Variable holographic spectrum identification method and device for shaft frequency and blade frequency of propeller of underwater vehicle

Abstract

The invention discloses an underwater vehicle propeller shaft frequency and blade frequency variational holographic spectrum identification method and device, the variational holographic spectrum method utilizes the idea of holographic representation to realize robust high-dimensional holographic representation of nonlinear and non-stationary signals to obtain a variational holographic spectrogram, the variational holographic spectrogram is subjected to feature extraction, and the characteristic of the variational holographic spectrogram is extracted. The shaft frequency, the blade frequency and the blade number of the propeller of the underwater vehicle are identified and estimated; according to the method, variational mode decomposition is introduced, and the mode aliasing problem of EMD in a holographic Hilbert spectrum analysis method is effectively solved; according to the characteristic that a propeller modulation signal in radiation noise of an underwater vehicle is low-frequency and slow-change, segmented Fourier transform is introduced, and the problem that a Hilbert spectrum analysis method has a large error when processing the low-frequency and slow-change signal is solved. According to the method, the problem of insufficient characterization of the conventional common radiation noise analysis method LOFAR analysis, DEMON analysis and various improved algorithms during nonlinear and non-stationary signal analysis is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater target recognition, and in particular to a variational holographic spectrum recognition method and device for underwater vehicle propeller shaft frequency and blade frequency. Background Art

[0002] In the field of underwater acoustic target detection and identification, feature extraction of radiated noise signals is an effective means to achieve accurate detection of underwater targets. The radiated noise of underwater vehicles can be divided into three categories, including mechanical noise, propeller noise and hydrodynamic noise. According to the spectral characteristics, it can be divided into continuous spectrum, line spectrum and modulation spectrum. Among them, the continuous spectrum is mainly generated by propeller cavitation noise, the line spectrum is mainly generated by mechanical noise and propeller rotation noise, and the modulation spectrum is manifested as the high-frequency continuous spectrum in the propeller noise being modulated by the propeller shaft frequency and blade frequency.

[0003] Propeller noise is the main noise source for underwater targets such as ships, submarines, and underwater vehicles. When the propeller of an underwater vehicle rotates in a non-uniform wake, its propeller cavitation noise will be modulated. There are many discrete line spectra in the modulation spectrum, among which the fundamental frequency line spectrum with the lowest frequency represents the shaft frequency of the propeller, and the other line spectra with multiple frequencies are the harmonics of the propeller shaft frequency, and the number of harmonics corresponds to the number of propeller blades. Therefore, the modulation spectrum contains the structural characteristic information of the propeller of the underwater vehicle, and its spectral characteristics are often used to identify the type of underwater targets and estimate the target speed.

[0004] Theoretical studies have shown that the noise signal radiated by underwater vehicles is nonlinear and non-stationary. The commonly used radiation noise analysis methods LOFAR analysis, DEMON analysis and their various improved algorithms all have insufficient representation when analyzing nonlinear and non-stationary signals. LOFAR analysis uses the local characteristics of the signal to reveal the change of the instantaneous frequency of the signal over time through time-frequency representation, but this method cannot handle nonlinear modulated signals; DEMON analysis extracts the modulation frequency in the radiation noise signal through envelope demodulation, but the DEMON spectrum analysis method requires the artificial selection of the frequency band range of the bandpass filter and the selection of the bandpass filter will significantly affect the performance of the algorithm, which is very blind in practical applications.

[0005] Therefore, it is of great significance to detect and identify underwater vehicles by studying more accurate and robust target feature extraction methods based on nonlinear and non-stationary signals such as propeller modulation signals in the radiated noise of underwater vehicles. Summary of the invention

[0006] In order to solve the above-mentioned technical problems existing in the prior art, the present invention provides a variational holographic spectrum identification method and device for the propeller shaft frequency and blade frequency of an underwater vehicle, so as to solve the problem of insufficient representation of the currently commonly used radiation noise analysis methods LOFAR analysis, DEMON analysis and various improved algorithms thereof when analyzing nonlinear and non-stationary signals.

[0007] In order to achieve the above object, the technical solution of the embodiment of the present invention is:

[0008] A first aspect of the present invention provides a variational holographic spectrum recognition method for propeller shaft frequency and blade frequency of an underwater vehicle, the method comprising:

[0009] Perform time-frequency analysis on the underwater vehicle radiation noise signal, and intercept the signal in combination with the time domain diagram to obtain the intercepted radiation noise signal;

[0010] Performing empirical mode decomposition on the intercepted radiation noise signal to obtain a plurality of IMFs; and performing Hilbert spectrum analysis on each of the IMFs to obtain a relationship between the amplitude of each of the IMFs and the carrier frequency over time;

[0011] After screening the multiple IMFs, extracting the envelope of the screened IMFs, and performing variational mode decomposition on the envelope to obtain the decomposed IMFs;

[0012] Based on a time window of a preset length, performing a piecewise Fourier transform on the decomposed IMF to obtain a relationship between a modulation frequency and a modulation amplitude over time;

[0013] Integrating the time variables of the carrier frequency, the modulation frequency and the modulation amplitude to obtain a variational holographic spectrum;

[0014] Feature extraction is performed on the variational holographic spectrum to obtain estimation results of the shaft frequency, blade frequency and number of blades of the underwater vehicle propeller.

[0015] A second aspect of the present invention provides a variational holographic spectrum recognition device for propeller shaft frequency and blade frequency of an underwater vehicle, the method comprising:

[0016] The time-frequency analysis module is used to perform time-frequency analysis on the noise signal radiated by the underwater vehicle and intercept the signal in combination with the time domain diagram to obtain the intercepted noise signal radiated;

[0017] The HHT module is used to perform empirical mode decomposition on the intercepted radiation noise signal to obtain multiple IMFs; and perform Hilbert spectrum analysis on each of the IMFs to obtain the relationship between the amplitude and carrier frequency of each of the IMFs over time;

[0018] A decomposition module, used for extracting the envelope of the filtered IMF after filtering the multiple IMFs, and performing variational mode decomposition on the envelope to obtain the decomposed IMF;

[0019] A Fourier transform module, used for performing a piecewise Fourier transform on the decomposed IMF based on a time window of a preset length, to obtain a relationship between a modulation frequency and a modulation amplitude over time;

[0020] An integration module, used for integrating the time variables of the carrier frequency, the modulation frequency and the modulation amplitude to obtain a variational holographic spectrum;

[0021] The feature extraction module is used to extract features from the variational holographic spectrum to obtain the estimated results of the shaft frequency, blade frequency and number of blades of the underwater vehicle propeller.

[0022] In some embodiments, when performing empirical mode decomposition on the intercepted radiation noise signal, the intercepted radiation noise signal is expressed as:

[0023]

[0024] Among them, c j (t) represents the jth IMF, r n is the trend term or a constant value of the intercepted radiation noise signal;

[0025] When Hilbert spectrum analysis is performed on each of the IMFs, the intercepted radiation noise signal is expressed as:

[0026]

[0027] Among them, the amplitude a of each IMF component j (t) and frequency ω j (t) are respectively expressed as functions of time, and N is the number of IMFs.

[0028] In some embodiments, the decomposition module is further used to remove the IMF whose carrier frequency does not meet the modulation characteristics based on the requirement that the carrier frequency of the modulation signal is much greater than the modulation frequency, so as to obtain the screened IMF;

[0029] When performing variational mode decomposition on the envelope, the constraint model of the variational mode decomposition is:

[0030]

[0031] Among them, u k (t) is the kth mode, ω k is the center frequency of the kth mode, and K represents the total number of modes.

[0032] In some embodiments, when the decomposed IMF is subjected to a piecewise Fourier transform based on a time window of a preset length, the Fourier transform calculation formula is:

[0033]

[0034] Where ω represents the slowly changing envelope frequency and t represents time.

[0035] In some embodiments, the integration module is also used to obtain the corresponding relationship between the modulation frequency and the carrier frequency for the empirical mode decomposition and the filtered IMF at each moment, using time as an intermediate quantity; and determine the variational holographic spectrum with the carrier frequency as the x-axis, the modulation frequency as the y-axis, and the modulation amplitude as the z-axis.

[0036] An embodiment of the present invention provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned variational holographic spectrum recognition method for the shaft frequency and blade frequency of a propeller of an underwater vehicle when executing the executable instructions stored in the memory.

[0037] An embodiment of the present invention provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned variational holographic spectrum recognition method for the shaft frequency and blade frequency of a propeller of an underwater vehicle.

[0038] The present invention provides a variational holographic spectrum identification method for the shaft frequency and blade frequency of an underwater vehicle propeller. The variational holographic spectrum method utilizes the idea of ​​high-dimensional holographic representation to achieve a robust high-dimensional holographic representation of nonlinear and non-stationary signals, and obtains a variational holographic spectrum. By extracting features from the variational holographic spectrum, the identification and estimation of parameters such as the shaft frequency, blade frequency and number of blades of the underwater vehicle propeller are achieved. The method introduces variational modal decomposition, which effectively solves the modal aliasing problem of EMD in the holographic Hilbert spectrum analysis method. At the same time, according to the characteristic that the propeller modulation signal in the underwater vehicle radiation noise is low-frequency and slowly varying, the piecewise Fourier transform is introduced to solve the problem that the Hilbert spectrum analysis method has large errors when processing low-frequency and slowly varying signals. The method solves the problem of insufficient representation of the currently commonly used radiation noise analysis methods LOFAR analysis, DEMON analysis and various improved algorithms thereof when analyzing nonlinear and non-stationary signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of a variational holographic spectrum recognition method for the shaft frequency and blade frequency of an underwater vehicle propeller provided by the present invention;

[0040] Figure 2Another variational holographic spectrum recognition method for the shaft frequency and blade frequency of an underwater vehicle propeller provided by the present invention;

[0041] Figure 3 It is a time domain diagram of radiation noise when an AUV performs a task provided by the test example of the present invention;

[0042] Figure 4 It is a time-frequency diagram of radiation noise provided by the test example of the present invention;

[0043] Figure 5 This is a time domain diagram of a stable AUV radiation noise of 10 seconds provided in the test example of the present invention;

[0044] Figure 6 It is a time domain diagram of each IMF component after EMD decomposition of the intercepted radiation noise signal provided by the test example of the present invention;

[0045] Figure 7 It is a graph showing the frequency variation of some IMF components over time provided by the test example of the present invention;

[0046] Figure 8 is a variational holographic spectrum of the intercepted radiation noise signal provided in the experimental example of the present invention;

[0047] Fig. 9 is a schematic diagram of the variational holographic spectrum feature extraction result provided by the test example of the present invention;

[0048] Fig.10 It is a schematic diagram of the composition structure of a variational holographic spectrum recognition device for the shaft frequency and blade frequency of an underwater vehicle propeller provided by an embodiment of the present invention;

[0049] Fig.11 It is a schematic diagram of the composition structure of a variational holographic spectrum recognition device for the shaft frequency and blade frequency of an underwater vehicle propeller provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0051] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meaning as those commonly understood by those skilled in the art to which the embodiments of the present invention pertain. The terms used in the embodiments of the present invention are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0052] The embodiment of the present invention provides a variational holographic spectrum recognition method for the shaft frequency and blade frequency of an underwater vehicle propeller, see Figure 1 , Figure 1 is a flow chart of a variational holographic spectrum recognition method for propeller shaft frequency and blade frequency of an underwater vehicle provided by an embodiment of the present invention, which is combined with Figure 1 The steps shown are explained.

[0053] Step S110, performing time-frequency analysis on the underwater vehicle radiation noise signal, and performing signal interception in combination with the time domain diagram to obtain an intercepted radiation noise signal.

[0054] In some embodiments, before performing time-frequency analysis on the underwater vehicle radiation noise signal, the collected underwater vehicle radiation noise signal may also be preprocessed, such as denoising, filtering, etc., to reduce noise interference and improve signal quality.

[0055] In some embodiments, time domain analysis refers to observing the characteristics of a signal over time, such as amplitude, phase, etc. This helps identify periodicity, trends, or sudden changes in a signal.

[0056] In the present invention, after performing time-frequency analysis on the underwater vehicle radiated noise signal, further signal analysis can be performed in combination with the time domain diagram of the signal (i.e., a waveform diagram of the signal changing over time). By observing the time domain diagram and the STFT results, specific time intervals in the signal can be identified, which may contain features of interest. Afterwards, based on the analysis results, the time interval to be intercepted is determined on the time domain diagram, and the signal in this interval is intercepted using appropriate signal processing technology, that is, the intercepted radiated noise signal is obtained.

[0057] In some embodiments, the intercepted signal is usually purer or focused on a specific aspect than the original signal, and may be more suitable for subsequent tasks such as signal processing, feature extraction, target recognition or noise suppression.

[0058] In some embodiments, when performing time-frequency analysis on the underwater vehicle radiation noise signal, the calculation formula of the short-time Fourier transform is:

[0059]

[0060] Among them, x(t) is the original received signal, f is the signal frequency, t represents time, and j is the imaginary unit.

[0061] Step S120, performing empirical mode decomposition on the intercepted radiation noise signal to obtain a plurality of IMFs; and performing Hilbert spectrum analysis on each of the IMFs to obtain a relationship between the amplitude of each of the IMFs and the carrier frequency changes over time.

[0062] In the present invention, the purpose of Empirical Mode Decomposition (EDM) is to decompose complex radiated noise signals into a series of intrinsic mode function components (IMF) with specific frequency and time-frequency characteristics. Each IMF represents a component of different frequency scales in the original signal. Afterwards, Hilbert spectrum analysis is performed on each IMF to calculate its instantaneous amplitude and phase. Afterwards, the relationship between the carrier frequency of each IMF and time can be derived through the phase information, which can help understand the energy distribution and frequency characteristics of the signal at different times.

[0063] In some embodiments, when performing empirical mode decomposition on the intercepted radiation noise signal, the intercepted radiation noise signal is expressed as:

[0064]

[0065] Among them, c j (t) represents the jth IMF, r n is the trend term or a constant value of the intercepted radiation noise signal;

[0066] When the Hilbert spectrum analysis is performed on each of the IMFs, the intercepted radiation noise signal can be expressed as:

[0067]

[0068] Among them, the amplitude a of each IMF component j (t) and frequency ω j (t) are respectively expressed as functions of time, and N is the number of IMFs.

[0069] Step S130, after screening the multiple IMFs, extracting the envelope of the screened IMFs, and performing variational mode decomposition on the envelope to obtain the decomposed IMFs.

[0070] In some embodiments, variational mode decomposition (VMD) is a non-recursive signal decomposition method based on variational problems, which can simultaneously estimate the center frequency and bandwidth of each IMF, thereby providing more refined frequency information.

[0071] In the present invention, the IMFs of interest can be screened out according to preset criteria; then, the envelopes of these IMFs, ie, the variation curves of the instantaneous amplitudes, are extracted; VMD is performed on the extracted envelopes to further decompose more frequency components.

[0072] In some embodiments, the “screening the multiple IMFs” in the above step S130 may be implemented in the following manner:

[0073] Based on the requirement that the carrier frequency of the modulated signal is much greater than the modulation frequency, the IMF whose carrier frequency does not meet the modulation characteristics is removed to obtain the screened IMF.

[0074] In some embodiments, when performing variational mode decomposition on the envelope, the constraint model of the variational mode decomposition is:

[0075]

[0076] Among them, u k (t) is the kth mode, ω k is the center frequency of the kth mode, and K represents the total number of modes.

[0077] Step S140, based on a time window of a preset length, performing a piecewise Fourier transform on the decomposed IMF to obtain a relationship between a modulation frequency and a modulation amplitude changing with time.

[0078] In some embodiments, the IMF after VMD decomposition is segmented using a time window of a preset length, and each segment is Fourier transformed. The purpose of this step is to obtain the relationship between the modulation frequency and the modulation amplitude over time. The modulation frequency is usually related to a specific physical process of the signal (such as the rotation of a propeller).

[0079] In some embodiments, when the decomposed IMF is subjected to a piecewise Fourier transform based on a time window of a preset length, the Fourier transform calculation formula is:

[0080]

[0081] Where ω represents the slowly changing envelope frequency and t represents time.

[0082] Step S150, integrating the time variables of the carrier frequency, the modulation frequency and the modulation amplitude to obtain a variational holographic spectrum.

[0083] In some embodiments, a variational holographic spectrum is constructed by integrating the time variables of carrier frequency, modulation frequency and modulation amplitude. The spectrum can simultaneously display the modulation frequency and carrier frequency of nonlinear and non-stationary signals, providing intuitive and comprehensive information for subsequent feature extraction.

[0084] In some embodiments, the above step S150 can be implemented in the following manner:

[0085] For the empirical mode decomposition and the filtered IMF, each moment corresponds to a carrier frequency and a group of modulation frequencies, and the corresponding relationship between the modulation frequency and the carrier frequency is obtained with time as the intermediate quantity; with the carrier frequency as the x-axis, the modulation frequency as the y-axis, and the modulation amplitude as the z-axis, the variational holographic spectrum is determined.

[0086] Step S160, extracting features from the variational holographic spectrum to obtain estimation results of the shaft frequency, blade frequency and number of blades of the underwater vehicle propeller.

[0087] In some embodiments, finally, feature extraction is performed on the variational holographic spectrum, such as identifying specific frequency peaks and their corresponding modulation patterns. Through these features, key parameters such as shaft frequency, blade frequency, and number of blades of the propeller of the underwater vehicle can be estimated.

[0088] The present invention provides a variational holographic spectrum identification method for the shaft frequency and blade frequency of an underwater vehicle propeller. The variational holographic spectrum method uses the idea of ​​high-dimensional holographic representation to achieve a robust high-dimensional holographic representation of nonlinear and non-stationary signals, and obtains a variational holographic spectrum. By extracting features from the variational holographic spectrum, the identification and estimation of parameters such as the shaft frequency, blade frequency and number of blades of the underwater vehicle propeller are achieved. The method introduces variational modal decomposition, which effectively solves the modal aliasing problem of EMD in the holographic Hilbert spectrum analysis method; at the same time, based on the characteristic that the propeller modulation signal in the underwater vehicle radiation noise is low-frequency and slowly changing, the segmented Fourier transform is introduced to solve the problem that the Hilbert spectrum analysis method has large errors when processing low-frequency and slowly changing signals. The method solves the problem of insufficient representation of the currently commonly used radiation noise analysis methods LOFAR analysis, DEMON analysis and various improved algorithms when analyzing nonlinear and non-stationary signals.

[0089] The following describes an exemplary application of an embodiment of the present invention in a practical application scenario.

[0090] The present invention provides another variational holographic spectrum recognition method for propeller shaft frequency and blade frequency of underwater vehicle, such as Figure 2 As shown, the implementation steps are as follows:

[0091] S1, perform time-frequency analysis on the noise signal radiated by the underwater vehicle, and intercept the stable time domain signal in combination with the time domain diagram;

[0092] The short-time Fourier transform calculation formula is:

[0093]

[0094] S2, performing Hilbert-Huang transform on the signal intercepted in step S1, first performing empirical mode decomposition on the signal, decomposing the signal into a number of intrinsic mode functions (IMFs), then performing Hilbert spectrum analysis on each intrinsic mode function after the empirical mode decomposition, and obtaining a function of the amplitude and frequency of each IMF component changing with time.

[0095] S3, screening the intrinsic mode functions, removing the IMF components whose carrier frequencies do not meet the modulation characteristics, extracting the envelopes of the screened IMF components, and performing VMD decomposition;

[0096] The constraint model of variational mode decomposition is:

[0097]

[0098] Among them, u k ={u1,u2,…,u K} is each mode function; ω k ={ω1,ω2,…,ω K} is the center frequency of each mode.

[0099] In order to solve the above optimization problem, the augmented Lagrangian function is introduced, as shown in the formula:

[0100]

[0101] Among them, α is the penalty factor and λ is the Lagrange multiplier.

[0102] For all ω ≥ 0, update u k ,ω k and λ,

[0103]

[0104] Where n is the number of iterations, γ represents noise, and the symbol ^ represents Fourier transform.

[0105] When the iteration constraint shown in formula (8) is met, the iteration stops.

[0106]

[0107] S4, select a time window of appropriate length, perform piecewise Fourier transform on each eigenmode function after envelope VMD decomposition, and determine the relationship between the modulation frequency and modulation amplitude over time;

[0108] The calculation formula of Fourier transform is:

[0109]

[0110] Where ω represents the slowly changing envelope frequency and t represents time.

[0111] Since the envelope modulation signal is a low-frequency, slowly varying signal, it can be considered that within a certain time window, the signal has the same modulation frequency.

[0112] S5, using the idea of ​​holographic representation, integrate time. For each IMF component after empirical mode decomposition and screening, each moment corresponds to a carrier frequency and a set of modulation frequencies, and the corresponding relationship between the modulation frequency and the carrier frequency is obtained with time t as the intermediate quantity. The variational holographic spectrum is drawn with the carrier frequency as the x-axis, the modulation frequency as the y-axis, and the modulation amplitude (or energy) as the z-axis to achieve a three-dimensional full information representation of the modulated signal in the radiated noise.

[0113] S6, extracting features from the three-dimensional variational holographic spectrum obtained in step S5, so as to identify and estimate parameters such as the shaft frequency, blade frequency and number of blades of the underwater vehicle propeller.

[0114] In addition, the present invention also provides a test example, the specific content of the test example is as follows.

[0115] Select the radiation noise data of a certain AUV when performing a certain task, perform downsampling processing, and draw a time domain graph, such as Figure 3 As shown. The short-time Fourier transform is used to perform time-frequency analysis on the signal. The time-frequency diagram is shown in Figure 4 As shown in the figure, the collected signal is downsampled, and the sampling frequency is 10240Hz after downsampling. Combined with the time-frequency diagram and the time domain diagram, a stable time domain signal is intercepted. The signal duration is 10s. The time domain diagram is as follows Figure 5 shown.

[0116] The intercepted signal is subjected to Hilbert-Huang transform. First, the signal is subjected to empirical mode decomposition to decompose it into several intrinsic mode functions (IMFs), such as Figure 6 Then, the Hilbert spectrum analysis is performed on each eigenmode function after the empirical mode decomposition to obtain the function of the frequency of each IMF component changing with time, as shown in Figure 7 shown.

[0117] For modulated signals, the carrier frequency should be much larger than the modulation frequency. Based on this requirement, the IMF components whose carrier frequency does not meet the modulation characteristics are removed. Figure 7 It can be seen that as the number of IMF components increases, the carrier frequency decreases. For IMF components with numbers 5 and later, the carrier frequency is low and does not meet the modulation characteristics. In the subsequent analysis, in order to simplify the calculation and reduce interference, the IMF components with numbers 5 and later are discarded.

[0118] Extract the envelope of each IMF component after screening and perform variational mode decomposition. Select a time window of appropriate length, perform piecewise Fourier transform on each intrinsic mode function after envelope VMD decomposition, and determine the relationship between modulation frequency and modulation amplitude over time. Since the envelope modulation signal is a low-frequency and slowly changing signal, it can be considered that within a certain time window range, the signal has the same modulation frequency.

[0119] Using the idea of ​​holographic representation, time is integrated. For each IMF component after empirical mode decomposition and screening, each moment corresponds to a carrier frequency and a set of modulation frequencies, and the corresponding relationship between the modulation frequency and the carrier frequency is obtained with time t as the intermediate quantity. The variational holographic spectrum is drawn with the carrier frequency as the x-axis, the modulation frequency as the y-axis, and the modulation amplitude (or energy) as the z-axis to achieve a three-dimensional full information representation of the modulated signal in the radiated noise, such as Figure 8 shown.

[0120] Feature extraction is performed on the variational holographic spectrum, such as Fig. 9 As shown. It can be obtained that the modulation component in the intercepted radiation noise signal is mainly concentrated in the frequency band of 500-900Hz, and the modulation frequencies are 8.3Hz, 16.4Hz and 24.9Hz, which are harmonic relationships, of which 8.3Hz is the fundamental frequency. Based on the above analysis, it can be obtained that the propeller is a three-blade propeller with a shaft frequency of 8.3Hz.

[0121] It is known that the propeller of the aircraft has three blades. According to the propeller motor PWM frequency, the shaft frequency of the propeller in the intercepted time range is about 8.24 Hz. The estimated value is approximately equal to the theoretical value.

[0122] In summary, the present invention proposes a variational holographic spectrum recognition method for underwater vehicle propeller shaft frequency and blade number, realizes the robust three-dimensional full information representation of nonlinear and non-stationary signals such as propeller modulation signals in underwater vehicle radiated noise, and draws variational holographic spectra. By extracting features from the variational holographic spectra, the recognition and estimation of underwater vehicle propeller shaft frequency, blade frequency, and blade number parameters are realized.

[0123] Fig.10 FIG. 1 is a schematic diagram of the composition structure of a variational holographic spectrum recognition device for propeller shaft frequency and blade frequency of an underwater vehicle provided by an embodiment of the present invention. Fig.10As shown, the variational holographic spectrum identification device 1000 for the shaft frequency and blade frequency of the propeller of an underwater vehicle comprises: a time-frequency analysis module 1001, which is used to perform time-frequency analysis on the radiation noise signal of the underwater vehicle, and intercept the signal in combination with the time domain diagram to obtain the intercepted radiation noise signal; an HHT module 1002, which is used to perform empirical mode decomposition on the intercepted radiation noise signal to obtain multiple IMFs; and perform Hilbert spectrum analysis on each of the IMFs to obtain the relationship between the amplitude and carrier frequency of each IMF and the time; a decomposition module 1003, which is used to extract the screened IMFs after screening the multiple IMFs. The envelope of the selected IMF is obtained, and the envelope is subjected to variational mode decomposition to obtain the decomposed IMF; a Fourier transform module 1004 is used to perform a segmented Fourier transform on the decomposed IMF based on a time window of a preset length to obtain the relationship between the modulation frequency and the modulation amplitude over time; an integration module 1005 is used to integrate the time variables of the carrier frequency, the modulation frequency and the modulation amplitude to obtain a variational holographic spectrum; a feature extraction module 1006 is used to extract features from the variational holographic spectrum to obtain the estimation results of the shaft frequency, blade frequency and number of blades of the propeller of the underwater vehicle.

[0124] In some embodiments, when performing empirical mode decomposition on the intercepted radiation noise signal, the intercepted radiation noise signal is expressed as:

[0125]

[0126] Among them, c j (t) represents the jth IMF, r n is the trend term or a constant value of the intercepted radiation noise signal;

[0127] When Hilbert spectrum analysis is performed on each of the IMFs, the intercepted radiation noise signal is expressed as:

[0128]

[0129] Among them, the amplitude a of each IMF component j (t) and frequency ω j (t) are respectively expressed as functions of time, and N is the number of IMFs.

[0130] In some embodiments, the decomposition module is further used to remove the IMF whose carrier frequency does not meet the modulation characteristics based on the requirement that the carrier frequency of the modulation signal is much greater than the modulation frequency, so as to obtain the screened IMF;

[0131] When performing variational mode decomposition on the envelope, the constraint model of the variational mode decomposition is:

[0132]

[0133] Among them, u k (t) is the kth mode, ω k is the center frequency of the kth mode, and K represents the total number of modes.

[0134] In some embodiments, when the decomposed IMF is subjected to a piecewise Fourier transform based on a time window of a preset length, the Fourier transform calculation formula is:

[0135]

[0136] Where ω represents the slowly changing envelope frequency and t represents time.

[0137] In some embodiments, the integration module is also used to obtain the corresponding relationship between the modulation frequency and the carrier frequency for the empirical mode decomposition and the filtered IMF at each moment, using time as an intermediate quantity; and determine the variational holographic spectrum with the carrier frequency as the x-axis, the modulation frequency as the y-axis, and the modulation amplitude as the z-axis.

[0138] In some embodiments, it should be noted that the description of the device of the embodiment of the present invention is similar to the description of the method embodiment described above, and has similar beneficial effects as the same method embodiment, so it will not be repeated. For technical details not disclosed in the embodiment of the device, please refer to the description of the method embodiment of the present invention for understanding.

[0139] It should be noted that, in the embodiment of the present invention, if the variational holographic spectrum identification method of the underwater vehicle propeller shaft frequency and blade frequency is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for a terminal to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), disk or optical disk, etc. Various media that can store program codes. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0140] Correspondingly, an embodiment of the present invention provides a variational holographic spectrum recognition device for the shaft frequency and blade frequency of an underwater vehicle propeller. Fig.11 FIG. 1 is a schematic diagram of the composition structure of a variational holographic spectrum recognition device for propeller shaft frequency and blade frequency of an underwater vehicle provided by an embodiment of the present invention, such as Fig.11As shown, the variational holographic spectrum recognition device 1100 for underwater vehicle propeller shaft frequency and blade frequency includes at least: a processor 1101 and a computer-readable storage medium 1102 configured to store executable instructions, wherein the processor 1101 generally controls the overall operation of the variational holographic spectrum recognition device for underwater vehicle propeller shaft frequency and blade frequency. The computer-readable storage medium 1102 is configured to store instructions and applications executable by the processor 1101, and can also cache data to be processed or processed by each module in the processor 1101 and the variational holographic spectrum recognition device 1100 for underwater vehicle propeller shaft frequency and blade frequency, which can be implemented by flash memory (FLASH) or random access memory (RAM, Random Access Memory).

[0141] An embodiment of the present invention provides a storage medium storing executable instructions, wherein the executable instructions are stored. When the executable instructions are executed by a processor, the processor will be caused to execute the method provided by the embodiment of the present invention, for example, Figure 1 The method shown.

[0142] In some embodiments, the storage medium can be a computer-readable storage medium, for example, a ferroelectric random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disk, or a compact disk read-only memory (CD-ROM), etc.; it can also be various devices including one or any combination of the above memories.

[0143] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0144] As an example, executable instructions may, but need not necessarily, correspond to a file in a file system, may be stored as part of a file storing other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions). As an example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0145] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement and improvement made within the spirit and scope of the present invention are included in the protection scope of the present invention.

[0146] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0147] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, an element defined by the statement "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed.

[0148] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A variational holographic spectrum recognition method for propeller shaft frequency and blade frequency of an underwater vehicle, characterized in that: The method comprises: Perform time-frequency analysis on the underwater vehicle radiation noise signal, and intercept the signal in combination with the time domain diagram to obtain the intercepted radiation noise signal; Performing empirical mode decomposition on the intercepted radiation noise signal to obtain a plurality of IMFs; and performing Hilbert spectrum analysis on each of the IMFs to obtain a relationship between the amplitude of each of the IMFs and the carrier frequency over time; After screening the multiple IMFs, extracting the envelope of the screened IMFs, and performing variational mode decomposition on the envelope to obtain the decomposed IMFs; Based on a time window of a preset length, performing a piecewise Fourier transform on the decomposed IMF to obtain a relationship between a modulation frequency and a modulation amplitude over time; Integrating the time variables of the carrier frequency, the modulation frequency and the modulation amplitude to obtain a variational holographic spectrum; Feature extraction is performed on the variational holographic spectrum to obtain estimation results of the shaft frequency, blade frequency and number of blades of the underwater vehicle propeller.

2. The method according to claim 1, characterized in that When performing empirical mode decomposition on the intercepted radiation noise signal, the intercepted radiation noise signal is expressed as: Among them, c j (t) represents the jth IMF, r n is the trend term or a constant value of the intercepted radiation noise signal; When Hilbert spectrum analysis is performed on each of the IMFs, the intercepted radiation noise signal is expressed as: Among them, the amplitude a of each IMF component j (t) and frequency ω j (t) are respectively expressed as functions of time, and N is the number of IMFs.

3. The method according to claim 1, characterized in that The screening of the plurality of IMFs comprises: Based on the requirement that the carrier frequency of the modulation signal is much greater than the modulation frequency, the IMF component whose carrier frequency does not meet the modulation characteristics is removed to obtain the screened IMF; When performing variational mode decomposition on the envelope, the constraint model of the variational mode decomposition is: Among them, u k (t) is the kth mode, ω k is the center frequency of the kth mode, and K represents the total number of modes.

4. The method according to claim 1, characterized in that: When the decomposed IMF is subjected to piecewise Fourier transform based on the time window of preset length, the calculation formula of Fourier transform is: Where ω represents the slowly changing envelope frequency and t represents time.

5. The method according to claim 1, characterized in that The step of integrating the time variables of the carrier frequency, the modulation frequency and the modulation amplitude to obtain a variational holographic spectrum comprises: For the empirical mode decomposition and the filtered IMF, each moment corresponds to a carrier frequency and a group of modulation frequencies, and the corresponding relationship between the modulation frequency and the carrier frequency is obtained by taking time as an intermediate quantity; The variational holographic spectrum is determined with the carrier frequency as the x-axis, the modulation frequency as the y-axis, and the modulation amplitude as the z-axis.

6. A variational holographic spectrum recognition device for propeller shaft frequency and blade frequency of an underwater vehicle, characterized in that: The device comprises: The time-frequency analysis module is used to perform time-frequency analysis on the noise signal radiated by the underwater vehicle and intercept the signal in combination with the time domain diagram to obtain the intercepted noise signal radiated; The HHT module is used to perform empirical mode decomposition on the intercepted radiation noise signal to obtain multiple IMFs; and perform Hilbert spectrum analysis on each of the IMFs to obtain the relationship between the amplitude and carrier frequency of each of the IMFs over time; A decomposition module, used for extracting the envelope of the filtered IMF after filtering the multiple IMFs, and performing variational mode decomposition on the envelope to obtain the decomposed IMF; A Fourier transform module, used for performing a piecewise Fourier transform on the decomposed IMF based on a time window of a preset length, to obtain a relationship between a modulation frequency and a modulation amplitude over time; An integration module, used for integrating the time variables of the carrier frequency, the modulation frequency and the modulation amplitude to obtain a variational holographic spectrum; The feature extraction module is used to extract features from the variational holographic spectrum to obtain the estimated results of the shaft frequency, blade frequency and number of blades of the underwater vehicle propeller.

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