CN112287835A - Blade acoustic signal denoising method based on EWT-SE and wavelet threshold - Google Patents

Blade acoustic signal denoising method based on EWT-SE and wavelet threshold Download PDF

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CN112287835A
CN112287835A CN202011182502.2A CN202011182502A CN112287835A CN 112287835 A CN112287835 A CN 112287835A CN 202011182502 A CN202011182502 A CN 202011182502A CN 112287835 A CN112287835 A CN 112287835A
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blade
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CN112287835B (en
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张家安
姜皓龄
田家辉
徐超林
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Hebei University of Technology
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Abstract

The invention discloses a blade sound signal denoising method based on EWT-SE and a wavelet threshold, aiming at the problem that pure blade sound signals are difficult to obtain due to the influence of random wind noise on the blade sound of a wind driven generator, the sample entropy is improved; and modifying the wavelet threshold function according to the characteristics of the blade acoustic signals, and providing a blade acoustic signal denoising method based on Empirical Wavelet Transform (EWT) -Sample Entropy (SE) and wavelet threshold. Firstly, decomposing a signal into various mode functions through EWT, calculating a sample entropy value of each mode function through an improved sample entropy algorithm, and selecting a signal reconstruction component; then, performing wavelet threshold re-denoising on the boundary mode function; and finally, reconstructing the components to obtain the signal subjected to noise reduction. The method is analyzed and verified by using the blade sound data recorded on site, and the result shows that the noise reduction effect of the method is better, and a purer blade signal can be obtained, so that a foundation is laid for subsequently extracting the blade acoustic characteristics.

Description

Blade acoustic signal denoising method based on EWT-SE and wavelet threshold
Technical Field
The invention belongs to the field related to signal processing, and relates to a blade sound signal denoising method based on EWT-SE and a wavelet threshold, which has better denoising effect than a wavelet threshold denoising method, an EMD denoising method and an EMD-wavelet threshold denoising method.
Background
When the wind driven generator works in a complex open environment for a long time, the blades are easy to crack, corrode and other faults, and if the faults cannot be found in time, the risks are brought to the safe operation of the fan. Because the fault blade can generate abnormal signals, some scholars extract the state information characteristics of the blade by taking acoustic emission signals, vibration signals, pneumatic signals and the like of the wind turbine blade as research objects and further carry out fault diagnosis. However, the above methods require installation of corresponding sensors, which is costly. The sound signals of the blades can be acquired without stopping the machine, and an additional sensor is not needed, so that the method has a wide application prospect in fault diagnosis of the blades by using the sound signals. However, the sound recorded on site is affected by wind noise with strong randomness, so that a pure sound signal of the fan blade cannot be obtained. Therefore, the method has very important significance in noise reduction of the blade sound of the fan, and lays a foundation for subsequently extracting the acoustic characteristics of the blade so as to diagnose faults.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a blade acoustic signal denoising method based on EWT-SE and a wavelet threshold, which improves the traditional sample entropy algorithm and modifies the wavelet threshold function according to the characteristics of the blade acoustic signal; the specific process is as follows: decomposing an acoustic signal into various modal functions through Empirical Wavelet Transform (EWT), and then calculating a sample entropy value of each modal function through an improved sample entropy algorithm to select a signal reconstruction component; then, performing wavelet threshold re-denoising on the boundary mode function; and finally, reconstructing the components to obtain the noise-reduced acoustic signal.
The technical scheme for solving the technical problems is as follows: designing a blade acoustic signal denoising method based on EWT-SE and wavelet threshold, wherein the method comprises the following steps:
step 1: original fan blade by utilizing EWT decomposition methodDecomposing the chip sound signal time sequence to obtain the modal function component f under different frequenciesi(t)；
Step 2: calculating the sample entropy value of each mode function component by using an improved sample entropy algorithm;
and step 3: arranging the sample entropy values of all the modal function components from large to small, selecting the modal function component before sample entropy mutation as a signal reconstruction component, and extracting the first modal function component before sample entropy mutation as a boundary modal function;
and 4, step 4: carrying out re-denoising on the boundary modal function extracted in the step 3 by using the modified wavelet threshold function;
and 5: and (4) reconstructing the signal reconstruction component selected in the step (3) except the boundary mode function and the boundary mode function subjected to noise reduction processing in the step (4) to obtain the noise-reduced blade sound signal.
Compared with the prior art, the invention has the beneficial effects that: the method improves a sample entropy algorithm, modifies a wavelet threshold function, and creatively provides a blade acoustic signal denoising method based on Empirical Wavelet Transform (EWT) -Sample Entropy (SE) and a wavelet threshold. The method is used for denoising wind field fault blade sound and normal blade sound, and proves that the method has an obvious effect of filtering random wind noise, obtains a relatively pure blade sound signal, can highlight the fault sound of a fault blade and lays a foundation for extracting the sound signal characteristic of the blade in the later period.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings and tables used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a time-frequency plot of an acoustic signal for a fan blade;
FIG. 2 is a time-frequency plot of a wind noise signal for the acoustic signal of the fan blade shown in FIG. 1;
FIG. 3 is a flow chart of the method of the present invention;
FIG. 4 is a time series plot of the raw fan blade acoustic signal of example 1 (in the same units of amplitude as in the remaining figures);
FIG. 5 is a time series plot of 10 modal function components obtained by an EWT decomposition method from the time series plot of the original fan blade acoustic signal shown in FIG. 4;
FIG. 6 is a time series diagram of blade acoustic signals obtained after denoising the original fan blade acoustic signal time series shown in FIG. 4 by using a wavelet threshold denoising method;
FIG. 7 is a time series diagram of the acoustic signal of the blade obtained after the EMD denoising method is adopted to denoise the acoustic signal time series of the original fan blade shown in FIG. 4;
FIG. 8 is a time series diagram of blade acoustic signals obtained after denoising the original fan blade acoustic signal time series shown in FIG. 4 by using an EMD-wavelet threshold denoising method;
FIG. 9 is a time series plot of the acoustic signals of the blade obtained after denoising the time series of the acoustic signals of the original fan blade shown in FIG. 4 using the method of the present invention;
FIG. 10 is a graph showing the noise reduction effect of the method of the present invention on the acoustic signal of the No. 1 fan blade; wherein FIG. 10(a) is an original fan blade acoustic signal and FIG. 10(b) is a blade acoustic signal denoised by the method of the present invention;
FIG. 11 is a graph showing the noise reduction effect of the method of the present invention on the acoustic signal of the No. 2 fan blade; FIG. 11(a) is an original fan blade acoustic signal, and FIG. 11(b) is a blade acoustic signal after noise reduction by the method of the present invention;
FIG. 12 is a graph showing the noise reduction effect of the method of the present invention on the acoustic signal of the No. 3 fan blade; FIG. 12(a) is an original fan blade acoustic signal, and FIG. 12(b) is a blade acoustic signal after noise reduction using the method of the present invention;
FIG. 13 is a graph showing the noise reduction effect of the acoustic signal of the No. 4 fan blade by the method of the present invention; wherein FIG. 13(a) is the original fan blade acoustic signal and FIG. 13(b) is the blade acoustic signal after noise reduction using the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a blade acoustic signal denoising method (method for short) based on EWT (empirical wavelet transform) -SE (sample entropy) and a wavelet threshold, which comprises the following steps:
step 1: decomposing the original fan blade acoustic signal time sequence by utilizing an EWT (equivalent wavelet transform) decomposition method to obtain a modal function component f under different frequenciesi(t)；
Step 2: calculating the sample entropy value of each mode function component by using an improved sample entropy algorithm;
the traditional calculation method can only calculate the entropy of the signal sample with the length of 500-2000. However, as many as hundreds of thousands of modal function sampling points obtained by EWT decomposition cannot directly calculate the sample entropy thereof, the sample entropy algorithm needs to be improved, and the improved sample entropy algorithm has the following calculation steps:
(1) assuming that the length of an acoustic signal of a mode function component is L, decomposing the acoustic signal into M frames with the frame length of N, moving the frames into inc, and marking each frame as Xk(k ═ 1,2, … M), the framing formula is:
M＝(L-N+inc)/inc
(2) determining an embedding dimension m, and dividing a frame of acoustic signals X of length Nk(k ═ 1,2, … M) is reconstructed as an M-dimensional vector, i.e.:
xi＝[xi,xi+1,…xi+m-1],i＝1,2,…,N-m
(3) two vectors x are definediAnd xjDistance d ofijThe maximum value of the absolute value of the difference value of the corresponding elements of the two vectors is as follows:
Figure BDA0002750570340000051
(4) determining a similarity margin r, statistic dijNumber B less than riCalculating BiAnd dijThe ratio of the total number N-m-1 is recorded as
Figure BDA0002750570340000052
And find
Figure BDA0002750570340000053
I.e.:
Figure BDA0002750570340000054
(5) repeating the above steps (2) to (4) for the embedding dimension m +1 to obtain
Figure BDA0002750570340000055
And Bm+1(r)；
(6) Calculating the frame of acoustic signal XkSample entropy of (k ═ 1,2, … M):
SampEn(m,r,N)＝ln Bm(r)-ln Bm+1(r)
(7) and (5) repeating the steps (2) to (6), calculating and summing sample entropy values of the M frames of acoustic signals, and obtaining the sample entropy value of the corresponding modal function component.
And step 3: arranging the sample entropy values of all the modal function components from large to small, selecting the modal function component before sample entropy mutation as a signal reconstruction component, and extracting the first modal function component before sample entropy mutation as a boundary modal function;
and 4, step 4: and (4) performing re-denoising on the boundary modal function extracted in the step (3) by using the modified wavelet threshold function.
The traditional wavelet threshold denoising method considers noise as a high-frequency signal, so that a high-frequency coefficient is processed, but by comparing time-frequency graphs of blade acoustic signals and wind noise signals, as shown in fig. 1, it can be seen that wind noise is a low-frequency signal relative to the fan blade acoustic signals, and the traditional wavelet threshold denoising method cannot process the low-frequency signal. Selecting to modify the soft threshold function, wherein the modified wavelet threshold function is as follows:
Figure BDA0002750570340000061
wherein ω isj,kTaking threshold value for low-frequency coefficient obtained by wavelet transform
Figure BDA0002750570340000062
Wherein σ is ωj,kL is the acoustic signal length of the boundary mode function.
And 5: and (4) reconstructing (namely superposing) the signal reconstruction component selected in the step (3) except the boundary mode function and the boundary mode function subjected to noise reduction processing in the step (4) to obtain the noise-reduced blade acoustic signal.
Example 1
The embodiment provides a blade sound signal denoising method based on EWT (empirical wavelet transform) -SE (sample entropy) and a wavelet threshold, which adopts the following steps:
step 1: decomposing the original wind turbine blade acoustic signal time sequence (taking 3s recorded blade acoustic signal as an example, as shown in fig. 4) by using the EWT decomposition method to obtain 10 modal function components f under different frequenciesi(t), i is 1,2, … … 10, and a time series diagram of each mode function component is shown in fig. 5.
Step 2: calculating the sample entropy value of each mode function component by using an improved sample entropy algorithm;
the traditional calculation method can only calculate the entropy of the signal sample with the length of 500-2000. However, as many as hundreds of thousands of modal function sampling points obtained by EWT decomposition cannot directly calculate the sample entropy thereof, the sample entropy algorithm needs to be improved, and the improved sample entropy algorithm has the following calculation steps:
(1) assuming that the acoustic signal length of a mode function component is L, the acoustic signal is decomposed into M frames with the frame length of N, and the frames areShift to inc, and denote the acoustic signal of each frame as Xk(k ═ 1,2, … M), the framing formula is:
M＝(L-N+inc)/inc
(2) determining an embedding dimension m, and dividing a frame of acoustic signals X of length Nk(k ═ 1,2, … M) is reconstructed as an M-dimensional vector, i.e.:
xi＝[xi,xi+1,…xi+m-1],i＝1,2,…,N-m
(3) two vectors x are definediAnd xjDistance d ofijThe maximum value of the absolute value of the difference value of the corresponding elements of the two vectors is as follows:
Figure BDA0002750570340000071
(4) determining a similarity margin r, statistic dijNumber B less than riCalculating BiAnd dijThe ratio of the total number N-m-1 is recorded as
Figure BDA0002750570340000072
And find
Figure BDA0002750570340000073
I.e.:
Figure BDA0002750570340000074
(5) repeating the above steps (2) to (4) for the embedding dimension m +1 to obtain
Figure BDA0002750570340000075
And Bm+1(r)；
(6) Calculating the frame of acoustic signal XkSample entropy of (k ═ 1,2, … M):
SampEn(m,r,N)＝ln Bm(r)-ln Bm+1(r)
(7) and (5) repeating the steps (2) to (6), calculating and summing sample entropy values of the M frames of acoustic signals, and obtaining the sample entropy value of the corresponding modal function component.
In the improved sample entropy calculation, parameters m, r, inc and a frame length N need to be determined, where m is 2, r is 0.15Std (Std is a standard deviation of an original fan blade acoustic signal), inc is 300, and N is 1024.
The sample entropy values of the mode function components calculated using the improved sample entropy are shown in table 1.
TABLE 1 sample entropies of modal function components
EWT decomposition mode function Sample entropy value
Component
1 3.4212
Component 2 3.2582
Component 3 2.7388
Component 4 2.5875
Component 5 1.4899
Component 6 1.573×10-3
Component 7 1.112×10-4
Component 8 7.555×10-5
Component 9 5×10-5
Component 10 3.732×10-5
And step 3: arranging the sample entropy values of all the modal function components from large to small, selecting the modal function component before sample entropy mutation as a signal reconstruction component, and extracting the first modal function component before sample entropy mutation as a boundary modal function; referring to table 1, the sample entropy is mutated in the 6 th mode function component, and the sample entropy value is mutated to 10-nAnd the magnitude indicates that the modal function is less interfered by wind noise from the component, so that the signals are reconstructed from the 6 th to 10 th modal functions, and the 6 th component is extracted as the boundary modal function.
And 4, step 4: and (4) performing re-denoising on the boundary modal function extracted in the step (3) by using the modified wavelet threshold function.
The modified wavelet threshold function is:
Figure BDA0002750570340000081
wherein ω isj,kTaking threshold value for low-frequency coefficient obtained by wavelet transform
Figure BDA0002750570340000082
Wherein σ is ωj,kL is the acoustic signal length of the boundary mode function.
And 5: and (4) reconstructing (namely superposing) the signal reconstruction component selected in the step (3) except the boundary modal function and the boundary modal function subjected to noise reduction processing in the step (4) to obtain the noise-reduced blade acoustic signal.
To further illustrate the superiority of the method, the original fan blade acoustic signals in example 1 are respectively denoised by a wavelet threshold denoising method, an EMD denoising method, and an EMD-wavelet threshold denoising method, and then compared, and the results are shown in fig. 6 to 9. Through comparison, the acoustic signal subjected to noise reduction by the method is smoother, and the suppression effect on random wind noise is better. In order to make a more intuitive comparison of the noise reduction effects, the sample entropy of the acoustic signal after different noise reduction methods is calculated, as shown in table 2.
TABLE 2 entropy of signal samples for different noise reduction methods
Noise reduction method Sample entropy value
Nothing (original signal) 7.3748
Wavelet threshold denoising 6.1564
EMD noise reduction 3.5862
EMD-wavelet threshold denoising 1.1409
The method of the invention 0.0142
The method can find that the entropy value of the acoustic signal sample after noise reduction is minimum, shows the sequence rule of the acoustic signal, basically has no interference of random noise, and proves the feasibility and the superiority of the method for reducing the noise of the acoustic signal of the fan blade.
In order to test the denoising effect of the method in practical engineering application, denoising three single-blade fault fan sound signals and one normal fan blade sound signal recorded by a certain Tianjin wind power plant. For convenient differentiation, three fault fans are respectively No. 1, No. 2 and No. 3 fans, a normal fan is marked as a No. 4 fan, and the models and working environments of the four fans are the same. 18s acoustic signals of each fan blade are extracted, noise reduction is carried out by adopting the method, and the denoising result is shown in FIGS. 10-13.
By comparing the sound signal diagrams before and after denoising, the sound of a fault blade and the sound of a normal blade are completely submerged by the noise due to the influence of wind noise of a fan before denoising; the influence of wind noise on the denoised signals is obviously reduced, abnormal sounds of a fault fan are highlighted, and blade sounds of a normal fan are more stable, so that the denoising method has an obvious denoising effect on the fault blade sounds or the normal blade sounds, and a foundation is laid for subsequently extracting the blade acoustic features.
Nothing in this specification is said to apply to the prior art.

Claims (5)

1. A blade acoustic signal denoising method based on EWT-SE and wavelet threshold is characterized by comprising the following steps:
step 1: decomposing the original fan blade acoustic signal time sequence by utilizing an EWT (equivalent wavelet transform) decomposition method to obtain a modal function component f under different frequenciesi(t)；
Step 2: calculating the sample entropy value of each mode function component by using an improved sample entropy algorithm;
and step 3: arranging the sample entropy values of all the modal function components from large to small, selecting the modal function component before sample entropy mutation as a signal reconstruction component, and extracting the first modal function component before sample entropy mutation as a boundary modal function;
and 4, step 4: carrying out re-denoising on the boundary modal function extracted in the step 3 by using the modified wavelet threshold function;
and 5: and (4) reconstructing the signal reconstruction component selected in the step (3) except the boundary mode function and the boundary mode function subjected to noise reduction processing in the step (4) to obtain the noise-reduced blade sound signal.
2. The method for denoising blade acoustic signals based on EWT-SE and wavelet threshold as claimed in claim 1, wherein the improved sample entropy algorithm is calculated by the following steps:
(1) assuming that the length of an acoustic signal of a mode function component is L, decomposing the acoustic signal into M frames with the frame length of N, moving the frames into inc, and marking each frame as Xk(k ═ 1,2, … M), the framing formula is:
M＝(L-N+inc)/inc
(2) determining an embedding dimension m, and dividing a frame of acoustic signals X of length Nk(k ═ 1,2, … M) is reconstructed as an M-dimensional vector, i.e.:
xi＝[xi,xi+1,…xi+m-1],i＝1,2,…,N-m
(3) two vectors x are definediAnd xjDistance d ofijThe maximum value of the absolute value of the difference value of the corresponding elements of the two vectors is as follows:
Figure FDA0002750570330000021
(4) determining a similarity margin r, statistic dijNumber B less than riCalculating BiAnd dijThe ratio of the total number N-m-1 is recorded as
Figure FDA0002750570330000022
And find
Figure FDA0002750570330000023
I.e.:
Figure FDA0002750570330000024
(5) repeating the above steps (2) to (4) for the embedding dimension m +1 to obtain
Figure FDA0002750570330000025
And Bm+1(r)；
(6) Calculating the frame of acoustic signal XkSample entropy of (k ═ 1,2, … M):
SampEn(m,r,N)＝lnBm(r)-lnBm+1(r)
(7) and (5) repeating the steps (2) to (6), calculating and summing sample entropy values of the M frames of acoustic signals, and obtaining the sample entropy value of the corresponding modal function component.
3. The method for denoising blade acoustic signals based on EWT-SE and wavelet threshold as claimed in claim 2, wherein m-2, r-0.15 Std, inc-300, N-1024.
4. The method for denoising blade acoustic signals based on EWT-SE and wavelet threshold as claimed in claim 1, wherein the modified wavelet threshold function is:
Figure FDA0002750570330000026
wherein ω isj,kTaking threshold value for low-frequency coefficient obtained by wavelet transform
Figure FDA0002750570330000027
Wherein σ is ωj,kL is the acoustic signal length of the boundary mode function.
5. The method for denoising blade acoustic signals based on EWT-SE and wavelet threshold as claimed in claim 1, wherein the mode function component f at different frequencies in step 1iThe number of (t) is 10.
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