Vibration nonlinear signal energy analysis method and system based on variational mode decomposition

Abstract

The invention provides a vibration nonlinear signal energy analysis method and system based on variational mode decomposition, and relates to the technical field of signal processing, and the method comprises the following steps: obtaining a monitoring signal of a vibratory roller, determining a corresponding state based on root-mean-square data of the monitoring signal, and determining the state of the monitoring signal; performing segmentation processing on the monitoring signal based on the state corresponding to the monitoring signal to obtain a signal segmentation result; performing variational mode decomposition processing on the signal segmentation result to obtain at least two second mode components; performing Hilbert transformation processing on the second modal component, and combining the instantaneous frequency and the instantaneous amplitude of each modal component obtained through transformation to obtain frequency spectrum information of the monitoring signal; and performing marginal spectrum calculation and integration on the frequency spectrum information to obtain the total energy of the monitoring signal. According to the method, interference signals can be efficiently identified and eliminated in the vibration signals, so that the precision and robustness of compaction quality evaluation are improved.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and more specifically, to a method and system for energy analysis of vibration nonlinear signals based on variational mode decomposition. Background Technology

[0002] Currently, vibratory rollers widely use acceleration signals to evaluate the compaction quality of fill material during highway subgrade compaction. Conventional acceleration signal analysis methods mainly rely on spectral analysis of vibration signals to assess the compaction degree of the fill material. Common techniques include harmonic ratio calculation based on acceleration signals, resonance value (RMV), and total harmonic distortion (THD). However, these methods often suffer from severe signal noise interference and poor real-time processing, especially during vibration. Due to the instability of vibration frequency and the influence of external environmental interference, existing signal analysis methods struggle to accurately reflect compaction quality. Existing technologies primarily suppress interference through simple filtering or spectral analysis, but these traditional methods cannot effectively address nonlinear interference and complex noise issues in vibration signals, resulting in a lack of accuracy and robustness in the final evaluation results. Furthermore, conventional techniques fail to fully utilize the time-frequency characteristics of the signal and do not perform in-depth adaptive analysis, thus exhibiting significant shortcomings in improving accuracy and stability.

[0003] Therefore, there is an urgent need for a variational mode decomposition method and system for analyzing the energy of vibration nonlinear signals to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for energy analysis of vibration nonlinear signals using variational mode decomposition, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows: In a first aspect, this application provides a method for energy analysis of vibration nonlinear signals based on variational mode decomposition, including: The monitoring signal of the vibratory roller is acquired according to preset conditions, and the monitoring signal includes the acceleration information of the vibratory roller. The state corresponding to the monitoring signal is determined based on the root mean square data of the monitoring signal, and the monitoring signal is segmented based on the state corresponding to the monitoring signal to obtain the signal segmentation result. The signal segmentation results are subjected to variational mode decomposition, and the preset decomposition parameters are optimized based on the first mode component obtained by decomposition. Then, the monitoring signal is decomposed using the optimized decomposition parameters to obtain at least two second mode components. The second mode component is subjected to Hilbert transform, and the instantaneous frequency and instantaneous amplitude of each mode component obtained by the transform are combined to obtain the spectrum information of the monitoring signal; The total energy of the monitoring signal is obtained by performing marginal spectrum calculation and integration on the spectral information.

[0005] Secondly, this application also provides a vibration nonlinear signal energy analysis system based on variational mode decomposition, comprising: The acquisition unit is used to acquire the monitoring signal of the vibratory roller according to preset conditions, wherein the monitoring signal includes the acceleration information of the vibratory roller; The segmentation unit is used to determine the corresponding state based on the root mean square data of the monitoring signal, and to segment the monitoring signal based on the corresponding state to obtain the signal segmentation result. The decomposition unit is used to perform variational mode decomposition on the signal segmentation result, optimize the preset decomposition parameters based on the first mode component obtained by decomposition, and then decompose the monitoring signal through the optimized decomposition parameters to obtain at least two second mode components. The transformation unit is used to perform Hilbert transform on the second mode component and combine the instantaneous frequency and instantaneous amplitude of each mode component obtained by the transformation to obtain the spectrum information of the monitoring signal; The calculation unit is used to perform marginal spectrum calculation and integration on the spectrum information to obtain the total energy of the monitoring signal.

[0006] The beneficial effects of this invention are as follows: This invention calculates the root mean square (RMS) value of the monitored signal to determine its coupling or chaotic state, and then segments the signal according to different states. Next, variational mode decomposition (VMD) is used to decompose the segmented signal, and an adaptive evolutionary strategy based on the covariance matrix is ​​employed to optimize the decomposition parameters, achieving accurate signal decomposition. Subsequently, Hilbert transform is used to process the modal components, extracting the instantaneous frequency and amplitude of the signal, and combining this with spectral information to calculate the marginal spectrum, thereby obtaining the total energy characteristics of the signal. This invention can efficiently identify and eliminate interference signals in vibration signals, thereby improving the accuracy and robustness of compaction quality assessment, especially in complex environments with high signal noise, strong nonlinearity, and drastic vibration frequency changes. It exhibits superior signal processing capabilities and can provide a more accurate and reliable basis for real-time assessment of compaction quality.

[0007] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of the vibration nonlinear signal energy analysis method based on variational mode decomposition as described in this embodiment of the invention; Figure 2 This is a schematic diagram of the vibration nonlinear signal energy analysis system based on variational mode decomposition as described in this embodiment of the invention. Figure 3 The variational mode components and their fast Fourier transform spectra of the monitoring signal in the coupled state described in this embodiment of the invention; Figure 4 The variational mode components and their fast Fourier transform spectra of the monitoring signal in a chaotic state described in this embodiment of the invention; Figure 5 The total energy and dry density curves are obtained by the vibration nonlinear signal energy analysis method of variational mode decomposition described in this embodiment of the invention.

[0010] In the diagram: 701, acquisition unit; 702, segmentation unit; 703, decomposition unit; 704, transformation unit; 705, calculation unit. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0012] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0013] Example 1: This embodiment provides a method for energy analysis of vibration nonlinear signals based on variational mode decomposition.

[0014] See Figure 1 , Figure 3 , Figure 4 and Figure 5 The figure shows that the method includes steps S1, S2, S3, S4 and S5.

[0015] Step S1: Obtain the monitoring signal of the vibratory roller according to preset conditions, wherein the monitoring signal includes the acceleration information of the vibratory roller; Understandably, this step begins by acquiring the monitoring signals of the vibratory roller under preset conditions. This step is fundamental to signal analysis and involves data acquisition and sensor settings. The monitoring signals of the vibratory roller are typically acquired using accelerometers, which are usually mounted on the roller's vibrating drum to ensure the capture of dynamic responses during vibration. The sensor's sensitivity, range, frequency response, and other parameters need to be set according to the roller's operating environment and vibration characteristics to ensure sufficient accuracy and reliability of the acquired data. For example, the accelerometer's sensitivity typically needs to be greater than 10 mV / (m / s²) to accurately capture vibration information from low to high frequencies; the range should be greater than 10 g to accommodate vibrations of varying intensities; and the frequency response generally needs to be less than 5 kHz to effectively capture the high-frequency components of the vibration signal.

[0016] In this step, the collected monitoring signals include the acceleration information of the road roller, which is the core data reflecting the vibration compaction process. Since acceleration signals are affected by vibration frequency, compaction degree, filler type, and environmental factors (such as temperature and humidity), accurate acquisition of these signals is crucial for subsequent analysis. In practical applications, signal acquisition conditions are often influenced by different working environments. Therefore, the setting of preset conditions should take into account external interference and variables in actual use, such as the range of vibration frequency variation and external noise.

[0017] Step S2: Determine the corresponding state based on the root mean square data of the monitoring signal, and perform segmentation processing on the monitoring signal based on the corresponding state to obtain the signal segmentation result; It is understandable that this step, through state judgment and segmentation based on the root mean square value, can effectively distinguish different operating modes in the vibration signal, thereby providing more accurate input data for subsequent signal decomposition, feature extraction, and energy analysis. Furthermore, the state-segmentation-based signal processing method has strong adaptive capabilities, automatically adjusting the processing strategy according to the dynamic changes of the signal, improving the accuracy and stability of signal analysis. Especially when processing complex signals with nonlinearity and significant noise interference, it can effectively improve the robustness of signal analysis. In this step, step S2 includes steps S21 and S22.

[0018] Step S21: Perform root mean square calculation on the monitoring signal, and determine the status information of the monitoring signal based on the root mean square value at each time point; Understandably, the root mean square (RMS) value is a commonly used statistical measure in signal processing, used to quantify the energy magnitude and fluctuation amplitude of a signal. In vibration signal analysis, the RMS value, obtained by averaging and then taking the square root of the squared value of the signal, reflects the intensity of signal fluctuations within a certain time window. Specifically, periods with larger RMS values ​​indicate greater vibration intensity, potentially corresponding to a vibratory roller operating at high energy input or in an unstable state; while smaller RMS values ​​indicate less signal fluctuation, usually associated with a stable operating state of the vibratory roller.

[0019] The formula for calculating the root mean square is as follows: ; in, The root mean square value, Indicates monitoring signal, No. Seconds Indicates the first Seconds Indicates the first Time point.

[0020] Step S22: Segment the monitoring signal according to its corresponding state information to obtain at least two monitoring signal segments, and use them as the signal segmentation results.

[0021] It is understandable that the monitoring signal in this step is a continuous signal containing multiple dynamic changes, and the signal state may change significantly at different points in time. For example, during the operation of a vibratory roller, when the fill material is in a loose state, the vibration signal is usually relatively stable, exhibiting low-amplitude periodic vibration (coupled state); however, as the fill material gradually becomes denser, the vibration signal may be disturbed by phenomena such as bouncing, leading to nonlinear and irregular fluctuations in the signal (chaotic state). By segmenting the monitoring signal, signals under different operating states can be effectively processed separately, thereby avoiding analysis errors caused by the mixing of signals under different states.

[0022] Specifically, the signal's time interval is first divided based on the changes in the RMS value. When the RMS value is stable and at a low level, it indicates that the signal is in a coupled state, and the signal is divided into one segment; while when the RMS value fluctuates significantly, it indicates that the signal has entered a chaotic state, and another segment of the signal is separated. The advantage of this approach is that each segment of the signal has similar characteristics, facilitating subsequent processing and analysis.

[0023] Step S3: Perform variational mode decomposition on the signal segmentation result, and optimize the preset decomposition parameters based on the first mode component obtained by decomposition. Then, decompose the monitoring signal using the optimized decomposition parameters to obtain at least two second mode components. It is understandable that this step, through variational mode decomposition (VMD) and optimized parameter adjustment, can effectively reduce mode aliasing and accurately extract useful frequency components from the signal, especially showing significant advantages when processing complex nonlinear and time-varying signals. The optimized decomposition parameters ensure that every part of the signal can be accurately captured, making the decomposition results more consistent with actual vibration characteristics, providing more reliable data support, and laying a solid foundation for subsequent signal feature extraction and energy analysis. In this step, step S3 includes steps S31, S32, and S33.

[0024] Step S31: Perform variational mode decomposition on each monitoring signal segment in the signal segmentation result to obtain the first mode component of each monitoring signal segment; It is understandable that in the signal processing of vibratory rollers, the first modal component typically represents the low-frequency components of the signal, reflecting the dominant vibration mode of the vibration system. These low-frequency components usually contain the main vibration characteristics of the signal, representing the coupling effect between the roller and the filler, thus making the extraction of this mode crucial. Through VMD decomposition, the spectrum of each signal segment can be effectively separated, avoiding mode aliasing and ensuring that each modal component clearly corresponds to an independent vibration mode in the signal.

[0025] Specifically, the time series of each monitored signal is first input into the VMD algorithm. The VMD algorithm decomposes the signal into multiple modal components through an iterative optimization process, ensuring that each modal component has a specific center frequency and bandwidth. For each signal segment, the VMD algorithm automatically selects the most suitable decomposition parameters (such as the number of modes and penalty factor) to ensure that the decomposition result for each signal segment is as accurate as possible. The first modal component is typically a low-frequency component, representing the main characteristics of the signal.

[0026] Step S32: Optimize the number of modes and the penalty factor based on the first mode component of each monitoring signal. The optimization of the number of modes and the penalty factor is achieved by using an adaptive evolution strategy of the covariance matrix to obtain the optimized decomposition parameters, which are the number of modes and the penalty factor. It is understandable that this step, by optimizing the number of modes and the penalty factor in VMD, can effectively improve the accuracy and precision of signal decomposition. The introduction of the covariance matrix adaptive evolution strategy algorithm makes the parameter optimization process more adaptive and intelligent, automatically adjusting parameters to adapt to the characteristics of different signals and avoiding the errors and limitations of manual parameter setting. This not only improves the quality of the decomposition results but also enhances the robustness of the entire signal processing process, especially when dealing with nonlinear and time-varying signals. It ensures more accurate extraction of key signal features, providing more reliable data support for subsequent signal analysis and compaction quality assessment.

[0027] The covariance matrix adaptive evolutionary strategy can automatically learn the correlation between parameters, dynamically adjust the search step size, and retain the best individuals in each generation. In constructing the optimal parameters, the initial values ​​of K and α can be set to 5 and 2000, respectively. During optimization, the maximum number of iterations is set to 100-200, the fitness change threshold is set to 1-6, and the maximum number of iterations without improvement is set to 20.

[0028] The optimal selection of parameters is mainly determined by the fitness function, which is generally the information entropy, including power spectral entropy, singular spectral entropy, energy entropy, approximate entropy, sample entropy, fuzzy entropy, permutation entropy, etc. Here, energy entropy is chosen as the fitness function. A higher information entropy indicates greater uncertainty and less information content; therefore, the optimal parameter combination corresponds to the minimum information entropy.

[0029] In this step, the formula for calculating the energy entropy of the coupled state is as follows: ; The formula for calculating the energy entropy of a chaotic state is as follows: ; in, The energy entropy of a signal in a coupled state. Indicates the time of the boundary point. Indicates time, These represent the modal components obtained after performing VMD decomposition on the coupled signal. Represents modal components exist Data at any given time It represents a very small non-zero value, used to avoid mathematical errors caused by taking the logarithm of zero; The energy entropy of a signal representing a chaotic state. Indicates the duration of the signal. These represent the modal components obtained after performing VMD decomposition on a signal in a chaotic state. Represents modal components exist Data at any given time Indicates the first Time point, Indicates the coupling state. Indicates a chaotic state. Indicates the first A signal in a coupled state, Indicates the first A signal in a chaotic state.

[0030] Step S33: Perform variational mode decomposition on each monitoring signal segment again based on the optimized decomposition parameters to obtain the second mode component of each monitoring signal segment.

[0031] It is understandable that the optimized number of modes and the penalty factor in this step determine the decomposition quality of each monitoring signal segment. The selection of the number of modes ensures that the signal can be decomposed into an appropriate number of modal components, while the penalty factor controls the bandwidth of each modal component, affecting the degree of separation between the modal components. By optimizing these parameters, mode aliasing can be effectively avoided, making the signal decomposition more accurate and clear. Especially for nonlinear and time-varying signals, the optimized parameters can ensure the independence between modal components, thereby reducing information loss and errors.

[0032] The second modal component of each monitoring signal typically represents the high-frequency components of the signal. These components may reflect detailed features of the signal or be related to subtle vibration changes in the vibratory roller. This decomposition effectively separates the low-frequency and high-frequency components of the signal, avoiding interference from low-frequency components and allowing for clearer extraction of the high-frequency components. This not only captures the overall characteristics of the signal but also enables precise identification of minute fluctuations and subtle changes.

[0033] Step S4: Perform Hilbert transform on the second mode component, and combine the instantaneous frequency and instantaneous amplitude of each mode component obtained by the transform to obtain the spectrum information of the monitoring signal; It is understandable that this step, through Hilbert transform, can accurately extract the instantaneous frequency and instantaneous amplitude of the signal, thereby obtaining the dynamic spectrum of the signal. This not only helps in the in-depth analysis of the time-frequency characteristics of the signal, but also effectively improves the accuracy of signal processing, especially when dealing with complex nonlinear and time-varying signals, better revealing the detailed changes in the signal. By combining the instantaneous frequency and instantaneous amplitude, the obtained spectral information provides reliable data support for subsequent energy analysis, making the evaluation of vibration signals more accurate and comprehensive. In this step, step S4 includes steps S41, S42, and S43.

[0034] Step S41: Perform Hilbert transform on the second modal component to obtain the transformed modal component; The Hilbert transform is a mathematical tool used to generate an orthogonal companion signal to a given signal, such that the original and companion signals have the same amplitude but a 90-degree phase difference. In signal processing, the Hilbert transform is widely used to calculate the instantaneous frequency and amplitude of a signal. By performing the Hilbert transform on each second-mode component, a complex signal orthogonal to the original signal can be obtained. The real and imaginary parts of this complex signal represent the original amplitude and the phase information obtained through the transform, respectively. In this step, the Hilbert transform formula is as follows: ; ; in, The Hilbert transform results of the modal components of the signal in the coupled state. Indicates Cauchy's principal value. Represents pi (π). The signal representing the coupling state at a frequency of Modal components at time, Indicates the first Time point, This indicates differentiation. Indicates frequency, The Hilbert transform results of the modal components of a signal representing a chaotic state. This indicates a chaotic state at a frequency of Data at that time, Indicates the first The signal in the coupled state has a frequency of Modal components at time, Indicates the first A chaotic signal at a frequency of Modal components at time, Indicates the coupling state. This indicates a chaotic state.

[0035] Step S42: Analyze based on the transformed modal components, wherein the instantaneous frequency and instantaneous amplitude of each transformed modal component are extracted by analyzing the phase and amplitude of the transformed modal components; Understandably, the Hilbert transform converts a signal into a complex form. The real part of the complex signal represents its original amplitude, while the imaginary part contains its phase information. Phase and amplitude information are two fundamental characteristics of a signal, effectively describing its time and frequency domain behavior. By analyzing the phase and amplitude of the transformed modal components, we can extract the instantaneous frequency and instantaneous amplitude of the signal.

[0036] Instantaneous frequency refers to the frequency of a signal at a specific instant, reflecting the trend of frequency change. In signal analysis, instantaneous frequency is usually obtained by differentiating the phase information, i.e., calculating the rate of change of phase over time. Instantaneous amplitude is a measure of signal strength, usually represented by the amplitude of a complex signal, i.e., the square root of the sum of the squares of the real and imaginary parts. Instantaneous amplitude reflects the energy change of the signal, indicating its instantaneous strength.

[0037] By utilizing these two instantaneous characteristics—instantaneous frequency and instantaneous amplitude—we can obtain the time-frequency characteristics of a signal and further analyze its frequency and amplitude variations at different time points. This information is crucial for further energy analysis because it helps us identify key points of change in the signal, especially in nonlinear and time-varying signals, where this analytical method offers significant advantages. In this way, we can gain a detailed understanding of the signal's local dynamic behavior, thus providing reliable data support for subsequent spectral analysis, energy assessment, and judgment of the signal's quality.

[0038] Step S43: Combine the instantaneous frequency and instantaneous amplitude of each transformed modal component to obtain the spectrum information of the monitoring signal. The spectrum information of the monitoring signal includes the instantaneous frequency and instantaneous amplitude of each time point corresponding to each transformed modal component.

[0039] Understandably, this involves combining the instantaneous frequency and amplitude of each transformed modal component to obtain the spectral information of the monitoring signal. The core purpose of this step is to combine the frequency characteristics and intensity information of each modal component in the time domain, thereby comprehensively reflecting the distribution characteristics of the monitoring signal in time and frequency.

[0040] First, after the Hilbert transform, we obtain the instantaneous frequency and instantaneous amplitude of each modal component. The instantaneous frequency represents the frequency value of the signal at a certain moment, while the instantaneous amplitude represents the amplitude or energy level of the signal at that moment. By combining the instantaneous frequency and instantaneous amplitude of each transformed modal component, we can obtain a time-frequency domain spectrum. This spectrum not only describes the frequency distribution of the signal at various time points but also provides the intensity or energy level of that frequency component.

[0041] Specifically, the spectral information includes the instantaneous frequency and instantaneous amplitude of each transformed modal component, and this information is arranged in a time series. In the monitoring signal of a vibratory roller, the frequency and amplitude of the signal may change over time. The spectral information combining the instantaneous frequency and instantaneous amplitude can reveal the frequency change trend and signal strength fluctuations over different time periods. For example, when the signal is in a relatively stable coupling state, the instantaneous frequency changes little and the instantaneous amplitude is relatively stable; while in a chaotic state, the instantaneous frequency and amplitude may fluctuate significantly, reflecting the characteristics of nonlinear and irregular vibration. The formula for combining the instantaneous frequency and instantaneous amplitude of each transformed modal component is shown below: ; in, The Hilbert spectrum represents the combination of the instantaneous frequencies and instantaneous amplitudes of the transformed modal components. Indicates instantaneous frequency. Indicates the first One data point, q Show the first q One modal component, The first state represents the coupling state. q The square of each modal component, express The Hilbert transform result, express instantaneous frequency, e Represents the natural constant. t Indicates the first t Time point, d This indicates differentiation. j Indicates a negative unit. Indicates the coupling state. This indicates a chaotic state.

[0042] Step S5: Perform marginal spectrum calculation and integration on the spectrum information to obtain the total energy of the monitoring signal.

[0043] Understandably, this step, through marginal spectrum calculation and integration, can accurately quantify the energy distribution of the signal and provide a quantitative basis for assessing the compaction quality through the calculation of the total energy. Compared with traditional signal processing methods, this analysis method based on spectral information and marginal spectrum is more accurate, capable of handling complex nonlinear and time-varying signals, and better reveals the energy characteristics of the signal. Ultimately, the obtained total signal energy provides more scientific and reliable data support for assessing the compaction quality of vibratory rollers, helping to improve the monitoring accuracy and real-time performance of the compaction process. In this step, step S5 includes steps S51 and S52.

[0044] Step S51: Integrate the spectrum information over time to obtain the marginal spectrum; The integral formula is as follows: ; in, Represents the marginal spectrum; The Hilbert spectrum is obtained by combining the instantaneous frequency and instantaneous amplitude of the transformed modal components. Indicates frequency; Indicates the first Time point, This indicates differentiation.

[0045] Step S52: Calculate the marginal spectrum according to the preset total energy calculation formula to obtain the total energy of the monitoring signal.

[0046] The formula for calculating total energy is as follows: ; in, Represents total energy. Represents the marginal spectrum. Indicates frequency, This indicates differentiation.

[0047] Example 2: like Figure 2 As shown, this embodiment provides a vibration nonlinear signal energy analysis system based on variational mode decomposition. (See also...) Figure 2 The system includes an acquisition unit 701, a segmentation unit 702, a decomposition unit 703, a transformation unit 704, and a calculation unit 705.

[0048] The acquisition unit 701 is used to acquire the monitoring signal of the vibratory roller according to preset conditions, wherein the monitoring signal includes the acceleration information of the vibratory roller; The segmentation unit 702 is used to determine the corresponding state based on the root mean square data of the monitoring signal, and to segment the monitoring signal based on the corresponding state of the monitoring signal to obtain the signal segmentation result. The decomposition unit 703 is used to perform variational mode decomposition on the signal segmentation result, optimize the preset decomposition parameters based on the first mode component obtained by decomposition, and then decompose the monitoring signal through the optimized decomposition parameters to obtain at least two second mode components. The transformation unit 704 is used to perform Hilbert transformation on the second mode component and combine the instantaneous frequency and instantaneous amplitude of each mode component obtained by the transformation to obtain the spectrum information of the monitoring signal. The calculation unit 705 is used to perform marginal spectrum calculation and integration on the spectrum information to obtain the total energy of the monitoring signal.

[0049] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0051] 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 method of analyzing energy of a vibration nonlinear signal by variational mode decomposition, characterized in that, The method comprises the following steps: obtaining a monitoring signal of the vibrating roller under preset conditions, wherein the monitoring signal comprises acceleration information of the vibrating roller; judging the corresponding state of the monitoring signal based on the root mean square data of the monitoring signal, and performing segmented processing on the monitoring signal based on the corresponding state of the monitoring signal to obtain a signal segmentation result; performing variational modal decomposition processing on the signal segmentation result, and optimizing the preset decomposition parameters based on the first modal component obtained by the decomposition, and then decomposing the monitoring signal by using the optimized decomposition parameters to obtain at least two second modal components; performing Hilbert transform processing on the second modal components, and combining the instantaneous frequency and the instantaneous amplitude of each modal component obtained by the transform to obtain the frequency spectrum information of the monitoring signal; performing marginal spectrum calculation and integration on the frequency spectrum information to obtain the total energy of the monitoring signal.

2. The variational modality decomposition based vibration nonlinear signal energy analysis method according to claim 1, characterized in that, The method comprises the following steps: performing root mean square calculation on the monitoring signal, and judging the state information of the monitoring signal based on the root mean square value of each time node; segmenting the monitoring signal according to the corresponding state information to obtain at least two segments of the monitoring signal, and taking the segments as the signal segmentation result. 3.The variational modality decomposition-based vibration nonlinear signal energy analysis method according to claim 1, characterized in that, The method comprises the following steps: performing variational modal decomposition processing on each segment of the monitoring signal in the signal segmentation result to obtain the first modal component of each segment of the monitoring signal; optimizing the modal number and the penalty factor based on the first modal component of each segment of the monitoring signal, wherein the value of the modal number and the penalty factor is optimized by using a covariance matrix adaptive evolution strategy to obtain the optimized decomposition parameters, and the decomposition parameters are the modal number and the penalty factor; performing variational modal decomposition on each segment of the monitoring signal again according to the optimized decomposition parameters to obtain the second modal component of each segment of the monitoring signal. 4.The variational modality decomposition-based vibration nonlinear signal energy analysis method according to claim 1, characterized in that, The method comprises the following steps: performing Hilbert transform on the second modal components to obtain the transformed modal components; performing analysis based on the transformed modal components, wherein the instantaneous frequency and the instantaneous amplitude of each transformed modal component are extracted by analyzing the phase and the amplitude of the transformed modal components; combining the instantaneous frequency and the instantaneous amplitude of each transformed modal component to obtain the frequency spectrum information of the monitoring signal, wherein the frequency spectrum information of the monitoring signal comprises the instantaneous frequency and the instantaneous amplitude of each time node corresponding to each transformed modal component. 5.The variational modality decomposition-based vibration nonlinear signal energy analysis method according to claim 1, characterized in that, The method comprises the following steps: integrating the frequency spectrum information in time to obtain a marginal spectrum; calculating the total energy of the monitoring signal by using a preset total energy calculation formula on the marginal spectrum.

6. A vibration nonlinear signal energy analysis system based on variational mode decomposition, characterized in that, The method comprises the following steps: The acquisition unit is configured to acquire a monitoring signal of the vibratory roller according to a preset condition, and the monitoring signal comprises acceleration information of the vibratory roller. The segmentation unit is configured to determine a corresponding state of the monitoring signal based on root mean square data of the monitoring signal, and perform segmentation processing on the monitoring signal based on the corresponding state of the monitoring signal to obtain a signal segmentation result. The decomposition unit is configured to perform variational modal decomposition processing on the signal segmentation result, optimize a preset decomposition parameter based on a first modal component obtained by the decomposition, and then perform decomposition on the monitoring signal by using the optimized decomposition parameter to obtain at least two second modal components. The transformation unit is configured to perform Hilbert transformation processing on the second modal components, and combine an instantaneous frequency and an instantaneous amplitude of each modal component obtained by the transformation to obtain frequency spectrum information of the monitoring signal. The calculation unit is configured to perform marginal spectrum calculation and integration on the frequency spectrum information to obtain total energy of the monitoring signal.

7. The system for variational modal decomposition of a vibration nonlinear signal energy analysis of claim 6, wherein, The segmentation unit comprises: The first segmentation subunit is configured to perform root mean square calculation on the monitoring signal, and determine state information of the monitoring signal based on a root mean square value of each time node. The second segmentation subunit is configured to segment the monitoring signal according to the corresponding state information of the monitoring signal to obtain at least two segments of the monitoring signal, and take the at least two segments of the monitoring signal as the signal segmentation result.

8. The system for variational modal decomposition of a vibration nonlinear signal energy analysis of claim 6, wherein, The decomposition unit comprises: The first decomposition subunit is configured to perform variational modal decomposition processing on each segment of the monitoring signal in the signal segmentation result to obtain a first modal component of each segment of the monitoring signal. The second decomposition subunit is configured to optimize a modal number and a penalty factor based on the first modal component of each segment of the monitoring signal, wherein the value of the modal number and the penalty factor is optimized by using a covariance matrix adaptive evolution strategy to obtain an optimized decomposition parameter, and the decomposition parameter is the modal number and the penalty factor. The third decomposition subunit is configured to perform variational modal decomposition on each segment of the monitoring signal again according to the optimized decomposition parameter to obtain a second modal component of each segment of the monitoring signal.

9. The system for variational modal decomposition of a vibration nonlinear signal energy analysis of claim 6, wherein, The transformation unit comprises: The first transformation subunit is configured to perform Hilbert transformation on the second modal components to obtain transformed modal components. The second transformation subunit is configured to analyze the transformed modal components, wherein the instantaneous frequency and the instantaneous amplitude of each transformed modal component are extracted by analyzing the phase and the amplitude of the transformed modal components. The third transformation subunit is configured to combine the instantaneous frequency and the instantaneous amplitude of each transformed modal component to obtain the frequency spectrum information of the monitoring signal, and the frequency spectrum information of the monitoring signal comprises the instantaneous frequency and the instantaneous amplitude of each time node corresponding to each transformed modal component.

10. The system for variational modal decomposition of a vibration nonlinear signal energy analysis of claim 6, wherein, The calculation unit comprises: The first calculation subunit is configured to integrate the frequency spectrum information in time to obtain a marginal spectrum. The second calculation subunit is configured to calculate the total energy of the monitoring signal by using a preset total energy calculation formula on the marginal spectrum.

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