Rock damage characterization method based on acoustic emission

Abstract

The invention discloses a rock damage characterization method based on acoustic emission. The rock damage characterization method comprises the steps that 1, acoustic emission signals in the whole rock damage process are collected through a piezoelectric transducer; step 2, preprocessing the acoustic emission signal; step 3, performing time segmentation processing on the preprocessed acoustic emission signal by using a sliding window method; 4, calculating the energy release rate and the signal zero-crossing rate of the acoustic emission signal; step 5, constructing a comprehensive index damage variable F by utilizing the energy release rate and the signal zero-crossing rate and coupling the strain rate; and 6, representing the damage evolution process of the rock by using the damage variable F. By combining the energy release rate and the zero-crossing rate of the acoustic emission signal, the damage variable F is established to represent the rock damage process, the problem that a traditional method cannot accurately monitor rock damage evolution in real time is effectively solved, and the method has important engineering application value and is particularly suitable for damage detection and evaluation of rock mechanics and engineering structures.

Description

Technical Field

[0001] The invention relates to the field of information processing, and in particular to a rock damage characterization method based on acoustic emission. Background Art

[0002] The damage process of rock usually occurs deep underground or in other inaccessible environments, so it is difficult to observe the damage process directly. When the damage accumulates to a certain extent, it will lead to a more violent rockburst. Therefore, how to monitor the evolution of rock damage during loading has become an important task in fields such as geotechnical engineering.

[0003] The acoustic emission method has become a common means of studying and monitoring rock damage because of its simple principle, convenient implementation and high efficiency. At present, in rock damage monitoring, the analysis method of acoustic emission mainly characterizes the damage state of rock by energy or ringing count. These methods are usually used for qualitative analysis and cannot directly give a quantitative evaluation of damage. Specifically:

[0004] (1) Energy analysis: The energy of the acoustic emission signal is closely related to the degree of rock damage. By analyzing the energy of the acoustic emission signal, it is possible to evaluate the scale and degree of damage to the rock when it is subjected to external forces. Generally speaking, the greater the energy of the acoustic emission signal, the more serious the rock damage and the more significant the expansion and combination of microcracks.

[0005] (2) Ring count analysis: Ring counts characterize the extent of damage by counting the number of acoustic emission events generated by the rock during loading. Each time a crack expands or slips, a certain amount of sound waves are released. These sound wave signals are received by the piezoelectric transducer and converted into event counts. An increase in ring counts usually indicates that the number of microcracks in the rock is increasing and damage is accumulating.

[0006] Although energy analysis and ring count analysis have achieved certain success in the qualitative analysis of rock damage, their quantitative analysis capabilities are still limited. The magnitude of energy and the number of ring counts can reflect the overall trend of rock damage, but cannot accurately quantify the specific nature of rock damage and the speed of damage evolution. Summary of the invention

[0007] The present invention proposes a rock damage characterization method based on acoustic emission, which solves the technical problem of inaccurate quantitative characterization of rock damage in the prior art.

[0008] The present invention provides a rock damage characterization method based on acoustic emission, comprising the following steps:

[0009] S1. Use piezoelectric transducers to collect acoustic emission signals during the entire rock destruction process;

[0010] S2, preprocessing the acoustic emission signal to obtain a preprocessed signal;

[0011] S3, using a sliding window method to perform time segmentation processing on the preprocessed acoustic emission signal to obtain a plurality of segmented signals;

[0012] S4, calculating the energy release rate and signal zero crossing rate of each time interval signal after segmentation;

[0013] S5, normalize the acoustic emission energy release rate and signal zero crossing rate, and couple the strain rate to construct the comprehensive indicator damage variable F;

[0014] S6. Use the damage variable F to characterize the damage evolution process of rock.

[0015] The beneficial effect provided by the present invention is: by analyzing the energy release rate and signal zero-crossing rate of the acoustic emission signal during the rock failure process and coupling the strain rate, a normalized damage variable F is constructed. When F=1, it indicates that the rock is completely destroyed, which provides a reliable technical means for accurately characterizing the rock damage evolution process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of the method of the present invention;

[0017] Figure 2 It is a schematic diagram of the energy release rate in the rock destruction process in the method of the present invention;

[0018] Figure 3 It is a schematic diagram of the zero-crossing rate of the rock destruction process signal in the method of the present invention;

[0019] Figure 4 It is a schematic diagram of the effect of the method of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0021] Before formally describing the present invention, the scheme of the present invention is first generally described for easy understanding.

[0022] Please refer to Figure 1 , Figure 1 It is a schematic flow diagram of the method of the present invention.

[0023] The present invention provides a rock damage characterization method based on acoustic emission, comprising the following steps:

[0024] S1. Use piezoelectric transducers to collect acoustic emission signals during the entire rock destruction process;

[0025] As an example, a piezoelectric transducer is placed on the rock surface to monitor and record the acoustic emission activity during the rock destruction process in real time, and convert the acoustic emission signal into an electrical signal. The piezoelectric transducer has high sensitivity and a wide frequency response range, and can effectively capture the high-frequency sound wave signals generated by the expansion and rupture of microcracks in the rock when it is subjected to external forces.

[0026] S2, preprocessing the acoustic emission signal to obtain a preprocessed signal;

[0027] It should be noted that the preprocessing in step S2 specifically includes: discretization processing and wavelet packet threshold denoising.

[0028] The acoustic emission signal is usually expressed in the form of a continuous time function. Assuming that the amplitude of the acoustic emission signal at time t is x(t), the acoustic emission signal can be expressed as: x(t), t∈[t0,t N ]. Among them, t0 is the initial time, t N It's the end time.

[0029] In the present invention, the acoustic emission signal is discretized. In order to digitally process the acoustic emission signal, it is necessary to convert the continuous time signal x(t) into a discrete time series signal. The core of discretization is to sample the signal at a certain time interval Δt. The time series of the sampling points can be expressed as: t n =t0+n·Δt,n=0,1,2,…,N.

[0030] According to the Nyquist sampling theorem, the sampling interval Δt should satisfy: where f max is the highest frequency of the signal.

[0031] Sampling frequency f s is the inverse of the sampling interval:

[0032] The continuous signal x(t) is transformed into n Sampling can obtain a discrete signal x[n], where n represents the nth sampling point. The discrete signal can be expressed as: x[n] = x(t n ),n=0,1,2,...,N. Further expansion is: x[n]=x(t0+n·Δt),n=0,1,2,…,N. The discrete signal x[n] is a time series vector consisting of the values ​​of sampling points.

[0033] In addition, the present invention also performs noise reduction processing on the collected acoustic emission signals to remove unnecessary redundant signals. The acoustic emission signals of the rock fracture process are usually non-stationary. Considering that the wavelet packet transform has good time-frequency localization characteristics, it can better capture the non-stationary characteristics of the signal, effectively extract the essential components of the signal, and suppress noise.

[0034] Since the Db2 wavelet basis function has good localization characteristics in the time domain, it can better capture the transient components in the signal. Then the discrete signal x[n] is decomposed into j layers (the number of decomposition layers can be selected according to the characteristics and needs of the signal, usually 3 to 5 layers). The decomposition of the wavelet packet transform divides the signal into multiple frequency subbands, each of which contains different components of the signal and noise. The wavelet packet decomposition result can be expressed as:

[0035]

[0036] Among them, W k [n] is the wavelet packet coefficient of the kth frequency band.

[0037] According to the coefficients of each sub-band after wavelet packet decomposition, selecting a suitable threshold method to distinguish between signal and noise can effectively improve the quality of the signal and reduce noise interference. Threshold selection method:

[0038] (1) VisuShrink (based on fixed threshold)

[0039] The VisuShrink method assumes that the noise follows a normal distribution and the standard deviation of the noise is known as σ, which is usually used for simpler noise models. The threshold λ is calculated as:

[0040]

[0041] Where N is the length of the signal.

[0042] (2) SURE (based on Stein unbiased risk estimation)

[0043] The SURE method is an adaptive threshold selection method that can select an appropriate threshold based on the characteristics of the data itself without fully knowing the statistical characteristics of the noise. It is suitable for situations where the signal and noise components vary greatly, because it can adjust the threshold based on the actual data to better balance signal recovery and noise suppression.

[0044] (3) Minimax Threshold

[0045] The Minimax method is suitable for scenarios where the noise in the signal is relatively stable and has known statistical characteristics. This method solves a "minimize the maximum risk" problem to select a threshold suitable for global noise reduction.

[0046] The sub-band coefficients W obtained after wavelet packet decomposition k [n]Threshold processing is performed to remove noise.

[0047] (1) Soft Thresholding

[0048] The wavelet coefficients are processed as follows:

[0049]

[0050] (2) Hard Thresholding

[0051] The wavelet coefficients are processed as follows:

[0052]

[0053] After threshold processing, the effective components of the signal are retained, and the wavelet packet coefficients of the noise components are removed. The inverse wavelet packet transform (IWPT) is used to reconstruct these coefficients and obtain the denoised signal.

[0054] After the acoustic emission signal is denoised by wavelet packet threshold, the essential components of the signal are retained and unnecessary noise interference is removed, providing more reliable and efficient data support for subsequent analysis, monitoring and diagnosis.

[0055] S3, using a sliding window method to perform time segmentation processing on the preprocessed acoustic emission signal to obtain a plurality of segmented signals;

[0056] As an embodiment, the present invention divides the acoustic emission signal into multiple time windows, and the signal in each window is used for subsequent analysis. The sliding window method is used to divide the signal.

[0057] The specific process is as follows:

[0058] Assume that the acoustic emission signal x[n], the sampling rate f s =1MHz, that is, the sampling interval is Δt=1μs, and 1 second is used as the length of the unit time window. Then each time window contains N=1×10 6 sampling points.

[0059] The i-th window signal can be expressed as x i[n] = x[(i-1)·W+1,i·W], where the window size W = N = 1×10 6 sampling points; that is, each window does not overlap.

[0060] S4, calculating the energy release rate and signal zero crossing rate of each time interval signal after segmentation;

[0061] As an embodiment, the energy release rate represents the amount of energy released per unit time. For a signal x[n], the energy release amount is calculated based on the amplitude of the signal and can be defined as the sum of the square values ​​of the signal in each window. The specific calculation steps are as follows:

[0062] E i =∑x i [n] 2

[0063] where x i [n] represents the signal amplitude at the nth sampling point in the i-th window.

[0064] The energy release rate calculation steps for the i-th window are as follows:

[0065]

[0066] From the numerical point of view, since the length of each time window is 1 second, the energy release rate E of the i-th window is r (i) is equal to the energy release E of the i-th window i , that is, E r (i) = E i .

[0067] As an embodiment, the signal zero-crossing rate is defined as the number of times the signal passes through the zero value per unit time. In layman's terms, it represents the number of times the signal changes from positive to negative or from negative to positive in its amplitude. The signal zero-crossing rate calculation formula can be expressed as:

[0068]

[0069] Among them, Z r (i) is the signal zero-crossing rate of the i-th time window; x i [n] represents the signal amplitude of the nth sampling point in the i-th window; sgn(·) is the zero-crossing judgment function, which is defined as:

[0070] S5. Normalize the acoustic emission energy release rate and signal zero-crossing rate, and couple the strain rate to construct the comprehensive indicator damage variable F.

[0071] It should be noted that in step S5, according to the characteristics of energy release rate and signal zero-crossing rate, a comprehensive damage assessment index F is constructed by adopting the weighted average and strain rate coupling method.

[0072] The specific calculation steps are as follows:

[0073]

[0074] Where ω1 and ω2 are the weight coefficients of energy release rate and signal zero crossing rate, satisfying ω1+ω2=1; E z is the normalized energy release rate; Z z is the normalized signal zero-crossing rate; is the strain rate.

[0075] S6. Use the damage variable F to characterize the damage evolution process of rock.

[0076] As an example, the value of the comprehensive damage variable F is between [0, 1]. When F=1, it indicates that the rock is completely destroyed.

[0077] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A rock damage characterization method based on acoustic emission, characterized in that: The method comprises the following steps: S1. Use piezoelectric transducers to collect acoustic emission signals during the entire rock destruction process; S2, preprocessing the acoustic emission signal to obtain a preprocessed signal; S3, using a sliding window method to perform time segmentation processing on the preprocessed acoustic emission signal to obtain a plurality of segmented signals; S4, calculating the energy release rate and signal zero crossing rate of each time interval signal after segmentation; S5, normalize the acoustic emission energy release rate and signal zero crossing rate, and couple the strain rate to construct the comprehensive indicator damage variable F; S6. Use the damage variable F to characterize the damage evolution process of rock.

2. A rock damage characterization method based on acoustic emission according to claim 1, characterized in that: The preprocessing in step S2 specifically includes: discretization processing and wavelet packet threshold denoising.

3. A rock damage characterization method based on acoustic emission according to claim 1, characterized in that: In step S3, a sliding window method is used to perform time segmentation processing to obtain a plurality of continuous segmented signals.

4. A rock damage characterization method based on acoustic emission according to claim 1, characterized in that: In step S4, the energy release rate E is calculated by the following formula: r (i) The signal zero crossing rate Z is calculated by the following formula r (i) In the above formula, W is the specified window size, f s is the signal sampling frequency, sgn is the zero-crossing judgment function.

5. The rock damage characterization method based on acoustic emission according to claim 1, characterized in that: In step S5, the following comprehensive indicator damage variable F is constructed: Where ω1 and ω2 are the weight coefficients of energy release rate and signal zero crossing rate, satisfying ω1+ω2=1, E z is the normalized energy release rate, Z z is the normalized signal zero-crossing rate, is the strain rate.

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