Coal body compression mechanics test acoustic emission signal processing method and system

Abstract

The application discloses a coal body compression mechanics test acoustic emission signal processing method and system, relates to coal and rock mass analysis, and comprises the following steps: collecting acoustic emission original data sets of the whole process of coal body uniaxial compression destruction under the condition of supercritical water coupling; respectively performing time domain processing, frequency domain conversion and space positioning calculation on the original data sets, outputting time domain characteristic parameters, a frequency domain characteristic parameter set and a space position parameter set; fusing the time domain characteristic parameter set, the frequency domain characteristic parameter set and the space position parameter set, establishing a multi-dimensional characteristic data matrix through time synchronization, and analyzing acoustic emission signal rules of different destruction stages of the coal body under the condition of supercritical water coupling. The application improves the recognition accuracy by performing time domain processing, frequency domain conversion and space positioning calculation on the acoustic emission original data set, and solves the problem of low recognition accuracy of the coal body destruction stage under the condition of supercritical water coupling.

Description

Technical Field

[0001] This application relates to the field of coal and rock mass analysis, and in particular to a method and system for processing acoustic emission signals for coal compression mechanics testing. Background Technology

[0002] With the increasing depth of coal mining in my country, the safe and efficient mining of deep coal seams faces increasingly severe challenges. Supercritical... Water-coupled fracturing technology, as a novel coal seam permeability enhancement technology, utilizes supercritical fluid dynamics to induce fracturing. The high permeability and low viscosity of supercritical fluid, combined with the synergistic effect of water, can effectively improve coal seam permeability and increase gas extraction efficiency. This technology has broad application prospects in coalbed methane development and gas disaster prevention. However, supercritical fluid... The failure mechanism of coal under water coupling is complex. Accurately identifying different stages in the coal failure process is of great significance for optimizing fracturing parameters and preventing engineering accidents.

[0003] Acoustic emission (AE) technology, as a real-time, non-destructive monitoring method, has been widely applied in rock mechanics research. By monitoring the elastic wave signals released during the initiation and propagation of microcracks within materials, rich information on the material's damage evolution can be obtained. In monitoring the coal and rock failure process, AE signals contain information about the entire process from crack initiation, propagation, penetration, to final failure. Accurately identifying different stages of coal failure, such as the initial compaction stage, elastic deformation stage, stable crack propagation stage, unstable crack propagation stage, and post-failure stage, plays a crucial role in understanding the failure mechanism and predicting the timing of failure.

[0004] However, in supercritical Identifying the failure stages of coal under water-coupled conditions presents numerous technical challenges. First, traditional acoustic emission signal processing methods often employ single-dimensional characteristic parameter analysis, such as analyzing only time-domain parameters like ring counts, energy, or amplitude, which fails to comprehensively reflect the failure characteristics of coal under complex fluid-structure interaction. Second, supercritical... Water coupling alters the mechanical properties and failure modes of coal, rendering failure criteria established under conventional conditions inapplicable and leading to misjudgments of the failure stage. Furthermore, existing methods lack comprehensive utilization of the time, frequency, and spatial information of acoustic emission signals, failing to accurately capture the characteristic transition points of different failure stages. In addition, the transition from tensile failure to shear failure in coal lacks quantitative characterization methods, hindering a deeper understanding of the failure mechanism.

[0005] Therefore, there is an urgent need for a method targeting supercritical fluids. A method for processing acoustic emission signals under water coupling conditions to improve the identification accuracy of coal body failure stages. Summary of the Invention

[0006] For supercritical The identification accuracy of coal body failure stages under water coupling is low. This application provides a method for processing acoustic emission signals in coal body compression mechanics testing. By performing time-domain processing, frequency-domain conversion, and spatial positioning calculation on the original acoustic emission dataset, the identification accuracy is improved.

[0007] One aspect of this application provides a method for processing acoustic emission signals in coal compressibility testing, comprising: S1, in supercritical... Under water coupling conditions, a raw acoustic emission dataset of the entire uniaxial compression failure process of coal was collected. The raw dataset includes ring count, ring count rate, energy, amplitude, rise time, duration, average frequency, peak frequency, and reception time data from each sensor for each acoustic emission event. Steps S2 and S3 involve time-domain processing of the raw dataset to output a time-domain feature parameter set. Step S4 involves frequency-domain transformation of the raw dataset to output a frequency-domain feature parameter set. Step S5 involves spatial positioning calculation of the raw dataset to output a spatial location parameter set. Step S6 involves data fusion of the time-domain, frequency-domain, and spatial location parameter sets, establishing a multi-dimensional feature data matrix through time synchronization, and analyzing the supercritical... Acoustic emission signal patterns at different stages of coal failure under water coupling.

[0008] Furthermore, S2 outputs a set of time-domain feature parameters, including: a sequence of ring count parameters extracted from the original dataset. Where i = 1, 2, ..., n represents the time sequence number; for the ringing count parameter sequence Perform timing accumulation operations to generate a cumulative ringing count sequence. , as the first time-domain feature parameter; extract the amplitude parameter sequence from the original dataset. A sliding window is constructed, and the time series of b-values ​​is generated by fitting the data using the least squares method. As the second time-domain feature parameter; extract the energy parameter sequence from the original dataset. and corresponding time series Construct a sliding window with a fixed number of events to calculate the energy release index. ,generate Value Time Series As the third time-domain feature parameter; the rise time of each acoustic emission event is extracted in parallel from the original dataset. Amplitude Ringing count and duration Calculate the ascending angle matrix and average frequency matrix Generate RA-AF value pair dataset As the fourth time-domain feature parameter; the RA-AF value is calculated using kernel density estimation on the dataset. The two-dimensional probability density is used to generate a density distribution matrix, which is used as the fifth time-domain feature parameter; the first to fifth time-domain feature parameters are used as the time-domain feature parameter set.

[0009] In particular, supercritical Water coupling produces complex physicochemical effects within the coal seam, including supercritical... The extraction process leads to softening of the coal matrix, stress redistribution caused by pore pressure gradients, and the coupling effect of fluid seepage and solid deformation. These factors collectively result in a multi-scale, nonlinear coal failure process. A single time-domain parameter is insufficient to capture the full picture of this complex failure process; therefore, this application constructs five complementary time-domain characteristic parameters:

[0010] Cumulative ring count sequence As an indicator of macroscopic damage accumulation, its slope change directly reflects the stage-wise transformation of crack propagation rate. In supercritical... Under the action of water coupling, the damage accumulation of coal exhibits obvious nonlinear characteristics. By tracking the inflection point of the accumulation curve, the transition moment from stable failure to unstable failure can be accurately identified.

[0011] b-value time series Based on the magnitude-frequency relationship in seismology, the dynamic evolution of crack-scale distribution within the coal seam was characterized. Supercritical The infiltration process alters the heterogeneity of the coal body, causing the b-value to exhibit characteristic changes at different failure stages: the b-value is relatively high in the initial stage, indicating that small-scale cracks dominate; the b-value drops sharply near failure, indicating the formation of large-scale cracks.

[0012] Energy release index Value Time Series The acceleration characteristics of the destructive process were quantified from the perspective of energy dissipation. In supercritical... - During water-coupled fracturing, the effect of fluid pressure alters the energy release pattern. The temporal evolution of the value can sensitively reflect the change in this energy release mechanism.

[0013] RA-AF value pairs dataset Quantitative identification of failure modes was achieved through a two-dimensional mapping of rise angle and average frequency. Supercritical Under the action of water coupling, the coal body undergoes a transformation process from tensile failure to shear failure. This evolution of failure mode is clearly characterized by the RA-AF feature space, overcoming the limitation of traditional methods that cannot distinguish 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, ensuring a one-to-one correspondence between energy values ​​and timestamps; construct a sliding window processor with a fixed number of events, setting the number of acoustic emission events within the window N = 500, and the sliding step size to 1 event, to generate a window sequence. , where k=1,2,......,n-N+1.

[0020] For each window Perform the following processing: Extract the energy subsequence within the window. and time subsequence ;Calculate the cumulative energy within the window Where N represents the event sequence number within the window; construct a cumulative energy-time data pair set. ,in, The window start time is used; a logarithmic transformation is performed on the data pairs to obtain... The least squares method is used to perform linear fitting on the transformed data. By minimizing the sum of squared errors Solve for parameters Extract the fitted slope As the energy release index of this window; [the following is a list of windows] The values ​​are sorted by the time of the event at the center of the window, and generated. Value Time Series Each of them Corresponding window Central event moment Output Value Time Series As a third time-domain feature parameter.

[0021] Specifically, the energy release index By constructing a power-law relationship between accumulated energy W and time t This quantitatively characterizes the degree of acceleration in energy release. In supercritical... Under water coupling, the damage evolution inside the coal body follows the principle of self-organized criticality: when When this occurs, it indicates that energy release is slowing down, corresponding to the stable crack propagation stage; when... When this occurs, it indicates an accelerating trend in energy release, suggesting the system is approaching a critical failure state. The physical essence of this power-law relationship lies in the fact that supercritical... The coupling effect of infiltration and pore water pressure alters the stress field distribution inside the coal body, enhancing the interaction between microcracks and forming a positive feedback mechanism, which leads to accelerated damage accumulation.

[0022] This application, firstly, employs a sliding window design with a fixed number of events N=500, ensuring sufficient statistical samples for power-law fitting within each window. Simultaneously, the sliding step size of one event guarantees temporal resolution, enabling fine tracking. The dynamic change of values. This design overcomes the limitations of a fixed time window when the density of acoustic emission events varies greatly, and is particularly suitable for supercritical applications. Non-uniform distribution characteristics of acoustic emission activity under water coupling.

[0023] Secondly, by linearizing the power-law relationship through logarithmic transformation and using the least squares method to solve for the βt value, not only is computational efficiency improved, but more importantly, the statistically optimal estimate is obtained by minimizing the sum of squared errors. This method is more stable than direct nonlinear fitting, reduces the uncertainty of numerical calculation, and improves efficiency. Reliability of the value.

[0024] From the perspective of identifying the stage of destruction Value Time Series It provides a sensitive precursor indicator: during the initial compaction and elastic deformation stages, The value is usually less than 1 and relatively stable; after entering the stable crack propagation stage, The value begins to fluctuate and rise; as it approaches peak intensity, A sharp increase in the value exceeding 1 indicates the onset of unstable crack propagation. This stage-specific characteristic is observed in supercritical environments. This is particularly evident under water coupling, as the presence of fluid exacerbates the nonlinear characteristics of energy release.

[0025] Furthermore, four parameters for each acoustic emission event i are extracted in parallel from the original dataset: rise time. Amplitude Ringing count and duration , where i = 1, 2, ..., n.

[0026] For supercritical Characteristics of coal body under water coupling, and calculation of failure mode parameters: calculation of the rise angle. The unit is ms / dB, which characterizes the rise rate of the stress wave; the average frequency is calculated. The unit is kHz, representing the frequency characteristics of the signal; a RA-AF two-dimensional feature space is constructed, and a value pair dataset is generated. .

[0027] Based on supercritical The mechanism of coal body failure under water coupling is determined by setting a failure mode classification threshold: when When, it is determined to be a tension failure mode; when When the condition is met, it is determined to be a shear failure mode; where α is the supercritical value. The characteristic threshold under water coupling has a value range of -1.5 to -0.5.

[0028] The proportion of tension failure and shear failure events within each time window is statistically analyzed to generate a failure mode evolution sequence.

[0029] Pair the RA-AF values ​​with the dataset The evolution sequence of the destruction mode is used as a fourth time-domain characteristic parameter to characterize supercriticality. The dynamic process of coal body transforming from tensile failure to shear failure under water coupling.

[0030] In particular, the RA (Rise Angle) and AF (Average Frequency) parameters characterize the essential features of the acoustic emission signal from the time domain and frequency domain, respectively. It reflects the steepness of the stress wave front, which physically corresponds to the crack propagation rate: tension cracks propagate rapidly and brittlely, producing a steep wave front (low RA value); while shear cracks propagate slowly due to friction, forming a gentle wave front (high RA value). The RA / AF ratio characterizes the dominant frequency properties of the signal: tensile failure releases elastic waves dominated by high-frequency components (high AF value); shear failure generates low-frequency components due to friction on the crack surface (low AF value). Therefore, the RA / AF ratio quantitatively distinguishes the two failure modes from a physical mechanism perspective.

[0031] supercritical Water coupling fundamentally alters the evolutionary patterns of coal seam failure. Supercritical The strong permeability and extraction properties of the pore water weaken the strength of the coal matrix, promoting the formation of initial tensile cracks; as the load increases, the pore water pressure and... The coupling effect of pressure alters the stress field distribution, inducing the transformation of cracks into shear modes. This gradual transformation process of failure modes can be precisely quantified through the RA-AF characteristic space: in the initial stage, cracks accumulate in the low RA-high AF region (tension-dominated), and then migrate to the high RA-low AF region (shear-dominated).

[0032] Furthermore, kernel density estimation is used to calculate the RA-AF values ​​for the dataset. The two-dimensional probability density is used to generate a density distribution matrix, which serves as the fifth time-domain feature parameter. This includes obtaining RA-AF values ​​from the fourth time-domain feature parameter dataset. Where i = 1, 2, ..., n; a multivariate Gaussian function is used as the kernel function to calculate the dataset. The density is obtained by calculating the density distribution matrix D, which is used as the fifth time-domain characteristic 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 abrupt density change caused by phase transition can lead to local distortion of sound velocity; water- Reflection and refraction at the interface cause some sensors to receive delayed signals. The least squares method's squared amplification effect on outliers can severely impact positioning accuracy.

[0038] This application adopts the minimum absolute deviation criterion. Its linear penalty characteristic limits the impact of abnormal time data to a linear range, preventing it from being excessively amplified by the squared term, thus ensuring positioning stability in complex fluid environments.

[0039] supercritical Water coupling causes coal bodies to exhibit strong heterogeneity and complex sound velocity field distribution, resulting in a multi-peaked and non-convex objective function. Gradient-based optimization algorithms are prone to getting trapped in local optima. The simplex algorithm, as a direct search method, does not rely on gradient information and searches the solution space through geometric deformation (reflection, expansion, and contraction), effectively escaping local optima and is particularly suitable for handling non-smooth and non-convex optimization problems. Specifically, in this application, the simplex optimization algorithm refers to the Nelder-Mead simplex algorithm, which is a direct search optimization method that does not require gradient calculation. A simplex is a geometric solid in n-dimensional space consisting of n+1 vertices. In the four-dimensional optimization problem of this application... A simplex is a four-dimensional geometric solid consisting of five vertices, each vertex representing a candidate solution. Objective function Containing absolute value terms, and not differentiable at some points, the simplex algorithm avoids the difficulty of gradient calculation. Furthermore, for supercritical conditions... - In complex sound fields under water coupling environments, the simplex algorithm, through geometric transformation search, is less likely to get trapped in local extrema caused by sound velocity anomalies.

[0040] This application optimizes the occurrence time t as a variable to be solved along with the spatial coordinate x, instead of treating it as a known quantity as in traditional methods. This approach fully considers the clock synchronization error and triggering time uncertainty in supercritical environments. Water coupling can generate a continuous cluster of micro-fractures, making it difficult to accurately determine the start time of a single event. Spatiotemporal coupling solutions avoid the transmission of time errors to spatial positioning.

[0041] Finally, the average wave velocity obtained through calibration tests was adopted. Rather than a complex variable speed model, this is based on supercritical... - Engineering treatments were implemented to address the unique characteristics of the aquatic environment. Although the sound velocity within the coal seam varies spatially, within the optimization framework, the average wave velocity combined with the minimum absolute deviation criterion can adaptively reduce the impact of local sound velocity anomalies, achieving a balance between computational 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 water coupling conditions, raw acoustic emission datasets of the entire uniaxial compression failure process of coal were collected. The raw datasets include ring counts, ring count rate, energy, amplitude, rise time, duration, average frequency, peak frequency, and reception time data from each sensor for each acoustic emission event. The raw datasets were then processed in the time domain to output a set of time-domain characteristic parameters; frequency domain transformation was performed to output a set of frequency-domain characteristic parameters; and spatial positioning calculations were performed on the raw datasets to output a set of spatial location parameters. The time-domain, frequency-domain, and spatial location parameter sets were fused, and a multidimensional feature data matrix was established through time synchronization to analyze the supercritical... Acoustic emission signal patterns at different stages of coal failure under water coupling.

[0056] The acoustic emission sensor probe in the acoustic emission acquisition system can collect multi-parameter information on acoustic emission signal waveform events within the coal body during destruction, including ring count, ring count rate, energy, amplitude, rise time, duration, average frequency (AF), and peak frequency. Figure 2 As shown, the spatial localization information parameters of acoustic emission of internal failure events in coal can also be obtained by calibrating the sound velocity of the sample. By analyzing and processing the collected acoustic emission characteristic signal information parameters of coal, more parameters such as rise angle (RA), dominant frequency and its amplitude can be obtained, thus enabling more effective inversion analysis of rock fracture damage mechanisms and interpretation of acoustic emission information of coal fracture propagation.

[0057] Acoustic emission ringing count refers to the number of oscillations of the acoustic emission event signal waveform collected during the coal body failure process that exceed a set threshold value. Therefore, ringing count analysis can be used to further study the degree of internal fracture damage in coal samples during the damage and deterioration process. The cumulative acoustic emission ringing count is the cumulative value of the ringing count.

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

[0059] This paper employs a sliding window algorithm to statistically analyze relevant parameters such as arrival time and amplitude of acoustic emission signals when extracting and processing acoustic emission b-value parameters. The acoustic emission b-value is then calculated using the least squares method. During the extraction and calculation of acoustic emission b-value parameters, the sliding window algorithm parameters are set as follows: the calculation window is 2000, the end time of the calculation window is taken as the occurrence time of the acoustic emission signal, and the sliding window is 500. A schematic diagram of the algorithm is shown below. Figure 3 As shown.

[0060] The calculated dynamic distribution of acoustic emission b-values ​​over time reflects information related to the scale of the destructive event and has specific physical significance. If small-scale destructive events dominate within the coal sample, the acoustic emission b-value will be relatively large; if large-scale destructive events dominate within the sample, the acoustic emission b-value will be relatively small.

[0061] Therefore, further considering the characteristics of the acoustic emission b-value changes at different failure stages in the uniaxially compressed coal samples in this paper, the patterns can be interpreted as follows: if the acoustic emission b-value increases continuously during uniaxial loading, it indicates that the proportion of small-scale failure events occurring within the coal body is increasing; conversely, it indicates an increase in large-scale failure events within the coal body. If the acoustic emission b-value fluctuates in a relatively stable state, it means that the probability and frequency of failure events of different scales within the coal body are similar. Similarly, when the acoustic emission b-value fluctuates continuously within a small time span, and failure events of different scales occur slowly and gradually, we define the state of fracture development within the coal body as a gradual stable expansion state; if the acoustic emission b-value changes and fluctuates within a larger time span, and failure events of different scales have relatively large and rapid failure amplitudes, we define this as a sudden unstable expansion state.

[0062] The absolute energy W in the acoustic emission signal parameters satisfies the following relationship with time: ; Where t represents the test time for recording the acoustic emission signal; For the test time; The energy release index is represented by N; N represents the total number of acoustic emission events that occurred during the test period. This represents the total number of acoustic emission events.

[0063] This article discusses acoustic emission. The value extraction and processing employs a sliding window algorithm similar to that used in the acoustic emission b-value calculation method, and sets a fixed acoustic emission event N of 500 to perform acoustic emission analysis. The calculation of acoustic emission during coal body destruction was obtained through analysis and research. The variation of values ​​at different stages of failure can illustrate the stability of the internal structure of the coal body and the degree of fracture damage. This indicates that the coal body is in a relatively stable state (metastable state) at this stage. This means that the coal body destruction process is relatively slow at this stage, energy dissipation is reduced, and the sample destruction tends to be stable. This means that the internal structure of the coal body is less stable at this time, the rate of rupture events increases rapidly, and energy dissipation becomes greater.

[0064] The failure mode of a material can be analyzed by examining the relationship between the rise angle RA and the average frequency AF of the acoustic emission waveform. RA is the ratio of rise time RT to amplitude A; AF is the ratio of ring count C to duration DT. The formulas for calculating the acoustic emission values ​​RA and AF are as follows: ; The damage mode classification based on RA-AF is obtained as follows: As shown in the figure, the waveform generated when a tension failure event occurs inside the coal body mainly propagates as a longitudinal wave, with rapid energy release and a short time to reach the maximum amplitude, resulting in a relatively small waveform rise angle RA. Conversely, the waveform generated when a shear failure event occurs mainly propagates as a shear wave, with relatively slow energy release and a long time to reach the maximum amplitude, resulting in a relatively large waveform rise angle RA.

[0065] In the critical density function estimation method, each element of the multi-parameter of the acoustic emission signal waveform acquired from the uniaxial compression acoustic emission experiment in this paper contributes to the probability density distribution. Its multivariate data true density estimation function is: In the formula, It is an estimate of the true density. Represents the number of data points. For the first One data point, For smoothing parameters.

[0066] Key density function We use a multivariate Gaussian function as an arbitrary local function that can satisfy certain constraints: ;in, It is the dimension of the data space.

[0067] Smoothing parameter values Verification and determination are performed using least squares cross-validation. and true density Minimize the squared error between them: Due to the true density function Since it cannot be obtained directly, a quadratic fitting Newton-type method is used here to achieve the optimal smoothing parameters. The calculation is as follows: For multivariate distributions, parameters Determined according to the following formula: .

[0068] A coal body failure mode classification method based on RA-AF values ​​was implemented using the kernel density function estimation method (KDE) in MATLAB programming language. Different RGB values ​​were assigned to the RA-AF scatter plot according to different probability densities, allowing the density distribution to be represented in different colors. This ultimately achieved visualization of the acoustic emission RA-AF value distribution density, resulting in a coal body failure acoustic emission RA-AF value cloud map, as shown below. Figure 5 As shown.

[0069] By extracting and processing the characteristic parameters of acoustic emission time-domain signals during coal body failure as described above, some fracture characteristics and failure modes reflecting coal body failure events were obtained. In addition to extracting and analyzing the characteristic parameters of acoustic emission time-domain signals, wavelet waveforms of acoustic emission events can also be processed. By converting the acoustic emission event waveforms from the time domain to the frequency domain, the acoustic emission time-domain characteristic signal waveforms are transformed into frequency-domain characteristic waveforms, thus obtaining more obvious acoustic emission signal evolution characteristics from another perspective.

[0070] For the time-domain characteristic signal of the obtained acoustic emission signal waveform event of the coal body, since any continuous waveform can be composed of countless sine / cosine waves, the Fourier transform (FT) method can be used to convert the time-domain parameter information of the signal waveform at each moment obtained by acoustic emission monitoring into the corresponding frequency-domain parameter information.

[0071] First, we introduce the basic principles of the Fourier Transform (FT). Let's assume... If the acoustic emission time-domain waveform signal to be processed is given, then the Fourier transform process is as follows: The corresponding inverse Fourier transform is: ;in, Includes The relevant characteristic parameters such as the frequency of the time-domain signal are therefore... Called The spectrum of its signal. And its signal phase. ,frequency and amplitude The relationship between them is expressed as: The Fourier Transform (FT) is represented by the Discrete Fourier Transform (DFT) and the Fast Fourier Transform (FFT) based on the processing procedure. The Fast Fourier Transform is a transformation method that takes the Discrete Fourier Transform as a prerequisite.

[0072] First, for the Discrete Fourier Transform method, assuming a set of data signals consists of N discrete points, then this set of data signals is: ,Will The expressions for the continuous Fourier transform and inverse Fourier transform are as follows: ; .

[0073] After undergoing Discrete Fourier Transform The function has become continuous; to transform it into finite discrete data, we will... Discrete approximation is a finite number of discrete points, and these discretized points... Perform the discrete Fourier transform and inverse Fourier transform respectively: ; .

[0074] The above transformation process can be expressed in matrix form as follows, let... ,but: .

[0075] The Fast Fourier Transform (FFT) is an efficient and fast computational algorithm for the Discrete Fourier Transform, and it has significant advantages in analyzing dynamic non-stationary acoustic emission signals recorded in monitoring data, such as those from uniaxial compression failure tests of coal bodies, as studied in this paper. Its principle involves dividing the acoustic emission signal data into a series of discrete data sets, performing a Discrete Fourier Transform on each set, and then integrating the calculation results to obtain the FFT result of the original data sets. Therefore, using the Fast Fourier Transform (FFT) method to transform existing acoustic emission signal event wavelet waveform data allows for more efficient and convenient acquisition of the key frequency parameters and characteristics of the acoustic emission radio frequency domain characteristic signals during coal body failure.

[0076] Taking the acoustic emission monitoring data set of coal seam A-0-3 in test group A-0 as an example, specifically the waveform of a certain event in the acoustic emission signal waveform event during its entire destruction process, an example of the frequency domain signal waveform spectrum obtained by performing a fast Fourier transform on its acoustic emission time domain signal waveform data is shown below. As shown.

[0077] By acquiring spatial location information of acoustic emission events during the uniaxial failure process of coal, i.e., spatial positioning, it is possible to further analyze and visualize information such as the location and energy of acoustic emission events and fracture development and evolution within the coal body.

[0078] The acoustic emission localization technique for coal and rock masses mainly utilizes the time difference of the same acoustic emission fracture event inside the coal and rock mass collected by acoustic emission sensor probes fixed on various surfaces (at different orientations) of the coal and rock mass. The specific location information of the acoustic emission signal event, including three-dimensional coordinates and energy, is calculated by means of these time differences.

[0079] Since the distance between the signal source of the internal coal failure event and the surface sensor probe is much greater than the length of the internal micro-cracks, we assume that the location point of the micro-crack event is a point source. Therefore, the acoustic emission localization equation is: Where X and Xi are the coordinate positions of the signal source of the destructive event and the receiving source of sensor i, respectively; cp represents the wave velocity of the coal body; t represents the time when the destructive event occurs; and ti represents the time when sensor i senses the signal.

[0080] By fixing the acoustic emission probe of the sample and calibrating the sound velocity of the coal body during the test preparation stage before the experiment, the coordinates of each sensor can be determined and the specific wave velocity can be measured, as well as the coordinates of the micro-crack event location point. The time t of the acoustic emission event is unknown.

[0081] The occurrence time t of an acoustic emission event can be simply regarded as being directly obtained through acoustic emission signal monitoring. However, in actual acoustic emission monitoring and spatial positioning calculations, considering the influence of wave velocity in the model and the existence of some large outliers in positioning accuracy, it is necessary to process the acoustic emission event location and time data through certain methods.

[0082] In this paper, the specific method for determining the location and occurrence time-related parameters of acoustic emission spatial positioning events employs the minimum absolute deviation and simplex numerical optimization algorithms to further refine the positioning. The objective function f(x) is defined as the absolute value of the difference between the occurrence time of the disruptive event and the reception time of the signal sensed by the sensor. The solution is the value corresponding to the minimum value of the objective function f(x), as shown in the following formula: Through the above-described methods for calculating, processing, and analyzing acoustic emission spatial positioning data, the final result can be obtained as follows: The image shows the results of acoustic emission spatial positioning feature information.

[0083] S5, Time synchronization processing for each parameter set: Extracting the cumulative ringing count sequence from the time-domain feature parameter set. b-value time series , Value Time Series Extract the master frequency-amplitude parameter sequence and its corresponding timestamp from the frequency domain feature parameter set; extract the spatial coordinate sequence from the spatial location parameter set. and its corresponding timestamp Establish a unified time reference axis and map all parameter sequences to the same time coordinate system;

[0084] Perform multi-scale data alignment and interpolation: For parameter sequences with different sampling frequencies, use linear interpolation or spline interpolation methods to unify them to the same time resolution; for parameter sequences generated by sliding windows, use the window center time as the effective time point of the parameter; for event-level parameters, keep the original timestamp unchanged and fill in null values ​​at non-event times.

[0085] Combine all time-synchronized parameters column-wise to form a feature matrix. Where T is the time vector. Let b be the cumulative ring count sequence, and b be the time series of b values. Let RA and AF be the energy release exponential sequence, RA and AF be the rising angle and average frequency sequences, and D be the RA-AF probability density. and The main control frequency and corresponding amplitude are given, with x, y, and z as spatial coordinates; each row represents a multidimensional feature vector at a time point; the matrix is ​​Z-score standardized to eliminate the influence of different parameter dimensions, so that the mean of each parameter is 0 and the standard deviation is 1.

[0086] Coal body failure stages are segmented based on multi-parameter coupling criteria: through the cumulative ringing count sequence. Calculate the first derivative and second derivative Identify the acceleration points of acoustic emission activity; monitor the b-value time series. rate of change ,when The time marker is used as the point where stress concentration intensifies; the stage transition index is calculated. ,in, This represents the normalized value of the first derivative of the cumulative ringing count; when STI exceeds the threshold of 0.6, it is determined to be a stage transition point; based on the above criteria, the loading process is divided into: the initial compaction stage (b value is stable at 2.0±0.2, During the elastic deformation stage (the b-value slowly decreases to 1.5 ± 0.2), ), the plastic yielding stage (b-value rapidly drops below 1.0, (b) and the instability and failure stage (b value drops sharply to below 0.5, and the cumulative ringing count increases exponentially).

[0087] Extracting characteristic parameter statistics for each failure stage: Initial compaction stage: Calculation Value, statistics on tension failure events The proportion exceeds 80%; the mean of the master control frequency is concentrated in 30-50kHz, with a standard deviation of less than 10kHz; the spatial distribution clustering index (SDI) is less than 0.3, showing a random distribution; the b-value remains at 2.0±0.2. The value remained stable at 0.8±0.1.

[0088] Elastic deformation stage: Tension failure accounts for 60-80%, shear failure events occur. The frequency begins to increase; the master control frequency shifts towards 50kHz to 100kHz, and the shift rate... The Spatial Aggregation Index (SDI) rose to 0.3-0.5; the b-value decreased at a rate of 0.01-0.02 / min. The value increased to 1.0-1.5.

[0089] In the plastic yielding stage: shear failure accounts for 40-60%, the RA-AF probability density exhibits a bimodal characteristic, and the peak spacing ΔP > 0.5; the dominant frequency further evolves towards 100-150kHz, with high-frequency events (>100kHz) accounting for over 50%; spatial localization shows that acoustic emission sources concentrate in 2-3 regions, and SDI increases to 0.5-0.8; the b-value rapidly decreases to below 1.0. The value exceeds 1.5.

[0090] Instability and failure stage: Shear failure accounts for more than 60%, probability density D shows a single-peak high-density clustering, with a peak density >0.8; the dominant frequency-amplitude parameter shows a surge in high-frequency, high-amplitude (>80dB) events above 150kHz; spatial localization reveals the formation of a macroscopic fracture surface, SDI >0.8; the b-value suddenly drops below 0.5. The value increased dramatically.

[0091] Generate supercritical Destruction evolution under water coupling: Establishing a characteristic parameter evolution model: Constructing a b-value-time model. - Fitting curves of time and master frequency centroid-time; determine the precursor characteristic combinations of each stage of transition, including b-value decrease rate > 0.05 / min, The growth rate is >0.1 / min, and the growth rate of shear failure ratio is >5% / min; establish a coupled relationship model of "parameter evolution-failure mode-spatial distribution" and output the discrimination criterion: when b<1.2 is satisfied simultaneously, When SDI > 0.5, the coal body enters a dangerous state; supercritical coal is formed. A complete set of rules for identifying progressive coal body failure under water coupling, including the threshold range of characteristic parameters and state transition criteria for each stage.

[0092] The foregoing illustrative description of the present application and its embodiments is not restrictive and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. The accompanying drawings are only one embodiment of the present application, and the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present application, such designs should fall within the scope of protection of this application. Furthermore, the use of the word "include" does not exclude other elements or steps, and a single word preceding an element does not exclude the inclusion of multiple such elements. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

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 on the original data set, output time domain feature parameter set; S3, frequency domain conversion on the original data set, output frequency domain feature parameter set; S4, spatial positioning calculation on the original data set, output spatial position parameter set; 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 signals of different damage stages of coal under the action of water coupling are analyzed The acoustic emission signal law of different damage stages of coal under the action of water coupling 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; S3, frequency domain conversion on the original data set, output frequency domain feature parameter set, comprising: time domain waveform signal for each acoustic emission event in the original signal data set performing a fast Fourier transform to generate a 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 failure under water coupling; S4, spatial positioning calculation on the original data set, output spatial position parameter set, 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; cp represents the average propagation speed of sound waves in the coal body; S5, through time synchronization, establish multi-dimensional feature data matrix, comprising: Time synchronization processing on the time domain feature parameter set, the frequency domain feature parameter set and the spatial position parameter set, establish a unified time reference axis; Based on the unified time reference axis, data alignment and interpolation processing on the parameter sequence with different sampling frequencies and time scales; All parameter sequences after time alignment are combined according to column 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 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 calculate the statistical indicators of each damage stage, including the 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.

2. The acoustic emission signal processing method for coal body compression mechanics test according to claim 1, characterized in that: Constructing a second time domain feature parameter, comprising: 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 subsequences 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 sum of squares of errors; 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 time series of b values wherein each corresponding window has a center time .

3. The acoustic emission signal processing method for coal body compression mechanics test according to claim 1, characterized in that: Constructing a third time domain feature parameter, comprising: 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 time of the center event of the window .​​​ 4. The acoustic emission signal processing method for coal body compression mechanics test according to claim 1, characterized in that: Constructing a fourth time domain feature parameter, comprising: 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 RA-AF ratio relationship, judging the damage mode of acoustic emission events: When the tensile failure mode is determined. When shear failure mode; wherein, alpha is the characteristic threshold value of supercritical water coupling, ranging from -1.5 to -0.

5. Statistically calculating the proportion of tensile damage and shear damage events in each time window, and generating 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.

5. The acoustic emission signal processing method for coal body compression mechanics test according to claim 1, characterized in that: Constructing a fifth time domain feature parameter, comprising: 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.

6. A coal body compression mechanics test acoustic emission signal processing system for implementing the method of any one of claims 1 to 5, characterized in that, Comprise: Data acquisition module, in supercritical Under water coupling conditions, raw acoustic emission data of the entire process of uniaxial compression failure of coal were collected; The time domain processing module, time domain processing on the original data, output time domain feature parameter set; The frequency domain processing module, frequency domain processing on the original data, output frequency domain feature parameter set; The spatial positioning module, spatial positioning calculation on the original data, output spatial position parameter set; The data fusion module, data fusion on the time domain feature parameter set, the frequency domain feature parameter set and the spatial position parameter set, through time synchronization, establish multi-dimensional feature data matrix, generate multi-dimensional quantitative analysis results of the whole process from initial damage to final damage of coal body under supercritical CO2 water coupling action.

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