Method for picking up arrival time of acoustic emission signal

Abstract

The invention provides an acoustic emission signal arrival time pickup method. The method comprises the following steps: acquiring an acoustic emission signal sampled at a target position; determining a time window where sound wave arrival time is located based on a sliding window method, and extracting an acoustic emission signal in the time window where the sound wave arrival time is located as a target signal; and determining the accurate arrival time of the acoustic emission signal according to the target signal. The time window where the arrival time is located is preliminarily determined through the sliding window method, then the accurate arrival time is determined according to the signals in the time window, and the arrival time of the sound wave emission signals can be rapidly and accurately picked up.

Description

Technical Field

[0001] The invention relates to the technical field of sound wave detection, and in particular to a method for picking up the arrival time of an acoustic emission signal. Background Art

[0002] Tunnel lining is an important part of the tunnel structure, playing a key role in supporting the surrounding rock, preventing collapse, and improving the overall stability of the tunnel. During tunnel excavation and long-term operation, the lining structure may suffer structural damage such as cracks, spalling, and displacement due to changes in surrounding geological conditions or long-term loads. If these damages are not discovered and repaired in time, they may affect the service life of the tunnel and even cause safety accidents.

[0003] As a non-destructive detection method, acoustic emission technology can monitor and evaluate the damage status of lining structures in real time by monitoring the stress wave signals inside the material. Acoustic emission technology captures the stress waves released when the lining is stressed or damaged and analyzes their temporal and spatial distribution, thereby locating and evaluating the location and extent of the damage. Therefore, accurately obtaining the arrival time information of the acoustic emission signal is crucial for timely evaluating the health status of the tunnel lining.

[0004] In the prior art, energy ratio algorithms, AIC algorithms, machine learning algorithms, and other algorithms are usually used to determine the precise time of sound wave emission. However, the energy ratio algorithm and the AIC algorithm are greatly affected by environmental noise and have poor robustness. The machine learning algorithm depends on the selection of samples and parameters. The quantity and quality of samples will affect the performance of the model, and the parameters in the algorithm will also affect the prediction ability of the model. Therefore, none of the methods in the prior art can achieve accurate and rapid picking of the precise time of sound wave emission. Summary of the invention

[0005] The embodiment of the present invention provides a method for picking up the arrival time of an acoustic emission signal, so as to solve the problem that the existing methods are unable to achieve accurate and rapid picking up of the acoustic emission signal.

[0006] In a first aspect, an embodiment of the present invention provides a method for picking up an arrival time of an acoustic emission signal, comprising:

[0007] Acquire an acoustic emission signal sampled at a target location;

[0008] Determine the time window where the sound wave arrives based on the sliding window method, and extract the acoustic emission signal in the time window where the sound wave arrives as the target signal;

[0009] According to the target signal, the precise arrival time of the acoustic emission signal is determined.

[0010] The embodiment of the present invention provides a method for picking up the arrival time of an acoustic emission signal. The above method includes: obtaining an acoustic emission signal sampled at a target position; determining the time window where the sound wave arrives based on a sliding window method, and extracting the acoustic emission signal in the time window where the sound wave arrives as a target signal; and determining the precise arrival time of the acoustic emission signal based on the target signal. In the embodiment of the present invention, a sliding window method is used to preliminarily determine the time window where the arrival time is located, and then the precise arrival time of the sound wave is determined based on the signal in the time window. The calculation process is simple, the picking speed is fast and the accuracy is high, and it is less affected by noise, and the arrival time of the acoustic wave emission signal can be picked up quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0012] Figure 1 It is a flow chart of an implementation method of picking up the arrival time of an acoustic emission signal provided by an embodiment of the present invention;

[0013] Figure 2 is a waveform diagram of an acoustic emission signal provided by an embodiment of the present invention;

[0014] Figure 3 is a signal waveform diagram within a time window provided by an embodiment of the present invention;

[0015] Figure 4 The embodiment of the present invention provides Figure 2 The shape dimension sequence diagram corresponding to the acoustic emission signal shown;

[0016] Figure 5 It is a schematic diagram of target time corresponding to wavelet coefficients of each layer provided by an embodiment of the present invention;

[0017] Figure 6 It is a structural schematic diagram of a device for picking up the arrival time of an acoustic emission signal provided by an embodiment of the present invention;

[0018] Figure 7 It is a schematic diagram of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of the implementation of the method for picking up the arrival time of the acoustic emission signal provided by an embodiment of the present invention, and is described in detail as follows;

[0022] The above-mentioned method for picking up the arrival time of the acoustic emission signal comprises:

[0023] S101: Acquire an acoustic emission signal sampled at a target position;

[0024] In the embodiment of the present invention, a sound wave receiving device is arranged at the target position to sample and acquire the sound wave emission signal.

[0025] S102: Determine the time window where the sound wave arrives based on the sliding window method, and extract the acoustic emission signal in the time window where the sound wave arrives as the target signal;

[0026] The embodiment of the present invention preliminarily determines the time window where the sound wave arrival time is located based on the sliding window method, and extracts the acoustic emission signal in the time window as the target signal for subsequent data processing. On the one hand, the data processing amount is greatly reduced, and on the other hand, useless data is discarded, the data processing is more targeted, the influence of noise is reduced, and the accuracy of the sound wave arrival time calculation is improved.

[0027] S103: Determine the precise arrival time of the acoustic emission signal according to the target signal.

[0028] Based on the target signal extracted in S101, the embodiment of the present invention specifically determines the precise arrival time based on the target signal, which greatly improves the accuracy of the sound wave arrival time calculation.

[0029] In the embodiment of the present invention, a sliding window method is used to preliminarily determine the time window in which the arrival time is located, and then the precise arrival time is determined according to the signal in the time window. The calculation process is simple, the picking speed is fast and the accuracy is high. It is independent of the selection of samples and parameters, the accuracy is higher, and data processing is performed in a targeted manner. The noise impact is smaller, the robustness is stronger, and the calculation amount is small, so that the arrival time of the sound wave can be quickly and accurately picked up.

[0030] In a possible implementation, S102 may include:

[0031] S1021: Taking the sampling starting point of the acoustic emission signal as the starting time, extracting the signal in each time window according to the sliding window size and sliding distance;

[0032] Exemplarily, the time window length may be 270 μs, and the sliding step length may be 1 μs.

[0033] S1022: for any time window, determine the fractal dimension of the time window according to the signal in the time window;

[0034] S1023: sorting the fractal dimensions in chronological order to obtain a fractal dimension sequence;

[0035] S1024: Determine a mutation point in the fractal dimension sequence, and use the signal in the time window where the mutation point is located as the target signal.

[0036] Fractal dimension reflects the effectiveness of space occupied by complex shapes, and it is a measure of the irregularity of complex shapes. The acoustic emission signal exhibits fractal behavior in its waveform. In the early stage of the acoustic emission arrival, the calculated fractal dimension fluctuates continuously because the amplitude of the acoustic emission signal changes very little, and when the window moves to the arrival time of the acoustic emission signal, the fractal dimension will have an extreme value. Based on this, in an embodiment of the present invention, the fractal dimension of each time window is determined, and when the fractal dimension mutates, it indicates that the acoustic emission signal has arrived.

[0037] For example, Figure 2 The waveform of the acoustic emission signal obtained by sampling is shown, and the sliding window extracts the signal in each time window. Figure 3 . Correspondingly, the fractal dimension of each time window is calculated to obtain Figure 4 The fractal dimension sequence diagram shown in Figure 4 It can be seen that the fractal dimension suddenly changes at 10μs, so the signal in the time window of 10μs is taken as the target signal (the time window of 10μs to 279μs, the time window length is 270μs), thereby preliminarily determining the time window where the sound wave arrives, and realizing accurate data extraction.

[0038] Specifically, a specific method for determining a mutation point in a fractal dimension sequence may be: if the difference between a certain value in the fractal dimension sequence and the previous fractal dimension and the difference between a certain value and the next fractal dimension are both greater than a preset difference, then the value is determined as a mutation point.

[0039] Based on the above, in a possible implementation, S1022 may include:

[0040] 1. Perform shift operation on the signal within the time window;

[0041] Determine the minimum value y_min of the amplitude of the signal in the time window, subtract y_min from the amplitude of each point in the signal, and thus obtain the converted vertical coordinate y_shift for subsequent data processing. The formula is as follows:

[0042] y_shift=y i -y_min

[0043] Among them, i = 1, 2, 3, ... 270.

[0044] 2. Resampling

[0045] The above shift operation processes the ordinate, and then normalizes the original abscissa of the signal to obtain the normalized abscissa x_ord. The formula is as follows:

[0046]

[0047] Among them, x i =1, 2, ..., 1024.

[0048] Then the normalized horizontal coordinate is interpolated to obtain the normalized interpolated horizontal coordinate xx_ord. The formula is as follows:

[0049]

[0050] Among them, cellmax is the maximum side length of the square, x j =1, 2, …cellmax.

[0051] Finally, according to the given interpolation horizontal coordinate xx_ord, one-dimensional linear interpolation is performed in the horizontal coordinate xx_ord and the corresponding vertical coordinate y_shift to obtain the corresponding interpolated vertical coordinate y_interp. Then the side length of the square is normalized, and the formula is as follows:

[0052]

[0053] Among them, cellsize is the side length of the square, x k =1, 2, …cellmax.

[0054] 3. Zoom the vertical axis

[0055] Normalize the interpolated ordinate y_interp to get the reduced ordinate y_shrink. The formula is as follows:

[0056]

[0057] 4. Divide the horizontal axis equally

[0058] Assuming that the horizontal axis is divided into NumSeg segments, the formula is as follows:

[0059]

[0060] 5. Calculate the number of boxes that cumulatively cover the signal

[0061] From the above analysis, we can see that the horizontal axis is divided into NumSeg segments. Starting from the first segment, the cumulative number of grids spanned by the vertical axis is calculated, Q(1). The calculation formula of Q(1) is as follows:

[0062]

[0063] Among them, y_1max is the maximum value of the ordinate in the first segment, and y_1min is the minimum value of the ordinate in the first segment.

[0064] Similarly, we can get the cumulative number of grids Q(2) in the second segment, the cumulative number of grids Q(3) in the third segment, and so on, the cumulative number of grids Q(NumSeg) in the NumSeg segment. The total number of grids N(cellsize) occupied by the signal curve can be calculated from the cumulative number of grids in each segment. The calculation formula is as follows:

[0065] N(cellsize)=Q(1)+Q(2)+Q(3)+......+Q(Numseg)

[0066] 6. Calculate fractal dimension

[0067] From the above, we can see that when cellsize is 1 / cellmax, 2 / cellmax, 4 / cellmax, 8 / cellmax, ..., 1, the corresponding N(1 / cellmax), N(2 / cellmax), N(4 / cellmax), N(8 / cellmax), ..., N(1) can be obtained.

[0068] Select log 10 (cellsize) is the horizontal axis, log 10 (N(cellsize)) is the ordinate, and linear fitting is performed to obtain the fractal dimension D H , the formula is as follows:

[0069]

[0070] In a possible implementation, S103 may include:

[0071] S1031: De-noising the target signal to obtain a denoised target signal;

[0072] S1032: Perform singularity detection on the denoised target signal to obtain a singular point of the denoised target signal, and use the time corresponding to the singular point as the precise arrival time of the acoustic emission signal.

[0073] In order to reduce the influence of noise, in the embodiment of the present invention, the target signal is denoised, and after denoising, a singular point is determined as the precise arrival time.

[0074] In a possible implementation, S1031 includes:

[0075] 1. Perform wavelet decomposition on the target signal to obtain the decomposed wavelet coefficients;

[0076] According to the time window of fractal dimension mutation, the target signal is extracted and then wavelet decomposition is performed to obtain the wavelet coefficients.

[0077] In a possible implementation manner, the number of decomposition levels of the wavelet decomposition may be 3.

[0078] Specifically, the decomposition formula is as follows:

[0079]

[0080] Among them, a k j-1 is the low-frequency wavelet coefficient, h[m-2k] is the low-pass filter; b k j-1 is the high-frequency wavelet coefficient, and g[m-2k] is the high-pass filter.

[0081] 2. Use the improved threshold algorithm to reduce the noise of the decomposed wavelet coefficients to obtain the denoised wavelet coefficients;

[0082] The embodiment of the present invention adopts an improved threshold method to perform noise reduction processing on the decomposed wavelet coefficients.

[0083] In a possible implementation, the calculation formula of the improved threshold algorithm may be:

[0084]

[0085] Among them, ujk is the wavelet coefficient after denoising, vjk is the wavelet coefficient after decomposition, λ is a fixed threshold; N is the decomposition scale, w1, w2, w3, σ is a constant.

[0086] Specifically, w1 can be 0.2, w2 can be 0.4, w3 can be 2, It can be 0.5 and σ can be 0.1.

[0087] 3. Reconstruct the denoised wavelet coefficients to obtain the denoised target signal.

[0088] According to the signal reconstruction algorithm, the wavelet coefficients after noise reduction are reconstructed. The calculation formula is as follows:

[0089]

[0090] Among them, a k j is the reconstructed low-frequency wavelet coefficient, b k j is the reconstructed high frequency wavelet coefficient.

[0091] In a possible implementation, S1032 may include:

[0092] 1. Determine the threshold coefficient according to the low-frequency wavelet coefficient of the denoised target signal;

[0093] First, determine the threshold coefficient, which can be determined based on the low-frequency wavelet coefficient of the target signal after denoising. The specific calculation formula is as follows:

[0094]

[0095] Where L is the threshold coefficient, x i is the low-frequency wavelet coefficient corresponding to the i-th moment, i=1,…N.

[0096] 2. For any moment, if the low-frequency wavelet coefficient corresponding to the moment is greater than the threshold coefficient, and the moment is less than the threshold moment, the moment is taken as the target moment; wherein the threshold moment is the inherent threshold moment of the receiving device determined according to the sampled acoustic emission signal;

[0097] The receiving device will determine the threshold time, that is, the original sound wave arrival time, which is inaccurate and usually later than the normal arrival time. Therefore, the accurate arrival time should be before the threshold time, thereby obtaining multiple possible arrival times, that is, the target time.

[0098] For example, refer to Figure 5 , 13 target moments x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x10, x11, x12, x13 are extracted, which is the possible accurate time. At the same time, multiple points are obtained on the other three high-frequency wavelet coefficient graphs.

[0099] 3. For any target moment, if the wavelet coefficient corresponding to the target moment meets the preset conditions, the point on the denoised target signal corresponding to the target moment is taken as a singular point.

[0100] Based on the characteristics of sound waves, at the moment corresponding to the precise time of the sound wave, its amplitude should increase with the increase of the number of decomposition layers, and the sign of the amplitude (positive and negative signs remain unchanged) will not change. Based on this, the preset conditions are determined, and the point corresponding to the target moment that meets the preset conditions is taken as the singular point.

[0101] Based on the above analysis, the preset conditions can be:

[0102] |A m (t)|>|A m-1 (t)|

[0103] A m (t)×A m-1 (t)>0

[0104] Among them, A m (t) is the amplitude of the wavelet coefficient of the mth layer corresponding to the target time t.

[0105] Depend on Figure 5 It can be seen that at the moment corresponding to x12, the amplitude gradually increases with the increase in the number of decomposition layers, and the sign of the amplitude is always positive and does not change. Therefore, the moment x12, 264μs, is determined as the accurate time.

[0106] It has been verified that the precise arrival time is basically consistent with the actual arrival time, has high accuracy, and the data processing is simple, and the precise arrival time of the sound wave can be determined quickly and accurately.

[0107] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0108] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0109] Figure 6 The structure diagram of the device for picking up the arrival time of the acoustic emission signal provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0110] like Figure 6 As shown, the device for picking up the arrival time of the acoustic emission signal includes:

[0111] The acoustic wave signal sampling module 21 is used to obtain the acoustic emission signal sampled at the target position;

[0112] A preliminary arrival time determination module 22 is used to determine the time window where the sound wave arrival time is located based on a sliding window method, and extract the acoustic emission signal within the time window where the sound wave arrival time is located as a target signal;

[0113] The precise arrival time determination module 23 is used to determine the precise arrival time of the acoustic emission signal according to the target signal.

[0114] In a possible implementation, the preliminary arrival determination module 22 may include:

[0115] The signal segmentation extraction unit is used to extract the signal in each time window according to the sliding window size and sliding distance, taking the sampling starting point of the acoustic emission signal as the starting time;

[0116] A fractal dimension calculation unit, used for determining the fractal dimension of any time window according to the signal in the time window;

[0117] A sorting unit is used to sort the fractal dimensions in chronological order to obtain a fractal dimension sequence;

[0118] The target signal output unit is used to determine the mutation point in the fractal dimension sequence and take the signal in the time window where the mutation point is located as the target signal.

[0119] In a possible implementation, the accurate time determination module 23 may include:

[0120] A denoising unit, used for denoising the target signal to obtain a denoised target signal;

[0121] The singularity detection unit is used to perform singularity detection on the denoised target signal, obtain the singularity point of the denoised target signal, and use the time corresponding to the singularity point as the precise arrival time of the acoustic emission signal.

[0122] In a possible implementation, the denoising unit may include:

[0123] The wavelet decomposition subunit is used to perform wavelet decomposition on the target signal to obtain the decomposed wavelet coefficients;

[0124] A coefficient denoising subunit is used to denoise the decomposed wavelet coefficients by using an improved threshold algorithm to obtain denoised wavelet coefficients;

[0125] The reconstruction subunit is used to reconstruct the denoised wavelet coefficients to obtain the denoised target signal.

[0126] In a possible implementation manner, the number of decomposition levels of the wavelet decomposition may be 3.

[0127] In a possible implementation, the calculation formula of the improved threshold algorithm may be:

[0128]

[0129] Among them, ujk is the wavelet coefficient after denoising, vjk is the wavelet coefficient after decomposition, λ is a fixed threshold; N is the decomposition scale, w1, w2, w3, σ is a constant.

[0130] In a possible implementation, the singularity detection unit may include:

[0131] A threshold coefficient determination subunit, used to determine the threshold coefficient according to the low-frequency wavelet coefficient of the target signal after denoising;

[0132] The target time determination subunit is used for determining any time as the target time if the low-frequency wavelet coefficient corresponding to the time is greater than the threshold coefficient and the time is less than the threshold time; wherein the threshold time is the inherent threshold time of the receiving device determined according to the sampled acoustic emission signal;

[0133] The singular point determination subunit is used to determine, for any target moment, a point on the denoised target signal corresponding to the target moment as a singular point if the wavelet coefficient corresponding to the target moment meets a preset condition.

[0134] In a possible implementation, the calculation formula of the threshold coefficient may be:

[0135]

[0136] Where L is the threshold coefficient, x i is the low-frequency wavelet coefficient corresponding to the i-th moment, i=1,…N.

[0137] In a possible implementation, the preset condition may be:

[0138] |A m (t)|>|A m-1 (t)|

[0139] A m (t)×A m-1 (t)>0

[0140] Among them, A m (t) is the amplitude of the wavelet coefficient of the mth layer corresponding to the target time t.

[0141] In a possible implementation manner, the time window length of the sliding window method may be 270 μs, and the sliding step length may be 1 μs.

[0142] Figure 7 Schematic diagram of a terminal device 3 provided in an embodiment of the present invention. Figure 7As shown, the terminal device 3 of this embodiment includes: a processor 30 and a memory 31. The memory 31 is used to store a computer program 32, and the processor 30 is used to call and run the computer program 32 stored in the memory 31 to perform the steps in the above-mentioned embodiments of the method for picking up the arrival time of each acoustic emission signal, for example Figure 1 Alternatively, the processor 30 is used to call and run the computer program 32 stored in the memory 31 to implement the functions of each module / unit in the above-mentioned device embodiments, such as Figure 6 The functions of modules 21 to 23 are shown.

[0143] Exemplarily, the computer program 32 may be divided into one or more modules / units, one or more modules / units are stored in the memory 31, and are executed by the processor 30 to implement the present invention. One or more modules / units may be a series of computer program instruction segments that can implement specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the computer program 32 may be divided into Figure 6 Modules / units 21 to 23 are shown.

[0144] The terminal device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 7 It is only an example of terminal device 3 and does not constitute a limitation on terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0145] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0146] The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used to store computer programs and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0147] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0148] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0149] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0150] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0151] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0153] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0154] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for picking up the arrival time of an acoustic emission signal, characterized in that: include: Acquire an acoustic emission signal sampled at a target location; Determine the time window where the sound wave arrives based on the sliding window method, and extract the acoustic emission signal in the time window where the sound wave arrives as the target signal; The precise arrival time of the acoustic emission signal is determined according to the target signal.

2. The method for picking up the arrival time of the acoustic emission signal according to claim 1, characterized in that: The method of determining the time window where the sound wave arrives based on the sliding window method, and extracting the acoustic emission signal in the time window where the sound wave arrives as the target signal, includes: Taking the sampling starting point of the acoustic emission signal as the starting time, extracting the signal in each time window according to the sliding window size and sliding distance; For any time window, the fractal dimension of the time window is determined according to the signal in the time window; Sort each fractal dimension in chronological order to obtain a fractal dimension sequence; A mutation point in the fractal dimension sequence is determined, and a signal within a time window where the mutation point is located is used as the target signal.

3. The method for picking up the arrival time of the acoustic emission signal according to claim 1, characterized in that: Determining the precise arrival time of the acoustic emission signal according to the target signal includes: De-noising the target signal to obtain a de-noised target signal; A singularity detection is performed on the denoised target signal to obtain a singular point of the denoised target signal, and a time corresponding to the singular point is used as the precise arrival time of the acoustic emission signal.

4. The method for picking up the arrival time of the acoustic emission signal according to claim 3, characterized in that: The denoising the target signal to obtain a denoised target signal includes: Performing wavelet decomposition on the target signal to obtain decomposed wavelet coefficients; Using an improved threshold algorithm to reduce noise on the decomposed wavelet coefficients to obtain reduced noise wavelet coefficients; The denoised wavelet coefficients are reconstructed to obtain the denoised target signal.

5. The method for picking up the arrival time of the acoustic emission signal according to claim 4, characterized in that: The decomposition layer number of the wavelet decomposition is 3 layers.

6. The method for picking up the arrival time of the acoustic emission signal according to claim 4, characterized in that: The calculation formula of the improved threshold algorithm is: Among them, ujk is the wavelet coefficient after denoising, vjk is the wavelet coefficient after decomposition, λ is a fixed threshold; N is the decomposition scale, w1, w2, w3, σ is a constant.

7. The method for picking up the arrival time of the acoustic emission signal according to claim 3, characterized in that: The performing singularity detection on the denoised target signal to obtain the singularity point of the denoised target signal includes: Determine a threshold coefficient according to the low-frequency wavelet coefficient of the denoised target signal; For any moment, if the low-frequency wavelet coefficient corresponding to the moment is greater than the threshold coefficient, and the moment is less than the threshold moment, the moment is taken as the target moment; wherein the threshold moment is the inherent threshold moment of the receiving device determined according to the sampled acoustic emission signal; For any target moment, if the wavelet coefficient corresponding to the target moment meets the preset condition, the point on the denoised target signal corresponding to the target moment is taken as the singular point.

8. The method for picking up the arrival time of the acoustic emission signal according to claim 7, characterized in that: The calculation formula of the threshold coefficient is: Wherein, L is the threshold coefficient, x i is the low-frequency wavelet coefficient corresponding to the i-th moment, i=1,…N.

9. The method for picking up the arrival time of the acoustic emission signal according to claim 7, characterized in that: The preset conditions are: A m (t)|>|A m-1 (t)| A m (t)×A m-1 (t)>0 Among them, A m (t) is the amplitude of the wavelet coefficient of the mth layer corresponding to the target time t.

10. The method for picking up the arrival time of an acoustic emission signal according to claim 1, characterized in that: The time window length of the sliding window method is 270 μs, and the sliding step length is 1 μs.

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