A method and device for identifying the shaft frequency and blade frequency of a propeller of an underwater vehicle by means of variational holographic spectrum

Abstract

The application discloses a kind of underwater vehicle propeller shaft frequency and blade frequency variational holographic spectrum identification method and device, variational holographic spectrum method utilizes the thought of holographic representation, realizes the robust high-dimensional holographic representation of nonlinear non-stationary signal, obtains variational holographic spectrum diagram, by feature extraction to variational holographic spectrum diagram, realize the identification and estimation of underwater vehicle propeller shaft frequency, blade frequency and blade number;The method introduces variational mode decomposition, effectively solves the mode aliasing problem of EMD in holographic hilbert spectrum analysis method;According to the characteristics that propeller modulation signal in underwater vehicle radiation noise is low frequency slow change, segmented fourier transform is introduced, the problem that there is large error when hilbert spectrum analysis method is processed low frequency slow change signal is solved.The method solves the problem of insufficient representation when the commonly used radiation noise analysis method LOFAR analysis, DEMON analysis and various improved algorithms are analyzed in nonlinear non-stationary signal.

Description

Technical Field

[0001] This invention relates to the field of underwater target recognition technology, and in particular to a variational holographic spectrum recognition method and apparatus for underwater vehicle propeller shaft frequency and blade frequency. Background Technology

[0002] In the field of underwater acoustic target detection and identification, feature extraction of radiated noise signals is an effective means to achieve accurate underwater target detection. The radiated noise of underwater vehicles can be divided into three categories: mechanical noise, propeller noise, and hydrodynamic noise. According to spectral characteristics, it can be further divided into continuous spectrum, line spectrum, and modulation spectrum. 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 characterized by the high-frequency continuous spectrum of propeller noise being modulated by the propeller shaft frequency and blade frequency.

[0003] Propeller noise is a major noise source for underwater targets such as ships, submarines, and underwater vehicles. When an underwater vehicle's propeller rotates in a non-uniform wake, its cavitation noise exhibits modulation. The modulation spectrum contains numerous discrete line spectra, with the lowest frequency fundamental frequency representing the propeller's shaft frequency. The other line spectra, whose frequencies are multiples of each other, represent the harmonics of the propeller's shaft frequency, with the number of harmonics corresponding to the number of propeller blades. Therefore, the modulation spectrum contains structural characteristic information about the underwater vehicle's propeller, and its spectral characteristics are often used for underwater target type identification and target velocity estimation.

[0004] Theoretical studies have shown that radiated noise signals from underwater vehicles are nonlinear and nonstationary. Currently used radiated noise analysis methods, such as LOFAR analysis, DEMON analysis, and their various improved algorithms, all suffer from insufficient characterization when analyzing nonlinear and nonstationary signals. LOFAR analysis utilizes local signal features to reveal the instantaneous frequency variation over time through time-frequency representation; however, this method cannot handle nonlinearly modulated signals. DEMON analysis extracts the modulation frequency from the radiated noise signal through envelope demodulation; however, the DEMON spectral analysis method requires manual selection of the bandpass filter's bandwidth, and the choice of the bandpass filter significantly affects the algorithm's performance, making it highly arbitrary in practical applications.

[0005] Therefore, researching 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 is of great significance for detecting and identifying underwater vehicles. Summary of the Invention

[0006] To address the aforementioned technical problems in existing technologies, this invention provides a variational holographic spectrum identification method and apparatus for the shaft frequency and blade frequency of underwater vehicle propellers, thereby solving the problem of insufficient characterization in the analysis of nonlinear and non-stationary signals by commonly used radiated noise analysis methods such as LOFAR analysis, DEMON analysis, and their various improved algorithms.

[0007] To achieve the above objectives, the technical solution of this invention is as follows:

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

[0009] Time-frequency analysis was performed on the radiated noise signal of the underwater vehicle, and the signal was truncated by combining the time domain plot to obtain the truncated radiated noise signal;

[0010] Empirical mode decomposition is performed on the truncated radiated noise signal to obtain multiple IMFs; and Hilbert spectrum analysis is performed on each IMF to obtain the relationship between the amplitude and carrier frequency of each IMF over time.

[0011] After filtering multiple IMFs, the envelopes of the filtered IMFs are extracted, and variational mode decomposition is performed on the envelopes to obtain the decomposed IMFs.

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

[0013] Integrating the time variables of the carrier frequency, the modulation frequency, and the modulation amplitude yields a variational holographic spectrum.

[0014] Feature extraction is performed on the variational holographic spectrum to obtain estimates 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 identification device for the shaft frequency and blade frequency of an underwater vehicle propeller, the method comprising:

[0016] The time-frequency analysis module is used to perform time-frequency analysis on the radiated noise signal of the underwater vehicle, and to extract the signal by combining the time domain diagram to obtain the extracted radiated noise signal.

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

[0018] The decomposition module is used to filter multiple IMFs, extract the envelopes of the filtered IMFs, and perform variational mode decomposition on the envelopes to obtain the decomposed IMFs.

[0019] The Fourier transform module is used to perform piecewise Fourier transform on the decomposed IMF based on a time window of preset length to obtain the relationship between the modulation frequency and modulation amplitude over time.

[0020] An integration module is used to integrate 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 estimates 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 truncated radiated noise signal, the truncated radiated noise signal is represented as follows:

[0023]

[0024] Among them, c j (t) represents the j-th IMF, r n This is either a trend term or a constant value for the extracted radiated noise signal;

[0025] When performing Hilbert spectral analysis on each of the IMFs, the truncated radiated noise signal is represented as follows:

[0026]

[0027] Wherein, the amplitude a of each IMF component j (t) and frequency ω j (t) represents a function of time, and N is the number of IMFs.

[0028] In some embodiments, the decomposition module is further configured to remove IMFs whose carrier frequencies do not meet the modulation characteristics based on the requirement that the carrier frequency of the modulation signal is much greater than the modulation frequency, thereby obtaining the filtered IMFs.

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

[0030]

[0031] Among them, u k (t) represents the k-th mode, ω k Let be the center frequency of the k-th mode, and K represent the total number of modes.

[0032] In some embodiments, when performing a piecewise Fourier transform on the decomposed IMF based on a time window of a preset length, the formula for calculating the Fourier transform is as follows:

[0033]

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

[0035] In some embodiments, the integration module is further configured to, for the empirical mode decomposition and the filtered IMF, at each moment correspond to a carrier frequency and a set of modulation frequencies, obtain the correspondence between the modulation frequency and the carrier frequency using time as an intermediate quantity; and determine the variational holographic spectrum using the carrier frequency as the x-axis, the modulation frequency as the y-axis, and the modulation amplitude as the z-axis.

[0036] This invention provides an electronic device, including: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the variational holographic spectrum recognition method for the shaft frequency and blade frequency of an underwater vehicle propeller.

[0037] This invention provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, enable the aforementioned variational holographic spectrum identification method for the shaft frequency and blade frequency of an underwater vehicle propeller.

[0038] This invention provides a variational holographic spectrum identification method for the shaft frequency and blade frequency of underwater vehicle propellers. The variational holographic spectrum method utilizes the idea of ​​high-dimensional holographic representation to achieve robust high-dimensional holographic representation of nonlinear and non-stationary signals, obtaining a variational holographic spectrum. By extracting features from the variational holographic spectrum, the method identifies and estimates parameters such as the shaft frequency, blade frequency, and number of blades of the underwater vehicle propeller. This method introduces variational mode decomposition, effectively solving the mode aliasing problem of EMD in the holographic Hilbert spectrum analysis method. Simultaneously, based on the characteristic that the propeller modulation signal in the radiated noise of underwater vehicles is a low-frequency, slowly changing signal, a piecewise Fourier transform is introduced to solve the problem of large errors in the Hilbert spectrum analysis method when processing low-frequency, slowly changing signals. This method also addresses the shortcomings of commonly used radiated noise analysis methods such as LOFAR analysis, DEMON analysis, and their various improved algorithms in representing nonlinear and non-stationary signals. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a variational holographic spectrum identification method for the shaft frequency and blade frequency of an underwater vehicle propeller provided by the present invention.

[0040] Figure 2This invention provides another variational holographic spectrum identification method for the shaft frequency and blade frequency of underwater vehicle propellers;

[0041] Figure 3 This is a time-domain diagram of the radiated noise of an AUV performing a task, provided in the experimental example of this invention.

[0042] Figure 4 This is a time-frequency diagram of radiated noise provided in the experimental examples of this invention;

[0043] Figure 5 This is a 10-second segment of a stable AUV radiation noise time-domain plot provided in the experimental example of this invention;

[0044] Figure 6 This is a time-domain diagram of each IMF component after EMD decomposition of the radiated noise signal provided in the experimental example of this invention;

[0045] Figure 7 This is a graph showing the frequency variation of some IMF components over time, provided in the experimental examples of this invention.

[0046] Figure 8 This is a variational holographic spectrum of the radiated noise signal intercepted in the experimental example of this invention;

[0047] Figure 9 This is a schematic diagram of the variational holographic spectrum feature extraction results provided in the experimental examples of this invention;

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

[0049] Figure 11 This is a schematic diagram of the composition structure of the variational holographic spectrum recognition device for the shaft frequency and blade frequency of an underwater vehicle propeller provided in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] In the following description, references to "some embodiments" refer to a subset of all possible embodiments; however, it is understood that "some embodiments" may be the same 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 invention have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of the invention pertain. The terminology used in the embodiments of the invention is for the purpose of describing the embodiments of the invention only and is not intended to limit the invention.

[0052] This invention provides a variational holographic spectrum identification method for the shaft frequency and blade frequency of an underwater vehicle propeller. (See also...) Figure 1 , Figure 1 This is a flowchart illustrating a variational holographic spectrum identification method for the shaft frequency and blade frequency of an underwater vehicle propeller provided in an embodiment of the present invention. Figure 1 The steps shown are explained.

[0053] Step S110: Perform time-frequency analysis on the radiated noise signal of the underwater vehicle, and extract the signal by combining the time domain diagram to obtain the extracted radiated noise signal.

[0054] In some embodiments, before performing time-frequency analysis on the radiated noise signal of an underwater vehicle, the collected radiated noise signal can be preprocessed, such as by denoising and filtering, to reduce noise interference and improve signal quality.

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

[0056] In this invention, after performing time-frequency analysis on the radiated noise signal of an underwater vehicle, further signal analysis can be performed by combining the signal's time-domain plot (i.e., the waveform of the signal changing over time). By observing the time-domain plot and the STFT results, specific time intervals in the signal can be identified, which may contain features of interest. Subsequently, based on the analysis results, the time interval to be extracted is determined on the time-domain plot, and appropriate signal processing techniques are used to extract the signal within this interval, thus obtaining the extracted radiated noise signal.

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

[0058] In some embodiments, when performing time-frequency analysis on the radiated noise signal of an underwater vehicle, the formula for calculating the short-time Fourier transform is as follows:

[0059]

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

[0061] Step S120: Perform empirical mode decomposition on the intercepted radiated noise signal to obtain multiple IMFs; and perform Hilbert spectrum analysis on each IMF to obtain the relationship between the amplitude and carrier frequency of each IMF and time.

[0062] In this invention, the purpose of Empirical Mode Decomposition (EDM) is to decompose complex radiated noise signals into a series of Intrinsic Mode Functions (IMFs) with specific frequency and time-frequency characteristics. Each IMF represents a component of the original signal at a different frequency scale. Then, Hilbert spectral analysis is performed on each IMF to calculate its instantaneous amplitude and phase. Subsequently, the phase information allows for the derivation of the carrier frequency variation of each IMF over time, which is beneficial for understanding the energy distribution and frequency characteristics of the signal at different times.

[0063] In some embodiments, when performing empirical mode decomposition on the truncated radiated noise signal, the truncated radiated noise signal is represented as follows:

[0064]

[0065] Among them, c j (t) represents the j-th IMF, r n This is either a trend term or a constant value for the extracted radiated noise signal;

[0066] When performing Hilbert spectral analysis on each of the IMFs, the truncated radiated noise signal can be expressed as:

[0067]

[0068] Wherein, the amplitude a of each IMF component j (t) and frequency ω j (t) represents a function of time, and N is the number of IMFs.

[0069] Step S130: After screening multiple IMFs, extract the envelope of the screened IMFs, and perform 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 that can simultaneously estimate the center frequency and bandwidth of each IMF, thereby providing more refined frequency information.

[0071] In this invention, IMFs of interest can be selected according to preset criteria; then, the envelopes of these IMFs, i.e., the instantaneous amplitude variation curves, are extracted; and VMD is performed on the extracted envelopes to further decompose more frequency components.

[0072] In some embodiments, the "screening of the plurality of IMFs" in step S130 above can be achieved in the following ways:

[0073] Based on the requirement that the carrier frequency of the modulated signal is much greater than the modulation frequency, IMFs whose carrier frequencies do not meet the modulation characteristics are removed to obtain the filtered IMFs.

[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) represents the k-th mode, ω k Let be the center frequency of the k-th mode, and K represent the total number of modes.

[0077] Step S140: Based on a preset time window, perform a piecewise Fourier transform on the decomposed IMF to obtain the relationship between the modulation frequency and modulation amplitude over time.

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

[0079] In some embodiments, when performing a piecewise Fourier transform on the decomposed IMF based on a preset time window, the formula for calculating the Fourier transform is as follows:

[0080]

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

[0082] Step S150: Integrate 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 using the time variables of the integrated carrier frequency, modulation frequency, and modulation amplitude. This spectrum can simultaneously display the modulation frequency and carrier frequency of the nonlinear, non-stationary signal, providing intuitive and comprehensive information for subsequent feature extraction.

[0084] In some embodiments, step S150 above 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 set of modulation frequencies. The correspondence between the modulation frequency and the carrier frequency is obtained by using time as an intermediate quantity. The variational holographic spectrum is determined by using the carrier frequency as the x-axis, the modulation frequency as the y-axis, and the modulation amplitude as the z-axis.

[0086] Step S160: 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.

[0087] In some embodiments, feature extraction is then performed on the variational holographic spectrum, such as identifying specific frequency peaks and their corresponding modulation modes. These features can be used to estimate key parameters of the underwater vehicle propeller, including shaft frequency, blade frequency, and number of blades.

[0088] This invention provides a variational holographic spectrum identification method for the shaft frequency and blade frequency of underwater vehicle propellers. 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, obtaining a variational holographic spectrum. By extracting features from the variational holographic spectrum, the method identifies and estimates parameters such as the shaft frequency, blade frequency, and number of blades of the underwater vehicle propeller. This method introduces variational mode decomposition, effectively solving the mode aliasing problem of EMD in the holographic Hilbert spectrum analysis method. Simultaneously, based on the characteristic that the propeller modulation signal in the radiated noise of underwater vehicles is a low-frequency, slowly changing signal, a piecewise Fourier transform is introduced to solve the problem of large errors in the Hilbert spectrum analysis method when processing low-frequency, slowly changing signals. This method addresses the shortcomings of commonly used radiated noise analysis methods such as LOFAR analysis, DEMON analysis, and their various improved algorithms in representing nonlinear and non-stationary signals.

[0089] The following will describe an exemplary application of the embodiments of the present invention in a practical application scenario.

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

[0091] S1, perform time-frequency analysis on the radiated noise signal of the underwater vehicle, and extract stable time-domain signals by combining the time-domain plot;

[0092] The formula for calculating the short-time Fourier transform is:

[0093]

[0094] S2. Perform Hilbert-Huang transform on the signal intercepted in step S1. First, perform empirical mode decomposition on the signal to decompose it into several intrinsic mode functions (IMFs). Then, perform Hilbert spectral analysis on each IMF after empirical mode decomposition to obtain the amplitude and frequency of each IMF component as a function of time.

[0095] S3. The intrinsic mode functions are filtered to remove IMF components whose carrier frequency does not meet the modulation characteristics. The envelopes of each filtered IMF component are extracted and VMD decomposition is performed.

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

[0097]

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

[0099] To solve the above optimization problem, an augmented Lagrangian function is introduced, as shown in the following equation:

[0100]

[0101] Where 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] The iteration stops when the iterative constraints shown in formula (8) are met.

[0106]

[0107] S4. Select an appropriate time window and perform piecewise Fourier transform on each intrinsic mode function after envelope VMD decomposition to determine the relationship between modulation frequency and modulation amplitude over time.

[0108] The formula for calculating the 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 changing signal, it can be assumed that the signal has the same modulation frequency within a certain time window.

[0112] S5 utilizes the concept of holographic representation to integrate over time. For each IMF component after Empirical Mode Decomposition and filtering, each moment corresponds to a carrier frequency and a set of modulation frequencies. The correspondence between the modulation frequency and the carrier frequency is obtained using time t as an intermediate quantity. A variational holographic spectrum is plotted 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, realizing a three-dimensional full-information representation of the modulated signal in radiated noise.

[0113] S6. Feature extraction is performed on the three-dimensional variational holographic spectrum obtained in step S5 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 provides an experimental example, the specific details of which are as follows.

[0115] Radiated noise data from a specific AUV performing a particular task is selected, downsampled, and a time-domain plot is generated, as shown below. 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 below. Figure 4 As shown, the acquired signal was downsampled, resulting in a sampling frequency of 10240Hz. A stable time-domain signal segment with a duration of 10 seconds was extracted by combining the time-frequency plot and the time-domain plot. The time-domain plot is shown below. Figure 5 As shown.

[0116] The Hilbert-Huang transform is applied to the truncated signal. First, empirical mode decomposition is performed on the signal, decomposing it into several intrinsic mode functions (IMFs), such as... Figure 6 As shown. Then, Hilbert spectral analysis was performed on each intrinsic mode function after empirical mode decomposition to obtain the frequency-time variation function of each IMF component, as shown. Figure 7 As shown.

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

[0118] The envelopes of each filtered IMF component are extracted and variational mode decomposition (VMD) is performed. A suitable time window is selected, and piecewise Fourier transforms are applied to each intrinsic mode function (IMF) after envelope VMD decomposition to determine the relationship between modulation frequency and modulation amplitude over time. Since the envelope modulation signal is a low-frequency, slowly changing signal, it can be assumed that the signal has the same modulation frequency within a certain time window.

[0119] Using the concept of holographic representation, time is integrated. For each IMF component after Empirical Mode Decomposition and filtering, each time step corresponds to a carrier frequency and a set of modulation frequencies. The correspondence between the modulation frequency and the carrier frequency is obtained using time t as an intermediate quantity. A variational holographic spectrum is plotted 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, realizing a three-dimensional full-information representation of the modulated signal in radiated noise, such as... Figure 8 As shown.

[0120] Feature extraction is performed on the variational holographic spectrum, such as... Figure 9 As shown, the modulation components in the extracted radiated noise signal are mainly concentrated in the 500-900Hz frequency band, with modulation frequencies of 8.3Hz, 16.4Hz, and 24.9Hz, which are harmonics, with 8.3Hz being the fundamental frequency. Based on the above analysis, the propeller is a three-bladed propeller with a shaft frequency of 8.3Hz.

[0121] Given that the propeller of this aircraft has three blades, the shaft frequency of the propeller within the selected time range can be calculated to be approximately 8.24 Hz based on the PWM frequency of the propeller motor. The estimated value is approximately equal to the theoretical value.

[0122] In summary, this invention proposes a variational holographic spectrum identification method for underwater vehicle propeller shaft frequency and blade number. This method achieves robust three-dimensional full-information representation of nonlinear and non-stationary signals such as propeller modulation signals in underwater vehicle radiated noise, and generates variational holographic spectra. By extracting features from the variational holographic spectra, the method can identify and estimate parameters such as propeller shaft frequency, blade frequency, and blade number.

[0123] Figure 10 This is a schematic diagram of the composition structure of the variational holographic spectrum recognition device for the shaft frequency and blade frequency of an underwater vehicle propeller provided in an embodiment of the present invention, as shown below. Figure 10As shown, the variational holographic spectrum identification device 1000 for the shaft frequency and blade frequency of an underwater vehicle propeller includes: a time-frequency analysis module 1001, used to perform time-frequency analysis on the radiated noise signal of the underwater vehicle and to extract the signal by combining it with a time-domain plot, thereby obtaining the extracted radiated noise signal; an HHT module 1002, used to perform empirical mode decomposition on the extracted radiated noise signal to obtain multiple IMFs; and to perform Hilbert spectrum analysis on each IMF to obtain the relationship between the amplitude and carrier frequency of each IMF and time; and a decomposition module 1003, used to filter the multiple IMFs and extract the selected components. The envelope of the selected IMF is obtained, and variational mode decomposition is performed on the envelope to obtain the decomposed IMF; Fourier transform module 1004 is used to perform piecewise Fourier transform on the decomposed IMF based on a preset time window to obtain the relationship between the modulation frequency and modulation amplitude over time; 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; feature extraction module 1006 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.

[0124] In some embodiments, when performing empirical mode decomposition on the truncated radiated noise signal, the truncated radiated noise signal is represented as follows:

[0125]

[0126] Among them, c j (t) represents the j-th IMF, r n This is either a trend term or a constant value for the extracted radiated noise signal;

[0127] When performing Hilbert spectral analysis on each of the IMFs, the truncated radiated noise signal is represented as follows:

[0128]

[0129] Wherein, the amplitude a of each IMF component j (t) and frequency ω j (t) represents a function of time, and N is the number of IMFs.

[0130] In some embodiments, the decomposition module is further configured to remove IMFs whose carrier frequencies do not meet the modulation characteristics based on the requirement that the carrier frequency of the modulation signal is much greater than the modulation frequency, thereby obtaining the filtered IMFs.

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

[0132]

[0133] Among them, u k (t) represents the k-th mode, ω k Let be the center frequency of the k-th mode, and K represent the total number of modes.

[0134] In some embodiments, when performing a piecewise Fourier transform on the decomposed IMF based on a time window of a preset length, the formula for calculating the Fourier transform is as follows:

[0135]

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

[0137] In some embodiments, the integration module is further configured to, for the empirical mode decomposition and the filtered IMF, at each moment correspond to a carrier frequency and a set of modulation frequencies, obtain the correspondence between the modulation frequency and the carrier frequency using time as an intermediate quantity; and determine the variational holographic spectrum using 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 apparatus of the present invention is similar to the description of the method embodiments described above, and has similar beneficial effects to the same method embodiments, therefore, it will not be repeated. For technical details not disclosed in the embodiments of this apparatus, please refer to the description of the method embodiments of the present invention for understanding.

[0139] It should be noted that, in the embodiments of the present invention, if the above-mentioned variational holographic spectrum identification method for the shaft frequency and blade frequency of an underwater vehicle propeller is implemented in the form of a software functional 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 embodiments of the present invention, or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a terminal to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a magnetic disk, or an optical disk. Thus, the embodiments of the present invention are not limited to any specific hardware and software combination.

[0140] Correspondingly, embodiments of the present invention provide a variational holographic spectrum recognition device for the shaft frequency and blade frequency of an underwater vehicle propeller. Figure 11 This is a schematic diagram of the composition structure of the variational holographic spectrum recognition device for the shaft frequency and blade frequency of an underwater vehicle propeller provided in an embodiment of the present invention, as shown below. Figure 11As shown, the variational holographic spectrum identification device 1100 for the shaft frequency and blade frequency of an underwater vehicle propeller 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 identification device for the shaft frequency and blade frequency of the underwater vehicle propeller. 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 the various modules in the processor 1101 and the variational holographic spectrum identification device for the shaft frequency and blade frequency of the underwater vehicle propeller, which can be implemented by flash memory or random access memory (RAM).

[0141] This invention provides a storage medium storing executable instructions. When these executable instructions are executed by a processor, they cause the processor to perform the method provided in this invention, for example... Figure 1 The method shown.

[0142] In some embodiments, the storage medium may be a computer-readable storage medium, such as a ferromagnetic 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), flash memory, magnetic surface memory, optical disc, or a compact disk-read-only memory (CD-ROM); or it may be a device that includes one or any combination of the above-mentioned memories.

[0143] In some embodiments, executable instructions may take 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 standalone 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 do not necessarily, correspond to files in a file system. They may be stored as part of a file containing other programs or data, for example, in one or more scripts within a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple co-located files (e.g., files storing one or more modules, subroutines, or code sections). As an example, executable instructions may be deployed to execute on a single electronic device, or on multiple electronic devices located in one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0145] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of the present invention are included within the scope of protection of the present invention.

[0146] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of the invention, the sequence numbers of the above-described processes do not imply a sequential 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 embodiments of the invention. The sequence numbers of the above-described embodiments of the invention are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0147] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. In the several embodiments provided by this invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not performed.

[0148] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A variational holographic spectrum identification method for the shaft frequency and blade frequency of an underwater vehicle propeller, characterized in that, The method includes: Time-frequency analysis was performed on the radiated noise signal of the underwater vehicle, and the signal was truncated by combining the time domain plot to obtain the truncated radiated noise signal; Empirical mode decomposition is performed on the truncated radiated noise signal to obtain multiple IMFs; and Hilbert spectrum analysis is performed on each IMF to obtain the relationship between the amplitude and carrier frequency of each IMF over time. After filtering multiple IMFs, the envelopes of the filtered IMFs are extracted, and variational mode decomposition is performed on the envelopes to obtain the decomposed IMFs. Based on a time window of preset length, a piecewise Fourier transform is performed on the decomposed IMF to obtain the relationship between the modulation frequency and modulation amplitude over time. Integrating the time variables of the carrier frequency, the modulation frequency, and the modulation amplitude yields a variational holographic spectrum. Feature extraction was performed on the variational holographic spectrum to obtain estimates of the shaft frequency, blade frequency, and number of blades of the underwater vehicle propeller; The step of integrating the time variables of the carrier frequency, the modulation frequency, and the modulation amplitude to obtain a variational holographic spectrum includes: For the empirical mode decomposition and the filtered IMF, each moment corresponds to a carrier frequency and a set of modulation frequencies. The correspondence between the modulation frequency and the carrier frequency is obtained by using time as an intermediate quantity. The variational holographic spectrum is determined by using the carrier frequency as the x-axis, the modulation frequency as the y-axis, and the modulation amplitude as the z-axis.

2. The method according to claim 1, characterized in that, When performing empirical mode decomposition on the truncated radiated noise signal, the truncated radiated noise signal is represented as follows: ； Among them, c j (t) represents the j-th IMF, r n This is either a trend term or a constant value for the extracted radiated noise signal; When performing Hilbert spectral analysis on each of the IMFs, the truncated radiated noise signal is represented as follows: ； Wherein, the amplitude a of each IMF component j (t) and frequency ω j (t) represents a function of time, and N is the number of IMFs.

3. The method according to claim 1, characterized in that, The screening of multiple IMFs includes: Since the carrier frequency of the modulated signal is much greater than the modulation frequency requirement, the IMF components whose carrier frequency does not meet the modulation characteristics are removed to obtain the filtered IMF. When performing variational mode decomposition on the envelope, the constraint model for the variational mode decomposition is: ； ； Among them, u k (t) represents the k-th mode, ω k Let be the center frequency of the k-th mode, and K represent the total number of modes.

4. The method according to claim 1, characterized in that, When performing a piecewise Fourier transform on the decomposed IMF based on a preset time window, the formula for calculating the Fourier transform is as follows: ； Where ω represents the slowly changing envelope frequency, and t represents time.

5. A variational holographic spectrum identification device for the shaft frequency and blade frequency of an underwater vehicle propeller, characterized in that, The device include: The time-frequency analysis module is used to perform time-frequency analysis on the radiated noise signal of the underwater vehicle, and to extract the signal by combining the time domain diagram to obtain the extracted radiated noise signal. The HHT module is used to perform empirical mode decomposition on the intercepted radiated noise signal to obtain multiple IMFs; and to perform Hilbert spectrum analysis on each IMF to obtain the relationship between the amplitude and carrier frequency of each IMF over time. The decomposition module is used to filter multiple IMFs, extract the envelopes of the filtered IMFs, and perform variational mode decomposition on the envelopes to obtain the decomposed IMFs. The Fourier transform module is used to perform piecewise Fourier transform on the decomposed IMF based on a time window of preset length to obtain the relationship between the modulation frequency and modulation amplitude over time. An integration module is used to integrate 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 estimates of the shaft frequency, blade frequency, and number of blades of the underwater vehicle propeller.

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