible structural and / or methodical combinations, it is understood that various omissions and substitutions for a particular embodiment are considered to be equivalents which do not change either the essential characteristics of the structures and / or methods described herein, and / or the core of the claims. It is intended that the application be construed as not limited by the foregoing description, and as including all equivalents, whether now known or later devised, of the mechanisms and / or methods described herein. Furthermore, it is intended that the scope of the present application herein disclosed should include all features that neither minimally solve the specific problems as stated above, nor render the claims unpatentable over this prior art. Thus, to the extent that a figure is common to multiple claims, it is intended to be leveraged and applied individually to each claim. Moreover, it should be appreciated that embodiments of the present application have application regardless of the particular ordering of steps carried out, or the particular systems for carrying out those steps. Accordingly, the applicant intends to embrace all changes and modifications of the application that come within the spirit and scope of the following claims.

Claims

1. A coal body compression mechanics test acoustic emission signal processing method, characterized in that, Comprise: S1, under supercritical Under the coupling action of water, the acoustic emission raw data set of the whole process of coal uniaxial compression failure was collected, including the ring count, ring count rate, energy, amplitude, rise time, duration, average frequency, peak frequency and sensor receiving time data of each acoustic emission event. S2, time domain processing of the original data set, output time domain feature parameter set; S3, the original data set is converted to frequency domain, output frequency domain feature parameter set; S4, the original data set is calculated by spatial positioning, and the spatial position parameter set is output; S5, data fusion is performed on the time domain feature parameter set, the frequency domain feature parameter set and the spatial position parameter set, a multi-dimensional feature data matrix is established through time synchronization, and acoustic emission signal rules of different damage stages of coal under the action of water coupling are analyzed The acoustic emission signal rules of different damage stages of coal under the action of water coupling.

2. The coal compression mechanics test acoustic emission signal processing method of claim 1, characterized in that: S2, output time domain feature parameter set, comprising: Extracting ringing count parameter sequences from raw data sets where i = 1, 2,..., n represents the time index number; Ring count parameter sequence Timing accumulation operation is performed to generate a cumulative ring count value sequence as the first time domain feature parameter; Extracting amplitude parameter sequence from original data set , constructing sliding window, fitting by least square method, generating b value time sequence , as the second time domain feature parameter; extracting energy parameter sequence from original data set and corresponding time sequence , constructing fixed event number sliding window, calculating energy release index , generating value time sequence as third time domain feature parameter; extracting rise time, amplitude, ring count and duration of each acoustic emission event from the raw data set in parallel calculating rise angle matrix and average frequency matrix generating RA-AF value pair data set as the fourth time domain feature parameter;​​​ RA-AF values ​​were calculated for the dataset using kernel density estimation. The probability density is used as the fifth time-domain feature parameter; The first time domain feature parameter to the fifth time domain feature parameter is taken as the time domain feature parameter set.

3. The coal compression mechanics test acoustic emission signal processing method of claim 2, characterized in that: Constructing the second time domain feature parameter comprises: extracting the amplitude parameter sequence in time sequence from the original data set where i = 1, 2,..., n; constructing a sliding window processor, setting a calculation window length , a sliding step , generating a window sequence , wherein j = 1, 2,..., m, ; For each window : Extraction window Amplitude subsequence within ; Converting amplitude to magnitude parameter ; The magnitude parameter is divided into intervals, and the number of events in each interval is counted ;​ Constructing magnitude-frequency data pairs sets ; The data pairs are linearly fitted by least square method to establish a regression equation of ​ Solving parameters a and b by minimizing the error sum of squares; Extracting the fitting slope b as the b value of the corresponding window; Sort the b values of each window by the center time of the window to generate a b value time series wherein each corresponding window has a center time .

4. The coal compression mechanics test acoustic emission signal processing method of claim 2, characterized in that: Constructing the third time domain feature parameter comprises: extracting energy parameter sequences in time sequence from the original data set and corresponding timestamp sequences where i = 1, 2,..., n; A fixed number of events sliding window processor is constructed, a number of acoustic emission events N in the window is set, a sliding step is S2, and a window sequence is generated wherein k = 1, 2,..., n-N+1; For each window : Subsequences of energy within an extraction window and time ; Cumulative energy within a computation window where N represents the event number within the window. Constructing cumulative energy-time data pairs sets wherein, is the window start time; The data pairs are logarithmically transformed to obtain ; linear fitting of the transformed data using least squares method ; Solving parameters by minimizing sum of squared errors ; Extracting the slope of the fit as the energy release index of the window; The values of each window are ordered by the time of the center event of the window, generating a time series of values where each corresponds to the center event time of the window .​​​ 5. The coal compression mechanics test acoustic emission signal processing method of claim 2, characterized in that: Constructing the fourth time domain feature parameter comprises: From the raw data set, four parameters are extracted in parallel for each acoustic emission event i: rise time , amplitude , ring count and duration , where i = 1, 2,..., n. Respectively calculating: angle of ascent characterizes the rate of ascent of the stress wave; Average frequency characterizing the signal frequency characteristic; constructing a RA-AF two-dimensional feature space, generating value pair datasets ; According to the ratio relationship of RA-AF, the damage mode of acoustic emission event is judged: When the tensile failure mode is determined. When shear failure mode; wherein, alpha is the characteristic threshold value of supercritical water coupling, the value range is -1.5~ -0.5; The proportion of tensile damage and shear damage events in each time window is counted to generate a damage mode evolution sequence; The RA-AF values are plotted against the data sets and failure mode evolution sequence, as the fourth time domain characteristic parameter, are used to characterize the dynamic process of coal body from tensile failure to shear failure under the coupling action of supercritical water.

6. The coal compression mechanics test acoustic emission signal processing method of claim 2, characterized in that: Constructing the fifth time domain feature parameter comprises: Obtaining RA-AF values from fourth time-domain feature parameters for a data set where i = 1, 2,..., n; calculating a probability density estimation value for each position point z = (RA, AF) in the RA-AF two-dimensional space as the fifth time domain feature wherein, is an estimate of the true density; n is the number of data points, is the ith data point, z is an arbitrary location point in the RA-AF two-dimensional space for which the density is to be estimated; h is a smoothing parameter; and K is a Gaussian kernel function.

7. The coal compression mechanics test acoustic emission signal processing method of claim 1, characterized in that: S3, the original data set is converted to frequency domain, output frequency domain feature parameter set, comprising: time domain waveform signal for each acoustic emission event in the raw signal data set performing a fast Fourier transform to generate a corresponding frequency domain signal ; The frequency corresponding to the maximum amplitude is extracted from each frequency domain signal as the main frequency, and the amplitude value corresponding to the frequency, to generate a main frequency-amplitude parameter sequence as a frequency domain feature parameter set, to identify the characteristic frequency distribution of coal body failure under supercritical water coupling action. water coupling action.

8. The coal compression mechanics test acoustic emission signal processing method of claim 7, characterized in that: S4, the original data set is calculated by spatial positioning, and the spatial position parameter set is output, comprising: extracting from the raw data set the reception time data of each acoustic emission event at multiple sensors where i is the event number and j is the sensor number, a time difference matrix is constructed ; Based on time difference matrix and preset sensor spatial coordinates , a sound emission source spatial positioning equation set is established; Determination of average sound wave propagation velocity of coal body by wave velocity calibration test with sensor coordinates combination, constitute parameter set of positioning calculation; The acoustic emission source spatial positioning equation set is converted into an optimization problem, a target function is constructed by using a minimum absolute deviation criterion, a simplex optimization algorithm is applied to iteratively minimize the target function, and optimal spatial coordinates of each acoustic emission event are obtained by solving and occurrence time ; Wherein, the objective function: where N represents the number of sensors participating in the positioning calculation, ti represents the time of the acoustic emission signal received by the i-th sensor, t represents the occurrence time of the acoustic emission event; x represents the three-dimensional spatial coordinate vector of the acoustic emission source; xi represents the three-dimensional spatial coordinate vector of the i-th sensor; and cp represents the average propagation speed of the acoustic wave in the coal body.

9. The coal compression mechanics test acoustic emission signal processing method of claim 8, characterized in that: S5, a multi-dimensional feature data matrix is established by time synchronization, comprising: Time synchronization processing is performed on the time domain feature parameter set, the frequency domain feature parameter set and the spatial position parameter set to establish a unified time reference axis; Based on the unified time reference axis, data alignment and interpolation processing are performed on the parameter sequences with different sampling frequencies and time scales; All parameter sequences after time alignment are combined in columns to construct a multi-dimensional feature data matrix M; Slope change points and b-value time series based on cumulative ring count value sequences Slope change points and b-value time series based on cumulative ring count value sequences The phase transition index of the coal body failure process is calculated according to the mutation point, and the whole loading process is divided into multiple failure stages with different acoustic emission activity characteristics. Respectively calculating the statistical indicators of each damage stage, the statistical indicators include damage mode proportion, frequency distribution, spatial aggregation characteristics and time domain evolution characteristics; Based on the statistical indicators of each damage stage, output the supercritical water coupling effect under the whole process of coal from initial damage to final destruction of multi-dimensional quantitative analysis results.

10. A coal body compression mechanics test acoustic emission signal processing system, characterized in that, Comprise: A data acquisition module acquires acoustic emission raw data of the whole process of uniaxial compression failure of the coal body under the supercritical water coupling action condition. A data acquisition module acquires acoustic emission raw data of the whole process of uniaxial compression failure of the coal body under the supercritical water coupling action condition. The time domain processing module performs time domain processing on the original data, and outputs the time domain feature parameter set; The frequency domain processing module performs frequency domain processing on the original data, and outputs the frequency domain feature parameter set; The spatial positioning module calculates the original data by spatial positioning, and outputs the spatial position parameter set; The data fusion module fuses the time domain characteristic parameter set, the frequency domain characteristic parameter set and the spatial position parameter set, establishes a multi-dimensional characteristic data matrix through time synchronization, and generates a multi-dimensional quantitative analysis result of the whole process from initial damage to final failure of the coal body under the water coupling effect of supercritical CO2.

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