A method for recovering missing aircraft mechanical data based on random phase

Abstract

This invention discloses a method for recovering missing mechanical data of an aircraft based on random phase, comprising: zero-padding the received mechanical telemetry signal of the aircraft with the signal sampling frequency to obtain a zero-padding signal; performing power spectral density analysis on the zero-padding signal to obtain a power spectrum; assigning random phase values ​​at each frequency in the power spectrum to obtain a random phase sequence; calculating the random spectrum at each frequency based on the power spectrum and the random phase sequence; then performing an inverse Fourier transform on the random spectrum at all frequencies to obtain a reconstructed time-domain signal; calculating the standard deviation of the overlapping part between the telemetry signal and the reconstructed time-domain signal using the Cauchy convergence criterion to evaluate the difference between the telemetry signal and the reconstructed time-domain signal; if the standard deviation of the overlapping part is within a preset deviation threshold range, the reconstructed time-domain signal is considered to be an ideal recovered signal of the telemetry signal. This invention can obtain an ideal reconstructed signal, enriching the measured samples of the aircraft.

Description

Technical Field

[0001] This invention relates to the field of aircraft environmental adaptability assessment, and more specifically to a method for recovering missing mechanical data of aircraft based on random phase. Background Technology

[0002] In the field of aircraft environmental adaptability assessment, telemetry is commonly used to transmit vibration and shock data of aircraft, thereby collecting mechanical environmental data. However, due to factors such as poor telemetry system reception, electromagnetic interference, and network packet loss, the mechanical measurement data received from the ground often suffers from partial frame loss. Unlike ground tests, aircraft flight data is often more difficult to obtain, characterized by high testing costs and small sample sizes. Therefore, researching methods for recovering missing mechanical data from aircraft is of great significance for accurately assessing aircraft flight performance and mechanical environmental adaptability.

[0003] Missing local data can shorten the data length used for analysis, leading to decreased frequency resolution and increased errors in the analysis results. Unlike other signals, aircraft flight vibrations are mostly broadband random vibrations with short-term stationary characteristics. Although Hermite interpolation, tensor decomposition, and deep learning methods have been used for mechanical data recovery in recent years, Hermite interpolation suffers from the problem of missing mid-to-high frequency features; tensor decomposition is suitable for mechanical signals with obvious local features; and deep learning requires a large number of samples for model training. Therefore, existing methods are insufficient for recovering missing mechanical data from aircraft. Summary of the Invention

[0004] The purpose of this invention is to provide a method for recovering missing mechanical data of aircraft based on random phase, so as to overcome the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention employs the following technical solution: A method for recovering missing aircraft mechanical data based on random phase includes: The received aircraft mechanical telemetry signal is zero-padded by combining the signal sampling frequency to obtain the zero-padded signal; The power spectrum is obtained by performing power spectral density analysis on the zero-padded signal; Random phase values ​​are assigned to each frequency in the power spectrum to obtain a random phase sequence; Based on the power spectrum and random phase sequence, the random spectrum at each frequency is calculated; then, the random spectrum at all frequencies is subjected to inverse Fourier transform to obtain the reconstructed time-domain signal. Calculate the standard deviation of the overlap between the telemetry signal and the reconstructed time-domain signal to assess the difference between them; If the standard deviation of the overlapping portion is within the preset deviation threshold range, the reconstructed time-domain signal is considered to be the ideal recovered signal of the telemetry signal.

[0006] Furthermore, the received aircraft mechanical telemetry signals are zero-padded based on the signal sampling frequency to obtain a zero-padded signal, including: Recording the mechanical telemetry signals of the aircraft The signal sampling frequency is Calculate the time interval between every two adjacent telemetry data points in the telemetry signal. ; such as time interval Greater than Then in the time interval Interval interpolation A zero is obtained to receive the zero-padding signal. Zero-filling signal The data length is N .

[0007] Furthermore, the power spectrum is obtained by performing power spectral density analysis on the zero-padded signal, and is expressed as:

[0008] in, Represents the frequency in the power spectrum Power at that location, Indicates zero-padding signal The One sampling point, It is a natural constant. It is the imaginary unit.

[0009] Furthermore, frequency random spectrum The calculation formula is as follows:

[0010] in For frequency The result of random phase assignment at the location.

[0011] Furthermore, the standard deviation of the overlapping portion between the telemetry signal and the reconstructed time-domain signal is calculated and expressed as:

[0012] in, Indicates the current iteration number. , , They represent the first Telemetry signal at the next iteration Reconstructing the time-domain signal Compared with standard deviation.

[0013] Furthermore, if the standard deviation of the overlapping part is not within the preset deviation threshold range, the next iteration is performed; after re-assigning random phase to obtain a random phase sequence, the reconstructed time-domain signal of the current iteration is determined, and the standard deviation is calculated; the iteration is repeated until the standard deviation is within the preset deviation threshold range, at which point the reconstructed time-domain signal of the current iteration is output.

[0014] Furthermore, the deviation threshold range is 0.2-0.3.

[0015] A terminal device includes a processor, a memory, and a computer program stored in the memory; when the processor executes the computer program, it implements the method for recovering missing aircraft mechanical data based on random phase.

[0016] A computer-readable storage medium storing a computer program; when executed by a processor, the computer program implements the method for recovering missing aircraft mechanical data based on random phase.

[0017] Compared with the prior art, the present invention has the following technical features: This invention combines the characteristics of the unmissing signal with the random vibration of the aircraft. By assigning random phase values ​​to the power spectral density, the random characteristics of the signal are fully preserved. The reconstructed signal that is most similar to the unmissing signal is selected as the recovery signal, which can obtain an ideal reconstructed signal, enrich the actual test samples of the aircraft, and provide strong support for the effective evaluation of the flight performance and environmental adaptability of the aircraft. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a time-domain plot of the vibration signal acquired via telemetry. Figure 3 The above are time-domain and frequency-domain plots of the original vibration signal within the target frequency band, as shown in this embodiment of the invention. Figure 4 This is a time-domain diagram and a magnified view of the leakage of signal components within the target frequency band to the spectrum outside the target frequency band, according to an embodiment of the present invention. Figure 5 This is a time-domain diagram and a magnified view of the spectral leakage of signal components outside the target frequency band into the target frequency band according to an embodiment of the present invention. Figure 6 The images show the time-domain and frequency-domain plots of the vibration signal components within the target frequency band after eliminating the endpoint effect, according to an embodiment of the present invention. Detailed Implementation

[0019] This invention provides a method for recovering missing mechanical data of aircraft based on random phase. Starting with the unmissing signal, the phase of the signal power spectrum is randomly assigned, resulting in an inverse transformation to the time domain. The reconstructed signal is then compared with the unmissing signal; the signal with the smallest error is the recovered signal. (See appendix) Figure 1 The specific steps of the method of the present invention are as follows: Step 1: Zero-padding is performed on the received aircraft mechanical telemetry signal based on the signal sampling frequency to obtain the zero-padding signal.

[0020] Recording the mechanical telemetry signals of the aircraft The signal sampling frequency is Calculate the time interval between every two adjacent telemetry data points in the telemetry signal. ; such as time interval Greater than Then in the time interval Medium interval interpolation A zero is obtained to receive the zero-padding signal. Zero-filling signal The data length is N .

[0021] Step 2, zero-padding signal The power spectrum is obtained by performing power spectral density analysis; the calculation formula is as follows:

[0022] in, Represents the frequency in the power spectrum Power at that location, Indicates zero-padding signal The One sampling point, It is a natural constant. It is the imaginary unit.

[0023] Step 3, proceed with the first The next iteration involves assigning random phase values ​​at each frequency in the power spectrum to obtain a random phase sequence.

[0024] Step 4: Based on the power spectrum and random phase sequence, calculate the random spectrum at each frequency; then perform an inverse Fourier transform on the random spectra at all frequencies to obtain the reconstructed time-domain signal. .

[0025] Among them, frequency random spectrum The calculation formula is as follows:

[0026] in For frequency The result of random phase assignment at the location.

[0027] Step 5, Calculate telemetry signals With reconstructed time-domain signal Standard deviation of the overlapping portion To assess telemetry signals With reconstructed time-domain signal The difference is calculated using the following formula:

[0028] in, Indicates the current iteration number. , , They represent the first Telemetry signal at the next iteration Reconstructing the time-domain signal With respect to standard deviation.

[0029] Step 6, if the standard deviation of the overlapping part If the deviation is within the preset threshold range, the reconstructed time-domain signal is considered to be within the threshold range. Telemetry signal The ideal recovery signal is output; otherwise, the next step is performed. The next iteration, that is, after returning to step 3 to reassign the random phase, repeats the calculation process of steps 4 and 5 until the standard deviation is reached. When the preset deviation threshold is within the range, the reconstructed time-domain signal for that iteration is output. Preferably, the deviation threshold range is 0.2-0.3.

[0030] Example: Step 1: Select a 2-second vibration telemetry signal received during a specific flight of the aircraft. ,like Figure 2 As shown in the figure, a significant data gap can be observed at 0.76s. The signal sampling frequency is 5000Hz. Calculating the time interval between adjacent points in the telemetry data reveals that the time interval between 0.76s and the next point is 0.02s, which is much larger than 1 / 5000s. By interpolating 99 zeros at equal intervals within this 0.2s interval, a zero-padded signal can be obtained. ,like Figure 3 As shown, zero-padding signal The data length is 10000.

[0031] Step 2, zero-padding signal Power spectral density analysis is performed to obtain the power spectrum. ,like Figure 4 As shown.

[0032] Step 3, perform the first iteration: assign random phase values ​​at each frequency in the power spectrum to obtain a random phase sequence, as shown in the random phase spectrum diagram. Figure 5 As shown.

[0033] Step 4: Combining Steps 2 and 3, calculate the random spectrum at each frequency; denoted as... Then, the reconstructed time-domain signal is obtained through inverse Fourier transform. Reconstructing the time-domain signal, such as Figure 6 As shown.

[0034] Step 5, Calculate telemetry signals With reconstructed time-domain signal Standard deviation of the overlapping portion Assess telemetry signals With reconstructed time-domain signal The differences; in the first iteration of this embodiment .

[0035] Step 6: Since the overlap of the two signals is within the preset deviation threshold range of 0.2-0.3, it can be considered as... For the ideal recovery signal of the telemetry signal S, such as Figure 6 As shown.

[0036] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for recovering missing aircraft mechanical data based on random phase, characterized in that, include: The received aircraft mechanical telemetry signal is zero-padded by combining the signal sampling frequency to obtain the zero-padded signal; The power spectrum is obtained by performing power spectral density analysis on the zero-padded signal; Random phase values ​​are assigned to each frequency in the power spectrum to obtain a random phase sequence; Based on the power spectrum and random phase sequence, the random spectrum at each frequency is calculated; then, the random spectrum at all frequencies is subjected to inverse Fourier transform to obtain the reconstructed time-domain signal. Calculate the standard deviation of the overlap between the telemetry signal and the reconstructed time-domain signal to assess the difference between them; If the standard deviation of the overlapping portion is within the preset deviation threshold range, the reconstructed time-domain signal is considered to be the ideal recovered signal of the telemetry signal.

2. The method for recovering missing aircraft mechanical data based on random phase according to claim 1, characterized in that, The received aircraft mechanical telemetry signals are zero-padded by combining the signal sampling frequency to obtain a zero-padded signal, including: Recording the mechanical telemetry signals of the aircraft The signal sampling frequency is Calculate the time interval between every two adjacent telemetry data points in the telemetry signal. ; such as time interval Greater than Then in the time interval Medium interval interpolation A zero is obtained to receive the zero-padding signal. Zero-filling signal The data length is N .

3. The method for recovering missing aircraft mechanical data based on random phase according to claim 2, characterized in that, The power spectrum is obtained by performing power spectral density analysis on the zero-padded signal, and is expressed as: in, Represents the frequency in the power spectrum Power at that location, Indicates zero-padding signal The One sampling point, It is a natural constant. It is the imaginary unit.

4. The method for recovering missing aircraft mechanical data based on random phase according to claim 3, characterized in that, frequency random spectrum The calculation formula is as follows: in For frequency The result of random phase assignment at the location.

5. The method for recovering missing aircraft mechanical data based on random phase according to claim 1, characterized in that, The standard deviation of the overlap between the telemetry signal and the reconstructed time-domain signal is calculated and expressed as: in, Indicates the current iteration number. , , They represent the first Telemetry signal at the next iteration Reconstructing the time-domain signal Compared with standard deviation.

6. The method for recovering missing aircraft mechanical data based on random phase according to claim 1, characterized in that, If the standard deviation of the overlapping part is not within the preset deviation threshold range, the next iteration is performed; after re-assigning random phase to obtain a random phase sequence, the reconstructed time domain signal of the current iteration is determined, and the standard deviation is calculated; the iteration is repeated until the standard deviation is within the preset deviation threshold range, at which point the reconstructed time domain signal of the current iteration is output.

7. The method for recovering missing aircraft mechanical data based on random phase according to claim 1, characterized in that, The deviation threshold range is 0.2-0.

3.

8. A terminal device, comprising a processor, a memory, and a computer program stored in the memory; characterized in that, When the processor executes the computer program, it implements the method for recovering missing aircraft mechanical data based on random phase as described in any one of claims 1-7.

9. A computer-readable storage medium storing a computer program; characterized in that, When the computer program is executed by the processor, it implements the method for recovering missing aircraft mechanical data based on random phase as described in any one of claims 1-7.

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