Sensor position selection method, device and medium in modal test process

Abstract

The application discloses a sensor position selection method, device and medium in a modal test process, which is characterized by the following steps: establishing a correlation matrix MAC 12 , MAC 13 ,..., MAC ij , calculating the contribution degree value of a preselected sensor preselected position point to each order modal, deleting the preselected sensor position point with a negative contribution degree, and continuously adjusting the correlation matrix so that the remaining preselected sensor position points meet the different order modal vibration mode requirements, thereby realizing automatic matching of the preselected sensor position points to the measurement requirements, improving the sensor positioning accuracy, and eliminating the need for manual repeated intervention for modal measurement of the sensor arrangement position.

Description

Technical Field

[0001] This invention belongs to the field of automotive NVH technology, specifically relating to the sensor location selection method, equipment, and medium during modal testing. Background Technology

[0002] In existing vehicle modal testing processes, the selection of sensor locations often relies on the experience of test engineers, leading to uncertainties in the test results. Furthermore, even experienced test engineers may miss some modalities when faced with entirely new vehicle structures.

[0003] To address the aforementioned issues, the common practice in actual testing is to deploy as many sensors as possible. However, this approach is inefficient and increases testing time and cost. Patent CN111089695A discloses an automated modal testing method, which mentions a method for selecting sensor placement points. This method involves repeatedly tapping the same point to obtain the frequency response function of the selected point on the structure, and then performing singular value decomposition, modal indication function calculation, and modal statistics on the frequency response function matrix to obtain the final sensor position. The drawbacks of this method are: when dealing with entirely new structures, the selected points may not fully represent all modes, resulting in missing test modes. Furthermore, this method requires manual testing, is time-consuming, and is highly dependent on equipment.

[0004] Therefore, there is an urgent need to study a method that can traverse the complete vehicle NVH structural modes while using a small number of sensors, and has high testing efficiency, thereby improving the accuracy and efficiency of sensor placement in meeting NVH mode requirements. Summary of the Invention

[0005] The sensor position selection method disclosed in this invention during modal testing effectively improves the efficiency of vehicle structure modal testing and fully reflects the vehicle structure modes, thereby accurately determining the sensor placement position.

[0006] The present invention also discloses an electronic device that effectively improves the efficiency of vehicle structural modal testing and fully reflects the vehicle structural modalities, thereby accurately determining the sensor placement position.

[0007] The present invention also discloses a computer-readable storage medium that effectively improves the efficiency of vehicle structural modal testing and fully reflects the vehicle structural modalities, thereby accurately determining the sensor placement positions.

[0008] The sensor position selection method disclosed in this invention during modal testing includes the following steps:

[0009] Step 1: Calculate the sensor placement area modes based on the finite element data model of the vehicle under test, and select a series of points as the pre-selected sensor locations based on the mode shapes of each order;

[0010] Step 2: Establish the correlation matrix for each pair of different mode shapes corresponding to the pre-selected location points, and calculate the contribution of the pre-selected location points to each pair of different mode shapes;

[0011] Step 3: Delete the pre-selected location points with negative contribution values;

[0012] Step 4: Update the correlation matrix of each pair of different mode shapes, and update the contribution value of the remaining pre-selected location points;

[0013] Step 5: Determine whether the overall level of the relevance matrix is ​​acceptable. If acceptable, proceed to Step 6; if unacceptable, filter out the relevance values ​​in the relevance matrix that do not meet the requirements, delete the corresponding pre-selected position points to make the relevance values ​​meet the requirements, and return to Step 4.

[0014] Step 6: Output the coordinates of the remaining pre-selected locations that meet the requirements, and use them as the positions of the sensors during the vehicle structure modal test. Place the sensors at the pre-selected locations during the modal test.

[0015] Furthermore, in step 1, selecting the pre-selected location point of the sensor specifically includes the following steps:

[0016] Step 11: Establish a finite metadata model of the vehicle structure to be measured and calculate the modalities of the sensor placement area according to the test requirements;

[0017] Step 12: Based on the mode shapes exhibited by each mode, select a series of points as the pre-selected location points for the sensor.

[0018] Furthermore, in step 2, the correlation matrix for each pair of different mode shapes specifically includes the following steps:

[0019] Step 21: Calculate the degree of freedom values ​​corresponding to the pre-selected locations of each sensor under each mode shape;

[0020] Step 22: Establish the correlation matrix for each pair of different mode shapes corresponding to the pre-selected location points, as follows:

[0021]

[0022] in, These are the values ​​of the i-th and j-th mode shapes at the s-th sensor pre-selected position point, respectively. The smaller the MAC value, the smaller the angle between the two modes, that is, the smaller the correlation between the mode shapes, and the better the mode can be identified.

[0023] Furthermore, step 2 involves calculating the contribution of the pre-selected location points to pairwise different mode shapes, specifically including the following steps:

[0024] Step 23: Calculate the contribution K of the s-th sensor pre-selected location point to the MAC value between the i-th and j-th modes. ij_s ,as follows:

[0025] K ij_s =MAC ij_in_s -MAC ij_nin_s

[0026] Among them, MAC ij_in_s This represents the MAC value between the i-th and j-th mode shapes obtained from all measurement point data; MAC ij_nin_ns This represents the MAC value between the i-th and j-th mode shapes obtained based on the data from the pre-selected position points of the other sensors excluding the s-th sensor pre-selected position point.

[0027] in, and The matrix consists of the values ​​of the i-th and j-th mode shapes at the degrees of freedom corresponding to the sensor pre-selected position points, including the S-th sensor;

[0028] and The matrix consists of the values ​​of the i-th and j-th mode shapes at the degrees of freedom corresponding to the sensor pre-selected position points excluding the S-th sensor, respectively.

[0029] If K ij_s If K ≥ 0, then the s-th sensor pre-selected location point is said to have a negative contribution to the MAC value between the i-th and j-th modes; if K ij_s If <0, then the s-th sensor pre-selected location point is said to have a positive contribution to the MAC value between the i-th and j-th modes.

[0030] Furthermore, in step 3, delete the MAC address. 12 MAC 13 , ..., MAC ij Sensor pre-selected location points with a negative contribution value in the public domain.

[0031] Further, in step 5, it is determined whether the overall level of the relevance matrix is ​​acceptable. If it is acceptable, proceed to step 6; if it is not acceptable, filter out the relevance values ​​in the relevance matrix that do not meet the requirements, delete the corresponding pre-selected position points so that the relevance values ​​meet the requirements, and return to step 4.

[0032] Furthermore, in step 5, the correlation matrix (MAC) is selected from left to right. 12 MAC 13 , ..., MAC ijIf the first MAC value in the dataset does not meet the requirements, delete the corresponding pre-selected location point so that the correlation value meets the requirements.

[0033] Furthermore, in step 1, a finite metadata model of the vehicle structure to be measured is established using Hypermesh.

[0034] The present invention also discloses an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the sensor position selection method described above during modal testing.

[0035] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the sensor position selection method described above during modal testing.

[0036] The beneficial technical effects of this invention are: by establishing a correlation matrix (MAC) ij The system calculates the contribution of pre-selected sensor locations to each mode, removes pre-selected sensor locations with negative contributions, and continuously adjusts the correlation matrix to ensure that the remaining pre-selected sensor locations meet the requirements of different mode shapes. This achieves automated matching and measurement of pre-selected sensor locations, improving sensor positioning accuracy. It eliminates the need for repeated manual intervention in modal measurements of sensor placement. Attached Figure Description

[0037] Figure 1 This is an example diagram of the relevance matrix disclosed in this invention;

[0038] Figure 2 This is a flowchart of the sensor position selection method during modal testing of the present invention. Detailed Implementation

[0039] The present invention will now be described in detail with reference to the accompanying drawings.

[0040] like Figure 1 , Figure 2 As shown, the sensor position selection method during modal testing disclosed in this invention includes the following parts:

[0041] Step 1: Calculate the modalities of the sensor placement area based on the finite element data model of the vehicle under test, and select a series of points as pre-selected sensor locations based on the mode shapes of each order; specifically,

[0042] Step 11: Establish a finite metadata model of the vehicle structure to be measured based on Hypermesh and calculate the modes of the sensor placement area according to the test requirements. For ease of description, the first six modes of the structure (excluding rigid body modes) are taken here.

[0043] Step 12: View the modal results using Hyperview. Based on the mode shapes exhibited by the first six modes, select a series of points as the pre-selected location points for the sensor. The selected pre-selected location points should fully reflect the characteristics of each mode shape.

[0044] Step 2: Establish the correlation matrix for each pair of different mode shapes corresponding to the pre-selected location points, and calculate the contribution of the pre-selected location points to each pair of different mode shapes; specifically,

[0045] Step 21: Calculate the degree of freedom values ​​corresponding to the pre-selected locations of each sensor under each mode shape.

[0046] Step 22: Establish the correlation matrix between each pair of mode shapes in the first six modes corresponding to the pre-selected location points, as follows:

[0047]

[0048] in, These represent the values ​​of the i-th and j-th modal modes at the s-th pre-selected sensor location, respectively. A smaller MAC value indicates a smaller angle between the two modes, meaning a lower correlation between the modes and a better ability to identify them. The sixth-order modal correlation matrix is ​​shown below. Figure 1 As shown, the correlation MAC values ​​between all i-th and j-th mode shapes are obtained by iterating through them. ij .

[0049] Step 23: Calculate the contribution K of the s-th sensor pre-selected location point to the MAC value between the i-th and j-th modes. ij_s ,as follows:

[0050] K ij_s =MAC ij_in_s -MAC ij_nin_s

[0051] Among them, MAC ij_in_s This represents the MAC value between the i-th and j-th mode shapes obtained from all measurement point data; MAC ij_nin_s This represents the MAC value between the i-th and j-th mode shapes obtained based on the data from the pre-selected position points of the other sensors excluding the s-th sensor pre-selected position point.

[0052] in, and The matrix consists of the values ​​of the i-th and j-th mode shapes at the pre-selected sensor location points, including the S-th sensor:

[0053] and The matrix consists of the values ​​of the i-th and j-th mode shapes at the pre-selected sensor location points excluding the S-th sensor, respectively.

[0054] If K ij_s If K ≥ 0, then the s-th sensor pre-selected location point is said to have a negative contribution to the MAC value between the i-th and j-th modes; if K ij_s If <0, then the s-th sensor pre-selected location point is said to have a positive contribution to the MAC value between the i-th and j-th modes.

[0055] Step 3: Delete pre-selected location points with negative contribution values; delete those from MAC. 12 MAC 13 To MAC ij Sensor pre-selected location points with a negative contribution value in the common data. (From MAC) 12 MAC 13 To MAC ij The number of negative contribution points are H 12 H 13 To H ij The number of common negative contribution points is H0. After deleting the common negative contribution points, there are a total of H0 points.

[0056] Step 4: Update the correlation matrix of each pair of different mode shapes, and update the contribution value of the remaining pre-selected location points;

[0057] Step 5: Determine if the overall level of the correlation matrix is ​​acceptable. If acceptable, proceed to Step 6; if unacceptable, delete the corresponding pre-selected location points to make the correlation value meet the requirements. The principle for deleting measurement points is: first delete the measurement points with larger K values ​​among the negative contribution points, such as MAC... 12 If the conditions for going are not met, then the MAC address will be changed. 12 The remaining negative contribution points are sorted according to their K values, and the points with larger K values ​​are deleted first. Then return to step 4.

[0058] Step 6: Output the coordinates of the remaining pre-selected locations that meet the requirements, and use them as the positions of the sensors during the vehicle structure modal test. Place the sensors at the pre-selected locations during the modal test.

[0059] The present invention also discloses an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the sensor position selection method described above during modal testing.

[0060] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the sensor position selection method described above during modal testing.

Claims

1. A method for selecting sensor positions during modal testing, characterized in that: Includes the following steps, Step 1: Calculate the sensor placement area modes based on the finite element data model of the vehicle under test, and select a series of points as the pre-selected sensor locations based on the mode shapes of each order; Step 2: Establish the correlation matrix for each pair of different mode shapes corresponding to the pre-selected location points, and calculate the contribution of the pre-selected location points to each pair of different mode shapes, including: Step 21: Calculate the degree of freedom values ​​corresponding to each sensor's pre-selected location point under each mode shape; Step 22: Establish the correlation matrix for each pair of different mode shapes corresponding to the pre-selected location points, as follows: ； in, , The first First-order mode shape and the second-order mode shape The first mode shape is in the 1st order. The value at the degree of freedom corresponding to the pre-selected location point of each sensor; The smaller the value, the smaller the angle between the two vibration modes, that is, the smaller the correlation between the vibration modes, and the better the mode can be identified; Step 23: Calculate the first... The sensor pre-selected location point pairs with the first First-order mode and the second-order mode Contribution of MAC values ​​between first and second modes ,as follows: ； in, This represents the first result obtained based on all measurement point data. The first mode shape and the second mode shape Between mode shapes value; Indicates based on except the first The data obtained from the pre-selected locations of the other sensors outside the first sensor pre-selected location point. The first mode shape and the second mode shape Between mode shapes value; in, , and The first First-order mode shape and the second-order mode shape The matrix consisting of the values ​​of the first mode shape at the pre-selected sensor location point, including the S-th sensor; , and The first First-order mode shape and the second-order mode shape The matrix consisting of the values ​​of the first mode shapes at the pre-selected sensor location points excluding the S-th sensor; like Then the first The sensor pre-selected location point pairs with the first First-order mode and the second-order mode The MAC value between the first and second modes has a negative contribution; if Then the first The sensor pre-selected location point pairs with the first First-order mode and the second-order mode The MAC value contributes positively to the interaction between the first and second modes; Step 3: Delete Sensor pre-selected location points with a negative contribution value in the public domain; Step 4: Update the correlation matrix of each pair of different mode shapes, and update the contribution value of the remaining pre-selected location points; Step 5: Determine whether the overall level of the relevance matrix is ​​acceptable. If acceptable, proceed to Step 6; if unacceptable, filter out the relevance values ​​in the relevance matrix that do not meet the requirements, delete the corresponding pre-selected position points to make the relevance values ​​meet the requirements, and return to Step 4. Step 6: Output the coordinates of the remaining pre-selected locations that meet the requirements, and use them as the positions of the sensors during the vehicle structure modal test. Place the sensors at the pre-selected locations during the modal test.

2. The sensor position selection method during modal testing as described in claim 1, characterized in that: Step 1 involves selecting the pre-selected location point for the sensor, specifically including the following steps: Step 11: Establish a finite metadata model of the vehicle structure to be measured and calculate the modalities of the sensor placement area according to the test requirements; Step 12: Based on the mode shapes exhibited by each mode, select a series of points as the pre-selected location points for the sensor.

3. The sensor position selection method during modal testing as described in claim 1, characterized in that: In step 5, determine whether the overall level of the relevance matrix is ​​acceptable. If it is acceptable, proceed to step 6; if it is not acceptable, filter out the relevance values ​​in the relevance matrix that do not meet the requirements, delete the corresponding pre-selected position points so that the relevance values ​​meet the requirements, and return to step 4.

4. The sensor position selection method during modal testing as described in claim 3, characterized in that: In step 5, the relevance matrix is ​​selected from left to right. The first one that does not meet the requirements The value is then used to delete the corresponding pre-selected location points so that the correlation value meets the requirements.

5. The sensor position selection method during modal testing as described in claim 4, characterized in that: In step 1, a finite metadata model of the vehicle structure to be measured is established using Hypermesh.

6. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the sensor position selection method during modal testing as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the sensor position selection method during modal testing as described in any one of claims 1 to 5.

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