Synchronous compression adaptive transformation method and device for multi-component strong time-varying signals

Abstract

The present application relates to a synchronous compression adaptive transformation method and device for multi-component strong time-varying signals. The method includes: performing short-time Fourier transform on the multi-component strong frequency-varying signal to obtain a time-frequency diagram; performing synchronous compression transformation on the time-frequency diagram according to the two-dimensional instantaneous frequency estimation to obtain a candidate transformation result; performing frequency change estimation on the points of the time-frequency diagram to obtain a frequency change rate estimation value; calculating the bandwidth of the time-frequency diagram according to the frequency change rate estimation value, setting the local search range value to the bandwidth of the time-frequency diagram, and obtaining an estimation value of the local search range value; optimizing the two-dimensional instantaneous frequency estimation using the estimation value of the local search range value to obtain an adaptive estimation of the two-dimensional instantaneous frequency; performing synchronous compression adaptive transformation on the time-frequency diagram according to the adaptive estimation to obtain an adaptive transformation result. The use of this method can improve the time-frequency aggregation and reconstruction accuracy of multi-component strong time-varying signals.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a synchronous compression adaptive transformation method, device, computer equipment and storage medium for multi-component strongly time-varying signals. Background Art

[0002] The vibration signal of complex equipment can characterize its dynamic characteristics and the operating status of its components. Faults such as structural defects of the rotor-support system, poor assembly, and abnormal coordination of moving and static parts will be reflected in its vibration signal. Considering that there are limited locations on the equipment casing where vibration sensors can be installed, the collected vibration signals are often mixed with transmission signals from many excitation sources. It is impossible to accurately locate the abnormal vibration excitation source of the equipment based solely on the vibration bandwidth amplitude. Therefore, separating the key vibration components from the mixed signal of the casing is one of the core technologies for equipment health monitoring and traceability diagnosis. The vibration signals of complex equipment are often complex multi-component signals with different strong modulation rules, that is, strong time-varying non-stationary signals. [2] Therefore, the classic Fourier transform is not suitable for its analysis, and it needs to be accurately described by a two-dimensional combination of time and frequency. Traditional time-frequency analysis includes linear time-frequency analysis and nonlinear time-frequency analysis. Limited by the Heisenberg uncertainty principle, the linear time-frequency analysis results have low resolution and severe energy dispersion, such as Gabor transform, short-time Fourier transform (STFT) and wavelet transform (WT). [3-4] Nonlinear time-frequency analysis can provide highly aggregated time-frequency distribution for single-component signals, but this method will introduce cross terms when processing multi-component signals, such as Wigner-Ville distribution (WVD). [5] Therefore, when the above two traditional time-frequency analysis methods are applied to multi-component strong time-varying signals, the analysis performance will be significantly reduced, seriously affecting the characterization effect of each vibration vector feature.

[0003] Time-frequency aggregation, reconstruction performance and computational efficiency are three key indicators for measuring the performance of time-frequency analysis methods, and are also indicators for measuring whether time-frequency analysis methods can be used for strong time-varying signal analysis. Time-frequency rearrangement (RM) effectively improves the aggregation of diffuse energy in the STFT time-frequency representation by transferring the time-frequency energy of the signal from the initial position to the center of gravity of the energy distribution. However, time-frequency rearrangement is based on the time-frequency spectrum and redistributes energy in both the time and frequency directions, which easily leads to the loss of signal reconstruction capability. Synchronous compression transform (SST) produces a concentrated time-frequency representation by concentrating the time-frequency energy around the instantaneous frequency estimation trajectory along the frequency direction. [7] For weak time-varying signals, SST can focus the divergent energy in a way that is comparable to ideal time-frequency analysis. [8], has made great progress in signal processing, and, unlike RM, SST can retain perfect signal reconstruction capabilities. However, when SST is used to analyze strong non-stationary signals, the strong amplitude-frequency modulation characteristics of the signal will cause the energy in the SST results to be still relatively dispersed, the time-frequency aggregation will be seriously reduced, and the reconstruction accuracy of the components will also be affected. [9] How to make the time-frequency analysis method have both high time-frequency aggregation and reconstruction performance is the key to improving the analysis accuracy of engine strong time-varying signals.

[0004] The proposed LMSST transform virtually eliminates the problem of multiple point assignments, offering significant advantages for processing strongly time-varying signals. However, the effectiveness of LMSST analysis is significantly affected by its parameter—the local search range—and limited research has examined the parameters for setting this parameter. If the range is set too small, the local maximum criterion's estimated range will not cover the diffusion range of multiple vibration vectors in the time-frequency plane. This makes it difficult to fully rearrange the time-frequency energy near the component's instantaneous frequency trajectory into the trajectory, resulting in leakage of vibration vector energy and, in turn, affecting vector extraction accuracy. If the search range is set too large, energy aliasing between closely adjacent components of the equipment vibration signal or the inclusion of irrelevant noise components can occur, similarly impacting vector extraction accuracy and increasing computational time. Summary of the Invention

[0005] Based on this, it is necessary to provide a synchronous compression adaptive transformation method, device, computer equipment and storage medium for multi-component strong time-varying signals that can improve the time-frequency aggregation and reconstruction accuracy of multi-component strong time-varying signals to address the above technical problems.

[0006] A synchronous compression adaptive transformation method for multi-component strongly time-varying signals, the method comprising:

[0007] Acquire vibration signals of complex equipment;

[0008] The complex equipment vibration signal is simplified into a multi-component strong frequency signal, and the multi-component strong frequency signal is subjected to short-time Fourier transform to obtain a time-frequency diagram;

[0009] Performing synchronous compression transformation on the time-frequency graph according to the two-dimensional instantaneous frequency estimation to obtain candidate transformation results; the candidate transformation results include a signal local search range value;

[0010] Estimating the frequency change of the points in the time-frequency diagram to obtain the estimated value of the frequency change rate;

[0011] Calculating the bandwidth of the time-frequency graph according to the estimated value of the frequency change rate, setting the local search range value to the bandwidth of the time-frequency graph, and obtaining an estimated value of the local search range value;

[0012] Optimize the two-dimensional instantaneous frequency estimation using the estimated value of the local search range value to obtain the adaptive estimation of the two-dimensional instantaneous frequency;

[0013] Perform synchronous compression adaptive transformation on the time-frequency diagram according to the adaptive estimation to obtain the adaptive transformation result.

[0014] In one embodiment, perform synchronous compression transformation on the time-frequency diagram according to the two-dimensional instantaneous frequency estimation to obtain the candidate transformation result, including:

[0015] Perform synchronous compression transformation on the time-frequency diagram according to the two-dimensional instantaneous frequency estimation, and the obtained candidate transformation result is

[0016]

[0017] where, represents the two-dimensional instantaneous frequency estimation, Δ represents the local search range value, ω represents the frequency variable, δ() is the Dirac function, G(t, ω) represents the time-frequency diagram, ξ represents the frequency variable, and t represents the time variable.

[0018] In one embodiment, estimate the frequency change of the points on the time-frequency diagram to obtain the estimated value of the frequency change rate, including:

[0019] Estimate the frequency change of the points on the time-frequency diagram, and the obtained estimated value of the frequency change rate is

[0020]

[0021] where, Dh(t) = dh(t) / dt is the differential of the window function h(t), D 2 h(t) = d 2 h(t) / dt 2 is the double differential of the window function h(t), and τDh(t) = t·dh(t) / dt is the STFT expression of the window function.

[0022] In one embodiment, calculate the frequency bandwidth of the time-frequency diagram according to the estimated value of the frequency change rate, including:

[0023] Calculate the frequency bandwidth of the time-frequency diagram according to the estimated value of the frequency change rate, and the obtained frequency bandwidth of the time-frequency diagram is

[0024]

[0025] where, 0 < t0 < 1, t0 represents the time width required for estimating the frequency bandwidth, and K represents the total number of vibration components contained in the signal.

[0026] In one embodiment, optimizing the two-dimensional instantaneous frequency estimate based on the estimated value of the local search range value to obtain an adaptive estimate of the two-dimensional instantaneous frequency includes:

[0027] The two-dimensional instantaneous frequency estimation is optimized according to the estimated value of the local search range value, and the adaptive estimation of the two-dimensional instantaneous frequency is obtained as

[0028]

[0029] In one embodiment, performing synchronous compression adaptive transformation on the time-frequency graph according to the adaptive estimation to obtain the adaptive transformation result includes:

[0030] According to the adaptive estimation, the time-frequency graph is synchronously compressed and adaptively transformed, and the adaptive transformation result is obtained as follows:

[0031]

[0032] In one embodiment, a complex equipment vibration signal is simplified into a multi-component emphasized frequency signal, and a short-time Fourier transform is performed on the multi-component emphasized frequency signal to obtain a time-frequency diagram, including:

[0033] The complex equipment vibration signal is simplified into a multi-component stressed frequency signal, and the multi-component stressed frequency signal is subjected to short-time Fourier transform to obtain the time-frequency diagram:

[0034]

[0035] Among them, A k (t) represents the instantaneous amplitude of the kth component, represents the instantaneous phase of the kth component, represents the instantaneous frequency of the kth component, and i represents the imaginary term.

[0036] A synchronous compression adaptive transformation device for multi-component strongly time-varying signals, the device comprising:

[0037] The short-time Fourier transform module is used to obtain the vibration signal of complex equipment; the complex equipment vibration signal is simplified into a multi-component strong frequency signal, and the multi-component strong frequency signal is subjected to short-time Fourier transform to obtain a time-frequency diagram;

[0038] A synchronous compression transformation module is used to perform synchronous compression transformation on the time-frequency diagram according to the two-dimensional instantaneous frequency estimation to obtain candidate transformation results; the candidate transformation results include a signal local search range value;

[0039] A local search range value setting module is used to estimate the frequency change of the points of the time-frequency graph to obtain an estimated value of the frequency change rate; calculate the bandwidth of the time-frequency graph based on the estimated value of the frequency change rate, set the local search range value to the bandwidth of the time-frequency graph, and obtain an estimated value of the local search range value;

[0040] The synchronous compression adaptive transformation module is used to optimize the two-dimensional instantaneous frequency estimation using the estimated value of the local search range value to obtain an adaptive estimation of the two-dimensional instantaneous frequency; based on the adaptive estimation, the time-frequency diagram is subjected to synchronous compression adaptive transformation to obtain an adaptive transformation result.

[0041] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0042] Acquire vibration signals of complex equipment;

[0043] The complex equipment vibration signal is simplified into a multi-component strong frequency signal, and the multi-component strong frequency signal is subjected to short-time Fourier transform to obtain a time-frequency diagram;

[0044] Performing synchronous compression transformation on the time-frequency graph according to the two-dimensional instantaneous frequency estimation to obtain candidate transformation results; the candidate transformation results include a signal local search range value;

[0045] Estimating the frequency change of the points in the time-frequency diagram to obtain the estimated value of the frequency change rate;

[0046] Calculating the bandwidth of the time-frequency graph according to the estimated value of the frequency change rate, setting the local search range value to the bandwidth of the time-frequency graph, and obtaining an estimated value of the local search range value;

[0047] The two-dimensional instantaneous frequency estimation is optimized using the estimated value of the local search range value to obtain an adaptive estimation of the two-dimensional instantaneous frequency;

[0048] The time-frequency graph is subjected to synchronous compression adaptive transformation according to the adaptive estimation to obtain an adaptive transformation result.

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0050] Acquire vibration signals of complex equipment;

[0051] The complex equipment vibration signal is simplified into a multi-component strong frequency signal, and the multi-component strong frequency signal is subjected to short-time Fourier transform to obtain a time-frequency diagram;

[0052] Performing synchronous compression transformation on the time-frequency graph according to the two-dimensional instantaneous frequency estimation to obtain candidate transformation results; the candidate transformation results include a signal local search range value;

[0053] Estimating the frequency change of the points in the time-frequency diagram to obtain the estimated value of the frequency change rate;

[0054] Calculating the bandwidth of the time-frequency graph according to the estimated value of the frequency change rate, setting the local search range value to the bandwidth of the time-frequency graph, and obtaining an estimated value of the local search range value;

[0055] The two-dimensional instantaneous frequency estimation is optimized using the estimated value of the local search range value to obtain an adaptive estimation of the two-dimensional instantaneous frequency;

[0056] The time-frequency graph is subjected to synchronous compression adaptive transformation according to the adaptive estimation to obtain an adaptive transformation result.

[0057] The above-mentioned synchronous compression adaptive transformation method, device, computer equipment and storage medium for multi-component strong time-varying signals, the present application performs synchronous compression transformation on the time-frequency diagram based on two-dimensional instantaneous frequency estimation, improves the energy concentration of the time-frequency representation by rearranging the energy in the frequency direction, and provides the setting conditions of the local search range value in the local maximum synchronous compression transformation method, that is, consistent with the bandwidth in the time-frequency diagram, by estimating the two-dimensional modulation characteristics of the signal, calculating the bandwidth range of each component in the short-time Fourier transform result, and realizing the adaptive setting of the local search range value. It can not only solve the problem of reduced analysis accuracy caused by the mismatch between the fixed local search range value and the modulation characteristics when processing strong time-varying signals, but also improve the reconstruction accuracy of multi-component strong time-varying signals, and meet the analysis requirements of multi-component signals with significantly different modulation characteristics. Therefore, when using the method provided by the present invention to analyze complex equipment vibration signals, parameter values ​​can be set quickly and effectively, thereby clearly and accurately describing the strong modulation characteristics of multiple components, and significantly reducing the amount of calculation when setting the experience-driven local search range value. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 1 is a flow chart of a synchronous compression adaptive transformation method for multi-component strongly time-varying signals in one embodiment;

[0059] Figure 2 1 is a structural block diagram of a synchronous compression adaptive transformation device for multi-component strongly time-varying signals in one embodiment;

[0060] Figure 3 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0062] In one embodiment, Figure 1 As shown, a synchronous compression adaptive transformation method for multi-component strongly time-varying signals is provided, comprising the following steps:

[0063] Step 102 : Acquire a complex equipment vibration signal; simplify the complex equipment vibration signal into a multi-component emphasized frequency signal, perform short-time Fourier transform on the multi-component emphasized frequency signal, and obtain a time-frequency diagram.

[0064] The complex equipment vibration signal is simplified into a multi-component strong frequency signal s(t) as shown in formula (1), where the phase function of the signal is described by using high-order Taylor expansion, and it is assumed that when Enough hours for Therefore, the second-order expansion of the signal is derived, which is u=t, A k (u)=A k (t), so The sum signal s(u) can be well approximated by

[0065]

[0066] Instantaneous amplitude A k (u)=A k (t), expand the instantaneous signal: s(u) is a multi-component high-frequency signal. represents the instantaneous frequency of the kth component, It represents the frequency modulation rate of the kth component, k represents the number of vibration components contained in the signal, and k refers to the kth component among 1 to K components.

[0067] Perform short-time Fourier transform on the multi-component stressed frequency signal s(t) shown in equation (1), and the window function h=exp(-(ut) 2 / 2σ), as shown in formula (2).

[0068]

[0069] G(t,ω) is the short-time Fourier transform signal of s(t), and σ is the Gaussian window parameter.

[0070] Step 104: Perform synchronous compression transformation on the time-frequency diagram according to the two-dimensional instantaneous frequency estimation to obtain candidate transformation results; the candidate transformation results include the signal local search range value.

[0071] The STFT result shown in formula (2) is subjected to synchronous compression transformation, that is, the energy concentration of the time-frequency representation is improved by rearranging the energy in the frequency direction, as shown in formula (3), where δ() is the Dirac function, and the two-dimensional instantaneous frequency estimation It can be calculated according to the local maximum criterion, as shown in Equation (4).

[0072]

[0073] In the formula,

[0074]

[0075] Ts(t, ξ) represents the time-frequency, represents the two-dimensional instantaneous frequency estimation, the Δ value represents the local search range value, and ω represents the frequency variable

[0076] In the previous scheme, the Δ value was set to a fixed value according to experience and then substituted into Equations (3) to (4) to participate in the transformation. However, it can be seen from Equation (4) that if the Δ value is too small, part of the energy of adjacent components will be lost; if the Δ value is too large, energy aliasing will occur between adjacent components. And for signals with significantly different modulation characteristics, a fixed Δ value is difficult to meet the analysis requirements of such multi-component signals.

[0077] Step 106: Estimate the frequency change of the points on the time-frequency diagram to obtain an estimated value of the frequency change rate; calculate the frequency bandwidth of the time-frequency diagram according to the estimated value of the frequency change rate, and set the local search range value to the frequency bandwidth of the time-frequency diagram to obtain an estimated value of the local search range value.

[0078] This application will estimate the frequency bandwidth of each component in the G(t, ω) time-frequency diagram, so as to achieve the adaptive setting of the Δ value.

[0079] Here, it is assumed that the Gaussian window parameter σ is 1, and the STFT expression shown in Equation (2) can be simplified as:[[]]

[0080]

[0081] The amplitude of Equation (5) can be expressed as:[[]]

[0082]

[0083] Similarly, the frequency bandwidth Δ of the STFT result G(t, ω) can be obtained according to the following formula ω :[[]]

[0084] <on

[0085] In the formula, 0 < t0 < 1 and It can be seen that the frequency spread Δ shown in Equation (7)[[]] ω will be affected by the modulation rate .[[]]

[0086] The fifth step, the modulation rate This is a priori knowledge. In most practical cases, it is difficult to know this frequency characteristic of the signal in advance. Therefore, for each point (t0, ω0) in the G(t, ω) time-frequency diagram, this application estimates the frequency change rate according to formula (8): Substituting it into formula (7) we can get Δ ω Estimated value Δ ω (t,ω)(σ=1), as shown in formula (9).

[0087]

[0088] Where Dh(t)=dh(t) / dt represents the differential of the window function h(t), D 2 h(t)=d 2 h(t) / dt 2 Denote double differentiation of the window function h(t) and τDh(t)=t·dh(t) / dt as the STFT expression of the window function.

[0089]

[0090] Step 108: Optimize the two-dimensional instantaneous frequency estimation using the estimated value of the local search range value to obtain an adaptive estimation of the two-dimensional instantaneous frequency; perform synchronous compression adaptive transformation on the time-frequency diagram according to the adaptive estimation to obtain an adaptive transformation result.

[0091] The estimated value Δ ω Substituting (t, ω) into equation (4), the adaptive estimation of the two-dimensional instantaneous frequency of the signal s(t) can be realized, as shown in equation (10). The two-dimensional instantaneous frequency ω obtained by adaptive estimation is m Substituting (t, ω) into equation (3), the synchronous compression adaptive transformation of the multi-component emphasis frequency signal s(t) can be realized, as shown in equation (11).

[0092]

[0093]

[0094] In the above-mentioned synchronous compression adaptive transformation method for multi-component strong time-varying signals, the present application performs synchronous compression transformation on the time-frequency diagram based on the two-dimensional instantaneous frequency estimation, improves the energy concentration of the time-frequency representation by rearranging the energy in the frequency direction, and provides the setting conditions of the local search range value in the local maximum synchronous compression transformation method, that is, consistent with the bandwidth in the time-frequency diagram, by estimating the two-dimensional modulation characteristics of the signal, calculating the bandwidth range of each component in the short-time Fourier transform result, and realizing the adaptive setting of the local search range value. This not only solves the problem of reduced analysis accuracy caused by the mismatch between the fixed local search range value and the modulation characteristics when processing strong time-varying signals, but also improves the reconstruction accuracy of multi-component strong time-varying signals, and can meet the analysis requirements of multi-component signals with significantly different modulation characteristics. Therefore, when using the method provided by the present invention to analyze complex equipment vibration signals, parameter values ​​can be set quickly and effectively, thereby clearly and accurately describing the strong modulation characteristics of multiple components, and greatly reducing the amount of calculation when setting the experience-driven local search range value.

[0095] In one embodiment, performing synchronous compression transformation on the time-frequency diagram according to the two-dimensional instantaneous frequency estimation to obtain candidate transformation results includes:

[0096] According to the two-dimensional instantaneous frequency estimation, the time-frequency diagram is subjected to synchronous compression transformation, and the candidate transformation result is obtained as

[0097]

[0098] in, represents the two-dimensional instantaneous frequency estimate, Δ represents the local search range value, ω represents the frequency variable, δ() is the Dirac function, G(t,ω) represents the time-frequency graph, ξ represents the frequency variable, and t represents the time variable.

[0099] In one embodiment, estimating the frequency change of a point in the time-frequency graph to obtain an estimated value of the frequency change rate includes:

[0100] The frequency change of the points in the time-frequency graph is estimated, and the estimated frequency change rate is

[0101]

[0102] Among them, Dh(t)=dh(t) / dt is the differential of the window function h(t), D 2 h(t)=d 2 h(t) / dt 2 The double differentiation of the window function h(t), τDh(t)=t·dh(t) / dt is the STFT expression of the window function.

[0103] In one embodiment, calculating the frequency bandwidth of the time-frequency diagram according to the estimated value of the frequency change rate includes:

[0104] Calculating the frequency bandwidth of the time-frequency diagram according to the estimated value of the frequency change rate, and obtaining the frequency bandwidth of the time-frequency diagram as

[0105]

[0106] where \(0 < t_0 < 1\), \(t_0\) represents the time width required for estimating the frequency bandwidth, and \(K\) represents the total number of vibration components included in the signal.

[0107] In one embodiment, optimizing the two-dimensional instantaneous frequency estimation according to the estimated value of the local search range value to obtain the adaptive estimation of the two-dimensional instantaneous frequency includes:

[0108] Optimizing the two-dimensional instantaneous frequency estimation according to the estimated value of the local search range value, and obtaining the adaptive estimation of the two-dimensional instantaneous frequency as

[0109]

[0110] In one embodiment, performing synchronous compression adaptive transformation on the time-frequency diagram according to the adaptive estimation to obtain the adaptive transformation result includes:

[0111] Performing synchronous compression adaptive transformation on the time-frequency diagram according to the adaptive estimation, and obtaining the adaptive transformation result as

[0112]

[0113] In one embodiment, simplifying the complex equipment vibration signal into a multi-component strong frequency modulation signal, and performing short-time Fourier transform on the multi-component strong frequency modulation signal to obtain a time-frequency diagram includes:

[0114] Simplifying the complex equipment vibration signal into a multi-component strong frequency modulation signal, and performing short-time Fourier transform on the multi-component strong frequency modulation signal, and obtaining the time-frequency diagram as

[0115]

[0116] where \(A\) k (t) represents the instantaneous amplitude of the \(k\)th component, represents the instantaneous phase of the \(k\)th component, represents the instantaneous frequency of the \(k\)th component, and \(i\) represents the imaginary term.

[0117] It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0118] In one embodiment, Figure 2 As shown, a synchronous compression adaptive transformation device for multi-component strong time-varying signals is provided, comprising: a short-time Fourier transform module 202, a synchronous compression transformation module 204, a local search range value setting module 206 and a synchronous compression adaptive transformation module 208, wherein:

[0119] The short-time Fourier transform module 202 is used to obtain a complex equipment vibration signal; simplify the complex equipment vibration signal into a multi-component emphasized frequency signal, and perform a short-time Fourier transform on the multi-component emphasized frequency signal to obtain a time-frequency diagram;

[0120] The synchronous compression transformation module 204 is used to perform synchronous compression transformation on the time-frequency diagram according to the two-dimensional instantaneous frequency estimation to obtain candidate transformation results; the candidate transformation results include the signal local search range value;

[0121] The local search range value setting module 206 is configured to estimate the frequency change of the points of the time-frequency graph to obtain an estimated value of the frequency change rate; calculate the bandwidth of the time-frequency graph based on the estimated value of the frequency change rate, set the local search range value to the bandwidth of the time-frequency graph, and obtain an estimated value of the local search range value;

[0122] The synchronous compression adaptive transformation module 208 is used to optimize the two-dimensional instantaneous frequency estimation using the estimated value of the local search range value to obtain an adaptive estimation of the two-dimensional instantaneous frequency; and perform synchronous compression adaptive transformation on the time-frequency diagram according to the adaptive estimation to obtain an adaptive transformation result.

[0123] In one embodiment, the synchronous compression transformation module 204 is further configured to perform synchronous compression transformation on the time-frequency graph according to the two-dimensional instantaneous frequency estimation to obtain candidate transformation results, including:

[0124] According to the two-dimensional instantaneous frequency estimation, the time-frequency diagram is subjected to synchronous compression transformation, and the candidate transformation result is obtained as

[0125]

[0126] Among them, represents the two-dimensional instantaneous frequency estimation, Δ represents the local search range value, ω represents the frequency variable, δ() is the Dirac function, G(t, ω) represents the time-frequency diagram, ξ represents the frequency variable, and t represents the time variable.

[0127] In one embodiment, the local search range value setting module 206 is further configured to estimate the frequency change of the points on the time-frequency diagram to obtain an estimated value of the frequency change rate, including:

[0128] Estimating the frequency change of the points on the time-frequency diagram, and the obtained estimated value of the frequency change rate is

[0129]

[0130] Among them, Dh(t) = dh(t) / dt is the differential of the window function h(t), D 2 h(t) = d 2 h(t) / dt 2 is the double differential of the window function h(t), and τDh(t) = t·dh(t) / dt is the STFT expression of the window function.

[0131] In one embodiment, the local search range value setting module 206 is further configured to calculate the frequency width of the time-frequency diagram according to the estimated value of the frequency change rate, including:

[0132] Calculating the frequency width of the time-frequency diagram according to the estimated value of the frequency change rate, and the obtained frequency width of the time-frequency diagram is

[0133]

[0134] Among them, 0 < t0 < 1, t0 represents the time width required for estimating the frequency width, and K represents the total number of vibration components included in the signal.

[0135] In one embodiment, the synchrosqueezing adaptive transform module 208 is further configured to optimize the two-dimensional instantaneous frequency estimation according to the estimated value of the local search range value to obtain an adaptive estimation of the two-dimensional instantaneous frequency, including:

[0136] Optimizing the two-dimensional instantaneous frequency estimation according to the estimated value of the local search range value, and the obtained adaptive estimation of the two-dimensional instantaneous frequency is

[0137]

[0138] In one embodiment, the synchrosqueezing adaptive transform module 208 is further configured to perform synchrosqueezing adaptive transform on the time-frequency diagram according to the adaptive estimation to obtain an adaptive transform result, including:

[0139] According to the adaptive estimation, the time-frequency graph is synchronously compressed and adaptively transformed, and the adaptive transformation result is obtained as follows:

[0140]

[0141] In one embodiment, the short-time Fourier transform module 202 is further configured to simplify the complex equipment vibration signal into a multi-component emphasized frequency signal, perform a short-time Fourier transform on the multi-component emphasized frequency signal, and obtain a time-frequency diagram, including:

[0142] The complex equipment vibration signal is simplified into a multi-component stressed frequency signal, and the multi-component stressed frequency signal is subjected to short-time Fourier transform to obtain the time-frequency diagram:

[0143]

[0144] Among them, A k (t) represents the instantaneous amplitude of the kth component, represents the instantaneous phase of the kth component, represents the instantaneous frequency of the kth component, and i represents the imaginary term.

[0145] Regarding the specific definition of the synchronous compression adaptive transformation device for multi-component strong time-varying signals, please refer to the definition of the synchronous compression adaptive transformation method for multi-component strong time-varying signals above, which will not be repeated here. The various modules in the above-mentioned synchronous compression adaptive transformation device for multi-component strong time-varying signals can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0146] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a synchronous compression adaptive transformation method for multi-component strong time-varying signals is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0147] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0148] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0149] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A synchronous compression adaptive transformation method for multi-component strongly time-varying signals, characterized in that: The method comprises: Acquire vibration signals of complex equipment; Simplifying the complex equipment vibration signal into a multi-component stressed frequency signal, and performing short-time Fourier transform on the multi-component stressed frequency signal to obtain a time-frequency diagram; Performing synchronous compression transformation on the time-frequency graph according to the two-dimensional instantaneous frequency estimation to obtain a candidate transformation result; the candidate transformation result includes a signal local search range value; Estimating the frequency change of the points of the time-frequency graph to obtain an estimated value of the frequency change rate; Calculating the frequency bandwidth of the time-frequency graph according to the estimated value of the frequency change rate, setting the local search range value to the frequency bandwidth of the time-frequency graph, and obtaining an estimated value of the local search range value; optimizing the two-dimensional instantaneous frequency estimate using the estimated value of the local search range value to obtain an adaptive estimate of the two-dimensional instantaneous frequency; Performing synchronous compression adaptive transformation on the time-frequency graph according to the adaptive estimation to obtain an adaptive transformation result; Estimating the frequency change of the points of the time-frequency graph to obtain an estimated value of the frequency change rate includes: The frequency change of the points in the time-frequency diagram is estimated, and the estimated frequency change rate is obtained as Among them, Dh(t)=dh(t) / dt is the differential of the window function h(t), D 2 h(t)=d 2 h(t) / dt 2 The double differentiation of the window function h(t), τDh(t) = t·dh(t) / dt is the STFT expression of the window function; Calculating the bandwidth of the time-frequency graph according to the frequency change rate estimate includes: The bandwidth of the time-frequency graph is calculated according to the estimated value of the frequency change rate, and the bandwidth of the time-frequency graph is obtained as Where, 0<t0<1, t0 represents the time width required to be set when estimating the bandwidth, and K represents the total number of vibration components contained in the signal; Optimizing the two-dimensional instantaneous frequency estimate according to the estimated value of the local search range value to obtain an adaptive estimate of the two-dimensional instantaneous frequency, comprising: The two-dimensional instantaneous frequency estimate is optimized according to the estimated value of the local search range value, and the adaptive estimate of the two-dimensional instantaneous frequency is obtained as follows:

2. The method according to claim 1, characterized in that Performing synchronous compression transformation on the time-frequency graph according to the two-dimensional instantaneous frequency estimation to obtain candidate transformation results, including: The time-frequency diagram is subjected to synchronous compression transformation according to the two-dimensional instantaneous frequency estimation, and the candidate transformation result is obtained as follows: in, represents the two-dimensional instantaneous frequency estimate, Δ represents the local search range value, ω represents the frequency variable, δ() is the Dirac function, G(t,ω) represents the time-frequency graph, ξ represents the frequency variable, and t represents the time variable.

3. The method according to claim 1, characterized in that Performing synchronous compression adaptive transformation on the time-frequency graph according to the adaptive estimation to obtain an adaptive transformation result includes: According to the adaptive estimation, the time-frequency graph is subjected to synchronous compression adaptive transformation, and the adaptive transformation result is obtained as follows:

4. The method according to claim 3, characterized in that Simplifying the complex equipment vibration signal into a multi-component stressed frequency signal, performing short-time Fourier transform on the multi-component stressed frequency signal, and obtaining a time-frequency diagram, including: The complex equipment vibration signal is simplified into a multi-component emphasized frequency signal, and the multi-component emphasized frequency signal is subjected to short-time Fourier transform to obtain a time-frequency diagram: Among them, A k (t) represents the instantaneous amplitude of the kth component, represents the instantaneous phase of the kth component, represents the instantaneous frequency of the kth component, and i represents the imaginary term.

5. A synchronous compression adaptive conversion device for multi-component strongly time-varying signals, characterized in that: The device comprises: A short-time Fourier transform module is used to obtain a complex equipment vibration signal; simplify the complex equipment vibration signal into a multi-component emphasized frequency signal, and perform a short-time Fourier transform on the multi-component emphasized frequency signal to obtain a time-frequency diagram; a synchronous compression transformation module, configured to perform synchronous compression transformation on the time-frequency diagram according to the two-dimensional instantaneous frequency estimation to obtain a candidate transformation result; the candidate transformation result includes a signal local search range value; The local search range value setting module is used to estimate the frequency change of the points of the time-frequency diagram to obtain the estimated value of the frequency change rate; calculate the bandwidth of the time-frequency diagram according to the estimated value of the frequency change rate, and obtain the bandwidth of the time-frequency diagram as Wherein, 0<t0<1, t0 represents the time width required to be set when estimating the bandwidth, and K represents the total number of vibration components contained in the signal; the local search range value is set to the bandwidth of the time-frequency graph to obtain an estimated value of the local search range value, including: The frequency change of the points in the time-frequency diagram is estimated, and the estimated frequency change rate is obtained as Among them, Dh(t)=dh(t) / dt is the differential of the window function h(t), D 2 h(t)=d 2 h(t) / dt 2 The double differentiation of the window function h(t), τDh(t) = t·dh(t) / dt is the STFT expression of the window function; A synchronous compression adaptive transformation module is used to optimize the two-dimensional instantaneous frequency estimate using the estimated value of the local search range value to obtain an adaptive estimate of the two-dimensional instantaneous frequency, including: The two-dimensional instantaneous frequency estimate is optimized according to the estimated value of the local search range value, and the adaptive estimate of the two-dimensional instantaneous frequency is obtained as follows: Performing synchronous compression adaptive transformation on the time-frequency graph according to the adaptive estimation to obtain an adaptive transformation result.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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