Impact load positioning method and system based on signal cross-correlation coefficient and medium

Abstract

The application relates to an impact load positioning method and system based on a signal cross-correlation coefficient, and a medium. The impact load positioning method based on the signal cross-correlation coefficient comprises the following steps: applying an impact load to a monitoring area of a detected plate member, and obtaining signals of a plurality of sensors arranged on the monitoring area of the detected plate member; determining a characteristic interval, calculating the similarity of the signals of four sensors around each rectangular area in the characteristic interval two by two, and obtaining six correlation coefficients for each rectangular area; for each rectangular area, taking the average value of the six correlation coefficients as the similarity coefficient of the signals of the sensors around the rectangular area; and based on the similarity coefficients of the signals of the sensors around all the rectangular areas, taking a rectangular area corresponding to the maximum similarity coefficient as an impact point occurrence area. The application realizes the reliability of inverting the impact load occurrence position of a honeycomb sandwich plate.

Description

Technical Field

[0001] This invention relates to the field of impact load inversion. In particular, it relates to an impact load localization method, system, and medium based on signal cross-correlation coefficients. Background Technology

[0002] Honeycomb sandwich panels are a lightweight material widely used in aerospace. In the aerospace field, structures often face the risk of impact, such as debris impacts from outer space. These potential threats are extremely harmful to structures, and it is necessary to develop specialized methods to invert the location of impact loads and play a role in damage localization.

[0003] After an impact load occurs, the stress is transmitted in the form of a wave, propagating outwards from the impact point. Therefore, the arrival time of the impact signal at various sensors around the impact area will differ. Traditional methods primarily utilize the time-of-arrival (TOA) method to locate the impact load, offering high reliability. However, the TOA in this method is often difficult to calculate precisely, typically employing a threshold method. This means that when the sensor signal exceeds a set threshold at a certain time, that moment is considered the TOA. Therefore, the effectiveness of this method heavily depends on the reasonableness of the threshold setting, resulting in insufficient robustness.

[0004] Improving the reliability of the impact load location in the inverted honeycomb sandwich panel is an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method, system, and medium for locating impact loads based on signal cross-correlation coefficients, in order to address the issues of low robustness and low reliability in retrieving the location of impact loads in cellular sandwich panels.

[0006] To achieve the above objectives, in a first aspect, the present invention relates to an impact load localization method based on signal cross-correlation coefficients, used to invert the location of impact loads occurring in a honeycomb sandwich panel, comprising:

[0007] An impact load is applied to the monitoring area of ​​the tested board, and signals from multiple sensors arranged on the monitoring area of ​​the tested board are acquired. The multiple sensors are arranged on the tested board in the form of a rectangular array sensor network, and the tested board is divided into multiple rectangular regions. A sensor is arranged at the vertex of each rectangular region.

[0008] A feature interval is determined, and the signals of the four sensors surrounding each rectangular region are used to calculate the similarity of each pair within the feature interval. Six correlation coefficients are obtained for each rectangular region. The similarity calculation is performed by combining the signals of two sensors, taking the absolute value, and then taking the inner product within the feature interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors.

[0009] For each rectangular region, the average of the six correlation coefficients is taken as the similarity coefficient of the signals from the sensors surrounding the rectangular region;

[0010] Based on the similarity coefficients of the signals from all the sensors surrounding the rectangular region, the rectangular region corresponding to the largest similarity coefficient is taken as the impact point occurrence region.

[0011] To achieve the above objective, in a second aspect, the present invention relates to an impact load localization system based on signal cross-correlation coefficients for inverting the location of impact loads on a honeycomb sandwich panel, comprising:

[0012] The signal acquisition module is used to apply an impact load to the monitoring area of ​​the tested board and acquire signals from multiple sensors arranged on the monitoring area of ​​the tested board. The multiple sensors are arranged on the tested board in the form of a rectangular array sensor network, dividing the tested board into multiple rectangular areas. Each vertex of the rectangular area is equipped with a sensor.

[0013] The correlation coefficient calculation module is used to determine the feature interval. It calculates the similarity of the signals of the four sensors around each rectangular region in the feature interval. Each rectangular region obtains 6 correlation coefficients. The similarity calculation is to combine the signals of two sensors, take the absolute value, and then take the inner product in the feature interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors.

[0014] The similarity coefficient calculation module is used to take the average of the six correlation coefficients as the similarity coefficient of the signals of the sensors around the rectangular region for each rectangular region.

[0015] The positioning module is used to select the rectangular area corresponding to the largest similarity coefficient as the impact point occurrence area based on the similarity coefficient of the signals from all the sensors around the rectangular area.

[0016] To achieve the above objectives, in a third aspect, the present invention also relates to a computer-readable storage medium storing instructions that, when executed, perform the above-described method for locating impact loads based on signal cross-correlation coefficients.

[0017] The present invention relates to an impact load positioning method, system, and medium based on signal cross-correlation coefficients, which has the following advantages compared to the prior art:

[0018] This patent proposes a new regional positioning algorithm by utilizing the similarity of signal fluctuations from four sensors around the impact signal generation area. This algorithm does not require manually setting a threshold and has high reliability. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an impact load positioning method based on signal cross-correlation coefficients according to Embodiment 1 of the present invention.

[0020] Figure 2 This is a schematic diagram of the impact load identification sensor network layout in Example 1 of an impact load positioning method based on signal cross-correlation coefficients according to Embodiment 1 of the present invention.

[0021] Figure 3 This is an example of sensor waveforms around different sub-regions in Example 1 of an impact load positioning method based on signal cross-correlation coefficients according to Embodiment 1 of the present invention.

[0022] Figure 4 This is a schematic diagram of an impact load positioning system based on signal cross-correlation coefficients in Embodiment 2 of the present invention. Detailed Implementation

[0023] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention and not the entire structure.

[0024] Example 1

[0025] For an impact load localization method based on signal cross-correlation coefficients, please refer to [link / reference]. Figure 1-3 As shown, the present invention provides an impact load location method based on signal cross-correlation coefficients, used to invert the impact load location of a cellular sandwich panel, comprising the following steps: S101 to S104.

[0026] S101 applies an impact load to the monitoring area of ​​the tested board and acquires signals from multiple sensors arranged on the monitoring area of ​​the tested board. The multiple sensors are arranged on the tested board in the form of a rectangular array sensor network, dividing the tested board into multiple rectangular areas. Each rectangular area has a sensor arranged at its vertex.

[0027] In this embodiment, as Figure 2 As shown, multiple sensors are arranged in a rectangular array sensor network on the board under test, dividing the board under test into multiple rectangular areas. This can be achieved by using 9 sensors arranged in a 3×3 rectangular array sensor network on the board under test, dividing the monitoring area on the board under test into 4 matrix areas of 2×2.

[0028] S102 determines the feature interval, calculates the similarity of the signals from the four sensors around each rectangular region pairwise within the feature interval, and obtains six correlation coefficients for each rectangular region. The similarity is calculated by combining the signals from the two sensors, taking the absolute value, and then taking the inner product within the feature interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors.

[0029] Because the inner product is larger when the signal fluctuations of two sets of sensors are similar, this integral can be used as a measure of the similarity between the two sensors. Each region can ultimately obtain... The correlation coefficients are further calculated, and the average of the six correlation coefficients is taken as a measure of the similarity of the sensor signals in the surrounding area.

[0030] In this embodiment, the similarity coefficient of the rectangular region is calculated using the following formula:

[0031]

[0032] Among them, S k Let f be the similarity coefficient of the k-th rectangular region. ki (t) represents the signal from the sensor in the i-th region or f kj (t) represents the signal of the sensor in the k-th region. The values ​​of i and j are integers between 1 and 4, corresponding to the 4 sensors in the k-th region. t0 to tn is the characteristic interval. t0 is the time when the signal fluctuation starts, and tn is the time when the signal falls back to zero after the first peak.

[0033] S103 For each rectangular region, the average of the six correlation coefficients is taken as the similarity coefficient of the signals from the sensors surrounding the rectangular region.

[0034] S104 selects the rectangular region corresponding to the largest similarity coefficient as the impact point occurrence region based on the similarity coefficient of the signals from the sensors surrounding all rectangular regions.

[0035] To better illustrate the solution of the present invention, an example is given below, such as... Figure 2-3 As shown, it includes the following steps:

[0036] To test the aforementioned method, a corresponding practical experiment was conducted. In the experiment, the honeycomb sandwich panel was 200mm in length and width, 15mm in thickness, and made of aluminum honeycomb structure. Figure 2 As shown, the 120mm × 120mm area at the center of the honeycomb sandwich panel was used as the monitoring area. The monitoring area was divided into four 2×2 rectangular sub-areas, each with the same size of 60mm × 60mm. The impact load was applied using the free-falling ball impact method in the experiment.

[0037] like Figure 3The image shows example waveforms of sensors around different impact zones. It can be seen that in the correct impact zone ④, the waveforms of the four surrounding sensors are highly similar, while the waveforms in other zones are quite different. By calculating the average cross-correlation coefficient of each zone, the zone with the maximum value is taken as the impact zone, thus completing the impact zone localization.

[0038] The impact region location method proposed in this patent was used to perform impact region inversion on 720 sets of experiments. Of the 720 sets of experimental condition predictions, 719 were correct and 1 was incorrect, resulting in an overall region identification accuracy of 99.86%, demonstrating the high reliability and stability of the impact location method. Ten sets were randomly selected, and the average cross-correlation coefficients for each region are shown. The coefficient values ​​for each set have been normalized to their maximum values, as shown in Table 1, which displays the average cross-correlation coefficients for impact load location. ①②③④ correspond to four rectangular regions, as shown in Table 1. Figure 2-3 As shown, in Figure 3 In the diagram, ①②③④ correspond to four rectangular monitoring areas. Figure 2 The image shows a small segment of change after the signals from the four sensors surrounding the area oscillate. The signal impact point falls in region ④, and the changes in the signal after oscillation in region ④ are more synchronized than in other regions. This demonstrates that using the cross-correlation coefficients of the four sensors surrounding the area to locate the impact region is reasonable. The cross-correlation values ​​between different regions differ significantly, making misjudgment less likely and exhibiting good robustness.

[0039]

[0040]

[0041] Example 2

[0042] An impact load positioning system based on signal cross-correlation coefficients is provided. It is implemented in hardware by an electronic device with a central processing unit, such as a personal computer, smart terminal, local area network, or server. For implementation details in this embodiment, please refer to [link to relevant documentation]. Figure 4 It includes a signal acquisition module 61, a correlation coefficient calculation module 62, a similarity coefficient calculation module 63, and a positioning module 64.

[0043] The locations of impact loads used to invert the honeycomb sandwich panel include:

[0044] The signal acquisition module 61 is used to apply an impact load to the monitoring area of ​​the tested board and acquire the signals of multiple sensors arranged on the monitoring area of ​​the tested board. The multiple sensors are arranged on the tested board in the form of a rectangular array sensor network, and the tested board is divided into multiple rectangular areas. Each region of the rectangular area has a sensor arranged at its vertex.

[0045] The correlation coefficient calculation module 62 is used to determine the feature interval. It calculates the similarity of the signals of the four sensors around each rectangular region in the feature interval, and obtains a correlation coefficient for each rectangular region. The similarity calculation is to combine the signals of the two sensors, take the absolute value, and then take the inner product in the feature interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors.

[0046] The similarity coefficient calculation module 63 is used to take the average of 6 correlation coefficients as the similarity coefficient of the signals of the sensors around the rectangular area for each rectangular area.

[0047] The positioning module 64 is used to select the rectangular area with the largest similarity coefficient as the impact point occurrence area based on the similarity coefficient of the signals from the sensors around all rectangular areas.

[0048] In this embodiment, the similarity coefficient of the rectangular region is calculated using the following formula:

[0049]

[0050] Where is the similarity coefficient of the i-th rectangular region, (t) represents the signal of the sensor in the i-th region or (t) represents the signal of the sensor in the i-th region, and are integers ranging from 1 to 4, corresponding to the 4 sensors in the i-th region, t0 to tn are the feature intervals, t0 is the start time of signal fluctuation, and tn is the time when the signal falls back to zero after the first peak.

[0051] In this embodiment, multiple sensors are arranged in a rectangular array sensor network on the board under test, dividing the board under test into multiple rectangular regions. Specifically, nine sensors are arranged in a 3×3 rectangular array sensor network on the board under test, dividing the monitoring area on the board under test into four 2×2 matrix regions.

[0052] The impact load positioning system based on signal cross-correlation coefficients in this embodiment is the same as the impact load positioning method based on signal cross-correlation coefficients described in Embodiment 1 in terms of implementation process, method and effect, and will not be repeated here.

[0053] Example 3

[0054] This invention relates to a computer-readable storage medium storing instructions. When the instructions are executed, they perform an impact load location method based on signal cross-correlation coefficients according to Embodiment 1. The execution process and effects are the same as those of the impact load location method based on signal cross-correlation coefficients described in Embodiment 1, and will not be repeated here.

[0055] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0056] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for locating impact loads based on signal cross-correlation coefficients, characterized in that, The locations of impact loads used to invert the honeycomb sandwich panel include: An impact load is applied to the monitoring area of ​​the monitored plate, and signals from multiple sensors arranged on the monitoring area of ​​the monitored plate are acquired. The multiple sensors are arranged on the monitored plate in the form of a rectangular array sensor network, dividing the monitored plate into multiple rectangular areas. Each vertex of the rectangular area is equipped with a sensor. Determine feature intervals, and calculate the similarity between pairs of signals from the four sensors surrounding each rectangular region within those feature intervals. Each rectangular region then yields... A set of correlation coefficients are obtained, wherein the similarity is calculated by combining the signals of the two sensors, taking the absolute value, and then taking the inner product in the feature interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors. For each rectangular region, the average of the six correlation coefficients is taken as the similarity coefficient of the signals from the sensors surrounding the rectangular region. The similarity coefficient of the rectangular region is calculated using the following formula: ， in, For the first The similarity coefficient of each rectangular region (t) represents the signal of the i-th sensor in the k-th region. (t) represents the th The signal from the j-th sensor in the region, and Integers ranging from 1 to 4, corresponding to the first... Four sensors in one area, from t0 to t n Let t0 be the characteristic interval, and t be the start time of signal fluctuation. n The moment when the signal drops back to zero after the first peak; Based on the similarity coefficients of the signals from all the sensors surrounding the rectangular region, the rectangular region corresponding to the largest similarity coefficient is taken as the impact point occurrence region.

2. The impact load positioning method based on signal cross-correlation coefficient according to claim 1, characterized in that, The multiple sensors are arranged in a rectangular array sensor network on the monitored board, dividing the monitored board into multiple rectangular areas. Specifically, nine sensors are arranged in a 3×3 rectangular array sensor network on the monitored board, dividing the monitored area into four 2×2 rectangular areas on the monitored board.

3. An impact load positioning system based on signal cross-correlation coefficients, characterized in that, The locations of impact loads used to invert the honeycomb sandwich panel include: The signal acquisition module is used to apply an impact load to the monitoring area of ​​the monitored board and acquire signals from multiple sensors arranged on the monitoring area of ​​the monitored board. The multiple sensors are arranged on the monitored board in the form of a rectangular array sensor network, dividing the monitored board into multiple rectangular areas. Each of the rectangular areas has a sensor arranged at its vertex. The correlation coefficient calculation module is used to determine the feature intervals. It calculates the similarity between pairs of signals from the four sensors surrounding each rectangular region within the feature interval, thus obtaining the correlation coefficient for each rectangular region. A set of correlation coefficients are obtained, wherein the similarity is calculated by combining the signals of the two sensors, taking the absolute value, and then taking the inner product in the feature interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors. The similarity coefficient calculation module is used to take the average of the six correlation coefficients as the similarity coefficient of the signals from the sensors surrounding the rectangular region for each rectangular region. The similarity coefficient of the rectangular region is calculated using the following formula: ， in, For the first The similarity coefficient of each rectangular region (t) represents the signal of the i-th sensor in the k-th region. (t) represents the th The signal from the j-th sensor in the region, and Integers ranging from 1 to 4, corresponding to the first... Four sensors in one area, from t0 to t n Let t0 be the characteristic interval, and t be the start time of signal fluctuation. n The moment when the signal drops back to zero after the first peak; The positioning module is used to select the rectangular area corresponding to the largest similarity coefficient of the signals from all the sensors around the rectangular area as the impact point occurrence area.

4. The impact load positioning system based on signal cross-correlation coefficient according to claim 3, characterized in that, The multiple sensors are arranged in a rectangular array sensor network on the monitored board, dividing the monitored board into multiple rectangular areas. Specifically, nine sensors are arranged in a 3×3 rectangular array sensor network on the monitored board, dividing the monitored area into four 2×2 rectangular areas on the monitored board.

5. A computer-readable storage medium, characterized in that: The storage medium stores instructions that, when executed, perform an impact load positioning method based on signal cross-correlation coefficients as described in any one of claims 1-2.

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