CN117056669A - Denoising method and system for vibration signal - Google Patents
Denoising method and system for vibration signal Download PDFInfo
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Abstract
The application relates to a denoising method and a denoising system for vibration signals, which belong to the technical field of signal processing, and comprise the following steps: carrying out complete set empirical mode decomposition on the original vibration signal to obtain a plurality of original intrinsic mode components; screening out noise-containing eigenmode components based on a plurality of original eigenmode components; decomposing the noise-containing eigenmode components to obtain initial wavelet packet coefficients of each node of the noise-containing eigenmode components under different scales; denoising the initial wavelet packet coefficient to obtain a signal-to-noise ratio, and taking the signal-to-noise ratio as an adaptability function of a gray wolf optimization algorithm; searching an optimal wavelet packet threshold based on a gray wolf optimization algorithm, and calculating an estimated wavelet packet coefficient; the method has the advantages that the estimated wavelet packet coefficients are subjected to inverse transformation to obtain denoising vibration signals, and the problems that the traditional denoising method is influenced by a threshold selection rule, the denoising effect is limited, and then the signal processing accuracy is influenced are solved.
Description
Technical Field
The application relates to the technical field of signal processing, in particular to a denoising method and system for vibration signals.
Background
The mechanical vibration signals contain important information in the running process of the equipment, and when monitoring the running state of the rotating machinery, vibration signal data of main components of the equipment set, such as rolling bearings, gear boxes, accelerators and the like, are usually required to be collected. And according to the data, the signal is converted into a frequency domain by using the theory of signal transformation and the like to process the signal, and then the equipment fault is monitored and diagnosed. In the monitoring environment of mechanical devices, strong noise is generated inside the device and outside the device, and the noise signals are inevitably doped into pure vibration signals. The difficulty of extracting the characteristics of the vibration signals is increased, and then the result of signal analysis and processing is negatively influenced.
There are many methods for denoising vibration signals at present, including a digital filter method, a fourier transform method, a wavelet packet denoising method, and the like. In most cases, the digital filter method and the fourier transform method are based on frequency domain or time domain to process and analyze signals, and cannot refine local features of the signals to perform independent processing, so that the denoising effect is not ideal. The wavelet packet denoising method is a time-frequency localization analysis method with a multi-resolution characteristic and has the characteristic of multi-resolution analysis; meanwhile, the method is a time-frequency localization analysis method with unchanged window area and changeable shape, and is particularly suitable for analyzing abrupt non-stationary signals. Therefore, the wavelet packet denoising method is the most ideal denoising method for the vibration signal.
At present, when the wavelet packet denoising theory is used for denoising the non-stationary vibration signal, a threshold value needs to be set for processing the decomposition coefficient, and the denoising effect is greatly influenced by whether the threshold value is set properly or not.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a denoising method and system for vibration signals, and solves the problems that the traditional denoising method is influenced by a threshold selection rule, has limited denoising effect and further influences signal processing accuracy.
In order to solve the technical problems, the application is solved by the following technical scheme:
a method of denoising a vibration signal, comprising the steps of:
carrying out complete set empirical mode decomposition on the original vibration signal to obtain a plurality of original intrinsic mode components;
screening out noise-containing eigenmode components based on a plurality of original eigenmode components;
decomposing the noise-containing eigenmode components to obtain initial wavelet packet coefficients of each node of the noise-containing eigenmode components under different scales;
denoising the initial wavelet packet coefficient to obtain a signal-to-noise ratio, and taking the signal-to-noise ratio as an adaptability function of a gray wolf optimization algorithm;
searching an optimal wavelet packet threshold based on the gray wolf optimization algorithm, and calculating and estimating wavelet packet coefficients;
and carrying out inverse transformation on the estimated wavelet packet coefficient to obtain a denoising vibration signal.
Optionally, the full set empirical mode decomposition of the original vibration signal includes the steps of:
adding Gaussian white noise for a plurality of times into the original vibration signal, and performing empirical mode decomposition on the original vibration signal added with the Gaussian white noise to obtain a plurality of natural mode components;
calculating the overall average value of a plurality of the intrinsic mode components to obtain a first-order original intrinsic mode component;
calculating a first signal margin based on the original vibration signal added with Gaussian white noise and the first-order original eigenmode component;
and continuously adding Gaussian white noise to the first signal margin, and repeating the steps until the decomposed signal margin cannot be continuously decomposed, so as to obtain a final decomposed signal and a plurality of original intrinsic mode components.
Optionally, screening out the noise-containing eigenmode components, including the following steps:
calculating pearson correlation coefficients of each original eigenmode component and an original vibration signal;
setting a threshold value, and screening the original eigenmode components with the pearson correlation coefficient smaller than the threshold value as noisy eigenmode components.
Optionally, decomposing the noisy eigenmode component to obtain an initial wavelet packet coefficient of each node of the noisy eigenmode component under different scales, including the following steps:
and selecting a decomposition layer number and a db series wavelet packet basis function, and decomposing the noise-containing eigenmode components according to the decomposition layer number and the db series wavelet packet basis function to obtain initial wavelet packet coefficients of each node of the noise-containing eigenmode components under different scales.
Alternatively, the formula for calculating the estimated wavelet packet coefficients is:
wherein (1)>To estimate wavelet packet coefficients, W j,k For the initial wavelet packet coefficients, sgn () is a sign function and λ is the optimal wavelet packet threshold.
Optionally, before the raw vibration Xin Ha is subjected to the complete set of empirical mode decomposition, the method further comprises acquiring a raw vibration signal.
Optionally, acquiring the original vibration signal includes the steps of:
and acquiring a first vibration signal, and amplifying the first vibration signal to obtain an original vibration signal.
A denoising system for a vibration signal, the denoising system performing the denoising method for a vibration signal according to any one of the above, comprising a first decomposition unit, a screening unit, a second decomposition unit, a signal denoising unit, an optimizing calculation unit, and an inverse transformation unit;
the first decomposition unit is used for carrying out complete set empirical mode decomposition on the original vibration signal to obtain a plurality of original intrinsic mode components;
the screening unit is used for screening out noise-containing intrinsic mode components based on a plurality of original intrinsic mode components;
the second decomposition unit is used for decomposing the noise-containing eigenmode component to obtain an initial wavelet packet coefficient of each node of the noise-containing eigenmode component under different scales;
the signal denoising unit is used for denoising the initial wavelet packet coefficient to obtain a signal-to-noise ratio, and taking the signal-to-noise ratio as an adaptability function of a gray wolf optimization algorithm;
the optimizing calculation unit is used for searching an optimal wavelet packet threshold based on the gray wolf optimizing algorithm and calculating an estimated wavelet packet coefficient;
the inverse transformation unit is used for carrying out inverse transformation on the estimated wavelet packet coefficient to obtain a denoising vibration signal.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the method of denoising a vibration signal of any one of the above.
Compared with the prior art, the technical scheme provided by the application has the following beneficial effects:
the problem of modal aliasing caused by EMD decomposition in the denoising process is avoided by completely integrating empirical mode decomposition on the original vibration signal, and the optimal threshold can be accurately selected in the wavelet packet threshold selecting process by combining with the gray wolf optimization algorithm, so that the accuracy of signal processing is improved.
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In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a flowchart of a denoising method for a vibration signal according to a first embodiment.
Detailed Description
The present application will be described in further detail with reference to the following examples, which are illustrative of the present application and are not intended to limit the present application thereto.
Example 1
As shown in fig. 1, a denoising method for a vibration signal includes the steps of: the method for acquiring the original vibration signal comprises the following steps: collecting a first vibration signal through an acceleration sensor, and amplifying the first vibration signal to obtain an original vibration signal; and then carrying out complete set empirical mode decomposition (CEEMDAN) on the original vibration signal to obtain a plurality of original intrinsic mode components.
The method comprises the following steps of: adding Gaussian white noise for a plurality of times into an original vibration signal, and performing empirical mode decomposition on the original vibration signal added with the Gaussian white noise to obtain a plurality of natural mode components; calculating the overall average value of a plurality of natural modal components to obtain a first-order original natural modal component; calculating a first signal margin based on the original vibration signal added with Gaussian white noise and the first-order original eigenmode component; and continuously adding the Gaussian white noise into the first signal margin, and repeating the steps until the decomposed signal margin cannot be continuously decomposed, so as to obtain a final decomposed signal and a plurality of original intrinsic mode components.
Specifically, N times Gaussian white noise z is added to the original vibration signal y (t) i (t), wherein i=1, 2, …, N, the original vibration signal y added with gaussian white noise is obtained i ,(t)＝y(t)+z i (t) (i '=1, 2, …, N'); then for the vibration signal y added with Gaussian white noise i (t) generating M intrinsic mode components IMF after empirical mode decomposition (EMD decomposition) m (m=1, 2, …, M), and performing ensemble average calculation to obtain a first-order original eigenmode component IMF 1 Then the vibration signal y added with Gaussian white noise i (t) and IMF 1 The subtraction produces a first signal margin h 1 (t) as follows:
h 1 (t)＝y(t)-IMF 1 。
then, the first signal margin h generated after the first stage operation 1 (t) generating a second order original eigenmode component IMF after continuing to add Gaussian white noise and performing EMD decomposition 2 And a second order signal margin h 2 (t) according to the calculation characteristics, performing EMD decomposition on the vibration signal for a plurality of times until the signal allowance after the Mth decomposition cannot be continuously decomposed, completing a CEEMDAN decomposition flow, and finally, obtaining a signal y' (t) after the CEEMDAN decomposition as follows:wherein, IMF m (t) represents an mth order natural mode component generated after mth decomposition; h is a M And (t) represents the margin when the vibration signal is subjected to EMD decomposition a plurality of times during the calculation until the signal margin after the Mth decomposition cannot be continuously decomposed.
Further, based on a plurality of original eigenmode components, the noise-containing eigenmode components are screened out, and the method specifically comprises the following steps: calculating pearson correlation coefficients of each original eigenmode component and an original vibration signal; setting a threshold value, and screening original eigenmode components with the Pearson correlation coefficient smaller than the threshold value as noisy eigenmode components.
Specifically, let two samples be X and Y, respectively, the pearson correlation coefficient be:wherein r is the pearson correlation coefficient of two variables, cov (X, Y) is the covariance of X and Y, < ->The variance of the variable X is represented,represents YVariance, in the present embodiment, describes the correlation between each original eigenmode component and the original vibration signal using pearson correlation coefficients, which yield correlation coefficients ranging from [ -1,1]The larger the absolute value of the correlation coefficient, the larger the linear correlation degree between the variables; on the contrary, the smaller the correlation coefficient is, the smaller the linear correlation degree between the variables is, according to the value range of the pearson correlation coefficient, the value below 0.2 can be set to be regarded as no correlation, the value range between 0.2 and 0.4 is regarded as weak correlation, the value range between 0.4 and 0.6 is regarded as medium-degree correlation, the value above 0.6 and 0.8 is regarded as strong correlation, the stronger and weaker the correlation coefficient can be adjusted according to the requirement, the specific limitation is not made in the embodiment, and the original intrinsic mode component with the correlation coefficient smaller than 0.6 is screened out as the noise-containing intrinsic mode component by taking the threshold value of 0.6 as an example.
Further, the noise-containing eigenmode component is decomposed to obtain an initial wavelet packet coefficient of each node of the noise-containing eigenmode component under different scales, and the method specifically comprises the following steps: and selecting the decomposition layer number and the db series wavelet packet basis function, and decomposing the noise-containing eigenmode components according to the decomposition layer number and the db series wavelet packet basis function to obtain the initial wavelet packet coefficients of each node of the noise-containing eigenmode components under different scales.
Specifically, the number of decomposition layers is set to be n, n is an integer, n is more than or equal to 10 and more than or equal to 3, and then the noise-containing eigenmode components are decomposed according to the decomposition level and the wavelet packet basis function to obtain:wherein (1)>And->The wavelet packet coefficients corresponding to the jth layer 2n node and the jth layer 2n+1 node are respectively represented, h and g represent filter coefficients, and n is the decomposition layer number.
The initial wavelet is then processedThe packet coefficient denoising processing is carried out, so that a signal-to-noise ratio is obtained, the signal-to-noise ratio is used as an adaptability function of a gray wolf optimization algorithm, and specifically, a calculation formula of the signal-to-noise ratio F is as follows:wherein x (t) represents a noisy eigenmode component, x +.>And N represents the length of the signal sequence, and the fitness function is a measure for the quality of the position of the wolf, and the larger the fitness function is, the better the position of the wolf is indicated.
Further, based on the wolf optimization algorithm, an optimal wavelet packet threshold is found, and an estimated wavelet packet coefficient is calculated, specifically, the wolf optimization algorithm (Grey Wolf Optimizer, GWO) is a global random optimization algorithm optimized by learning the predation behavior of the wolves in nature, the wolf clusters can be divided into 4 levels, and the levels are alpha, beta, delta and omega in sequence from high to low.
The hunting procedure is as follows: first, determining the upper and lower limits of optimizing wavelet packet threshold:wherein lambda is j For wavelet packet threshold on the j scale, n j For the length, sigma, of the wavelet packet detail coefficients on the j scale j Is the noise variance; then introduce the noise variance equation sigma j ：σ j ＝MAD(|d j，k I, 0.ltoreq.k.ltoreq.2j)/q, wherein MAD (. Cndot.) represents taking the median function, d j,k The wavelet packet coefficient of the kth node on the j scale is that q is a constant, the value of the parameter q is 0.1 and 1, and the maximum wavelet packet threshold lambda is calculated and obtained max And a minimum wavelet packet threshold lambda min 。
Then initializing a gray wolf optimization algorithm, setting the current iteration number to be 1, and initializing to obtain an a convergence factor and a A, C synergistic coefficient vector:in the iterative process, the value of a is linearly reduced from 2 to 0; vector r 1 、r 2 Is die taken [0,1 ]]The random number in between, calculate the individual fitness of the wolf, keep 3 wolves alpha, beta and delta that the fitness is the best, the optimization process of the wolf is mainly finished by alpha, beta and delta guide of each generation, then enclose the prey: d= |c·x p (t)-X(t)|；X(t+1)＝X p (t) -AD, wherein D is the distance between the wolf and the prey, X p For the prey location, X represents the gray wolf location and t is the current number of iterations.
Then capturing prey: under the guidance of alpha, beta and delta wolves, the wolf flock searches for the target prey and approaches the prey until the prey succeeds, and the mathematical model can be expressed as:X(t+1)＝(X 1 +X 2 +X 3 ) 3, wherein D a ，D β ，D δ Respectively represent the distance between other individuals in the wolf group and alpha, beta and delta wolves, X a ，X β ，X δ Representing the current positions of alpha, beta and delta wolf, X 1 ，X 2 ，X 3 Indicating the positions that other individuals of the population need to adjust, affected by alpha, beta and delta wolf, respectively. C (C) 1 、C 2 、C 3 Is a random vector, and has a value range of [0,2 ]]，|A|<1 represents the concentrated search of the wolves in certain areas, and X (t) represents the updated position of the omega wolves after capturing in the round.
When the prey stops moving, the wolf completes the hunting process by means of an attack, the value of a is gradually reduced in order to simulate approaching prey, so the fluctuation range of a is also reduced accordingly, in other words, in the iterative process, when the value of a is linearly reduced from 2 to 0, the corresponding value of a is also changed within the interval [ -a, a ]; when the value of A is within the interval, the next position of the wolf can be located at any position between the current position and the prey position; when |A| <1, the wolf group initiates attack to the prey, namely falls into local optimum; when |a| >1, the wolves are separated from the prey, and it is desirable to find a more suitable prey, i.e. globally optimal.
GWO algorithm has another component C to help discover new solutions, represented by a mathematical model:as can be seen, C is [0,2]The random value between C represents the random weight of the position of the wolf on the hunting object, C>1 represents a significant impact weight, whereas a small impact weight, which helps the GWO algorithm to perform more randomly and support exploration while avoiding falling into local optima during optimization.
In addition, unlike a, C is non-linearly decreasing, so that from the initial iteration to the final iteration it provides a global search in the decision space, and when the algorithm falls into a local optimum and is not easily jumped out, the randomness of C plays a very important role in avoiding the local optimum, especially in the iterations that finally need to obtain a globally optimal solution.
Further, judging whether the iteration times reach the maximum iteration times, outputting an optimal wavelet packet threshold under the condition that the iteration times are not smaller than the maximum iteration times, otherwise, returning to the step of calculating individual fitness of the gray wolves.
Finally, calculating an estimated wavelet packet coefficient, wherein a formula for calculating the estimated wavelet packet coefficient is as follows:wherein (1)>To estimate wavelet packet coefficients, W j,k For the initial wavelet packet coefficients, sgn () is a sign function and λ is the optimal wavelet packet threshold.
Furthermore, the method for denoising the noise-containing vibration signal by the algorithm can obtain the highest signal-to-noise ratio SNR and the lowest root mean square error RMSE compared with the traditional four threshold selection rules of WPT.
The method of applying the noisy vibration signal data in the milling cutter cutting experiment to the embodiment is used for verification, wherein the sampling frequency is 50KHz, 10000 sampling point data are selected for analysis, in the experiment, gaussian white noise disturbance of 30dB is superimposed on the vibration signal, and then four traditional threshold selection rules and the denoising method (CEEMDAN-GWO-WPT algorithm) of the embodiment are respectively adopted in Matlab (Matrix Laboratory ) for comparison experiments.
In the experiment, the iteration number of the gray wolf optimization algorithm is set to be 30, the population scale is 20, the number of wavelet packet decomposition layers is 4, the wavelet packet basis function is dB4, and the soft threshold function is used for processing the wavelet packet threshold, so that the experimental result is shown in the table 1:
TABLE 1
As can be seen from table 1, the denoising effect of denoising the vibration signal by using the denoising method of the present embodiment is superior to that of other methods in terms of both signal-to-noise ratio and root mean square error, wherein the larger the SNR value of the signal-to-noise ratio is, the better the denoising effect is, and the smaller the RMSE value of the root mean square error is, which is, the smaller the deviation of the denoising vibration signal from the real vibration signal is.
Example two
A vibration signal denoising system for performing the vibration signal denoising method according to any one of the embodiments, comprising a first decomposition unit, a screening unit, a second decomposition unit, a signal denoising unit, a optimizing calculation unit, and an inverse transformation unit; the first decomposition unit is used for carrying out complete set empirical mode decomposition on the original vibration signal to obtain a plurality of original intrinsic mode components; the screening unit is used for screening out noise-containing eigenmode components based on a plurality of original eigenmode components; the second decomposition unit is used for decomposing the noise-containing eigenmode components to obtain initial wavelet packet coefficients of each node of the noise-containing eigenmode components under different scales; the signal denoising unit is used for denoising the initial wavelet packet coefficient to obtain a signal to noise ratio, and taking the signal to noise ratio as an adaptability function of a gray wolf optimization algorithm; the optimizing calculation unit is used for searching an optimal wavelet packet threshold based on a gray wolf optimizing algorithm and calculating and estimating wavelet packet coefficients; the inverse transformation unit is used for carrying out inverse transformation on the estimated wavelet packet coefficient to obtain a denoising vibration signal.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the method for denoising a vibration signal according to any one of the embodiments.
More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wire segments, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and the division of modules, or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units, modules, or components may be combined or integrated into another apparatus, or some features may be omitted, or not performed.
The units may or may not be physically separate, and the components shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU). The computer readable medium of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing is merely illustrative of specific embodiments of the present application, and the scope of the present application is not limited thereto, but any changes or substitutions within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A method for denoising a vibration signal, comprising the steps of:
carrying out complete set empirical mode decomposition on the original vibration signal to obtain a plurality of original intrinsic mode components;
screening out noise-containing eigenmode components based on a plurality of original eigenmode components;
decomposing the noise-containing eigenmode components to obtain initial wavelet packet coefficients of each node of the noise-containing eigenmode components under different scales;
denoising the initial wavelet packet coefficient to obtain a signal-to-noise ratio, and taking the signal-to-noise ratio as an adaptability function of a gray wolf optimization algorithm;
searching an optimal wavelet packet threshold based on the gray wolf optimization algorithm, and calculating and estimating wavelet packet coefficients;
and carrying out inverse transformation on the estimated wavelet packet coefficient to obtain a denoising vibration signal.
2. The method of denoising a vibration signal according to claim 1, wherein the method comprises the steps of:
adding Gaussian white noise for a plurality of times into the original vibration signal, and performing empirical mode decomposition on the original vibration signal added with the Gaussian white noise to obtain a plurality of natural mode components;
calculating the overall average value of a plurality of the intrinsic mode components to obtain a first-order original intrinsic mode component;
calculating a first signal margin based on the original vibration signal added with Gaussian white noise and the first-order original eigenmode component;
and continuously adding Gaussian white noise to the first signal margin, and repeating the steps until the decomposed signal margin cannot be continuously decomposed, so as to obtain a final decomposed signal and a plurality of original intrinsic mode components.
3. The method of denoising a vibration signal according to claim 1, wherein the step of screening out the noise-containing eigenmode components comprises the steps of:
calculating pearson correlation coefficients of each original eigenmode component and an original vibration signal;
setting a threshold value, and screening the original eigenmode components with the pearson correlation coefficient smaller than the threshold value as noisy eigenmode components.
4. The method of denoising a vibration signal according to claim 1, wherein decomposing the noisy eigenmode component to obtain initial wavelet packet coefficients for each node of the noisy eigenmode component at different scales comprises the steps of:
and selecting a decomposition layer number and a db series wavelet packet basis function, and decomposing the noise-containing eigenmode components according to the decomposition layer number and the db series wavelet packet basis function to obtain initial wavelet packet coefficients of each node of the noise-containing eigenmode components under different scales.
5. The method of denoising a vibration signal according to claim 1, wherein the formula for calculating the estimated wavelet packet coefficients is:
wherein (1)>To estimate wavelet packet coefficients, W j,k For the initial wavelet packet coefficients, sgn () is a sign function and λ is the optimal wavelet packet threshold.
6. The method of claim 1, further comprising obtaining the raw vibration signal before subjecting the raw vibration Xin Ha to the complete set of empirical mode decomposition.
7. The method of denoising a vibration signal according to claim 6, wherein acquiring the original vibration signal comprises the steps of:
and acquiring a first vibration signal, and amplifying the first vibration signal to obtain an original vibration signal.
8. A system for denoising a vibration signal, wherein the denoising system performs the method for denoising a vibration signal according to any one of claims 1 to 7, and comprises a first decomposition unit, a screening unit, a second decomposition unit, a signal denoising unit, an optimizing calculation unit, and an inverse transformation unit;
the first decomposition unit is used for carrying out complete set empirical mode decomposition on the original vibration signal to obtain a plurality of original intrinsic mode components;
the screening unit is used for screening out noise-containing intrinsic mode components based on a plurality of original intrinsic mode components;
the second decomposition unit is used for decomposing the noise-containing eigenmode component to obtain an initial wavelet packet coefficient of each node of the noise-containing eigenmode component under different scales;
the signal denoising unit is used for denoising the initial wavelet packet coefficient to obtain a signal-to-noise ratio, and taking the signal-to-noise ratio as an adaptability function of a gray wolf optimization algorithm;
the optimizing calculation unit is used for searching an optimal wavelet packet threshold based on the gray wolf optimizing algorithm and calculating an estimated wavelet packet coefficient;
the inverse transformation unit is used for carrying out inverse transformation on the estimated wavelet packet coefficient to obtain a denoising vibration signal.
9. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method of denoising a vibration signal according to any one of claims 1-7.
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