Automatic identification method for electromagnetic emission signals of active cracks in concrete dam based on cross-correlation analysis

Abstract

An automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis includes: acquiring electromagnetic radiation signals from the space surrounding the concrete dam at different locations simultaneously using electromagnetic signal acquisition devices, resulting in N electromagnetic radiation signals; performing threshold judgment on the preprocessed N electromagnetic radiation signals, extracting and recording the number of electromagnetic radiation signals exceeding a warning threshold; determining whether the number of electromagnetic radiation signals exceeding the warning threshold is greater than or equal to 4; comparing the amplitudes of the electromagnetic radiation signals exceeding the warning threshold; performing cross-correlation analysis on the marked electromagnetic radiation signals to obtain a cross-correlation function, the absolute value of which is xcorr; determining whether the time corresponding to the maximum value satisfies the relation; and calculating the average value Ave(p) of the electromagnetic radiation signals within the interval; and obtaining the average value Ave of the electromagnetic radiation signals within the defined interval of the xcorr function. This method has the advantages of fast calculation speed and high identification accuracy.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic monitoring technology, specifically to an automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis. Background Technology

[0002] Electromagnetic monitoring technology is widely used in the safety inspection and defect assessment of large components such as bridge damage detection and coal mine detection. Electromagnetic monitoring of active cracks in concrete dams is an important guarantee for the safe and stable operation of concrete dams. However, concrete dams are often located in areas with complex stress conditions and steep terrain, and the monitoring of active cracks is often affected by stress changes and environmental noise. This makes the identification of signals in the electromagnetic monitoring of active cracks in concrete dams a current research challenge and hot topic. Existing electromagnetic signal identification methods have complex calculation models and long calculation times, making it difficult to effectively extract electromagnetic radiation signals from concrete.

[0003] In the prior art, the Chinese patent "A Method for Identifying Coal-Rock Interfaces" (Patent No.: 202110250593.7) can identify electromagnetic signals by their intensity, dominant frequency, bandwidth, and duration. However, its construction method is too simplistic and does not consider the interference of sudden external disturbances on the identification of electromagnetic signals. The Chinese patent "A Method and System for Monitoring and Analyzing Electromagnetic Radiation Data" (Patent No.: ZL202111095842.6) can use electromagnetic radiation time-series data to train a model to obtain a fluctuation trend model of electromagnetic radiation signals, thereby enabling the monitoring of electromagnetic radiation data. However, it requires the use of a Long Short-Term Memory (LSTM) network to train parameters, resulting in long computation time, low efficiency, and the parameters are easily affected by changes in the training samples. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis. The method utilizes an electromagnetic monitoring device for active cracks in the concrete dam to acquire electromagnetic radiation signals, extracts data with high electromagnetic radiation signal intensity, performs cross-correlation analysis on these signals, and selects the ratio of the interval average values ​​of the cross-correlation functions as the electromagnetic emission signal identification coefficient by comparing the relationships between the cross-correlation functions. This method has the advantages of fast calculation speed and high identification accuracy.

[0005] The technical solution adopted in this invention is as follows:

[0006] An automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis is characterized by the following steps:

[0007] Step 1: At the same time, electromagnetic radiation signals in the space surrounding the concrete dam are acquired by electromagnetic signal acquisition devices at different locations, resulting in N electromagnetic radiation signals.

[0008] Step 2: Preprocess the N electromagnetic radiation signals obtained in Step 1;

[0009] Step 3: Perform threshold judgment on the N electromagnetic radiation signals after preprocessing in Step 2, and extract and record the number of electromagnetic radiation signals that exceed the warning threshold;

[0010] Step 4: Determine if the number of electromagnetic radiation signals exceeding the warning threshold is greater than or equal to 4. If yes, proceed to Step 5; otherwise, discard the electromagnetic radiation signals collected in the current process and proceed to Step 1.

[0011] Step 5: Compare the amplitudes of electromagnetic radiation signals that exceed the warning threshold, arrange them in descending order of amplitude, and select the four electromagnetic radiation signals with the highest amplitudes as M1, M2, M3, and M4.

[0012] Step 6: Perform cross-correlation analysis on the electromagnetic radiation signals labeled M1, M2, M3, and M4 to obtain the cross-correlation function. The function obtained by taking the absolute value of the cross-correlation function is xcorr. Record the maximum amplitude values ​​of xcorr Max1, Max2, and Max3 and the corresponding times t1, t2, and t3.

[0013] Step 7: Determine whether the times t1, t2, and t3 corresponding to the maximum value satisfy the following relationship. If |t1-t|≤△t, |t2-t|≤△t, and |t3-t|≤△t, proceed to step 8; otherwise, discard the electromagnetic radiation signal collected in the current process and proceed to step 1.

[0014] Step 8: Obtain the xcorr function in The electromagnetic radiation signal within the interval is calculated, and the average value Ave(p) of the electromagnetic radiation signal within the interval is calculated.

[0015] Step 9: Obtain the average value Ave of the electromagnetic radiation signal within the defined interval of the xcorr function, and the electromagnetic emission signal identification coefficient.

[0016] If R≥3, then M1, M2, M3, and M4 are electromagnetic emission signals of active cracks in concrete dams with large cracking degree. Record and save the signals of M1, M2, M3, and M4.

[0017] If 2≤R<3, then M1, M2, M3, and M4 are electromagnetic emission signals of active cracks in concrete dams with small cracking degree. Record and save the signals of M1, M2, M3, and M4.

[0018] If 0 ≤ R < 2, then M1, M2, M3, and M4 are system interference or external noise, and are discarded. In step 1, multiple electromagnetic signal acquisition devices are arranged at equal intervals on the concrete dam body. Equal interval arrangement means that the longitudinal and transverse spacing of the high-speed electromagnetic signal acquisition devices on the dam body plane is 10m, the sampling rate of the high-speed electromagnetic signal acquisition devices is 10MHz, and the electromagnetic radiation signal is a signal sequence with a duration of 40us.

[0019] In step 2, a bandpass filter and a comb filter are constructed to preprocess the acquired N electromagnetic radiation signals. The lower cutoff frequency of the bandpass filter is 10kHz and the upper cutoff frequency is 1MHz. The comb filter filters out noise at 50Hz and its harmonics.

[0020] In step 3, the warning threshold refers to the amplitude of the electromagnetic radiation signal being 100nT, and exceeding the warning threshold refers to the maximum absolute value of the electromagnetic radiation signal being greater than 100nT.

[0021] In step 5, the amplitude of the electromagnetic radiation signal refers to the maximum value of the absolute value of the electromagnetic radiation signal.

[0022] In step 6, using the electromagnetic radiation signal M1 as a reference, cross-correlation analysis is performed on the electromagnetic radiation signals M1, M2, M1, M3, and M1, M4 respectively; the maximum amplitude values ​​Max1, Max2, and Max3 of xcorr refer to the maximum absolute values ​​of each cross-correlation function after the cross-correlation analysis of the electromagnetic radiation signals M1, M2, M1, M3, and M1, M4.

[0023] The cross-correlation function is as follows:

[0024] Let the electromagnetic radiation signals of M1, M2, M3 and M4 be x1(t), x2(t), x3(t) and x4(t), respectively;

[0025] Cross-correlation analysis of M1 and M2 yielded the cross-correlation function. Cross-correlation analysis of M1 and M3 yielded the cross-correlation function. Cross-correlation analysis of M1 and M4 yielded the cross-correlation function. In step 7, t = 40 μs, Δt = 5 μs, and the average value Ave(p) of the electromagnetic radiation signal within the interval refers to... The sum of the xcorr function values ​​at each time point within the interval is divided by the total number of sampling points within the interval.

[0026] Let the electromagnetic radiation signals of M1, M2, M3 and M4 be x1(t), x2(t), x3(t) and x4(t);

[0027] Cross-correlation analysis of M1 and M2 yields the cross-correlation function, which is the function obtained by taking the absolute value of the cross-correlation function as follows:

[0028]

[0029] Cross-correlation analysis of M1 and M3 yields the cross-correlation function, which is the function obtained by taking the absolute value of the cross-correlation function as follows:

[0030]

[0031] Cross-correlation analysis of M1 and M4 yields the cross-correlation function, which is the function obtained by taking the absolute value of the cross-correlation function as follows:

[0032]

[0033] In step 8, the interval is defined as the duration of the xcorr function [0, 2t]. The Ave refers to the sum of the xcorr function values ​​at each moment within the interval [0, 2t] divided by the total number of sampling points within the interval.

[0034] This invention provides an automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis, with the following technical advantages:

[0035] 1) In step 1 of the present invention, extracting electromagnetic radiation signals at different locations can avoid the influence of local interference noise when extracting signals at a single point.

[0036] 2) In step 2 of this invention, preprocessing refers to constructing a bandpass filter and a comb filter to preprocess the acquired N electromagnetic radiation signals. The lower cutoff frequency of the bandpass filter is 10kHz, and the upper cutoff frequency is 1MHz; the comb filter filters out noise at 50Hz and its harmonics. Preprocessing can remove some abnormal noise, facilitating subsequent cross-correlation analysis to determine the electromagnetic radiation signals.

[0037] 3) In step 3 of the present invention, the threshold judgment can remove some small fluctuation noise and abnormal sudden noise, which is suitable for the extraction of electromagnetic radiation signals of dam concrete, because after removing noise, the amplitude of electromagnetic radiation signal will be higher than that of noise signal and can be received by multiple devices.

[0038] 4) In step 5 of the present invention, by selecting the four electromagnetic radiation signals with the highest amplitude, the success rate of identifying the electromagnetic emission signal of the active crack can be improved, because the higher the signal amplitude, the better the signal-to-noise ratio, and the easier it is to extract the required electromagnetic emission signal of the active crack through cross-correlation analysis.

[0039] 6) In step 6 of the present invention, the electromagnetic radiation signal of the active crack can be determined by comparing the maximum value of the cross-correlation function and the time of its occurrence. This is because after the aforementioned preprocessing and threshold judgment, the interference noise in the remaining signal is mainly the tip pulse noise in the system. The probability of tip pulse noise appearing in multiple devices is very small. Even if it appears, the electromagnetic radiation signal and tip pulse noise can be distinguished by judging the similarity between their signals through cross-correlation.

[0040] 7) In step 7 of the present invention, it is further determined whether the electromagnetic radiation signal is generated by an active crack. Since the signals generated by the active crack are generated simultaneously, by judging the arrival time of the maximum value of the absolute value of their cross-correlation function, it can be determined whether the signals are generated simultaneously and whether their signals have similarity, thereby determining whether it is an active crack signal.

[0041] 8) In step 8 of the present invention, the correspondence between the signal and the size of the active crack can be established by comparing the average amplitude level of the cross-correlation function signal at different times. The judgment method is simple and easy to implement, and has a good suppression effect on random noise, impulse noise, etc. Attached Figure Description

[0042] Figure 1 This is a flowchart of the method of the present invention.

[0043] Figure 2(a) is a waveform diagram of the electromagnetic emission signal of concrete cracks after preprocessing according to an embodiment of the present invention;

[0044] Figure 2(b) is a cross-correlation diagram of the electromagnetic emission signals of concrete cracks after preprocessing according to an embodiment of the present invention;

[0045] Figure 3(a) is a waveform diagram of external interference noise after preprocessing in an embodiment of the present invention;

[0046] Figure 3(b) is a cross-correlation diagram of external interference noise after preprocessing in an embodiment of the present invention;

[0047] Figure 4 This is a comparison chart of the electromagnetic emission signal identification coefficient R between the electromagnetic emission signal of concrete cracks and the external interference signal in an embodiment of the present invention. Detailed Implementation

[0048] Example:

[0049] The following describes, with reference to the accompanying drawings, an automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis according to an embodiment of the present invention.

[0050] like Figure 1 As shown, the automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis includes the following steps:

[0051] S1: In one embodiment of the present invention, an electromagnetic signal acquisition device with a sampling rate of 10MHz is arranged on the plane of the concrete dam body, with a longitudinal and transverse spacing of 10m. At the same time, the electromagnetic radiation signals of the space around the concrete dam are acquired by the signal acquisition devices at different locations, resulting in 20 electromagnetic radiation signals with a duration of 40us.

[0052] The electromagnetic signal acquisition device includes an electromagnetic signal acquisition sensor, which can acquire electromagnetic radiation signals in space. It can be an air-core or magnetic core coil, or a magnetic field detection chip.

[0053] S2: Construct a bandpass filter with a lower cutoff frequency of 10kHz and an upper cutoff frequency of 1MHz, and a comb filter to filter out noise at 50Hz and its harmonics, and preprocess the acquired 20 electromagnetic radiation signals.

[0054] Preprocessing involves two aspects. First, it involves constructing bandpass and comb filters to filter the acquired N electromagnetic radiation signals. The lower cutoff frequency of the bandpass filter is 10kHz, and the upper cutoff frequency is 1MHz. The comb filter removes noise at 50Hz and its harmonics. Second, it involves amplifying the electromagnetic radiation signals by 1000 times using a multi-stage amplification circuit and transmitting them to a computer or central processing unit (CPU) via an analog-to-digital converter. In this preprocessing method, signal amplification and transmission are implemented through hardware circuitry, while filtering is achieved through a combination of hardware circuitry and software.

[0055] S3: Perform threshold judgment on the 20 preprocessed electromagnetic radiation signals, extract and record the number of electromagnetic radiation signals that exceed the warning threshold, which is 6;

[0056] S4: If the number of electromagnetic radiation signals exceeding the warning threshold is greater than or equal to 4, proceed to S5;

[0057] S5: Compare the amplitudes of electromagnetic radiation signals that exceed the warning threshold and arrange them in descending order of amplitude. The four highest amplitudes are 207nT, 198nT, 174nT, and 129nT. Select the four electromagnetic radiation signals with the highest amplitudes and label them as M1, M2, M3, and M4.

[0058] S6: Perform cross-correlation analysis on the electromagnetic radiation signals M1, M2, M3, and M4 to obtain the cross-correlation function. The function obtained by taking the absolute value of the cross-correlation function is xcorr. Record the maximum amplitude values ​​of xcorr, Max1, Max2, and Max3, and the corresponding times t1, t2, and t3. Max1 = 2.84 * 10 -13 T 2 Max2 = 3.04 * 10-13 T 2 Max1 = 2.10 * 10 -13 T 2 , t1=40us, t2=40.1us, t3=40us;

[0059] S7: Determine if the times t1, t2, and t3 corresponding to the maximum value satisfy the following relationship. If |t1-t|≤△t, |t2-t|≤△t, and |t3-t|≤△t, proceed to S8;

[0060] S8: Get the xcorr function in The electromagnetic radiation signal within the interval is calculated, and the average value of the electromagnetic radiation signal within the interval is Ave(p) = 9.21 * 10⁻⁶. -14 T 2 ;

[0061] S9: Obtain the average value of the electromagnetic radiation signal within the defined interval of the xcorr function: Ave = 2.41 * 10 -14 T 2 If the electromagnetic emission signal identification coefficient R = 3.82 and R ≥ 3, then M1, M2, M3, and M4 are electromagnetic emission signals of active cracks in concrete dams with large cracking degree. Record and save the signals M1, M2, M3, and M4.

[0062] The electromagnetic radiation signal of active concrete cracks obtained in this embodiment is the signal generated by cracks with a large degree of cracking.

[0063] Figure 2(a) shows the electromagnetic emission signal waveforms of active cracks in a concrete dam collected by four devices. The time-domain waveform of the electromagnetic radiation signal can be seen from Figure 2(a), but there is a lot of interference noise. Direct analysis makes it difficult to see the consistency between the signals and to accurately determine whether they are electromagnetic radiation signals generated by active cracks.

[0064] Figure 2(b) is a cross-correlation diagram obtained by the method described in this invention. It can be clearly seen from Figure 2(b) that the electromagnetic signals collected by the four devices have a high degree of consistency. By further examining the range of specific signal amplitudes and average values, it can be determined whether the electromagnetic radiation signal is caused by small or large cracks in the concrete.

[0065] Figure 3(a) shows the interference noise waveform of active cracks in a concrete dam. The time-domain waveform of the interference noise can be seen from Figure 3(a). Direct analysis makes it difficult to determine whether there is a signal and whether it is interference noise.

[0066] Figure 3(b) is a cross-correlation diagram obtained by the method described in this invention. It can be clearly seen from Figure 3(b) that the interference noise does not have obvious consistency. Furthermore, by observing the range of specific signal amplitude and average value changes, it can be further determined that the electromagnetic radiation signal is interference noise.

[0067] Figure 4 This invention presents a novel method for identifying active cracks in concrete dams using an identification coefficient. The horizontal and vertical axes represent the number of experiments, with the vertical axis representing the R-value. The proposed method constructs an identification coefficient R. By judging the magnitude of the R-value, the electromagnetic emission signal from the concrete can be distinguished from abrupt interference noise and environmental noise signals. This method is simple, easy to implement, and exhibits good stability. It can be seen that the method of this invention can accurately identify the electromagnetic emission signal of active cracks in concrete dams.

Claims

1. An automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis, characterized in that... Includes the following steps: Step 1: At the same time, electromagnetic radiation signals in the space surrounding the concrete dam are acquired by electromagnetic signal acquisition devices at different locations, resulting in N electromagnetic radiation signals. Step 2: Preprocess the N electromagnetic radiation signals obtained in Step 1; Step 3: Perform threshold judgment on the N electromagnetic radiation signals after preprocessing in Step 2, and extract and record the number of electromagnetic radiation signals that exceed the warning threshold; Step 4: Determine if the number of electromagnetic radiation signals exceeding the warning threshold is greater than or equal to 4. If yes, proceed to Step 5; otherwise, discard the electromagnetic radiation signals collected in the current process and proceed to Step 1. Step 5: Compare the amplitudes of electromagnetic radiation signals that exceed the warning threshold, arrange them in descending order of amplitude, and select the four electromagnetic radiation signals with the highest amplitudes as M1, M2, M3, and M4. Step 6: Perform cross-correlation analysis on the electromagnetic radiation signals labeled M1, M2, M3, and M4 to obtain the cross-correlation function. The function obtained by taking the absolute value of the cross-correlation function is xcorr. Record the maximum amplitude values ​​of xcorr Max1, Max2, and Max3 and the corresponding times t1, t2, and t3. Step 7: Determine whether the times t1, t2, and t3 corresponding to the maximum value satisfy the following relationship; if |t1-t|≤△t, |t2-t|≤△t, and |t3-t|≤△t, then proceed to step 8; otherwise, discard the electromagnetic radiation signal collected in the current process and proceed to step 1. Step 8: Obtain the xcorr function in The electromagnetic radiation signal within the interval is calculated, and the average value Ave(p) of the electromagnetic radiation signal within the interval is calculated. Step 9: Obtain the average value Ave of the electromagnetic radiation signal within the defined interval of the xcorr function, and the electromagnetic emission signal identification coefficient.

2. The automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis according to claim 1, characterized in that: In step 9, If R≥3, then M1, M2, M3, and M4 are electromagnetic emission signals of active cracks in concrete dams with large cracking degree. Record and save the signals of M1, M2, M3, and M4. If 2≤R<3, then M1, M2, M3, and M4 are electromagnetic emission signals of active cracks in concrete dams with small cracking degree. Record and save the signals of M1, M2, M3, and M4. If 0 ≤ R < 2, then M1, M2, M3, and M4 are system interference or external noise, and M1, M2, M3, and M4 are discarded.

3. The automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis according to claim 1, characterized in that: In step 1, multiple electromagnetic signal acquisition devices are arranged at equal intervals on the concrete dam body. The equal interval arrangement means that the longitudinal and transverse spacing of the high-speed electromagnetic signal acquisition devices on the dam body plane is 10m. The sampling rate of the high-speed electromagnetic signal acquisition devices is 10MHz, and the electromagnetic radiation signal is a signal sequence with a duration of 40us.

4. The automatic identification method for electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis according to claim 1, characterized in that: In step 2, a bandpass filter and a comb filter are constructed to preprocess the acquired N electromagnetic radiation signals. The lower cutoff frequency of the bandpass filter is 10kHz and the upper cutoff frequency is 1MHz. The comb filter filters out noise at 50Hz and its harmonics.

5. The method for automatic identification of electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis according to claim 1, characterized in that: In step 3, the warning threshold refers to the amplitude of the electromagnetic radiation signal being 100nT, and exceeding the warning threshold refers to the maximum absolute value of the electromagnetic radiation signal being greater than 100nT.

6. The method for automatic identification of electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis according to claim 1, characterized in that: In step 5, the amplitude of the electromagnetic radiation signal refers to the maximum value of the absolute value of the electromagnetic radiation signal.

7. The method for automatic identification of electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis according to claim 1, characterized in that: In step 6, using the electromagnetic radiation signal M1 as a reference, cross-correlation analysis is performed on the electromagnetic radiation signals M1, M2, M1, M3, and M1, M4 respectively. The maximum amplitude values ​​Max1, Max2, and Max3 of xcorr refer to the maximum absolute values ​​of each cross-correlation function after the cross-correlation analysis of the electromagnetic radiation signals M1, M2, M1, M3, and M1, M4. The specific cross-correlation functions are as follows: Let the electromagnetic radiation signals of M1, M2, M3 and M4 be x1(t), x2(t), x3(t) and x4(t), respectively; Cross-correlation analysis of M1 and M2 yielded the cross-correlation function. Cross-correlation analysis of M1 and M3 yielded the cross-correlation function. Cross-correlation analysis of M1 and M4 yielded the cross-correlation function.

8. The method for automatic identification of electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis according to claim 1, characterized in that: In step 7, t = 40 μs, Δt = 5 μs, and the average value Ave(p) of the electromagnetic radiation signal within the interval refers to... The sum of the xcorr function values ​​at each time point within the interval is divided by the total number of sampling points within the interval; Let the electromagnetic radiation signals of M1, M2, M3 and M4 be x1(t), x2(t), x3(t) and x4(t); Cross-correlation analysis of M1 and M2 yields the cross-correlation function, which is the function obtained by taking the absolute value of the cross-correlation function as follows: Cross-correlation analysis of M1 and M3 yields the cross-correlation function, which is the function obtained by taking the absolute value of the cross-correlation function as follows: Cross-correlation analysis of M1 and M4 yields the cross-correlation function, which is the function obtained by taking the absolute value of the cross-correlation function as follows:

9. The method for automatic identification of electromagnetic emission signals of active cracks in concrete dams based on cross-correlation analysis according to claim 1, characterized in that: In step 8, the interval is defined as the duration of the xcorr function [0, 2t]. The Ave refers to the sum of the xcorr function values ​​at each moment within the interval [0, 2t] divided by the total number of sampling points within the interval.

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