A method for detecting quality defects of anchor bolts

Abstract

The present invention relates to the technical field of data processing, and particularly to a method for detecting the quality defects of anchor bolts. For any anchor bolt to be detected, acoustic wave detection is performed on the anchor bolt to obtain an initial reflection signal; according to the distribution characteristics of the signals in the initial reflection signal and the influence degree of the noise signal, a decomposition layer adjustment coefficient for wavelet denoising of the initial reflection signal is obtained; the decomposition layer for wavelet denoising of the initial reflection signal is obtained, and according to the decomposition layer adjustment coefficient, the decomposition layer is adjusted to obtain the optimal decomposition layer for wavelet denoising of the initial reflection signal; the optimal decomposition layer is used as the decomposition layer for wavelet denoising, and wavelet denoising is performed on the initial reflection signal to obtain a denoised signal, and the quality defects of any anchor bolt to be detected are detected according to the denoised signal, thereby improving the accuracy of the detection of the quality defects of the anchor bolt.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method for detecting quality defects of anchor bolts. Background Art

[0002] An anchor bolt is a rod system structure for reinforcing rock and soil masses. As a tensile member penetrating deep into the formation, one end of it is connected to an engineering structure, and the other end penetrates deep into the formation. The reinforcement effect of the anchor bolt not only helps to improve the bearing capacity of the soil mass, but also can effectively resist the scouring and erosion of groundwater, ensuring the safety of the engineering structure. Therefore, the detection of quality defects of anchor bolts is an important link to ensure the safety and stability of building structures.

[0003] Traditional methods for detecting quality defects of anchor bolts usually use the acoustic wave reflection method. The acoustic wave reflection method is a commonly used non-destructive testing technique. By exciting impact elastic waves at the top of the anchor bolt through a vibration source, the elastic waves propagate along the axis of the anchor bolt. When encountering an interface with a change in wave impedance, reflection occurs, forming a reflected wave. The length of the anchor bolt and the position of the anchoring defect are evaluated based on the arrival time and energy attenuation of the reflected wave. However, due to the influence of instrument settings, environmental interference, and the surface state of the anchor bolt, etc., the reflected wave obtained during acoustic wave detection may be affected by noise interference, resulting in inaccurate judgment of anchor bolt defects. Therefore, it is necessary to perform denoising processing on the obtained reflected wave.

[0004] Traditional methods often use wavelet denoising to remove noise in acoustic wave detection. First, a suitable wavelet basis function and decomposition level are selected to perform wavelet transform on the initial reflected signal obtained from acoustic wave detection to obtain wavelet coefficients. Then, a suitable threshold function is selected to process the wavelet coefficients to remove the noise signals contained in the initial reflected signal. Finally, inverse wavelet transform is performed on the processed wavelet coefficients to obtain the denoised signal. However, in the process of wavelet denoising, the setting of the decomposition level has a great influence on the denoising result. If the level is too small, it may not be able to capture and remove the noise components in the signal. If the level is too large, it may lead to signal distortion. In traditional methods, the selection of the decomposition level in wavelet denoising usually depends on experience. The frequency of the initial reflected signal obtained from the acoustic wave detection of anchor bolts generally concentrates in the low-frequency part. At this time, a larger decomposition level is required for more detailed analysis. Therefore, the selected decomposition level in wavelet denoising in traditional methods is relatively small compared to the decomposition level required in the detection of anchor bolt quality defects, which may lead to poor denoising results for the initial reflected signal, making the acoustic wave reflection method unable to detect the quality of anchor bolts more accurately.

[0005] Therefore, how to accurately obtain the decomposition level during wavelet denoising to achieve better denoising effect in the detection of anchor bolt quality defects has become an urgent problem to be solved. Summary of the Invention

[0006] In view of this, an embodiment of the present invention provides a method for detecting the quality defects of anchor bolts to solve the problem of how to accurately obtain the decomposition level during wavelet denoising so as to achieve a better denoising effect in the detection of the quality defects of anchor bolts.

[0007] An embodiment of the present invention provides a method for detecting the quality defects of anchor bolts, and the method includes the following steps:

[0008] For any anchor bolt to be detected, perform acoustic wave detection on the any anchor bolt to be detected to obtain an initial reflection signal;

[0009] According to the distribution characteristics of the signals in the initial reflection signal, obtain the signal complexity of the initial reflection signal. According to the influence degree of the noise signals in the initial reflection signal, obtain the noise proportion of the initial reflection signal. According to the signal complexity and the noise proportion of the initial reflection signal, obtain an adjustment coefficient for the decomposition level when performing wavelet denoising on the initial reflection signal;

[0010] Obtain the decomposition level when performing wavelet denoising on the initial reflection signal, and adjust the decomposition level according to the decomposition level adjustment coefficient to obtain the optimal decomposition level for performing wavelet denoising on the initial reflection signal;

[0011] Use the optimal decomposition level as the decomposition level for wavelet denoising, perform wavelet denoising on the initial reflection signal to obtain a denoised signal, and perform quality defect detection on the any anchor bolt to be detected according to the denoised signal.

[0012] Preferably, the step of obtaining the signal complexity of the initial reflection signal according to the distribution characteristics of the signals in the initial reflection signal includes:

[0013] Perform wavelet denoising on the initial reflection signal to obtain a comparison signal, form the comparison signal into a comparison signal sequence, obtain the sample entropy of the comparison signal sequence, and perform normalization processing on the sample entropy to obtain the first complexity degree of the initial reflection signal;

[0014] Construct a spectrogram of the initial reflection signal, where the abscissa of the spectrogram is the frequency and the ordinate is the signal amplitude. Obtain the bandwidth of the initial reflection signal according to the spectrogram, and perform normalization processing on the bandwidth to obtain the second complexity degree of the initial reflection signal;

[0015] Perform weighted summation on the first complexity degree and the second complexity degree to obtain the signal complexity of the initial reflection signal.

[0016] Preferably, the step of obtaining the noise proportion of the initial reflection signal according to the influence degree of the noise signals in the initial reflection signal includes:

[0017] Perform a Fourier transform on the initial reflection signal to obtain a power spectrum curve graph of the initial reflection signal, where the abscissa of the power spectrum curve graph is frequency and the ordinate is power;

[0018] Obtain the power spectral density corresponding to each frequency in the frequency spectrum diagram in the power spectrum curve graph, and obtain the proportion of the noise energy of the initial reflection signal according to the difference in the power spectral density corresponding to each frequency in the frequency spectrum diagram;

[0019] According to the power spectrum curve graph, obtain the average power of the initial reflection signal, denoted as the average power of the initial reflection signal, obtain the power spectrum curve graph of the comparison signal, obtain the average power of the comparison signal according to the power spectrum curve graph of the comparison signal, denoted as the average power of the comparison signal, and obtain the proportion of the noise power of the initial reflection signal according to the difference between the average power of the initial reflection signal and the average power of the comparison signal;

[0020] Perform a weighted sum on the proportion of the noise energy and the proportion of the noise power to obtain the proportion of the noise of the initial reflection signal.

[0021] Preferably, the obtaining the proportion of the noise energy of the initial reflection signal according to the difference in the power spectral density corresponding to each frequency in the frequency spectrum diagram includes:

[0022] Obtain the maximum power spectral density among the power spectral densities corresponding to each frequency in the frequency spectrum diagram, and form a power spectral density sequence with all the power spectral densities except the maximum power spectral density;

[0023] Denote the frequency corresponding to the maximum power spectral density as the target frequency. For any power spectral density in the power spectral density sequence, obtain the absolute value of the frequency difference between the frequency corresponding to the any power spectral density and the target frequency, and obtain the proportion of the absolute value of the frequency difference in the absolute values of the frequency differences corresponding to all the power spectral densities in the power spectral density sequence to obtain the weight coefficient of the any power spectral density;

[0024] Obtain the weight coefficient of each power spectral density in the power spectral density sequence, perform a weighted sum on all the power spectral densities in the power spectral density sequence to obtain a noise energy value, and perform a normalization process on the ratio of the noise energy value to the maximum power spectral density to obtain the proportion of the noise energy of the initial reflection signal.

[0025] Preferably, the obtaining the proportion of the noise power of the initial reflection signal according to the difference between the average power of the initial reflection signal and the average power of the comparison signal includes:

[0026] Calculate the average power difference between the average power of the initial reflection signal and the average power of the comparison signal, and normalize the ratio of the average power difference to the average power of the initial reflection signal to obtain the noise power ratio of the initial reflection signal.

[0027] Preferably, the obtaining of the decomposition layer adjustment coefficient for wavelet denoising of the initial reflection signal according to the signal complexity and noise ratio of the initial reflection signal includes:

[0028] Perform a weighted sum of the signal complexity and noise ratio of the initial reflection signal to obtain the decomposition layer adjustment coefficient for wavelet denoising of the initial reflection signal.

[0029] Preferably, the adjusting of the decomposition layer according to the decomposition layer adjustment coefficient to obtain the optimal decomposition layer for wavelet denoising of the initial reflection signal includes:

[0030] When the decomposition layer adjustment coefficient is greater than a preset decomposition layer adjustment coefficient threshold, use the decomposition layer adjustment coefficient as the independent variable of the Sigmoid function to obtain the Sigmoid function value;

[0031] Obtain the addition result of the constant 1 and the Sigmoid function value to get the expansion coefficient of the decomposition layer, and round up the product of the expansion coefficient and the decomposition layer to obtain the optimal decomposition layer for wavelet denoising of the initial reflection signal.

[0032] Preferably, the adjusting of the decomposition layer according to the decomposition layer adjustment coefficient to obtain the optimal decomposition layer for wavelet denoising of the initial reflection signal further includes:

[0033] When the decomposition layer adjustment coefficient is less than or equal to the preset decomposition layer adjustment coefficient threshold, use the decomposition layer as the optimal decomposition layer for wavelet denoising of the initial reflection signal.

[0034] The beneficial effects of the embodiments of the present invention compared with the prior art are:

[0035] For any anchor rod to be detected, the present invention performs acoustic wave detection on the any anchor rod to be detected to obtain an initial reflection signal; according to the distribution characteristics of the signals in the initial reflection signal, the signal complexity of the initial reflection signal is obtained, according to the influence degree of the noise signal in the initial reflection signal, the noise proportion of the initial reflection signal is obtained, and according to the signal complexity and the noise proportion of the initial reflection signal, an adjustment coefficient for the decomposition level during wavelet denoising of the initial reflection signal is obtained; the decomposition level during wavelet denoising of the initial reflection signal is obtained, and according to the decomposition level adjustment coefficient, the decomposition level is adjusted to obtain the optimal decomposition level for wavelet denoising of the initial reflection signal; the optimal decomposition level is used as the decomposition level for wavelet denoising, and the initial reflection signal is subjected to wavelet denoising to obtain a denoised signal, and the quality defect of the any anchor rod to be detected is detected according to the denoised signal. Among them, according to the distribution characteristics of the signals in the obtained initial reflection signal and the influence degree of the noise signal, the adjustment coefficient for the decomposition level during wavelet denoising of the initial reflection signal is obtained, and then the decomposition level is adjusted according to the decomposition level adjustment coefficient, so as to accurately obtain the optimal decomposition level for wavelet denoising of the initial reflection signal, reducing the poor noise removal effect caused by the traditional method of relying on experience to select the wavelet denoising decomposition level. Moreover, the optimal decomposition level depends more on the characteristics of the initial reflection signal itself, enabling the acoustic wave reflection method to more accurately detect the quality defects of the anchor rod and improving the accuracy of the anchor rod quality defect detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0037] Figure 1 is a flowchart of a method for detecting the quality defects of an anchor rod provided in Embodiment 1 of the present invention;

[0038] Figure 2 is a physical diagram of an anchor rod provided in Embodiment 1 of the present invention;

[0039] Figure 3 is an example diagram of a spectrogram provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The following details the embodiments of the present disclosure, and the examples of the embodiments are shown in the drawings. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation to the present disclosure.

[0041] It should be noted that the terms "first", "second", etc. in the description of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are only examples of devices and methods consistent with some aspects of the present disclosure.

[0042] In order to illustrate the technical solution of the present invention, the following will be described through specific embodiments.

[0043] See Figure 1 , which is a flowchart of a method for detecting bolt quality defects provided in the first embodiment of the present invention. As Figure 1 shown, the method may include:

[0044] Step S101, for any bolt to be detected, perform acoustic wave detection on the any bolt to be detected to obtain an initial reflection signal.

[0045] Bolt support means implanting a rod body into the rock mass, so that the original mechanical state of the rock mass is modified, making the broken and incoherent rock mass, sand and gravel layer, etc. become stable and making it an integral body with high strength and good stability, so as to achieve the purpose of supporting the roadway. The physical diagram of the bolt is as Figure 2 shown.

[0046] In order to detect the quality defects of bolts, the acoustic wave reflection method is usually used to generate impact elastic waves at the top of the bolt through a vibration source (ultra-magnetic vibration source or hammer). The elastic waves propagate along the axis of the bolt. When encountering an interface with a change in wave impedance (grouting defect or bottom of the rod), reflection occurs, forming a reflected wave. The length of the bolt and the position of the anchoring defect are evaluated according to the arrival time and energy attenuation of the reflected wave. The acoustic wave reflection method belongs to the prior art and will not be elaborated here. However, due to the influence of instrument settings, environmental interference, and the surface state of the bolt, etc., the reflected wave obtained during acoustic wave detection may be interfered by noise, resulting in inaccurate judgment of bolt defects. Therefore, it is necessary to perform denoising processing on the obtained reflected wave.

[0047] Traditional methods often use wavelet denoising to remove noise in acoustic wave detection. First, a suitable wavelet basis function and decomposition level are selected to perform wavelet transform on the initial reflection signal obtained from acoustic wave detection to obtain wavelet coefficients. Then, a suitable threshold function is selected to process the wavelet coefficients to remove the noise signal contained in the initial reflection signal. Finally, inverse wavelet transform is performed on the processed wavelet coefficients to obtain the denoised signal. However, in the process of wavelet denoising, the setting of the decomposition level has a great influence on the denoising result. If the number of levels is small, it may not be able to capture and remove the noise components in the signal. If the number of levels is too large, it may cause signal distortion. In traditional methods, the selection of the decomposition level for wavelet denoising usually depends on experience. The frequency of the initial reflection signal obtained from the anchor rod in acoustic wave detection generally concentrates in the low-frequency part. At this time, a larger decomposition level is required for more detailed analysis. Therefore, the selected decomposition level in wavelet denoising in traditional methods is relatively small compared to the decomposition level required for detecting the quality defects of the anchor rod, which may lead to poor denoising results for the initial reflection signal and make the acoustic wave reflection method unable to detect the quality of the anchor rod more accurately.

[0048] Therefore, in this embodiment, for any anchor rod to be detected, acoustic wave detection is performed on any anchor rod to be detected to obtain an initial reflection signal. By analyzing the distribution characteristics of the signal in the initial reflection signal and the influence degree of the noise signal in the initial reflection signal, the optimal decomposition level for wavelet denoising of the initial reflection signal is obtained, the denoising effect of the initial reflection signal is improved, and the acoustic wave reflection method can more accurately detect the quality defects of the anchor rod.

[0049] First, any anchor rod to be detected is denoted as the target anchor rod, and acoustic wave detection is performed on the target anchor rod using the acoustic wave reflection method to obtain the initial reflection signal of the target anchor rod. Among them, obtaining the initial reflection signal using the acoustic wave reflection method belongs to the prior art and will not be elaborated here.

[0050] Step S102, according to the distribution characteristics of the signal in the initial reflection signal, obtain the signal complexity of the initial reflection signal. According to the influence degree of the noise signal in the initial reflection signal, obtain the noise proportion of the initial reflection signal. According to the signal complexity and noise proportion of the initial reflection signal, obtain the decomposition level adjustment coefficient for wavelet denoising of the initial reflection signal.

[0051] After obtaining the initial reflection signal, denoising processing is performed on the initial reflection signal. Since the selected decomposition level in wavelet denoising in traditional methods is relatively small compared to the decomposition level required for detecting the quality defects of the anchor rod, it may lead to poor denoising results for the initial reflection signal. Therefore, it is necessary to adjust the decomposition level selected by wavelet decomposition in traditional methods according to the characteristics of the initial reflection signal itself to obtain the optimal decomposition level for wavelet denoising of the initial reflection signal and improve the denoising effect of the initial reflection signal.

[0052] When performing wavelet denoising on the initial reflection signal, the more complex the signal of the initial reflection signal is, it means that wavelet denoising requires more decomposition levels to capture the subtle features in the initial reflection signal, so as to effectively remove the noise signal without losing the useful signal; the higher the proportion of the noise signal in the initial reflection signal is, it means that wavelet denoising needs to increase the decomposition levels to more effectively separate the noise signal and the useful signal and enhance the denoising effect.

[0053] Therefore, according to the signal complexity in the initial reflection signal and the noise proportion in the initial reflection signal, the decomposition level adjustment coefficient for wavelet denoising the initial reflection signal can be obtained, and then the decomposition level for wavelet denoising the initial reflection signal can be adjusted using the decomposition level adjustment coefficient to obtain the optimal decomposition level.

[0054] Among them, the method for obtaining the decomposition level adjustment coefficient for wavelet denoising the initial reflection signal according to the signal complexity in the initial reflection signal and the noise proportion in the initial reflection signal is as follows:

[0055] (1) According to the distribution characteristics of the signal in the initial reflection signal, obtain the signal complexity of the initial reflection signal.

[0056] Since sample entropy can measure the complexity of a signal sequence, the larger the entropy value, the greater the complexity of the signal, and the bandwidth can reflect the range of signal frequency changes. The larger the bandwidth, the more different frequency components the signal contains, which also indicates a higher signal complexity. Therefore, the signal complexity of the initial reflection signal can be obtained according to the sample entropy and bandwidth of the initial reflection signal.

[0057] Specifically, perform wavelet denoising on the initial reflection signal to obtain a comparison signal, form the comparison signal into a comparison signal sequence, obtain the sample entropy of the comparison signal sequence, perform normalization processing on the sample entropy to obtain the first complexity level of the initial reflection signal;

[0058] Construct the spectrogram of the initial reflection signal, as Figure 3 shown, where the abscissa of the spectrogram is the frequency and the ordinate is the signal amplitude. Obtain the bandwidth of the initial reflection signal according to the spectrogram, perform normalization processing on the bandwidth to obtain the second complexity level of the initial reflection signal;

[0059] Perform weighted summation on the first complexity level and the second complexity level to obtain the signal complexity of the initial reflection signal.

[0060] In an embodiment, the calculation formula for the signal complexity of the initial reflection signal is:

[0061]

[0062] Among them, is the signal complexity of the initial reflection signal; S is the sample entropy of the comparison signal sequence; B is the bandwidth of the initial reflection signal; norm() is the normalization function.

[0063] It should be noted that since the noise in the initial reflection signal may affect the calculation of the sample entropy, the sample entropy of the comparison signal sequence after wavelet denoising is used to represent the signal complexity in the initial reflection signal. The greater the sample entropy of the comparison signal sequence, the higher the signal complexity of the initial reflection signal; the greater the bandwidth of the initial reflection signal, the higher the signal complexity of the initial reflection signal.

[0064] (2) Obtain the noise proportion of the initial reflection signal according to the influence degree of the noise signal in the initial reflection signal.

[0065] Since the power spectral density is usually used to analyze the spectral characteristics of a signal and reflect the distribution of energy in the frequency domain, the Fourier transform is performed on the initial reflection signal to obtain the power spectral density curve of the initial reflection signal. Among them, the abscissa of the power spectral density curve is the frequency, and the ordinate is the power. The power spectral density corresponding to each frequency in the spectral diagram is obtained in the power spectral density curve. The greater the power spectral density corresponding to each frequency in the spectral diagram, the greater the energy corresponding to this frequency. The maximum power spectral density is obtained among the power spectral densities corresponding to each frequency in the spectral diagram. The frequency corresponding to the maximum power spectral density is denoted as the target frequency. The power spectral densities other than the maximum power spectral density form a power spectral density sequence. Except for the target frequency, the power spectral densities of the remaining frequencies should be small. However, if there is noise interference, the power spectral densities of the remaining frequencies will be large. Therefore, the noise energy proportion of the initial reflection signal can be obtained according to the difference in the power spectral density corresponding to each frequency in the spectral diagram.

[0066] Among them, obtaining the noise energy proportion of the initial reflection signal according to the difference in the power spectral density corresponding to each frequency in the spectral diagram includes:

[0067] For any power spectral density in the power spectral density sequence, obtain the absolute value of the frequency difference between the frequency corresponding to the any power spectral density and the target frequency, and obtain the proportion of the absolute value of the frequency difference in the absolute values of the frequency differences corresponding to all the power spectral densities in the power spectral density sequence to obtain the weight coefficient of the any power spectral density;

[0068] Obtain the weight coefficient of each power spectral density in the power spectral density sequence, perform weighted summation on all the power spectral densities in the power spectral density sequence to obtain the noise energy value, and perform normalization processing on the ratio of the noise energy value to the maximum power spectral density to obtain the noise energy proportion of the initial reflection signal.

[0069] In one embodiment, the calculation formula for the noise energy proportion of the initial reflection signal is:

[0070]

[0071] Wherein, is the noise energy proportion of the initial reflection signal; is the frequency corresponding to the i-th power spectral density in the power spectral density sequence; f is the target frequency; is the i-th power spectral density in the power spectral density sequence; M is the number of power spectral densities in the power spectral density sequence; is the maximum power spectral density; norm() is the normalization function; is the absolute value symbol.

[0072] It should be noted that, is the weighted summation result of all the power spectral densities in the power spectral density sequence. Since on the spectrogram, the farther the frequency is from the target frequency, the smaller the possibility that it is the main frequency of the signal, that is, the greater the probability that this frequency is a noise component. At this time, a larger weight should be given to this frequency to highlight the influence of the noise. Therefore, the proportion of the absolute value of the frequency difference between it and the target frequency in the absolute values of the frequency differences corresponding to all the power spectral densities in the power spectral density sequence is used as the weight coefficient of its corresponding power spectral density. The larger the , the greater the noise energy in the initial reflection signal, the greater the noise energy proportion of the initial reflection signal, and the greater the noise proportion of the initial reflection signal.

[0073] Furthermore, considering that when there are defects in the target bolt, it will cause the noise energy proportion of the initial reflection signal to be inaccurate, and the noise signal in the initial reflection signal will affect the average power of the initial reflection signal. The greater the noise proportion, the greater the average power of the initial reflection signal. Therefore, according to the power spectral curve graph, the average power of the initial reflection signal can be obtained, denoted as the average power of the initial reflection signal. Similarly, the power spectral curve graph of the comparison signal is obtained, and the average power of the comparison signal is obtained according to the power spectral curve graph of the comparison signal, denoted as the average power of the comparison signal. Then, the average power difference between the average power of the initial reflection signal and the average power of the comparison signal is calculated, and normalization processing is performed on the ratio of the average power difference to the average power of the initial reflection signal to obtain the noise power proportion of the initial reflection signal.

[0074] In one embodiment, the calculation formula for the proportion of the noise power of the initial reflection signal is:

[0075]

[0076] Wherein, is the proportion of the noise power of the initial reflection signal; P is the average power of the initial reflection signal; is the average power of the comparison signal; norm() is the normalization function.

[0077] It should be noted that is the average power difference between the average power of the initial reflection signal and the average power of the comparison signal. The larger it is, the greater the noise interference in the initial reflection signal, the greater the proportion of the noise power of the initial reflection signal, and the greater the proportion of the noise in the initial reflection signal.

[0078] Finally, combining the proportion of the noise energy and the proportion of the noise power of the initial reflection signal, the proportion of the noise of the initial reflection signal is obtained. Specifically: performing weighted summation on the proportion of the noise energy and the proportion of the noise power to obtain the proportion of the noise of the initial reflection signal.

[0079] In one embodiment, the calculation formula for the proportion of the noise of the initial reflection signal is:

[0080]

[0081] Wherein, is the proportion of the noise of the initial reflection signal; is the proportion of the noise energy of the initial reflection signal; is the proportion of the noise power of the initial reflection signal; is the weight of the proportion of the noise energy; is the weight of the proportion of the noise power.

[0082] It should be noted that since the proportion of the noise energy is more accurate than the proportion of the noise power, therefore, set , , which is not limited here and can be set according to the specific implementation scenario.

[0083] (3) Performing weighted summation on the signal complexity and the proportion of the noise of the initial reflection signal to obtain the decomposition layer adjustment coefficient when performing wavelet denoising on the initial reflection signal.

[0084] In one embodiment, the calculation formula for the decomposition layer adjustment coefficient is:

[0085]

[0086] Wherein, is the decomposition layer adjustment coefficient; is the signal complexity of the initial reflection signal; is the noise ratio of the initial reflection signal; is the weight of the signal complexity; is the weight of the noise ratio.

[0087] It should be noted that since the signal complexity and the noise ratio of the initial reflection signal are equally important for selecting the decomposition layer when performing wavelet denoising on the initial reflection signal, so is set. There is no limitation here and it can be set according to the specific implementation scenario; the greater the signal complexity of the initial reflection signal, it means that more decomposition layers are required for wavelet denoising to capture the subtle features in the initial reflection signal, and the decomposition layer adjustment coefficient is greater; the greater the noise ratio of the initial reflection signal, it means that the decomposition layer needs to be increased to more effectively separate the noise signal and the useful signal, and the decomposition layer adjustment coefficient is greater.

[0088] Thus, the decomposition layer adjustment coefficient when performing wavelet denoising on the initial reflection signal is obtained.

[0089] Step S103: Obtain the decomposition layer when performing wavelet denoising on the initial reflection signal, and adjust the decomposition layer according to the decomposition layer adjustment coefficient to obtain the optimal decomposition layer for performing wavelet denoising on the initial reflection signal.

[0090] Since the size of the decomposition layer has a great influence on the denoising result during wavelet denoising, a smaller decomposition layer may not be able to capture and remove the noise components in the signal, and an excessive decomposition layer may cause signal distortion. Therefore, it is necessary to use the obtained decomposition layer adjustment coefficient to adjust the decomposition layer when performing wavelet denoising on the initial reflection signal to obtain the optimal decomposition layer to improve the wavelet denoising effect. Since a higher decomposition layer can process the initial reflection signal at a finer scale and enhance the denoising effect, therefore, the higher the complexity of the initial reflection signal, it means that the initial reflection signal contains multiple frequency components, and at this time, more detailed decomposition is required to effectively remove the noise signal without losing the useful signal; the greater the noise ratio of the initial reflection signal, the more decomposition layers are needed to more effectively separate the noise signal and the useful signal.

[0091] Among them, the method of using the obtained decomposition layer adjustment coefficient to adjust the decomposition layer when performing wavelet denoising on the initial reflection signal to obtain the optimal decomposition layer is as follows:

[0092] According to the statistics of experimental results, the threshold of the decomposition layer adjustment coefficient is set to 0.6. When the decomposition layer adjustment coefficient is greater than 0.6, it means that the signal complexity in the initial reflection signal is relatively large and the noise proportion is relatively high. At this time, it is necessary to increase the number of decomposition layers to more effectively separate the noise signal and the useful signal and enhance the denoising effect. Therefore, the decomposition layer adjustment coefficient is used as the independent variable of the Sigmoid function to obtain the Sigmoid function value, and the sum of the constant 1 and the Sigmoid function value is obtained to get the expansion coefficient of the decomposition layer. The product of the expansion coefficient and the decomposition layer is rounded up to obtain the optimal decomposition layer for wavelet denoising of the initial reflection signal.

[0093] In one embodiment, the calculation formula for the optimal decomposition layer is:

[0094]

[0095] Wherein, is the optimal decomposition layer; is the decomposition layer adjustment coefficient; L is the number of decomposition layers for wavelet denoising of the initial reflection signal; Sigmoid() is the S function, also known as the S growth curve, which can map the output result to (0, 1); is the rounding-up symbol; 1 is a constant.

[0096] It should be noted that the larger the decomposition layer adjustment coefficient, the greater the signal complexity and the higher the noise proportion in the initial reflection signal, and the more necessary it is to adjust the number of decomposition layers for wavelet denoising of the initial reflection signal, and the larger the optimal decomposition layer.

[0097] When the decomposition layer adjustment coefficient is less than or equal to 0.6, it means that both the signal complexity and the noise proportion in the initial reflection signal are relatively low. At this time, the number of decomposition layers of wavelet denoising obtained by the traditional method is sufficient to denoise the initial reflection signal, and adjusting the decomposition layer may increase the computational complexity. Therefore, no adjustment is made at this time, and the decomposition layer is used as the optimal decomposition layer for wavelet denoising of the initial reflection signal.

[0098] Thus, the optimal decomposition layer for wavelet denoising of the initial reflection signal is obtained.

[0099] Step S104, use the optimal decomposition layer as the number of decomposition layers for wavelet denoising to perform wavelet denoising on the initial reflection signal to obtain a denoised signal, and perform quality defect detection on any of the anchor bolts to be detected according to the denoised signal.

[0100] After obtaining the optimal decomposition level for wavelet denoising of the initial reflection signal, use the optimal decomposition level as the decomposition level for wavelet denoising of the initial reflection signal, perform wavelet denoising on the initial reflection signal to obtain a denoised signal, and perform quality defect detection on the target anchor bolt based on the denoised signal. Among them, wavelet denoising and quality defect detection of the target anchor bolt belong to the prior art and will not be elaborated here.

[0101] In this embodiment, for any anchor bolt to be detected, acoustic detection is performed on the any anchor bolt to be detected to obtain an initial reflection signal; according to the distribution characteristics of the signals in the initial reflection signal, the signal complexity of the initial reflection signal is obtained, according to the influence degree of the noise signals in the initial reflection signal, the noise ratio of the initial reflection signal is obtained, according to the signal complexity and the noise ratio of the initial reflection signal, a decomposition level adjustment coefficient for wavelet denoising of the initial reflection signal is obtained; the decomposition level for wavelet denoising of the initial reflection signal is obtained, and according to the decomposition level adjustment coefficient, the decomposition level is adjusted to obtain the optimal decomposition level for wavelet denoising of the initial reflection signal; use the optimal decomposition level as the decomposition level for wavelet denoising, perform wavelet denoising on the initial reflection signal to obtain a denoised signal, and perform quality defect detection on the any anchor bolt to be detected based on the denoised signal. Among them, according to the obtained distribution characteristics of the signals in the initial reflection signal and the influence degree of the noise signals, the decomposition level adjustment coefficient for wavelet denoising of the initial reflection signal is obtained, and then the decomposition level is adjusted according to the decomposition level adjustment coefficient, so as to accurately obtain the optimal decomposition level for wavelet denoising of the initial reflection signal, reduce the poor noise removal effect caused by the traditional method of relying on experience to select the wavelet denoising decomposition level, and the optimal decomposition level depends more on the own characteristics of the initial reflection signal, enabling the acoustic wave reflection method to more accurately detect the quality defects of the anchor bolt and improving the accuracy of the quality defect detection of the anchor bolt.

[0102] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for detecting anchor quality defects, characterized in that: The method comprises: For any anchor rod to be detected, an acoustic wave detection is performed on the anchor rod to be detected by using an acoustic wave reflection method to obtain an initial reflection signal; According to the distribution characteristics of the signal in the initial reflection signal, the signal complexity of the initial reflection signal is obtained, according to the influence degree of the noise signal in the initial reflection signal, the noise proportion of the initial reflection signal is obtained, and according to the signal complexity and noise proportion of the initial reflection signal, the decomposition layer number adjustment coefficient when performing wavelet denoising on the initial reflection signal is obtained; Obtaining the number of decomposition layers when performing wavelet denoising on the initial reflection signal, and adjusting the number of decomposition layers according to the decomposition layer number adjustment coefficient to obtain the optimal number of decomposition layers for performing wavelet denoising on the initial reflection signal; Using the optimal decomposition layer number as the decomposition layer number of wavelet denoising, performing wavelet denoising on the initial reflection signal to obtain a denoised signal, and performing quality defect detection on any anchor rod to be inspected according to the denoised signal; The acquiring the signal complexity of the initial reflected signal according to the distribution characteristics of the signal in the initial reflected signal comprises: Performing wavelet denoising on the initial reflection signal to obtain a comparison signal, composing the comparison signal into a comparison signal sequence, obtaining sample entropy of the comparison signal sequence, and normalizing the sample entropy to obtain a first complexity of the initial reflection signal; Constructing a frequency spectrum of the initial reflection signal, wherein the abscissa of the frequency spectrum is the frequency and the ordinate is the signal amplitude, obtaining the bandwidth of the initial reflection signal according to the frequency spectrum, and performing normalization processing on the bandwidth to obtain the second complexity of the initial reflection signal; Performing a weighted summation on the first complexity and the second complexity to obtain a signal complexity of the initial reflected signal; The step of adjusting the number of decomposition layers according to the decomposition layer number adjustment coefficient to obtain an optimal number of decomposition layers for performing wavelet denoising on the initial reflection signal comprises: When the decomposition layer number adjustment coefficient is greater than a preset decomposition layer number adjustment coefficient threshold, the decomposition layer number adjustment coefficient is used as an independent variable of a Sigmoid function to obtain a Sigmoid function value; The addition result of the constant 1 and the Sigmoid function value is obtained to obtain the expansion coefficient of the decomposition layer number, and the product of the expansion coefficient and the decomposition layer number is rounded up to obtain the optimal decomposition layer number for wavelet denoising of the initial reflection signal.

2. The anchor bolt quality defect detection method according to claim 1, characterized in that: The obtaining, according to the influence degree of the noise signal in the initial reflection signal, the noise proportion of the initial reflection signal comprises: Performing Fourier transform on the initial reflected signal to obtain a power spectrum curve graph of the initial reflected signal, wherein the abscissa of the power spectrum curve graph is frequency and the ordinate is power; Obtaining the power spectrum density corresponding to each frequency in the spectrum graph in the power spectrum curve graph, and obtaining the noise energy proportion of the initial reflected signal according to the difference in the power spectrum density corresponding to each frequency in the spectrum graph; According to the power spectrum curve, the average power of the initial reflected signal is obtained, which is recorded as the initial reflected signal average power; the power spectrum curve of the comparison signal is obtained; according to the power spectrum curve of the comparison signal, the average power of the comparison signal is obtained, which is recorded as the comparison signal average power; according to the difference between the initial reflected signal average power and the comparison signal average power, the noise power ratio of the initial reflected signal is obtained; A weighted sum is performed on the noise energy proportion and the noise power proportion to obtain the noise proportion of the initial reflected signal.

3. The anchor bolt quality defect detection method according to claim 2, characterized in that: The obtaining the noise energy proportion of the initial reflected signal according to the difference of the power spectrum density corresponding to each frequency in the spectrum diagram includes: Obtaining a maximum power spectrum density from the power spectrum densities corresponding to each frequency in the spectrum graph, and composing a power spectrum density sequence from all power spectrum densities except the maximum power spectrum density; Record the frequency corresponding to the maximum power spectrum density as the target frequency, and for any power spectrum density in the power spectrum density sequence, obtain the absolute value of the frequency difference between the frequency corresponding to any power spectrum density and the target frequency, obtain the proportion of the absolute value of the frequency difference in the absolute values ​​of the frequency differences corresponding to all power spectrum densities in the power spectrum density sequence, and obtain the weight coefficient of any power spectrum density; Obtain a weight coefficient for each power spectral density in the power spectral density sequence, perform weighted summation on all power spectral densities in the power spectral density sequence to obtain a noise energy value, normalize the ratio of the noise energy value to the maximum power spectral density, and obtain the noise energy ratio of the initial reflection signal.

4. The anchor bolt quality defect detection method according to claim 2, characterized in that: The obtaining, according to the difference between the average power of the initial reflected signal and the average power of the comparison signal, the noise power ratio of the initial reflected signal comprises: The average power difference between the average power of the initial reflected signal and the average power of the comparison signal is calculated, and the ratio of the average power difference to the average power of the initial reflected signal is normalized to obtain the noise power ratio of the initial reflected signal.

5. The anchor bolt quality defect detection method according to claim 1, characterized in that: The step of obtaining a decomposition layer number adjustment coefficient when performing wavelet denoising on the initial reflection signal according to the signal complexity and noise proportion of the initial reflection signal comprises: A weighted sum is taken for the signal complexity and noise proportion of the initial reflection signal to obtain a decomposition layer number adjustment coefficient when performing wavelet denoising on the initial reflection signal.

6. The anchor bolt quality defect detection method according to claim 1, characterized in that: The step of adjusting the number of decomposition layers according to the decomposition layer number adjustment coefficient to obtain the optimal number of decomposition layers for wavelet denoising of the initial reflection signal further includes: When the decomposition layer number adjustment coefficient is less than or equal to a preset decomposition layer number adjustment coefficient threshold, the decomposition layer number is used as the optimal decomposition layer number for wavelet denoising of the initial reflection signal.

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