Vertical take-off and landing aircraft vibration reliability test mechanism and test method

Abstract

The invention relates to the technical field of vertical take-off and landing aircraft vibration reliability testing, in particular to a vertical take-off and landing aircraft vibration reliability testing mechanism and method. According to the method, firstly, different one-dimensional data are converted into a gray matrix so as to better represent spatial features of the data; subsequently, multiple gray matrixes with the same time scale are fused into a multi-channel fusion image, so that the data analysis efficiency is improved, and the risk of missing important information is reduced. According to the multi-modal information fusion strategy based on causal intensity, different types of vibration signals can be effectively fused, and spatial features among data are kept at the same time. The deep learning model can obtain more comprehensive and efficient feature representation, thereby better adapting to changes in different flight environments, and improving the test accuracy of the vibration reliability of the aircraft.

Description

Technical Field

[0001] The invention relates to the technical field of vertical take-off and landing aircraft vibration reliability testing, in particular to a vertical take-off and landing aircraft vibration reliability testing mechanism and a testing method. Background Art

[0002] An electric vertical take-off and landing vehicle is an electric aircraft that can take off and land in a vertical direction. This electric aircraft combines an electric drive system with the ability to take off and land vertically, so it has the advantage of not requiring a traditional runway and can be used in urban environments, providing a more efficient and environmentally friendly way of air travel. The design of electric vertical take-off and landing aircraft is usually required to be able to operate in complex urban environments, which means that the aircraft needs to be able to cope with the challenges of different flight environment variables (such as wind speed, wind direction, etc.) and needs to have high reliability. Therefore, it is particularly important to test the vibration reliability of the aircraft working in an urban environment.

[0003] Existing vibration reliability tests mainly simulate aircraft or test aircraft in actual environments. However, simple simulations are difficult to truly restore the actual environment, so the test accuracy is poor. When testing in an actual environment, the test environment is relatively simple due to the similarity of the environment within the same day or the same time period. If you want to achieve full-environment testing, a longer test cycle is required. It can be seen that existing test equipment is difficult to meet the requirements of full-environment testing.

[0004] The vibration reliability test of the electric vertical take-off and landing aircraft in flight is mainly to evaluate the responsiveness of the aircraft under various airflow conditions to ensure that it can maintain stable flight when encountering wind speed changes, turbulence or other airflow disturbances. When the electric vertical take-off and landing aircraft encounters airflow disturbances, vibration or dynamic load will be generated, so the vibration signal can be used to judge whether the reliability of the electric vertical take-off and landing aircraft meets the standards.

[0005] At present, deep learning is usually used for reliability testing and prediction. Deep learning is good at processing complex nonlinear relationships, can extract useful features from a large amount of experimental data, and perform modeling and prediction through deep learning models.

[0006] However, most traditional deep learning models are modeled through the correlation between data. This modeling method is affected by potential confounding variables, namely flight environment variables. Under the influence of flight environment variables, aircraft will produce a variety of dynamically changing vibration signals, making reliability testing more complicated, which in turn leads to a decrease in the accuracy of traditional deep learning model testing, making it difficult to meet the current requirements for higher test accuracy, so it needs to be solved urgently. Summary of the invention

[0007] In order to avoid and overcome the technical problems existing in the prior art, the present invention provides a vibration reliability testing mechanism and a testing method for a vertical take-off and landing aircraft. The testing mechanism in the present invention can simulate a variety of flight environments to improve the accuracy of aircraft testing. The testing method of the present invention can take into account the changes of a variety of vibration signals to improve the accuracy of the test.

[0008] To achieve the above object, the present invention provides the following technical solutions: A vibration reliability testing mechanism for a vertical take-off and landing aircraft comprises a hexahedral frame, on each of the six faces of the frame are mounted a plurality of fans arranged in a rectangular array, and each fan can operate independently, and the fans on the six faces enclose a test space for testing the vibration reliability of the vertical take-off and landing aircraft.

[0009] A vertical take-off and landing aircraft vibration reliability test method, the test method using the vertical take-off and landing aircraft vibration reliability test mechanism, including the following test steps: S1. Obtaining time series data of the aircraft during flight testing, where the time series data includes vibration signals, flight environment variables, and corresponding vibration reliability; S2, converting each time series data into data of the same type, and arranging the converted data into a corresponding grayscale matrix; S3. Calculate the weight of each time series data according to the mutual information between the flight environment variable and each vibration signal, weight the grayscale matrix by the weight to obtain a weighted matrix, and fuse each weighted matrix in turn to obtain a multi-channel fusion image; S4, training a deep learning model using the multi-channel fusion graph to generate a vibration reliability prediction model with the vibration signal as a feature and the vibration reliability as a label; S5. Acquire the real-time time series data of the aircraft and input it into the vibration reliability prediction model to predict the real-time vibration reliability of the aircraft.

[0010] As a further solution of the present invention: the specific content of step S3 is as follows: S31, calculating the mutual information between the flight environment variables and each vibration signal; S32, calculating the weight of each vibration signal based on the mutual information; S33, weighting the grayscale matrix of each vibration signal by weight to obtain a corresponding weighted matrix; S34, taking each weighted matrix as a channel and fusing them in sequence, so as to obtain a multi-channel fusion graph having a corresponding number of channels.

[0011] As a further solution of the present invention: the calculation formula of mutual information is as follows: ; In the formula, Indicates flight environment variables and vibration signal The mutual information between Represents the total number of data points selected from the time series data; Represents the density ratio of joint probability to marginal probability; Indicates vibration signal No. The value of the data point; Indicates flight environment variables No. The value of the data point; Indicates flight environment variables The value is And vibration signal The value is The joint probability when Indicates vibration signal The value is The marginal probability when Indicates flight environment variables The value is The marginal probability when The symbol for density ratio; Represents a logarithmic function.

[0012] As a further solution of the present invention: the calculation formula of the density ratio is as follows: ; In the formula, represents the digamma function; Indicates vibration signal The value is The number of times; Indicates flight environment variables The value is The number of times.

[0013] As a further solution of the present invention: the calculation formula of the weight is as follows: ; In the formula, Indicates Vibration signal The weight of Indicates the total number of vibration signals; Indicates flight environment variables and Vibration signal The mutual information between Represents a natural constant.

[0014] As a further solution of the present invention: the normalization operation is expressed as follows: ; In the formula, express Normalized value; Indicates the current vibration signal The minimum value among all values ​​of ; Indicates the current vibration signal The maximum value among all the values ​​of .

[0015] As a further solution of the present invention: the multi-channel fusion graph is represented as follows: ; In the formula, Indicates the number of channels Multi-channel fusion map; Represents a multi-channel fusion operation; Indicates the first vibration signal ; indicates the Vibration signal ; Indicates Vibration signal ; Represents the weight matrix composed of various weights.

[0016] As a further solution of the present invention: the grayscale matrix is ​​expressed as follows: ; In the formula, Indicates the current vibration signal Grayscale matrix of Indicates the number of rows of the grayscale matrix; Indicates the number of columns of the grayscale matrix; Represents the operation of producing grayscale matrix; As a further solution of the present invention: by multiplying the weight of the vibration signal with each element in the grayscale matrix of the vibration signal, the grayscale matrix can be converted into a corresponding weighted matrix.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention first converts different one-dimensional data into grayscale matrices, which can better represent the spatial characteristics of the data. Subsequently, multiple grayscale image matrices of the same time scale are fused into a multi-channel fusion image. This fusion method not only improves the efficiency of data analysis, but also reduces the risk of missing important information. The multimodal information fusion strategy based on causal strength can effectively fuse different types of vibration signals while maintaining the spatial characteristics between the data. The deep learning model can obtain a more comprehensive and efficient feature representation, thereby better adapting to changes in different flight environments and improving the diagnosis and optimization of aircraft performance.

[0018] 2. The method of the present invention quantifies the nonlinear correlation between vibration signals and flight environment variables through mutual information, dynamically calculates the weight of each vibration signal and performs exponential transformation, so as to realize directional enhancement and noise reduction in feature space; element-by-element fusion of weighted matrix and grayscale matrix is ​​used to construct a multi-channel fusion graph, breaking through the limitations of traditional linear superposition, highlighting key vibration modes while retaining the time-frequency characteristics of data; the real-time weight update mechanism enables the system to have adaptive capabilities, track changes in aircraft status, significantly improve the accuracy of vibration reliability prediction, and enhance the interpretability of the model through explicit weight distribution, providing an efficient solution for aircraft structural health monitoring.

[0019] 3. The mutual information weight calculation method achieves differentiated weighting of multi-source data by quantifying the correlation between vibration signals and flight environment variables. This mechanism can dynamically identify key vibration parameters, avoid redundant information interference, and improve data fusion efficiency. The weight allocation strategy based on mutual information provides a scientific basis for the construction of subsequent multi-channel fusion graphs and ensures the effectiveness of model training.

[0020] 4. Mutual information calculation formula This formula uses a non-parametric density ratio estimation method to avoid the model deviation caused by the traditional Gaussian assumption. By introducing a sign function to process logarithmic operations, it can effectively suppress noise interference and improve the stability of mutual information calculation.

[0021] 5. Density ratio estimation based on the double gamma function can accurately estimate the ratio of joint probability to marginal probability under finite sample conditions, avoiding the parameter selection problem of traditional kernel density estimation and significantly reducing computational complexity. It is especially suitable for non-stationary characteristic analysis of aircraft vibration signals and improving the timeliness of mutual information calculation.

[0022] 6. The weight calculation formula uses natural constants to perform exponential transformation on mutual information, which can effectively amplify the weight difference of key vibration signals. Combined with normalization processing, the weight distribution can maintain a relative proportional relationship and have absolute quantitative significance.

[0023] 7. Based on the extreme value normalization method, vibration signals of different dimensions can be unified into the interval [0,255], eliminating the influence of dimension differences on the analysis results, maintaining the relative distribution characteristics of the data, providing standardized input for subsequent gray matrix construction, and ensuring the consistency of multi-source data fusion.

[0024] 8. Through the element-by-element multiplication of the weight matrix and the vibration signal matrix, the differentiated enhancement of different channel data is achieved. The multi-channel fusion operation can effectively integrate multi-dimensional vibration features to form a composite feature map containing spatial distribution and weight information, providing richer input features for the deep learning model and significantly improving the model's feature expression ability.

[0025] 9. Convert the time-series vibration data into a two-dimensional grayscale matrix to achieve time-frequency domain visualization of the vibration signal. By optimizing the row and column parameters of the matrix, the time-varying characteristics and frequency components of the vibration signal can be effectively retained. The construction of the grayscale matrix provides an intuitive data form for subsequent image processing and deep learning model training, improving analysis efficiency.

[0026] 10. The element-by-element weighting strategy ensures that the importance of each vibration signal is accurately mapped to each element of the matrix. Through the linear combination of the weight matrix and the grayscale matrix, the key vibration features can be highlighted while maintaining the original data structure, providing an efficient data preprocessing method for the construction of multi-channel fusion graphs and ensuring the effectiveness of model training. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the test mechanism structure in the present invention.

[0028] Figure 2 Flow chart of the testing method in the present invention.

[0029] Figure 3 This is a pixel value distribution diagram after normalization of vibration acceleration in the present invention.

[0030] Figure 4 This is a pixel value distribution diagram after normalization of vibration displacement in the present invention.

[0031] Figure 5 This is a pixel value distribution diagram after normalization of vibration pressure in the present invention.

[0032] Figure 6 It is a pixel value distribution diagram after normalization of horizontal wind speed in the wind field environment in the present invention.

[0033] Figure 7 It is a grayscale matrix diagram of vibration acceleration in the present invention.

[0034] Figure 8 It is a grayscale matrix diagram of vibration displacement in the present invention.

[0035] Fig. 9 It is a grayscale matrix diagram of vibration pressure in the present invention.

[0036] Fig.10 This is the multi-channel fusion image in the present invention.

[0037] In the figure: 1. Frame; 2. Fan; 3. Test space. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] See also Figure 1 The test mechanism of the present invention includes a hexahedral frame 1 formed by three rectangular frames enclosed in sequence, and the frame 1 is hollow. Nine fans 2 are installed on the six faces inside the frame 1, respectively, and the nine fans 2 on the same face are arranged in a rectangular array. The center of the rectangular array coincides with the center of the face on which it is located. Thirty-six fans 2 installed on the frame 1 are connected to each other with the control terminal, and each fan 2 can be controlled and operated independently. The aircraft performs a vibration reliability test in the test space 3 formed inside the frame 1. At the same time, corresponding vibration sensors are set at various key positions of the aircraft to detect corresponding vibration signals and flight environment variables, and transmit the corresponding information to the control terminal. The control terminal determines the vibration reliability corresponding to the current vibration signal and flight environment variable according to the corresponding vibration reliability evaluation standard.

[0040] In order to simulate the distribution of different wind fields in a real environment, the layout and control of the Fan 2 matrix are crucial. By accurately designing the layout and startup sequence of Fan 2, different wind field distribution patterns can be simulated. The layout and control of Fan 2 not only need to take into account the overall direction of the wind flow, but also need to accurately control the intensity and distribution characteristics of the wind flow in order to accurately reproduce the complex environment. Specifically, the direction control of the wind field involves two important angles: the pitch angle and the yaw angle. In order to achieve precise adjustment of the wind direction in the wind field, each rotating shaft of Fan 2 needs to have an independent control function, and the accuracy and stability of the wind direction control are ensured by dynamically adjusting the pitch and yaw angles.

[0041] In addition, wind speed control is another important factor. In wind field simulation, changes in wind speed directly affect the intensity and distribution of airflow. In order to achieve precise control of wind speed, the control terminal changes the intensity of airflow by adjusting the speed of fan 2. By accurately controlling the speed of fan 2, different wind speed changes can be simulated in different areas and different wind directions, thereby providing a variety of wind field simulation effects.

[0042] In order to achieve these complex control goals, a PID control algorithm is used. The PID control algorithm calculates the error between the target angle and the current angle in real time and continuously adjusts the output of the motor to achieve precise control of the angle of Fan 2. This feedback mechanism not only ensures that Fan 2 quickly reaches the set target angle, but also effectively maintains stable operation in actual applications, thereby ensuring the accuracy of the simulated wind field in all directions.

[0043] In the vibration reliability test of vertical take-off and landing aircraft, the vibration acceleration, vibration displacement and vibration pressure in the vibration signal are three types of core signals, which respectively reflect the vibration characteristics of different parts of the aircraft. Flight environment variables include wind speed, wind volume, etc., which respectively reflect the specific state of the wind field environment in the test space. Table 1 shows the specific role, processing method and collaborative value of vibration signals in the test process.

[0044] Table 1 Vibration signal types ; See also Figure 2 The testing method of the present invention comprises the following testing steps: 1. Obtaining Time Series Data Obtain the time series data of the aircraft during the test in the test space, which includes vibration signals, flight environment variables, and corresponding vibration reliability. Obtain the vibration signals, flight environment variables, and corresponding vibration reliability of the aircraft that change over time under different test environments in the test space, that is, obtain the time series data of the aircraft during flight for subsequent analysis.

[0045] 2. Get the grayscale matrix Through the normalization operation, each time series data is converted into normalized data of the same type, and each normalized data is organized into a corresponding grayscale matrix.

[0046] The normalization operation is expressed as follows: ; In the formula, express Normalized value; Indicates the current vibration signal The minimum value among all values ​​of ; Indicates the current vibration signal The maximum value among all the values ​​of .

[0047] The necessity of normalization is to eliminate the dimension difference. The data of different sensors (such as vibration acceleration and vibration displacement) have different physical units and magnitudes. Direct fusion will cause the signal with large value to dominate the image features. After normalization, all data are presented in the image with grayscale changes of the same weight.

[0048] like Figure 3~Figure 5 As shown in the figure, the pixel values ​​of vibration acceleration, vibration displacement and vibration pressure at each time point are obtained after the vibration signal sample with high vibration reliability is normalized, and each vibration signal sample contains data points. Figure 6 As shown in the figure, the pixel values ​​of each time point are obtained after the horizontal wind speed of the wind field environment is normalized, which includes data points.

[0049] Through normalization, the raw data of vibration acceleration, vibration displacement, vibration pressure and horizontal wind speed in the wind field environment are uniformly mapped to a color intensity range of 0-255, so as to be subsequently converted into a grayscale matrix for multimodal fusion.

[0050] All vibration signal samples obtained are normalized, and then the corresponding grayscale matrix is ​​constructed, and the dimension of each grayscale matrix is .like Figure 7~Figure 9 As shown, there are grayscale matrices corresponding to the three groups of vibration signal samples, which are the grayscale matrices of vibration acceleration, the grayscale matrix of vibration displacement and the grayscale matrix of vibration pressure corresponding to high vibration reliability, medium vibration reliability and low vibration reliability respectively.

[0051] 3. Multi-channel fusion image Calculate the weight of each vibration signal, use the weight to weight the grayscale matrix to obtain a weighted matrix, and fuse each weighted matrix in turn to obtain a multi-channel fusion image. The specific steps are as follows: First, the mutual information between the flight environment variables and each vibration signal is calculated. The calculation formula of the mutual information is as follows: ; ; In the formula, Indicates flight environment variables and vibration signal The mutual information between Represents the total number of data points selected from the time series data; Represents the density ratio of joint probability to marginal probability; Indicates vibration signal No. The value of the data point; Indicates vibration signal No. The value of the data point; Indicates flight environment variables The value is And vibration signal The value is The joint probability when Indicates vibration signal The value is The marginal probability when Indicates flight environment variables The value is The marginal probability when The symbol for density ratio; represents the logarithmic function; represents the digamma function; Indicates vibration signal The value is The number of times; Indicates flight environment variables The value is The number of times.

[0052] Taking the high vibration reliability vibration signal data as an example, based on the above formula, we can get: ; ; ; in, represents the vibration acceleration, represents the vibration displacement, represents the vibration pressure, Indicates the horizontal wind speed in the wind farm environment.

[0053] Then, based on the mutual information, the weight of each vibration signal is calculated. The weight calculation formula is as follows: ; In the formula, Indicates Vibration signal The weight of Indicates the total number of vibration signals; Indicates flight environment variables and Vibration signal The mutual information between Represents a natural constant.

[0054] Based on the calculation results of the mutual information above, the calculation results of the weights are as follows: the weight of vibration acceleration is 0.28499471; the weight of vibration displacement is 0.70891309; and the weight of vibration pressure is 0.00609219.

[0055] The grayscale matrix can be converted into a corresponding weighted matrix by multiplying the weight of the vibration signal with each element in the grayscale matrix of the vibration signal.

[0056] Finally, each weighted matrix is ​​used as a channel and fused in sequence to obtain a multi-channel fusion graph with the corresponding number of channels. The multi-channel fusion graph is represented as follows: ; In the formula, Indicates the number of channels Multi-channel fusion map; Represents a multi-channel fusion operation; Indicates the first vibration signal ; Indicates Vibration signal ; Indicates Vibration signal ; Represents the weight matrix composed of various weights.

[0057] like Fig.10 As shown, the multi-channel fusion diagrams of high vibration reliability, medium vibration reliability and low vibration reliability are obtained respectively.

[0058] 4. Obtaining the vibration reliability prediction model Using the multi-channel fusion graph to predict the deep learning model to generate a vibration reliability prediction model characterized by the vibration signal and with vibration reliability as a label; 5. Real-time vibration reliability prediction The real-time vibration signal of the aircraft is obtained and processed according to the contents of steps S2-S4 to predict the real-time vibration reliability of the aircraft.

[0059] By applying the method of the present invention to various models, the difference between the accuracy of the model using the method and the accuracy of the model not using the method can be obtained, as shown in Table 2.

[0060] Table 2 Comparison of prediction accuracy ; Based on the data in Table 2, we can see that: Autoencoder: The accuracy was only 19.50% when the method was not used, but it increased to 34.75% after using it.

[0061] Convolutional neural network: The accuracy of the method before adoption was 40.05%, which jumped to 99.75% after adoption, a significant improvement.

[0062] AlexNet network: The rate before the method was adopted was 95.75%, which was further improved to 98.50% after the method was adopted.

[0063] Other models (such as bidirectional long short-term memory networks, multi-layer perceptrons, etc.) show similar patterns: The accuracy after adopting this method is higher than that without adopting this method.

[0064] It can be seen that under variable working conditions, the accuracy of the model using this method is generally improved compared to the model not using this method, which verifies the positive role of this method in optimizing the model training effect and proves that it can effectively enhance the performance of the model under complex working conditions.

[0065] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A vibration reliability test method for a vertical take-off and landing aircraft, characterized in that: The test steps include: S1. Obtaining time series data of the aircraft during flight testing, where the time series data includes vibration signals, flight environment variables, and corresponding vibration reliability; S2, converting each time series data into data of the same type, and arranging the converted data into a corresponding grayscale matrix; S3. Calculate the weight of each time series data according to the mutual information between the flight environment variable and each vibration signal, weight the grayscale matrix by the weight to obtain a weighted matrix, and fuse each weighted matrix in turn to obtain a multi-channel fusion image; S4, training a deep learning model using the multi-channel fusion graph to generate a vibration reliability prediction model with the vibration signal as a feature and the vibration reliability as a label; S5. Acquire the real-time time series data of the aircraft and input it into the vibration reliability prediction model to predict the real-time vibration reliability of the aircraft.

2. A vertical take-off and landing aircraft vibration reliability testing method according to claim 1, characterized in that: The calculation formula of mutual information is as follows: ； In the formula, Indicates flight environment variables and vibration signal The mutual information between Represents the total number of data points selected from the time series data; Represents the density ratio of joint probability to marginal probability; Indicates vibration signal No. The value of the data point; Indicates flight environment variables No. The value of the data point; Indicates flight environment variables The value is And vibration signal The value is The joint probability when Indicates vibration signal The value is The marginal probability when Indicates flight environment variables The value is The marginal probability when The symbol for density ratio; Represents a logarithmic function.

3. A vertical take-off and landing aircraft vibration reliability testing method according to claim 2, characterized in that: The density ratio is calculated as follows: ； In the formula, represents the digamma function; Indicates vibration signal The value is The number of times; Indicates flight environment variables The value is The number of times.

4. A vertical take-off and landing aircraft vibration reliability testing method according to claim 3, characterized in that: The weight is calculated as follows: ； In the formula, Indicates Vibration signal The weight of Indicates the total number of vibration signals; Indicates flight environment variables and Vibration signal The mutual information between Represents a natural constant.

5. A vertical take-off and landing aircraft vibration reliability testing method according to claim 4, characterized in that: Through normalization operation, each time series data is converted into data of the same type. The normalization operation is expressed as follows: ； In the formula, express Normalized value; Indicates the current vibration signal The minimum value among all values ​​of ; Indicates the current vibration signal The maximum value among all the values ​​of .

6. A vertical take-off and landing aircraft vibration reliability testing method according to claim 5, characterized in that: The multi-channel fusion graph is represented as follows: ； In the formula, Indicates the number of channels Multi-channel fusion map; Represents a multi-channel fusion operation; Indicates the first vibration signal ; indicates the Vibration signal ; Indicates Vibration signal ; Represents the weight matrix composed of various weights.

7. A vertical take-off and landing aircraft vibration reliability testing method according to claim 6, characterized in that: The grayscale matrix is ​​represented as follows: ； In the formula, Indicates the current vibration signal Grayscale matrix of Indicates the number of rows of the grayscale matrix; Indicates the number of columns of the grayscale matrix; Represents the operation of producing a grayscale matrix.

8. A vertical take-off and landing aircraft vibration reliability testing method according to claim 7, characterized in that: By multiplying the weight of the vibration signal with each element in the grayscale matrix of the vibration signal, the grayscale matrix can be converted into a corresponding weighted matrix.

9. A vibration reliability testing mechanism for a vertical take-off and landing aircraft, the vibration reliability testing mechanism being applied to a vibration reliability testing method for a vertical take-off and landing aircraft as claimed in claim 8, characterized in that: The invention comprises a hexahedral frame (1), wherein a plurality of independently operable fans (2) are mounted on each of the six faces of the frame, and the fans (2) on the six faces enclose a test space (3) for testing the vibration reliability of a vertical take-off and landing aircraft.

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