Electric power fitting vibration signal noise reduction method, system, equipment and medium

Abstract

The invention discloses an electric power fitting vibration signal noise reduction method, system and device and a medium, and the method comprises the steps: taking the sum of information entropies of all modal components as a fitness function, and searching an optimal value of a balance parameter based on the fitness function through employing a raccoon longnose optimization algorithm; after a balance parameter is set based on the optimal value, successive variational mode decomposition is carried out on a vibration signal, a plurality of mode components are obtained, and the vibration signal is an electric power fitting vibration signal to be subjected to noise reduction; respectively calculating a cross correlation coefficient and a threshold value between each modal component and the vibration signal; the modal components with the cross correlation coefficients exceeding a threshold value are determined as effective components, and the modal components with the cross correlation coefficients lower than the threshold value are determined as noise components; and reconstructing the effective component into a denoised vibration signal and outputting the denoised vibration signal. According to the method, the optimal parameter of successive variational mode decomposition is searched through the raccoon longnose optimization algorithm, so that the noise reduction effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric hardware, and in particular to a method, system, equipment and medium for reducing noise of vibration signals of electric hardware. Background Art

[0002] Power fittings are metal accessories that connect and combine various devices in a power system, transmitting mechanical and electrical loads and providing some form of protection. They are crucial to the safety of the power system. Even a single defective fitting can paralyze the transmission line. Therefore, monitoring the condition of power fittings without affecting their operation is crucial. Vibration signals are a key indicator of the working condition of power fittings, providing early warning before they become damaged and preventing further damage. Vibration signal characteristics such as amplitude, frequency, and waveform can be used to detect mechanical defects such as loose bolts, cracks, or wear in fittings. For example, internal cracks in porcelain insulators can trigger vibration signals in specific frequency bands. Sudden abnormal vibration signals can indicate conductor galloping, wind vibration, or other external impacts. Furthermore, detecting high-frequency abnormal vibration signals in fittings can help prevent accidents such as line breaks and tower collapses by installing anti-vibration hammers and adjusting conductor tension. Power fittings often operate in harsh outdoor environments, such as high and low temperatures, and strong electromagnetic fields. Strong electromagnetic fields, in particular, can severely impact sensor operation and signal transmission stability. Furthermore, power fittings are subject to a variety of vibration sources, including wind, mechanical vibration, and their own electromagnetic vibration. Therefore, obtaining accurate vibration signals from power fittings and performing noise reduction processing on these signals are essential.

[0003] Traditional signal denoising methods, such as Fourier transform thresholding (FFT), empirical mode decomposition (EMD), wavelet packet decomposition (WPT), and variational mode decomposition (VMD), all have shortcomings. The Fourier transform thresholding method converts the signal into a frequency domain signal for analysis and processing. However, the threshold is difficult to determine, and this method may filter out both noise and high-frequency components of the signal, resulting in signal distortion. The wavelet packet method relies on the setting of parameters such as the decomposition level and threshold, requiring multiple attempts to achieve good results, resulting in low noise reduction efficiency. The empirical mode decomposition method can effectively distinguish between signals and noise, but it cannot address modal aliasing and end-point effects. The variational mode decomposition method (VMD) does not suffer from modal aliasing and end-point effects, but the decomposition effect is overly dependent on the settings of the parameters K and α.

[0004] In view of the above, it is necessary to propose a noise reduction method for vibration signals of power fittings to solve the above problems. Summary of the Invention

[0005] The present invention provides a method, system, device and medium for reducing noise of vibration signals of electric hardware, which are used to find the optimal parameters of successive variational modal decomposition through the coati optimization algorithm to improve the noise reduction effect.

[0006] In view of this, a first aspect of the present invention provides a method for reducing noise of vibration signals of electric hardware, the method comprising:

[0007] The sum of the information entropy of each modal component is used as the fitness function, and the coati optimization algorithm is used based on the fitness function to find the optimal value of the balance parameter;

[0008] After setting the balance parameter based on the optimal value, performing successive variational modal decomposition on the vibration signal to obtain a plurality of modal components, wherein the vibration signal is a vibration signal of the power hardware to be de-noised;

[0009] respectively calculating the mutual correlation coefficient and threshold between each of the modal components and the vibration signal;

[0010] determining that a modal component whose cross-correlation coefficient exceeds a threshold value is a valid component and a modal component whose cross-correlation coefficient is lower than the threshold value is a noise component;

[0011] The effective component is reconstructed into a vibration signal after noise reduction and outputted.

[0012] Optionally, the fitness function is expressed as:

[0013]

[0014] Where, is the fitness function; For the The information entropy of the modal components; For the The signal value of the modal component falls in The probability of an interval; is the total number of intervals, Total number of modal components.

[0015] Optionally, the method of finding the optimal value of the balance parameter based on the fitness function using the coati optimization algorithm includes:

[0016] Parameter initialization: set the maximum number of iterations , randomly generated a coati;

[0017] Calculating the fitness of each coati based on the fitness function, and recording the best fitness and corresponding position;

[0018] Coatis engage in cooperative hunting of iguanas;

[0019] Coatis scatter to escape predators;

[0020] Calculate the fitness of the coati in the new position and update the best fitness and corresponding position;

[0021] Iterate until the maximum number of iterations , output the optimal value of the parameter to be optimized, where the parameter to be optimized is the balance parameter.

[0022] Optionally, performing successive variational modal decomposition on the vibration signal includes:

[0023] Assume that the vibration signal is decomposed into order modal components and residual signals, and The first modal components and residual signals are divided into first-order modal components and the residual signal Two parts;

[0024] By building constraints And minimize the bandwidth of each modal component to make it compact around the center frequency.

[0025] By building constraints , used to minimize and modal aliasing;

[0026] By building constraints , used to avoid First mode With the previous order mode repetition;

[0027] Will solve the First mode The problem is transformed into solving the problem with constraints 、 and The minimization problem is solved by constructing the augmented Lagrangian equation with constraints 、 and The minimization problem, where First mode is known;

[0028] For the first First mode , center frequency Iterate until the inner layer convergence condition is met and output the The first modal component;

[0029] Repeat the above steps to decompose order modes until the outer convergence condition is met.

[0030] Optionally, the constraint condition Expressed as:

[0031] ;

[0032] Where, represents the partial derivative with respect to time; is the Dirac function; Represents the convolution operation; For the The center frequency of the first mode, For the order mode, is a natural constant, is an imaginary unit; Indicates time.

[0033] Optionally, the constraint condition Expressed as:

[0034] ;

[0035] Where, is the impulse response of the filter, is the residual signal.

[0036] Optionally, the constraint condition Expressed as:

[0037] The constraints Expressed as:

[0038] ;

[0039] Where, is the impulse response of the filter, For the order mode.

[0040] Optionally, the inner layer convergence condition is expressed as:

[0041] ;

[0042] Where, is the inner loop convergence threshold, for.

[0043] Optionally, the outer layer convergence condition is expressed as:

[0044] ;

[0045] Where, is the outer loop convergence threshold; is the power of the noise signal, is the vibration signal, is the iteration period, For the The modal component, Indicates the modal component number.

[0046] Optionally, the mutual correlation coefficient and the threshold are respectively expressed as:

[0047] ;

[0048] ;

[0049] Where, is the mutual correlation coefficient, is the threshold, For the The data mean of the modal components; is the data mean of the vibration signal, is the vibration signal, For the modal components, is the total number of modal components.

[0050] A second aspect of the present invention provides a system for reducing noise of vibration signals of electric hardware, the system comprising:

[0051] An optimization unit, configured to use the sum of the information entropy of each modal component as a fitness function and to use a coati optimization algorithm based on the fitness function to find the optimal value of the balance parameter;

[0052] a decomposition unit, configured to perform successive variational modal decomposition on the vibration signal after setting the balance parameter based on the optimal value to obtain a plurality of modal components, wherein the vibration signal is a vibration signal of the power fitting to be de-noised;

[0053] a calculation unit, configured to respectively calculate a mutual correlation coefficient and a threshold value between each of the modal components and the vibration signal;

[0054] a determination unit, configured to determine that the modal component whose cross-correlation coefficient exceeds the threshold is a valid component, and the modal component whose cross-correlation coefficient is lower than the threshold is a noise component;

[0055] The output unit is used to reconstruct the effective component into a vibration signal after noise reduction and output it.

[0056] A third aspect of the present invention provides a device for reducing noise of vibration signals of electric hardware, the device comprising a processor and a memory:

[0057] The memory is used to store program code and transmit the program code to the processor;

[0058] The processor is configured to execute the steps of the method for reducing noise of vibration signals of electric power fittings as described in the first aspect according to the instructions in the program code.

[0059] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the method for reducing noise of vibration signals of electric power fittings described in the first aspect.

[0060] It can be seen from the above technical solutions that the present invention has the following advantages:

[0061] The present invention provides a method for reducing the noise of the vibration signal of an electric hardware. The method uses the sum of the information entropy of each modal component as the fitness function, and uses the coati optimization algorithm based on the fitness function to find the optimal value of the balance parameter; after setting the balance parameter based on the optimal value, the vibration signal is subjected to successive variational modal decomposition to obtain a number of modal components, wherein the vibration signal is the vibration signal of the electric hardware to be denoised; the mutual correlation coefficient and threshold between each modal component and the vibration signal are calculated respectively; the modal component with a mutual correlation coefficient exceeding the threshold is determined to be a valid component, and the modal component with a mutual correlation coefficient below the threshold is determined to be a noise component; the valid component is reconstructed into a vibration signal after denoising and output. The present invention uses the coati optimization algorithm to find the optimal parameters of the successive variational modal decomposition, thereby solving the problem of the balance parameter in the successive variational modal decomposition (SVMD). The problem that the value of depends on experience is solved; and the problem that the threshold μ is difficult to determine in the wavelet packet method is solved; thereby improving the noise reduction effect of the vibration signal of the power hardware. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 A schematic diagram of a method for reducing noise of vibration signals of electric hardware provided by an embodiment of the present invention Figure 1 ;

[0064] Figure 2 A schematic diagram of a method for reducing noise of vibration signals of electric hardware provided by an embodiment of the present invention Figure 2 ;

[0065] Figure 3 A flowchart of the Coati Optimization Algorithm (COA) provided in an embodiment of the present invention;

[0066] Figure 4 A flowchart of Successive Variational Mode Decomposition (SVMD) provided in an embodiment of the present invention;

[0067] Figure 5 A schematic structural diagram of a system for reducing noise of vibration signals of electric hardware provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0069] See also Figure 1 and Figure 2 , an embodiment of the present invention provides a method for reducing noise of vibration signals of electric hardware, comprising:

[0070] Step 101: The sum of the information entropy of each modal component is used as a fitness function, and the coati optimization algorithm is used based on the fitness function to find the optimal value of the balance parameter.

[0071] First, construct the fitness function , specifically: the optimization goal is to maximize the sum of the information entropy of the modal components (IMF), then the fitness function for:

[0072] ;

[0073] Where, is the fitness function; For the The information entropy of the modal components; For the The signal value of the modal component falls in The probability of an interval; is the total number of intervals, Total number of modal components.

[0074] It's important to note that in signal processing, modal components refer to the decomposition of a complex signal into several simple signal components with distinct characteristics. These simple signal components are called modal components. For example, using methods such as empirical mode decomposition (EMD), a complex time series signal can be decomposed into a series of intrinsic mode functions (IMFs). Each IMF is a modal component, each with distinct frequency and amplitude characteristics, reflecting the local characteristics of the signal at different scales.

[0075] Then, the Coati Optimization Algorithm (COA) is used to find the equilibrium parameters The optimal value of Figure 3 As shown, the specific steps include:

[0076] (a) Parameter initialization: maximum number of iterations , parameters to be optimized The value range is , set the number of coatis , randomly generate the position of each raccoon, and the position of each raccoon represents the parameter to be optimized A value of ;

[0077] (b) Calculate the fitness of each coati and record the best fitness and corresponding position;

[0078] (c) Update the coati position:

[0079] (1) 50% of coatis climb trees to hunt iguanas. Their positions are updated as follows:

[0080] ;

[0081] Where, Indicates in In the iteration The location of the coati; Indicates in In the iteration The location of the coati; is a positive integer 1 or 2; Indicates in The global optimal position of the coati in iterations; for A random number between is the number of coatis;

[0082] (2) The remaining 50% of coatis wait for the iguana to land, and their positions are updated as follows:

[0083] Where, Indicates in In the iteration The location of the coati; Indicates in In the iteration The location of the coati; is a positive integer 1 or 2; Indicates in The landing position of the iguana in the iteration; for A random number between is the number of coatis; represents the fitness function;

[0084] (d) If the coati needs to avoid a predator, its position is updated to:

[0085] ;

[0086] Where, Indicates in In the iteration The location of the coati; Indicates in In the iteration The location of the coati; and are the lower limit and upper limit of the value of the parameter to be optimized respectively; is the current iteration number; for A random number between is the number of coatis;

[0087] (e) If a coati crosses the boundary, reset its position as follows:

[0088] ;

[0089] Where, Indicates in In the iteration The location of the coati; and are the lower limit and upper limit of the value of the parameter to be optimized respectively; for A random number between

[0090] (f) Iterate to the maximum number of iterations , select the best fitness The value of .

[0091] It should be noted that the Coati Optimization Algorithm (COA) is a novel metaheuristic optimization algorithm inspired by the foraging behavior of coatis. It was proposed by Mohammad Dehghani et al. in 2023. Coatis are mammals native to the Americas that exhibit a variety of behaviors when foraging, such as searching, chasing, and mobbing prey. This algorithm simulates these behaviors to optimize search. In the algorithm, each coati individual represents a potential solution, and by simulating the coati's foraging behavior, these solutions are continuously updated to find the optimal solution.

[0092] The coati optimization algorithm mainly consists of the following stages:

[0093] Initialization phase: Randomly initialize a group of coati individuals (i.e., initial solution), which are distributed in the solution space.

[0094] Search phase: Simulating the behavior of coatis searching for food in the environment, individuals conduct random searches in the solution space and explore different areas.

[0095] Chasing stage: When an individual finds a better food source (better solution), other individuals move closer to it and try to obtain better food.

[0096] Siege phase: Multiple individuals collaborate to besiege the target food source (optimal solution) and further optimize the solution.

[0097] The coati optimization algorithm has the advantages of simple structure, easy implementation, and fast convergence speed. It can be used to solve various optimization problems, such as function optimization, engineering design optimization, and parameter optimization in machine learning.

[0098] Step 102 : After setting the balancing parameters based on the optimal values, perform successive variational modal decomposition on the vibration signal to obtain a plurality of modal components, wherein the vibration signal is a vibration signal of the electrical hardware to be de-noised.

[0099] It should be noted that, specifically, by using a vibration sensor to measure the vibration of a certain electrical hardware, the vibration signal of the hardware is obtained. .

[0100] It should be noted that this step first sets the balance parameters , and then perform successive variational mode decomposition (SVMD) on the vibration signal, such as Figure 4 As shown, the specific steps include:

[0101] (a) Initialization parameters ;

[0102] (b) Construct the constraint that the bandwidth of each modal component is compact around the center frequency :

[0103] ;

[0104] Where, represents the partial derivative with respect to time; is the Dirac function; Represents the convolution operation; For the The center frequency of the first mode; For the order mode; is a natural constant; is an imaginary unit; Expressed as time;

[0105] (c) Constructing constraints to minimize modal aliasing :

[0106] ;

[0107] Where, is the impulse response of the filter; is the residual signal; Represents the convolution operation;

[0108] (d) Constructing constraints for minimizing the energy of the residual signal :

[0109] ;

[0110] Where, is the impulse response of the filter; For the The first modal component; Represents the convolution operation;

[0111] (e) Due to First mode Known, we will solve First mode The problem is transformed into a minimization problem with constraints, namely:

[0112] ;

[0113] Where, For the The first modal component; is the residual signal; is the vibration signal; For the The center frequency of the first mode; To balance constraints 、 and ; The bandwidth of each modal component is constrained to be tight around the center frequency; Constraints to minimize modal aliasing; Constraints for minimizing the energy of the residual signal;

[0114] (f) Constructing the augmented Lagrangian equation Solve the minimization problem in step (e) and use Parseval's equality for variable substitution;

[0115] Among them, the augmented Lagrangian equation is expressed as:

[0116] ;

[0117] Where, is the Lagrange multiplier; For the The first modal component; For the The center frequency of the first mode; is the unprocessed part of the residual signal; is the vibration signal; To balance constraints 、 and ; The bandwidth of each modal component is constrained to be tight around the center frequency; Constraints to minimize modal aliasing; Constraints for minimizing the energy of the residual signal;

[0118] The augmented Lagrange equation after variable replacement is updated as follows:

[0119] ;

[0120] Where, is the augmented Lagrange equation; is the Lagrange multiplier; Indicates that the variable is a vector; For the Frequency representation of the first-order modal components; is the frequency representation of the filter impulse response; express function; Indicates frequency; For the The center frequency of the first mode; is the frequency representation of the unprocessed part of the residual signal; is the frequency representation of the vibration signal; To balance constraints 、 and ; The bandwidth of each modal component is constrained to be tight around the center frequency; Constraints to minimize modal aliasing; Constraints for minimizing the energy of the residual signal; Indicates a continuous multiplication operation;

[0121] (g) Iteratively update the modal components using the alternating direction method of multipliers (ADMM) and center frequency , the update formula is:

[0122] ; ;

[0123] Where, is the Lagrange multiplier; Indicates that the variable is a vector; For the The updated Frequency representation of the first-order modal components; For the The updated The center frequency of the first mode; is the frequency representation of the vibration signal; represents the frequency independent variable; To balance constraints 、 and ;

[0124] (h) Iterate until the inner and outer convergence conditions are met, and output the decomposed modal components.

[0125] It's understandable that this step simulates the behavior of a coati climbing a tree to hunt iguanas, adjusting the relevant parameters in the noise reduction process for the vibration signals of power fittings to optimize the noise reduction effect. This position update method, combined with the characteristics of the coati optimization algorithm, enables the noise reduction algorithm to better search for the optimal solution in the solution space when processing the vibration signals of power fittings. This effectively removes noise, improves the quality of the vibration signal, and provides a more reliable data foundation for subsequent accurate judgment of the power fitting status.

[0126] Step 103: Calculate the mutual correlation coefficient and threshold between each modal component and the vibration signal respectively.

[0127] It should be noted that this step calculates the mutual correlation coefficients between each modal component and the vibration signal. and threshold :

[0128] Among them, the cross-correlation coefficient and threshold are expressed as:

[0129] ;

[0130] ;

[0131] Where, is the mutual correlation coefficient, is the threshold, For the The data mean of the modal components; is the data mean of the vibration signal, is the vibration signal, for, for, for.

[0132] It can be understood that this step can effectively determine the degree of correlation between each modal component and the vibration signal by calculating the mutual correlation coefficient and threshold between each modal component and the vibration signal, so as to make decisions on subsequent processing based on these values.

[0133] Step 104 : Determine that the modal components whose cross-correlation coefficients exceed a threshold value are valid components, and the modal components whose cross-correlation coefficients are lower than the threshold value are noise components.

[0134] It should be noted that the correlation coefficient is greater than the threshold value. The modal component (IMF) is a valid component and is below the threshold The IMF component is the noise component.

[0135] It can be understood that this step accurately distinguishes the modal components into effective components and noise components by determining the degree of correlation between each modal component and the vibration signal, laying the foundation for subsequent targeted processing of the vibration signal of the power fitting. Through this distinction, it is possible to retain the effective components and remove the noise components in subsequent processing, thereby effectively reducing the noise interference in the vibration signal of the power fitting, improving the quality and reliability of the signal, and enabling the processed signal to more accurately reflect the actual vibration conditions of the power fitting, providing more accurate data support for operating status monitoring and fault diagnosis of the power fitting.

[0136] Step 105: reconstruct the effective component into a vibration signal after noise reduction and output it.

[0137] It should be noted that the effective component is reconstructed into a vibration signal after noise reduction , and then output the noise-reduced vibration signal reconstructed by the effective component Furthermore, the noise component can be reconstructed into a noise signal according to actual needs.

[0138] It is understandable that this step, by reconstructing the effective components, can integrate signals that accurately reflect the actual vibration conditions of the power fittings to form a noise-reduced vibration signal. This reconstruction method fully utilizes the effective components previously identified, so that the final output vibration signal restores the true vibration characteristics of the power fittings to the greatest extent possible. The output noise-reduced vibration signal provides a high-quality data source for subsequent analyses of the operating status of the power fittings. It is of vital importance for both real-time monitoring of the fittings' operating status and accurate diagnosis of potential faults, and helps to improve the stability and safety of the entire power system operation.

[0139] An embodiment of the present invention provides a method for reducing the noise of a vibration signal of an electric hardware. The method uses the sum of the information entropy of each modal component as a fitness function, and uses the coati optimization algorithm based on the fitness function to find the optimal value of the balance parameter. After setting the balance parameter based on the optimal value, the vibration signal is subjected to successive variational modal decomposition to obtain a number of modal components, wherein the vibration signal is the vibration signal of the electric hardware to be reduced in noise. The mutual correlation coefficient and threshold between each modal component and the vibration signal are calculated respectively. The modal component whose mutual correlation coefficient exceeds the threshold is determined to be a valid component, and the modal component whose mutual correlation coefficient is lower than the threshold is a noise component. The valid component is reconstructed into a vibration signal after noise reduction and outputted. The present invention uses the coati optimization algorithm to find the optimal parameters of the successive variational modal decomposition, thereby solving the problem of the balance parameters in the successive variational modal decomposition (SVMD). The problem that the value of depends on experience is solved; and the problem that the threshold μ is difficult to determine in the wavelet packet method is solved; thereby improving the noise reduction effect of the vibration signal of the power hardware.

[0140] The above is a method for reducing noise of a vibration signal of an electric hardware provided in an embodiment of the present invention. The following is a system for reducing noise of a vibration signal of an electric hardware provided in an embodiment of the present invention.

[0141] See also Figure 5 , an embodiment of the present invention provides a system for reducing noise of vibration signals of electric hardware, comprising:

[0142] The optimization unit 201 is configured to use the sum of the information entropies of the modal components as a fitness function and to use the coati optimization algorithm based on the fitness function to find the optimal value of the balance parameter.

[0143] The decomposition unit 202 is used to perform successive variational modal decomposition on the vibration signal after setting the balance parameters based on the optimal value to obtain a plurality of modal components, wherein the vibration signal is the vibration signal of the power hardware to be de-noised.

[0144] The calculation unit 203 is used to calculate the mutual correlation coefficient and threshold value between each modal component and the vibration signal.

[0145] The determination unit 204 is configured to determine that a modal component whose cross-correlation coefficient exceeds a threshold is a valid component, and a modal component whose cross-correlation coefficient is lower than the threshold is a noise component.

[0146] The output unit 205 is used to reconstruct the effective component into a vibration signal after noise reduction and output it.

[0147] Furthermore, an embodiment of the present invention also provides a device for reducing noise of vibration signals of electric hardware, the device comprising a processor and a memory:

[0148] The memory is used to store program code and transmit the program code to the processor;

[0149] The processor is used to execute the steps of the method for reducing noise of vibration signals of electric hardware as described in the above method embodiment according to the instructions in the program code.

[0150] Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the method for reducing noise of vibration signals of electric power fittings described in the above method embodiment.

[0151] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0153] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0154] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0155] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0156] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for reducing noise of vibration signals of electric power fittings, characterized in that: include: The sum of the information entropy of each modal component is used as the fitness function, and the coati optimization algorithm is used based on the fitness function to find the optimal value of the balance parameter; After setting the balance parameter based on the optimal value, performing successive variational modal decomposition on the vibration signal to obtain a plurality of modal components, wherein the vibration signal is a vibration signal of the power hardware to be de-noised; respectively calculating the mutual correlation coefficient and threshold between each of the modal components and the vibration signal; determining that a modal component whose cross-correlation coefficient exceeds a threshold value is a valid component and a modal component whose cross-correlation coefficient is lower than the threshold value is a noise component; The effective component is reconstructed into a vibration signal after noise reduction and outputted.

2. The method for reducing noise of vibration signals of electric power fittings according to claim 1, characterized in that: The expression of the fitness function is: Where, is the fitness function; For the The information entropy of the modal components; For the The signal value of the modal component falls in The probability of an interval; is the total number of intervals, Total number of modal components.

3. The method for reducing noise of vibration signals of electric power fittings according to claim 1, characterized in that: The method of finding the optimal value of the balance parameter based on the fitness function using the coati optimization algorithm includes: Parameter initialization: set the maximum number of iterations , randomly generated a coati; Calculating the fitness of each coati based on the fitness function, and recording the best fitness and corresponding position; Coatis engage in cooperative hunting of iguanas; Coatis scatter to escape predators; Calculate the fitness of the coati in the new position and update the best fitness and corresponding position; Iterate until the maximum number of iterations , output the optimal value of the parameter to be optimized, where the parameter to be optimized is the balance parameter.

4. The method for reducing noise of vibration signals of electric power fittings according to claim 1, characterized in that: The performing successive variational modal decomposition on the vibration signal includes: Assume that the vibration signal is decomposed into order modal components and residual signals, and The first modal components and residual signals are divided into first-order modal components and the residual signal Two parts; By building constraints And minimize the bandwidth of each modal component to make it compact around the center frequency. By building constraints , used to minimize and modal aliasing; By building constraints , used to avoid First mode With the previous order mode repetition; Will solve the First mode The problem is transformed into solving the problem with constraints 、 and The minimization problem is solved by constructing the augmented Lagrangian equation with constraints 、 and The minimization problem, where First mode is known; For the first First mode , center frequency Iterate until the inner layer convergence condition is met and output the The first modal component; Repeat the above steps to decompose order modes until the outer convergence condition is met.

5. The method for reducing noise of vibration signals of electric power fittings according to claim 4, characterized in that: The constraints Expressed as: ； Where, represents the partial derivative with respect to time; is the Dirac function; Represents the convolution operation; For the The center frequency of the first mode, For the order mode, is a natural constant, is an imaginary unit; Indicates time.

6. The method for reducing noise of vibration signals of electric power fittings according to claim 4, characterized in that: The constraints Expressed as: ； Where, is the impulse response of the filter, is the residual signal.

7. The method for reducing noise of vibration signals of electric power fittings according to claim 4, characterized in that: The constraints Expressed as: The constraints Expressed as: ； Where, is the impulse response of the filter, For the order mode.

8. The method for reducing noise of vibration signals of electric power fittings according to claim 4, characterized in that: The inner layer convergence condition is expressed as: ； Where, is the inner loop convergence threshold, for.

9. The method for reducing noise of vibration signals of electric power fittings according to claim 4, characterized in that: The outer convergence condition is expressed as: ； Where, is the outer loop convergence threshold; is the power of the noise signal, is the vibration signal, is the iteration period, For the The modal component, Indicates the modal component number.

10. The method for reducing noise of vibration signals of electric power fittings according to claim 1, characterized in that: The cross-correlation coefficient and the threshold are respectively expressed as: ； ； Where, is the mutual correlation coefficient, is the threshold, For the The data mean of the modal components; is the data mean of the vibration signal, is the vibration signal, For the modal components, is the total number of modal components.

11. A vibration signal noise reduction system for electric power fittings, characterized in that: include: An optimization unit, configured to use the sum of the information entropy of each modal component as a fitness function and to use a coati optimization algorithm based on the fitness function to find the optimal value of the balance parameter; a decomposition unit, configured to perform successive variational modal decomposition on the vibration signal after setting the balance parameter based on the optimal value to obtain a plurality of modal components, wherein the vibration signal is a vibration signal of the power fitting to be de-noised; a calculation unit, configured to respectively calculate a mutual correlation coefficient and a threshold value between each of the modal components and the vibration signal; a determination unit, configured to determine that the modal component whose cross-correlation coefficient exceeds the threshold is a valid component, and the modal component whose cross-correlation coefficient is lower than the threshold is a noise component; The output unit is used to reconstruct the effective component into a vibration signal after noise reduction and output it.

12. A device for reducing noise of vibration signals of electric power fittings, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the method for reducing noise of vibration signals of electric hardware according to any one of claims 1 to 10 according to the instructions in the program code.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the method for reducing noise of vibration signals of electric power fittings according to any one of claims 1 to 10.

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