An acoustic emission signal noise reduction method based on improved CEEMD-WPT and related equipment

Abstract

The present application provides an acoustic emission signal denoising method and related equipment based on improved CEEMD-WPT, which is applied to the field of data processing technology. The present application processes the collected original signal x(t) based on the first preset rule to generate a sorted signal x(t); performs noise enhancement processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noise; performs WPT denoising processing on the noisy signal group to generate a denoised signal group; performs EMD decomposition processing on the denoised signal group to generate multiple groups of IMFs components and residuals r(t); processes the IMFs components based on the second preset rule to generate preprocessed IMFs components; processes the preprocessed IMFs components based on the third preset rule to generate a denoised signal x'(t). By adding a WPT denoising step between the two steps of adding white noise and EMD decomposition, and changing the original averaging method to averaging in each cycle, the ability to remove high-frequency noise in the signal is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of signal noise reduction, and in particular to an acoustic emission signal noise reduction method based on improved CEEMD-WPT and related equipment. Background Art

[0002] Cracks, as a form of damage, seriously affect the performance of structures. They are widely present in all walks of life and pose hidden dangers for subsequent accidents. Cracks can be generated in a variety of locations. Cracks within certain materials and cracks in structural blind spots are difficult to detect, but they also weaken the performance of the structure and pose a greater risk. The development of acoustic emission technology has made detection easier. However, the application of acoustic emission technology also has difficulties. The recording of acoustic emission signals may be affected by the environment and contact areas, generating noise. Although traditional CEEMD and WPT noise reduction algorithms have performed well in a large number of acoustic emission studies, their noise reduction effect on signals with high signal-to-noise ratios is limited.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0004] The purpose of this application is to provide an acoustic emission signal denoising method and related equipment based on improved CEEMD-WPT, which at least to a certain extent overcomes the problems existing in the prior art. By improving the original CEEMD decomposition, a WPT denoising step is added between the two steps of adding white noise and EMD decomposition, and the original averaging method is changed to averaging in each cycle, thereby improving the ability to remove high-frequency noise in the signal.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0006] According to one aspect of the present application, a method for denoising an acoustic emission signal based on an improved CEEMD-WPT is provided, comprising: obtaining an original signal x(t); processing the collected original signal x(t) based on a first preset rule to generate a sorted signal x(t); performing noise addition processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noises; performing WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group; performing EMD decomposition processing on the noise-reduced signal group to generate multiple groups of IMFs components and residuals r(t); processing the IMFs components based on a second preset rule to generate preprocessed IMFs components; and processing the preprocessed IMFs components based on a third preset rule to generate a noise-reduced signal x'(t).

[0007] In one embodiment of the present application, the processing of the collected original signal x(t) based on the first preset rule to generate a sorted signal x(t) includes: processing the collected original signal x(t) based on the EMD method to generate a preprocessed signal; sorting the preprocessed signal from high to low according to frequency to generate a sorted signal x(t).

[0008] In one embodiment of the present application, the collected original signal x(t) is processed based on the EMD method to generate a preprocessed signal, including: processing the collected original signal x(t) based on the Savizky-Golay filtering denoising method to generate an initial denoised signal; and performing secondary denoising on the initial denoised signal based on the EMD threshold denoising method of LabVIEW to generate a deep denoised signal.

[0009] In one embodiment of the present application, the processing of the IMFs component based on the second preset rule to generate the preprocessed IMFs component includes: obtaining a preset average value IMFj; processing the denoised signal group based on the preset average value IMFj to generate a target signal R(t) of the denoised signal group; processing the target signal R(t) to generate the preprocessed IMFs component.

[0010] In one embodiment of the present application, the target signal R(t) is processed to generate a preprocessed IMFs component, including: step 1, processing the target signal R(t) based on a first preset rule to generate a new sorted signal x(t); step 2, performing noise addition processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noise; step 3, performing WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group; step 4, performing EMD decomposition processing on the noise-reduced signal group to generate multiple groups of IMFs components and residuals r(t); step 5, processing the noise-reduced signal group based on the preset average value IMFj to generate a target signal R(t) of the noise-reduced signal group; if the target signal R(t) is decomposable or the number of decompositions is lower than an upper threshold, returning to step 1; if the target signal R(t) is not decomposable or the number of decompositions is higher than an upper threshold, deleting the residual value r(t) to generate the resulting IMFs component after improved CEEMD decomposition.

[0011] In one embodiment of the present application, the processing of the preprocessed IMFs components based on a third preset rule to generate a denoised signal x'(t) includes: processing the IMFs components to generate a correlation coefficient corresponding to each component in the IMFs and the original signal; processing the correlation coefficient based on a preset threshold to divide the correlation coefficient into a strongly correlated component or a weakly correlated component; performing WPT denoising on the weakly correlated component to remove the noise component therein; and performing EMD reconstruction on the weakly correlated component and the strongly correlated component to generate a denoised signal x'(t).

[0012] In one embodiment of the present application, processing the correlation coefficient based on a preset threshold to divide the correlation coefficient into a strong correlation component or a weak correlation component includes:

[0013]

[0014]

[0015]

[0016] Where Ci is the correlation coefficient between the i-th IMF component and the original signal, and T is the final threshold; the correlation coefficient with T less than or equal to 0.3 is classified as a weakly correlated component, and the correlation coefficient with T greater than 0.3 is classified as a strongly correlated component.

[0017] Another aspect of the present application is an acoustic emission signal denoising device based on improved CEEMD-WPT, characterized in that it includes: an acquisition module, configured to acquire an original signal x(t); a processing module, configured to process the collected original signal x(t) based on a first preset rule to generate a sorted signal x(t); perform noise addition processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noises; perform WPT noise reduction processing on the noisy signal group to generate a denoised signal group; perform EMD decomposition processing on the denoised signal group to generate multiple groups of IMFs components and residuals r(t); process the IMFs components based on a second preset rule to generate preprocessed IMFs components; and process the preprocessed IMFs components based on a third preset rule to generate a denoised signal x'(t).

[0018] According to another aspect of the present application, an electronic device is characterized in that it includes: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to implement the above-mentioned acoustic emission signal noise reduction method based on improved CEEMD-WPT by executing the executable instructions.

[0019] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned acoustic emission signal noise reduction method based on improved CEEMD-WPT is implemented.

[0020] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a processor, the above-mentioned acoustic emission signal noise reduction method based on improved CEEMD-WPT is implemented.

[0021] The present application provides an acoustic emission signal denoising method based on improved CEEMD-WPT, which obtains an original signal x(t), processes the collected original signal x(t) based on a first preset rule to generate a sorted signal x(t), performs noise enhancement on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noises, performs WPT noise reduction on the noisy signal group to generate a denoised signal group. The denoised signal group is subjected to EMD decomposition to generate multiple groups of IMFs components and residuals r(t), the IMFs components are processed based on a second preset rule to generate preprocessed IMFs components, and the preprocessed IMFs components are processed based on a third preset rule to generate a denoised signal x'(t). By improving the original CEEMD decomposition, adding a WPT noise reduction step between the two steps of adding white noise and EMD decomposition, and changing the original averaging method to averaging in each cycle, the ability to remove high-frequency noise in the signal is improved. In addition, the average correlation coefficient threshold method is used to distinguish whether the decomposed IMF components are strongly correlated components, and WPT processing is performed on the low-correlation components. Finally, the processed strong and weak correlation components are reconstructed by EMD, thereby improving the ability to preserve the low-frequency characteristics of the signal.

[0022] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0024] Figure 1 A flowchart of an acoustic emission signal noise reduction method based on improved CEEMD-WPT provided in one embodiment of the present application is shown;

[0025] Figure 2 The figure shows a schematic structural diagram of an acoustic emission signal noise reduction device based on improved CEEMD-WPT provided in one embodiment of the present application;

[0026] Figure 3 A schematic structural diagram of an electronic device provided in one embodiment of the present application is shown;

[0027] Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown;

[0028] Figure 5 shows a simulation signal diagram provided by an embodiment of the present application;

[0029] Figure 6 shows a noise waveform diagram of an analog signal provided by an embodiment of the present application;

[0030] Figure 7 A diagram showing the noise reduction effect of a traditional CEEMD provided in one embodiment of the present application is shown;

[0031] Figure 8 A diagram showing the noise reduction effect of the improved CEEMD provided in one embodiment of the present application is shown;

[0032] Figure 9 The figure shows the noise reduction effect of the traditional CEEMD-WPT provided by an embodiment of the present application;

[0033] Figure 10 A diagram showing the noise reduction effect of the improved CEEMD-WPT provided in one embodiment of the present application is shown. DETAILED DESCRIPTION

[0034] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0035] It should be noted that those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of this application are indicated by the claims.

[0036] It should be understood that the present application is not limited to the precise structures described below and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

[0037] The following combination Figure 1 The following describes an acoustic emission signal noise reduction method based on improved CEEMD-WPT according to an exemplary embodiment of the present application. It should be noted that the following application scenarios are merely provided to facilitate understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this respect. Rather, the embodiments of the present application can be applied to any applicable scenario.

[0038] In one embodiment, the present application also proposes an acoustic emission signal noise reduction method based on improved CEEMD-WPT. Figure 1 The following schematically shows a flow chart of an acoustic emission signal noise reduction method based on improved CEEMD-WPT according to an embodiment of the present application. Figure 1 Shown, including:

[0039] S101, obtaining the original signal x(t).

[0040] S102: Process the collected original signal x(t) based on a first preset rule to generate a sorted signal x(t).

[0041] In one embodiment, the collected original signal x(t) is processed based on the EMD method to generate a preprocessed signal, which is then sorted from high to low frequency to generate a sorted signal x(t). The collected original signal x(t) is decomposed using the EMD method to obtain all IMF components and a residual sequence after the signal decomposition, and all decomposed signals are sorted from high to low frequency. According to the EMD algorithm, the original signal x(t) is composed of different IMF components. The maximum and minimum values ​​of the original signal x(t) are found, and the upper and lower envelopes xu(t) and xd(t) are obtained based on the found maximum and minimum values. Based on the upper and lower envelopes xu(t) and xd(t), the mean m(t) and difference d(t) are determined. If d(t) meets the two necessary conditions for an IMF component: 1. The number of local extreme points and zero crossings of the IMF component must be equal or differ by at most one over the entire time range; 2. At any time point, the envelopes of the local maximum (upper envelope) and the local minimum (lower envelope) must average zero. This is then taken as the first IMF component, denoted as c1. This first IMF component c1 is separated from the signal x(t) to obtain the residual sequence r1: r1 = x(t) - c1. When the residual sequence is monotonic or less than a preset constant value, the decomposition ends. At this time, the original signal x(t) will be decomposed into n-1 IMF components and the final residual sequence r will be obtained.

[0042] In another embodiment, the collected original signal x(t) is processed based on the Savizky-Golay filtering noise reduction method to generate an initial noise reduction signal. The Savizky-Golay filter fits the data of each point in a field (a window of length n) of each data point with a unary p-order polynomial. The coefficients of this polynomial can be determined by minimizing the fitting error according to the least squares criterion, thereby obtaining the best fitting value of the center point in the sliding window, which is the value after noise reduction processing. The sliding data window slides along each point in turn, thereby achieving smoothing processing. Among them, the use of the Savizky-Golay filtering noise reduction method can make the signal smooth, and the low-frequency part is relatively smooth.

[0043] In another embodiment, the EMD threshold denoising method based on LabVIEW performs secondary denoising on the initial denoised signal to generate a deep denoised signal. LabVIEW is a graphical programming language that is widely used in the field of measurement and control. Complex signal denoising software is designed and implemented based on the EMD denoising principle through LabVIEW. The implementation principle is divided into three modules, and the steps are as follows: (1) Constructing the envelope curve by cubic spline interpolation is mainly to construct the envelope curve based on the extreme points of the analyzed signal, and to achieve this with the help of the cubic spline interpolation function provided in LabVIEW. (2) Calculating the local mean of the envelope curve From step (1), the upper and lower envelope curves of the signal are obtained, and the local mean can be obtained by taking the average of the extreme values ​​of each point in the curve. (3) Residual component judgment During the signal decomposition process, it is necessary to limit the screening process. According to the simulated Cauchy convergence criterion of Huang et al., the threshold SD is defined as the termination condition of the EMD decomposition, as shown in formula (4).

[0044]

[0045] In formula (4), T is the duration of the signal. According to Huang's suggestion, SD is between [0.2 and 0.3]. In the present invention, the value is 0.25 based on the trial and error method. The EMD threshold denoising method using LabVIEW can effectively improve the denoising effect of high-frequency MF components.

[0046] S103 , performing noise addition processing on the sorted signal x(t) to generate a noisy signal group including multiple pairs of white noises.

[0047] In one embodiment, the added white noise is 20dB. In order to verify the noise reduction performance of the improved CEEMD-WPT algorithm, a simulation signal y(t) is used as the object for the experiment. Its waveform is as follows: Figure 2 shown.

[0048] y(t)=0.3sin(3.5πt)sin(πt)+0.758cos(πt)

[0049] The number of sampling points is 2000 (for easy viewing, Figure 5 Take the first 500 points), add white noise with a signal-to-noise ratio of 10, and the waveform after adding noise is as follows Figure 6 shown.

[0050] S104: Perform WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group.

[0051] In one implementation, WPT uses a four-layer decomposition and hard threshold processing method.

[0052] S105 , performing EMD decomposition processing on the denoised signal group to generate multiple groups of IMFs components and residuals r(t).

[0053] In one embodiment, the denoised signal is input into the improved EMD for decomposition, and the envelope function is used to draw an envelope line on the denoised signal to enhance the continuity of the signal. The signal with the envelope line is then used to extract its characteristic variables, namely, the IMF components, using the improved EMD, thereby reducing the workload of signal processing.

[0054] S106: Process the IMFs component based on a second preset rule to generate a preprocessed IMFs component.

[0055] In one embodiment, a preset mean value IMFj is obtained, and the denoised signal group is processed based on the preset mean value IMFj. Specifically, the preset mean value IMFj is subtracted from the denoised signal group to generate a target signal R(t) for the denoised signal group. The target signal R(t) is then processed to generate the preprocessed IMFs component. By adding a WPT denoising step between the white noise addition and EMD decomposition steps and changing the existing averaging method to averaging for each loop, the ability to remove high-frequency noise from the signal is improved.

[0056] In another embodiment, the target signal R(t) is processed to generate a preprocessed IMFs component, including: step 1, processing the target signal R(t) based on a first preset rule to generate a new sorted signal x(t); step 2, performing noise addition processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noises; step 3, performing WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group; step 4, performing EMD decomposition processing on the noise-reduced signal group to generate multiple groups of IMFs components and residuals r(t); step 5, processing the noise-reduced signal group based on a preset average value IMFj to generate a target signal R(t) of the noise-reduced signal group; if the target signal R(t) can be decomposed or the number of decompositions is lower than the upper threshold, returning to step 1; if the target signal R(t) cannot be decomposed or the number of decompositions is higher than the upper threshold, deleting the residual value r(t) to generate the resulting IMFs component after improved CEEMD decomposition.

[0057] S107 , processing the preprocessed IMFs components based on a third preset rule to generate a denoised signal x′(t).

[0058] In one embodiment, the IMFs components are processed to generate a correlation coefficient corresponding to each component in the IMFs and the original signal. The correlation coefficient is processed based on a preset threshold and divided into a strongly correlated component or a weakly correlated component. The weakly correlated component is subjected to WPT denoising to remove the noise component. The weakly correlated component and the strongly correlated component are reconstructed by EMD to generate a denoised signal x'(t).

[0059] The present application obtains the original signal x(t), processes the collected original signal x(t) based on the first preset rule, generates a sorted signal x(t), performs noise enhancement on the sorted signal x(t), generates a noisy signal group containing multiple pairs of white noise, performs WPT noise reduction on the noisy signal group, generates a noise-reduced signal group, performs EMD decomposition on the noise-reduced signal group, and generates multiple groups of IMFs components and residuals r(t). The IMFs components are processed based on the second preset rule to generate preprocessed IMFs components, and the preprocessed IMFs components are processed based on the third preset rule to generate a noise-reduced signal x'(t). By improving the original CEEMD decomposition, a WPT noise reduction step is added between the two steps of adding white noise and EMD decomposition, and the original averaging method is changed to averaging in each cycle, thereby improving the ability to remove high-frequency noise in the signal.

[0060] Optionally, in another embodiment of the above method of the present application, processing the correlation coefficient based on a preset threshold to divide the correlation coefficient into a strong correlation component or a weak correlation component includes:

[0061]

[0062]

[0063]

[0064] Where Ci is the correlation coefficient between the i-th IMF component and the original signal, and T is the final threshold; the correlation coefficient with T less than or equal to 0.3 is classified as a weakly correlated component, and the correlation coefficient with T greater than 0.3 is classified as a strongly correlated component.

[0065] In one embodiment, under normal and high signal-to-noise ratio conditions, the correlation coefficient between the effective signal and the original signal is large, while the correlation coefficient between the noise and the original signal is small. Therefore, the correlation coefficient between each IMF component and the original signal is calculated to determine whether it is a strong or weak correlation component. Using the mean of the correlation coefficient as the dividing point between the coefficients of strong and weak correlation components can be applied to most signal situations. In addition, there are cases where the correlation coefficient of a very small component IMF is extremely small, thereby lowering the average value. Here, the lower limit of the mean is set to 0.3. If it is lower than this lower limit, it is calculated as 0.3.

[0066] By applying the above technical solution, the original signal x(t) is obtained, and the collected original signal x(t) is processed based on the Savizky-Golay filter denoising method to generate an initial denoised signal. The initial denoised signal is subjected to secondary denoising based on the EMD threshold denoising method of LabVIEW to generate a deep denoised signal. The preprocessed signal is sorted from high to low according to frequency to generate a sorted signal x(t). The sorted signal x(t) is subjected to denoising to generate a noisy signal group containing multiple pairs of white noises. The noisy signal group is subjected to WPT denoising to generate a denoised signal group. The denoised signal group is subjected to EMD decomposition to generate multiple groups of IMFs components and residual r(t).

[0067] In addition, a preset average value IMFj is obtained, and the denoised signal group is processed based on the preset average value IMFj to generate a target signal R(t) of the denoised signal group. Step 1: Process the target signal R(t) based on the first preset rule to generate a new sorted signal x(t); Step 2: Perform noise enhancement processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noises; Step 3: Perform WPT denoising processing on the noisy signal group to generate a denoised signal group; Step 4: Perform EMD decomposition processing on the denoised signal group to generate multiple groups of IMFs components and residuals r(t); Step 5: Process the denoised signal group based on the preset average value IMFj to generate a denoised signal. The target signal R(t) of the signal group after the decomposition is obtained. If the target signal R(t) can be decomposed or the number of decompositions is lower than the upper threshold, the method returns to step 1. If the target signal R(t) cannot be decomposed or the number of decompositions is higher than the upper threshold, the residual value r(t) is deleted, and the IMFs components of the improved CEEMD decomposition are generated. The IMFs components are processed to generate the correlation coefficients corresponding to each component in the IMFs and the original signal. The correlation coefficients are processed based on the preset threshold and divided into strong correlation components or weak correlation components. The weak correlation components are subjected to WPT denoising to remove the noise components. The weak correlation components and strong correlation components are reconstructed by EMD to generate the denoised signal x'(t). By improving the original CEEMD decomposition, a WPT denoising step is added between the two steps of adding white noise and EMD decomposition, and the original averaging method is changed to averaging in each cycle, thereby improving the ability to remove high-frequency noise in the signal. In addition, the average correlation coefficient threshold method is used to distinguish whether the decomposed IMF components are strongly correlated components, and WPT processing is performed on the low-correlation components. Finally, the processed strong and weak correlation components are reconstructed by EMD, thereby improving the ability to preserve the low-frequency characteristics of the signal.

[0068] For the convenience of comparison, the four noise reduction methods in the table below were used for testing. The waveforms after noise reduction are as follows: Figure 4-7 shown.

[0069]

[0070] from Figure 7-10 It can be seen that all four methods can remove some noise, but the signal detail continuity of traditional CEEMD and traditional CEEMD-WPT is poor, and high-frequency noise remains. The signal detail preservation after improved CEEMD denoising is poor, and there is severe distortion. The signal continuity after improved CEEMD-WPT denoising is good, and signal detail is preserved. To quantitatively compare the denoising performance of the four methods, the common denoising performance indicators signal-to-noise ratio (SNR) and root mean square error (RMSE) are used here.

[0071]

[0072]

[0073] In the above formula, n is the signal length; y(t) is the pure signal; and Y(t) is the denoised signal.

[0074] According to Table 2, the SNRs of the four methods are all greater than the noisy signal, and all have certain noise reduction capabilities. Overall, the improved CEEMD-WPT method proposed in this paper has the largest SNR and the smallest RMSE, and its noise reduction effect is better than the other three methods.

[0075]

[0076] In one embodiment, Figure 2 As shown, the present application also provides an acoustic emission signal noise reduction device based on improved CEEMD-WPT, comprising:

[0077] An acquisition module 201 is configured to acquire an original signal x(t);

[0078] The processing module 202 is configured to process the collected original signal x(t) based on a first preset rule to generate a sorted signal x(t); perform noise addition processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noise; perform WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group; perform EMD decomposition processing on the noise-reduced signal group to generate multiple groups of IMFs components and residuals r(t); process the IMFs components based on a second preset rule to generate preprocessed IMFs components; and process the preprocessed IMFs components based on a third preset rule to generate a noise-reduced signal x'(t).

[0079] The present application obtains the original signal x(t), processes the collected original signal x(t) based on the first preset rule, generates a sorted signal x(t), performs noise enhancement on the sorted signal x(t), generates a noisy signal group containing multiple pairs of white noise, performs WPT noise reduction on the noisy signal group, generates a noise-reduced signal group, performs EMD decomposition on the noise-reduced signal group, and generates multiple groups of IMFs components and residuals r(t). The IMFs components are processed based on the second preset rule to generate preprocessed IMFs components, and the preprocessed IMFs components are processed based on the third preset rule to generate a noise-reduced signal x'(t). By improving the original CEEMD decomposition, a WPT noise reduction step is added between the two steps of adding white noise and EMD decomposition, and the original averaging method is changed to averaging in each cycle, thereby improving the ability to remove high-frequency noise in the signal.

[0080] In another embodiment of the present application, the processing module 202 is configured to process the collected original signal x(t) based on the first preset rule to generate a sorted signal x(t), including: processing the collected original signal x(t) based on the EMD method to generate a preprocessed signal; sorting the preprocessed signal from high to low according to frequency to generate a sorted signal x(t).

[0081] In another embodiment of the present application, the processing module 202 is configured to process the collected original signal x(t) based on the EMD method to generate a preprocessed signal, including: processing the collected original signal x(t) based on the Savizky-Golay filtering denoising method to generate an initial denoised signal; performing secondary denoising on the initial denoised signal based on the EMD threshold denoising method of LabVIEW to generate a deep denoised signal.

[0082] In another embodiment of the present application, the processing module 202 is configured to process the IMFs component based on the second preset rule to generate a preprocessed IMFs component, including: obtaining a preset average value IMFj; processing the denoised signal group based on the preset average value IMFj to generate a target signal R(t) of the denoised signal group; processing the target signal R(t) to generate a preprocessed IMFs component.

[0083] In another embodiment of the present application, the processing module 202 is configured to process the target signal R(t) to generate a preprocessed IMFs component, including: step 1, processing the target signal R(t) based on a first preset rule to generate a new sorted signal x(t); step 2, performing noise addition processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noises; step 3, performing WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group; step 4, performing noise reduction processing on the The denoised signal group is subjected to EMD decomposition processing to generate multiple groups of IMFs components and residuals r(t); step 5, processing the denoised signal group based on the preset average value IMFj to generate the target signal R(t) of the denoised signal group; if the target signal R(t) can be decomposed or the number of decompositions is lower than the upper limit threshold, returning to step 1; if the target signal R(t) cannot be decomposed or the number of decompositions is higher than the upper limit threshold, deleting the residual value r(t) to generate the result IMFs component after improved CEEMD decomposition.

[0084] In another embodiment of the present application, the processing module 202 is configured to process the preprocessed IMFs components based on a third preset rule to generate a denoised signal x'(t), including: processing the IMFs components to generate a correlation coefficient corresponding to each component in the IMFs and the original signal; processing the correlation coefficient based on a preset threshold to divide the correlation coefficient into a strongly correlated component or a weakly correlated component; performing WPT denoising on the weakly correlated component to remove the noise component therein; and performing EMD reconstruction on the weakly correlated component and the strongly correlated component to generate a denoised signal x'(t).

[0085] In another embodiment of the present application, the processing module 202 is configured to process the correlation coefficient based on a preset threshold and divide the correlation coefficient into a strong correlation component or a weak correlation component, including:

[0086]

[0087]

[0088]

[0089] Where Ci is the correlation coefficient between the i-th IMF component and the original signal, and T is the final threshold; the correlation coefficient with T less than or equal to 0.3 is classified as a weakly correlated component, and the correlation coefficient with T greater than 0.3 is classified as a strongly correlated component.

[0090] The present application obtains an original signal x(t), processes the collected original signal x(t) based on the Savizky-Golay filtering denoising method to generate an initial denoised signal, performs secondary denoising on the initial denoised signal based on the EMD threshold denoising method of LabVIEW to generate a deep denoised signal, sorts the preprocessed signal from high to low according to frequency to generate a sorted signal x(t), performs denoising on the sorted signal x(t), generates a noisy signal group containing multiple pairs of white noise, performs WPT denoising on the noisy signal group to generate a denoised signal group, performs EMD decomposition on the denoised signal group to generate multiple groups of IMFs components and residual r(t).

[0091] In addition, a preset average value IMFj is obtained, and the denoised signal group is processed based on the preset average value IMFj to generate a target signal R(t) of the denoised signal group. Step 1: Process the target signal R(t) based on the first preset rule to generate a new sorted signal x(t); Step 2: Perform noise enhancement processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noises; Step 3: Perform WPT denoising processing on the noisy signal group to generate a denoised signal group; Step 4: Perform EMD decomposition processing on the denoised signal group to generate multiple groups of IMFs components and residuals r(t); Step 5: Process the denoised signal group based on the preset average value IMFj to generate a denoised signal. The target signal R(t) of the signal group after the decomposition is obtained. If the target signal R(t) can be decomposed or the number of decompositions is lower than the upper threshold, the method returns to step 1. If the target signal R(t) cannot be decomposed or the number of decompositions is higher than the upper threshold, the residual value r(t) is deleted, and the IMFs components of the improved CEEMD decomposition are generated. The IMFs components are processed to generate the correlation coefficients corresponding to each component in the IMFs and the original signal. The correlation coefficients are processed based on the preset threshold and divided into strong correlation components or weak correlation components. The weak correlation components are subjected to WPT denoising to remove the noise components. The weak correlation components and strong correlation components are reconstructed by EMD to generate the denoised signal x'(t). By improving the original CEEMD decomposition, a WPT denoising step is added between the two steps of adding white noise and EMD decomposition, and the original averaging method is changed to averaging in each cycle, thereby improving the ability to remove high-frequency noise in the signal. In addition, the average correlation coefficient threshold method is used to distinguish whether the decomposed IMF components are strongly correlated components, and WPT processing is performed on the low-correlation components. Finally, the processed strong and weak correlation components are reconstructed by EMD, thereby improving the ability to preserve the low-frequency characteristics of the signal.

[0092] The present application embodiment provides an electronic device, such as Figure 3 As shown, it includes a processor 300, a memory 301, a bus 302 and a communication interface 303, wherein the processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the processor 300, and when the processor 300 runs the computer program, the acoustic emission signal denoising method based on the improved CEEMD-WPT provided in any of the aforementioned embodiments of the present application is executed.

[0093] The memory 301 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element communicates with at least one other network element via at least one communication interface 303 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0094] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs, and the processor 300 executes the programs after receiving execution instructions. The acoustic emission signal denoising method based on the improved CEEMD-WPT disclosed in any of the aforementioned embodiments of the present application may be applied to the processor 300 or implemented by the processor 300.

[0095] The processor 300 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 300 or by software instructions. The above processor 300 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of this application can be implemented as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory 301 , and the processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0096] The electronic device provided in the above-mentioned embodiments of the present application and the acoustic emission signal noise reduction method based on improved CEEMD-WPT provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0097] The present application provides a computer-readable storage medium. Figure 4 As shown, the computer-readable storage medium 401 stores a computer program. When the computer program is read and executed by the processor 402, the aforementioned acoustic emission signal noise reduction method based on the improved CEEMD-WPT is implemented.

[0098] The technical solution of the embodiments of the present application, or the portion that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for causing an electronic device (such as an air conditioner, a refrigeration device, a personal computer, a server, or a network device) or a processor to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0099] The computer-readable storage medium provided in the above-mentioned embodiment of the present application and the acoustic emission signal noise reduction method based on improved CEEMD-WPT provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0100] An embodiment of the present application provides a computer program product, including a computer program, wherein the computer program is executed by a processor to implement the method described above.

[0101] The computer program product provided in the above-mentioned embodiments of the present application and the acoustic emission signal noise reduction method based on improved CEEMD-WPT provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0102] It should be noted that, in this application, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further restrictions, an element defined by the statement "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0103] Each embodiment in this application is described in a related manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the acoustic emission signal noise reduction method based on improved CEEMD-WPT, electronic device, electronic device, and readable storage medium embodiment, since they are basically similar to the acoustic emission signal noise reduction method embodiment based on improved CEEMD-WPT described above, the description is relatively simple. For related parts, please refer to the partial description of the acoustic emission signal noise reduction method embodiment based on improved CEEMD-WPT described above.

[0104] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art may make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims.

Claims

1. A method for reducing the noise of acoustic emission signals based on improved CEEMD-WPT, characterized in that: include: Get the original signal x(t); The collected original signal x(t) is processed based on a first preset rule to generate a sorted signal x(t), including: processing the collected original signal x(t) based on an EMD method to generate a preprocessed signal; sorting the preprocessed signal from high to low according to frequency to generate a sorted signal x(t); Performing noise addition processing on the sorted signal x(t) to generate a noisy signal group including multiple pairs of white noises; Performing WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group; Performing EMD decomposition on the denoised signal group to generate multiple groups of IMFs components and residuals r(t); Processing the IMFs component based on a second preset rule to generate a preprocessed IMFs component; Processing the preprocessed IMFs components based on a third preset rule to generate a denoised signal x'(t), wherein the third preset rule is processing the IMFs components to generate a correlation coefficient corresponding to each component in the IMFs and the original signal; processing the correlation coefficient based on a preset threshold to classify the correlation coefficient into a strongly correlated component or a weakly correlated component; The processing of the IMFs component based on the second preset rule to generate a preprocessed IMFs component includes: Get the preset average value IMFj; Processing the noise-reduced signal group based on the preset average value IMFj to generate a target signal R(t) of the noise-reduced signal group; Processing the target signal R(t) to generate preprocessed IMFs components; The processing of the target signal R(t) to generate a preprocessed IMFs component includes: Step 1: Process the target signal R(t) based on a first preset rule to generate a new sorted signal x(t); Step 2: performing noise addition processing on the sorted signal x(t) to generate a noisy signal group including multiple pairs of white noises; Step 3: Perform WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group; Step 4: performing EMD decomposition on the denoised signal group to generate multiple groups of IMFs components and residuals r(t); Step 5: Process the noise-reduced signal group based on the preset average value IMFj to generate a target signal R(t) of the noise-reduced signal group; If the target signal R(t) can be decomposed or the number of decompositions is lower than the upper threshold, return to step 1; If the target signal R(t) cannot be decomposed or the number of decompositions is higher than an upper threshold, the residual r(t) is deleted to generate the result IMFs component after the improved CEEMD decomposition.

2. The method according to claim 1, characterized in that The method of processing the collected original signal x(t) based on the EMD method to generate a preprocessed signal includes: The collected original signal x(t) is processed based on the Savizky-Golay filtering noise reduction method to generate an initial noise reduction signal; The initial noise reduction signal is subjected to secondary noise reduction by the EMD threshold noise reduction method based on LabVIEW to generate a deep noise reduction signal.

3. The method according to claim 1, characterized in that The processing of the pre-processed IMFs components based on a third preset rule to generate a noise-reduced signal x'(t) includes: Processing the IMFs components to generate a correlation coefficient corresponding to each component in the IMFs and the original signal; Processing the correlation coefficient based on a preset threshold value, and dividing the correlation coefficient into a strong correlation component or a weak correlation component; Performing WPT noise reduction processing on the weakly correlated components to remove the noise components therein; The weakly correlated component and the strongly correlated component are subjected to EMD reconstruction to generate a noise-reduced signal x'(t).

4. The method according to claim 3, characterized in that The processing of the correlation coefficient based on a preset threshold to divide the correlation coefficient into a strong correlation component or a weak correlation component includes: Where Ci is the correlation coefficient between the i-th IMF component and the original signal, and T is the final threshold; The correlation coefficients with T less than or equal to 0.3 are classified as weak correlation components, and the correlation coefficients with T greater than 0.3 are classified as strong correlation components.

5. An acoustic emission signal noise reduction device based on improved CEEMD-WPT, characterized in that: include: An acquisition module is configured to acquire the original signal x(t); The processing module is configured to process the collected original signal x(t) based on a first preset rule to generate a sorted signal x(t); process the collected original signal x(t) based on an EMD method to generate a preprocessed signal; sort the preprocessed signal from high to low according to frequency to generate a sorted signal x(t); perform noise addition processing on the sorted signal x(t) to generate a noisy signal group containing multiple pairs of white noises; perform WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group; perform E MD decomposition processing to generate multiple groups of IMFs components and residuals r(t); processing the IMFs components based on a second preset rule to generate preprocessed IMFs components; processing the preprocessed IMFs components based on a third preset rule to generate a denoised signal x'(t); the third preset rule is to process the IMFs components to generate a correlation coefficient corresponding to each component in the IMFs and the original signal; processing the correlation coefficient based on a preset threshold to classify the correlation coefficient into a strongly correlated component or a weakly correlated component; The processing of the IMFs component based on the second preset rule to generate a preprocessed IMFs component includes: Get the preset average value IMFj; Processing the noise-reduced signal group based on the preset average value IMFj to generate a target signal R(t) of the noise-reduced signal group; Processing the target signal R(t) to generate preprocessed IMFs components; The processing of the target signal R(t) to generate a preprocessed IMFs component includes: Step 1: Process the target signal R(t) based on a first preset rule to generate a new sorted signal x(t); Step 2: performing noise addition processing on the sorted signal x(t) to generate a noisy signal group including multiple pairs of white noises; Step 3: Perform WPT noise reduction processing on the noisy signal group to generate a noise-reduced signal group; Step 4: performing EMD decomposition on the denoised signal group to generate multiple groups of IMFs components and residuals r(t); Step 5: Process the noise-reduced signal group based on the preset average value IMFj to generate a target signal R(t) of the noise-reduced signal group; If the target signal R(t) can be decomposed or the number of decompositions is lower than the upper threshold, return to step 1; If the target signal R(t) cannot be decomposed or the number of decompositions is higher than an upper threshold, the residual r(t) is deleted to generate the result IMFs component after the improved CEEMD decomposition.

6. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to execute the acoustic emission signal denoising method based on improved CEEMD-WPT according to any one of claims 1 to 4 by executing the executable instructions.

7. A computer-readable storage medium for storing computer-readable instructions, characterized in that: When the instruction is executed, the operation of the acoustic emission signal noise reduction method based on improved CEEMD-WPT according to any one of claims 1 to 4 is implemented.

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