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

Abstract

The invention relates to an impact load positioning method and system based on a signal cross correlation coefficient and a medium, and the method comprises the steps: applying an impact load to a monitoring region of a detected plate, and obtaining the signals of a plurality of sensors disposed on the monitoring region of the monitored plate; determining a feature interval, calculating the similarity between every two signals of the four sensors around each rectangular area in the feature interval, and obtaining six correlation coefficients in each rectangular area; for each rectangular region, taking the average value of the six correlation coefficients as the similarity coefficient of the signals of the sensors around the rectangular region; and based on the similarity coefficients of the signals of the sensors around all the rectangular areas, taking the rectangular area corresponding to the maximum similarity coefficient as an impact point generation area. According to the invention, the reliability of inversion of the impact load generation position of the honeycomb sandwich plate is realized.

Description

Technical Field

[0001] The present invention relates to the field of impact load inversion, and in particular to an impact load positioning method, system and medium based on signal cross-correlation coefficients. Background Art

[0002] Honeycomb sandwich panels are a lightweight material widely used in aerospace. Structures in the aerospace field are often at risk of impact, such as collisions with debris from outer space. These potential threats are extremely harmful to structures, and specialized methods need to be developed to invert the location of impact loads and locate damage.

[0003] After an impact load occurs, stress propagates in the form of waves, emanating from the impact point. Consequently, the impact signal arrives at different sensors around the impact area at different times. Traditional methods primarily utilize the time difference of arrival (TDOA) method to locate impact loads, which offers high reliability. However, these methods are often difficult to accurately calculate the TDOA. Instead, they typically employ a threshold method, where the sensor signal exceeds a set threshold at a certain time, defining that time as the TDOA. Consequently, this method's effectiveness relies heavily on the appropriateness of the threshold setting, and suffers from limited robustness.

[0004] How to improve the reliability of the inverse analysis of the impact load occurrence location of honeycomb sandwich panels is an urgent problem that needs to be solved. Summary of the Invention

[0005] The present invention provides an impact load positioning method, system and medium based on signal cross-correlation coefficient, so as to solve the problem of low robustness and low reliability of the impact load occurrence position of the inverted honeycomb sandwich panel.

[0006] To achieve the above-mentioned object, in a first aspect, the present invention relates to a method for locating an impact load based on a signal cross-correlation coefficient, which is used to invert the location of the impact load on a honeycomb sandwich panel, comprising:

[0007] Applying an impact load to a monitoring area of ​​the panel under test, and acquiring signals from a plurality of sensors arranged on the monitoring area of ​​the panel under test, wherein the plurality of sensors are arranged on the panel under test in the form of a rectangular array sensor network, dividing the panel under test into a plurality of rectangular areas, wherein a sensor is arranged at a vertex of each rectangular area;

[0008] Determine a characteristic interval, and calculate the similarity of the signals of the four sensors surrounding each rectangular area within the characteristic interval, obtaining six correlation coefficients for each rectangular area. The similarity is calculated by combining the signals of two sensors, taking the absolute value, and then taking the inner product within the characteristic interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors.

[0009] For each rectangular area, taking the average of the six correlation coefficients as the similarity coefficient of the signals of the sensors around the rectangular area;

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

[0011] To achieve the above-mentioned object, in a second aspect, the present invention relates to an impact load location system based on a signal cross-correlation coefficient, which is used to invert the location of the impact load on a honeycomb sandwich panel, comprising:

[0012] a signal acquisition module, configured to apply an impact load to a monitoring area of ​​the panel under test, and acquire signals from a plurality of sensors arranged on the monitoring area of ​​the panel under test, wherein the plurality of sensors are arranged on the panel under test in the form of a rectangular array sensor network, dividing the panel under test into a plurality of rectangular areas, wherein a sensor is arranged at a vertex of each of the rectangular areas;

[0013] a correlation coefficient calculation module, configured to determine a characteristic interval, calculate the similarity of the signals of the four sensors surrounding each rectangular area in the characteristic interval, and obtain six correlation coefficients for each rectangular area, wherein the similarity is calculated by combining the signals of two sensors, taking the absolute value, and then taking the inner product in the characteristic interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors;

[0014] a similarity coefficient calculation module, configured to take, for each rectangular area, an average value of the six correlation coefficients as a similarity coefficient of the signals of the sensors around the rectangular area;

[0015] The positioning module is configured to take the rectangular area corresponding to the largest similarity coefficient as the impact point occurrence area based on the similarity coefficients of the signals of the sensors around the rectangular area.

[0016] To achieve the above objectives, the third aspect of the present invention further relates to a computer-readable storage medium, in which instructions are stored. When the instructions are executed, the above-mentioned impact load positioning method based on the signal correlation coefficient is executed.

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

[0018] This patent uses the similarity of the signal fluctuations of four sensors around the impact signal generation area to propose a new regional positioning algorithm that does not require manual setting of thresholds and has high reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of a flow chart of a method for locating an impact load based on a signal cross-correlation coefficient in a first embodiment of the present invention.

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

[0021] Figure 3 This is an example diagram of sensor waveforms around different sub-areas of Example 1 of an impact load location method based on signal cross-correlation coefficient in Example 1 of the present invention.

[0022] Figure 4 Schematic diagram of the structure of an impact load positioning system based on signal cross-correlation coefficient in the second embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0024] Example 1

[0025] A method for locating impact loads based on signal cross-correlation coefficients, see Figure 1-3 As shown, the present invention provides an impact load locating method based on signal cross-correlation coefficient, which is used to invert the impact load occurrence position of the honeycomb sandwich panel, and includes the following steps: S101 to S104.

[0026] S101 applies an impact load to the monitoring area of ​​the panel under test, and obtains signals from multiple sensors arranged on the monitoring area of ​​the panel under test, wherein the multiple sensors are arranged on the panel under test in the form of a rectangular array sensor network, dividing the panel under test into multiple rectangular areas, wherein a sensor is arranged at each vertex of the rectangular area.

[0027] In this embodiment, Figure 2 As shown, multiple sensors are set on the board under test in the form of a rectangular array sensor network, dividing the board under test into multiple rectangular areas. It can be: 9 sensors are set on the board under test in the form of a 3×3 rectangular array sensor network, and the monitoring area on the board under test is divided into 4 2×2 matrix areas.

[0028] S102 determines the characteristic interval and calculates the similarity of the signals of the four sensors around each rectangular area in the characteristic interval. Six correlation coefficients are obtained for each rectangular area. The similarity is calculated by combining the signals of the two sensors, taking the absolute value, and then taking the inner product in the characteristic interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors.

[0029] Because the inner product value is larger when the signal fluctuations of two groups of sensors are similar, the integral can be used as a measure of the similarity between the two sensors. Each region can finally be obtained The average of the six correlation coefficients is taken as the measure of the similarity of the sensor signals around the area.

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

[0031]

[0032] Among them, S k is the similarity coefficient of the kth rectangular area, f ki (t) represents the signal of the sensor in the i-th region or f kj (t) represents the signal of the sensor in the kth region. i and j are integers ranging from 1 to 4, corresponding to the four sensors in the kth region. t0 to tn are the characteristic intervals, where t0 is the start time of signal fluctuation, and tn is the time when the signal falls back to zero after the first peak.

[0033] S103 For each rectangular area, take the average value of the six correlation coefficients as the similarity coefficient of the signals of the sensors around the rectangular area.

[0034] S104 takes the rectangular area corresponding to the largest similarity coefficient as the impact point occurrence area based on the similarity coefficients of the signals of the sensors around the rectangular area.

[0035] In order to better illustrate the solution of the present invention, an example is given below. Figure 2-3 As shown, the following steps are included:

[0036] In order to test the above method, actual experiments were carried out. 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 in the figure, the 120mm×120mm area in the center of the honeycomb sandwich panel is used as the monitoring area, which is divided into four 2×2 rectangular sub-areas, each with the same size of 60mm×60mm. The impact load is applied using the free-fall impact method of a falling ball.

[0037] like Figure 3The following diagram shows example waveforms of sensors around different impact areas. It can be seen that in the correct impact area (④), the waveforms of the four surrounding sensors are highly similar, while other areas show significant differences. By calculating the average cross-correlation coefficient for each area and taking the area with the maximum value as the impact area, the impact area is located.

[0038] The impact area positioning method proposed in this patent was used to perform impact area inversion on 720 groups of experiments. Among all 720 groups of experimental working condition predictions, 719 groups were correct and 1 group was wrong. The overall area recognition accuracy rate was as high as 99.86%, indicating that the impact positioning method is reliable and stable. From 10 groups, the average cross-correlation coefficient of each area is displayed. The coefficient value of each group has been normalized to the maximum value. Table 1 shows the average cross-correlation coefficient of impact load positioning, where ①②③④ corresponds to four rectangular areas. Figure 2-3 As shown in Figure 3 In the figure, ①②③④ correspond to 4 rectangular monitoring areas and Figure 2 The image shows the changes in the signals from the four sensors surrounding the area, corresponding to the layout in the middle. The signal impact point falls in area ④, and the changes in the signals from the area after onset are more synchronized than those in other areas. This demonstrates that using the cross-correlation coefficients of the four sensors surrounding the area is reasonable for locating the impact area. The cross-correlation values ​​vary significantly between different areas, making misjudgment less likely and demonstrating good robustness.

[0039]

[0040]

[0041] Example 2

[0042] An impact load positioning system based on signal cross-correlation coefficient is used to implement the electronic equipment hardware with a central processing unit, which can be implemented for personal computers, smart terminals, local area networks, servers, etc. Figure 4 , including a signal acquisition module 61, a correlation coefficient calculation module 62, a similarity coefficient calculation module 63 and a positioning module 64.

[0043] Used to invert the impact load occurrence position of honeycomb sandwich panels, including:

[0044] a signal acquisition module 61 for applying an impact load to a monitoring area of ​​a panel under test and acquiring signals from a plurality of sensors arranged in the monitoring area of ​​the panel under test, wherein the plurality of sensors are arranged on the panel under test in the form of a rectangular array sensor network, dividing the panel under test into a plurality of rectangular areas, wherein a sensor is arranged at each vertex of the rectangular area;

[0045] The correlation coefficient calculation module 62 is used to determine the characteristic interval and calculate the similarity of the signals of the four sensors around each rectangular area in the characteristic interval. The correlation coefficient is obtained for each rectangular area. The similarity is calculated by combining the signals of the two sensors, taking the absolute value and then taking the inner product in the characteristic interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors.

[0046] A similarity coefficient calculation module 63 is configured to take, for each rectangular area, an average value of the six correlation coefficients as a similarity coefficient of the signals of the sensors around the rectangular area;

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

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

[0049]

[0050] Among them, is the similarity coefficient of the ith rectangular area, (t) represents the signal of the sensor in the ith area or (t) represents the signal of the sensor in the ith area, and is an integer ranging from 1 to 4, corresponding to the four sensors in the ith area, 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 set on the board under test in the form of a rectangular array sensor network, dividing the board under test into multiple rectangular areas. Specifically, 9 sensors are set on the board under test in the form of a 3×3 rectangular array sensor network, and the monitoring area on the board under test is divided into 4 2×2 matrix areas.

[0052] The implementation process, method and effect of the impact load positioning system based on signal cross-correlation coefficient in this embodiment are the same as those of the impact load positioning method based on signal cross-correlation coefficient described in the first embodiment, and will not be repeated here.

[0053] Example 3

[0054] The present invention relates to a computer-readable storage medium, which stores instructions. When the instructions are executed, a method for locating an impact load based on a signal correlation coefficient is executed. The implementation process, method and effect of the method are the same as those of the method for locating an impact load based on a signal correlation coefficient described in Example 1, and will not be repeated here.

[0055] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0056] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for locating impact loads based on signal cross-correlation coefficients, characterized in that: Used to invert the impact load occurrence position of honeycomb sandwich panels, including: Applying an impact load to a monitoring area of ​​the panel under test, and acquiring signals from a plurality of sensors arranged on the monitoring area of ​​the panel under test, wherein the plurality of sensors are arranged on the panel under test in the form of a rectangular array sensor network, dividing the panel under test into a plurality of rectangular areas, wherein a sensor is arranged at a vertex of each rectangular area; Determine a characteristic interval, and calculate the similarity of the signals of the four sensors surrounding each rectangular area within the characteristic interval, obtaining six correlation coefficients for each rectangular area. The similarity is calculated by combining the signals of two sensors, taking the absolute value, and then taking the inner product within the characteristic interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors. For each rectangular area, taking the average of the six correlation coefficients as the similarity coefficient of the signals of the sensors around the rectangular area; Based on the similarity coefficients of the signals of all sensors around the rectangular area, the rectangular area corresponding to the largest similarity coefficient is taken as the impact point occurrence area.

2. The impact load location method based on signal cross-correlation coefficient according to claim 1, characterized in that: The similarity coefficient of the rectangular area is calculated using the following formula: Among them, S k is the similarity coefficient of the kth rectangular area, f ki (t) represents the signal of the sensor in the i-th region or f kj (t) represents the signal of the sensor in the k-th area, i and j are integers ranging from 1 to 4, corresponding to the four sensors in the k-th area, t0 to tn are the characteristic 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.

3. The impact load location method based on signal cross-correlation coefficient according to claim 2, characterized in that: The multiple sensors are arranged on the board under test in the form of a rectangular array sensor network, dividing the board under test into multiple rectangular areas. Specifically, 9 sensors are arranged on the board under test in the form of a 3×3 rectangular array sensor network, and the monitoring area on the board under test is divided into 4 2×2 matrix areas.

4. An impact load positioning system based on signal cross-correlation coefficient, characterized in that: Used to invert the impact load occurrence position of honeycomb sandwich panels, including: a signal acquisition module, configured to apply an impact load to a monitoring area of ​​the panel under test, and acquire signals from a plurality of sensors arranged on the monitoring area of ​​the panel under test, wherein the plurality of sensors are arranged on the panel under test in the form of a rectangular array sensor network, dividing the panel under test into a plurality of rectangular areas, wherein a sensor is arranged at a vertex of each of the rectangular areas; a correlation coefficient calculation module, configured to determine a characteristic interval, calculate the similarity of the signals of the four sensors surrounding each rectangular area in the characteristic interval, and obtain six correlation coefficients for each rectangular area, wherein the similarity is calculated by combining the signals of two sensors, taking the absolute value, and then taking the inner product in the characteristic interval to obtain a set of correlation coefficients as a measure of the similarity between the two sensors; a similarity coefficient calculation module, configured to take, for each rectangular area, an average value of the six correlation coefficients as a similarity coefficient of the signals of the sensors around the rectangular area; The positioning module is configured to take the rectangular area corresponding to the largest similarity coefficient as the impact point occurrence area based on the similarity coefficients of the signals of the sensors around the rectangular area.

5. The impact load location system based on signal cross-correlation coefficient according to claim 4, characterized in that: The similarity coefficient of the rectangular area is calculated using the following formula: Among them, S k is the similarity coefficient of the kth rectangular area, f ki (t) represents the signal of the sensor in the i-th region or f kj (t) represents the signal of the sensor in the k-th area, i and j are integers ranging from 1 to 4, corresponding to the four sensors in the k-th area, t0 to tn are the characteristic 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.

6. The impact load location system based on signal cross-correlation coefficient according to claim 4, characterized in that: The multiple sensors are arranged on the board under test in the form of a rectangular array sensor network, dividing the board under test into multiple rectangular areas. Specifically, 9 sensors are arranged on the board under test in the form of a 3×3 rectangular array sensor network, and the monitoring area on the board under test is divided into 4 2×2 matrix areas.

7. A computer-readable storage medium, characterized in that: The storage medium stores instructions, which, when executed, execute the impact load positioning method based on signal cross-correlation coefficient according to any one of claims 1 to 3.

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