CN117056675A - Magnetic flux leakage signal noise reduction method based on combination of wavelet transformation and particle filtering - Google Patents
Magnetic flux leakage signal noise reduction method based on combination of wavelet transformation and particle filtering Download PDFInfo
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- CN117056675A CN117056675A CN202311035634.6A CN202311035634A CN117056675A CN 117056675 A CN117056675 A CN 117056675A CN 202311035634 A CN202311035634 A CN 202311035634A CN 117056675 A CN117056675 A CN 117056675A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G01N27/72—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables
- G01N27/82—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws
- G01N27/83—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating magnetic variables for investigating the presence of flaws by investigating stray magnetic fields
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
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Abstract
The invention discloses a magnetic leakage signal noise reduction method based on combination of wavelet transformation and particle filtering, which comprises the following steps: and selecting proper wavelet bases and decomposition layers for wavelet decomposition of the initialized and selecting proper threshold values and threshold value wavelet denoising methods of threshold value functions for denoising, reconstructing the wavelet coefficients to form new particles, adding wavelet denoising in the process, keeping the low entropy characteristic of the signals, enabling the prior probability to be more concentrated, and improving the accuracy of the subsequent particle filtering algorithm. After new particles are generated, the importance weight of the particles is corrected and normalized, then resampling operation of resampling is performed, the particles with large weight are duplicated, the particles with small weight are removed, and the phenomenon of particle degradation is reduced. The subsequent sampling process after reconstruction is also accompanied by the participation of wavelet noise reduction, so that new particles are formed. The invention realizes more reliable noise reduction treatment on the magnetic leakage signal under the strong vibration background.
Description
Technical Field
The invention relates to a magnetic leakage signal noise reduction method based on combination of wavelet transformation and particle filtering, and belongs to the technical field of magnetic leakage signal noise reduction.
Background
In recent years, with the increasing demand of nondestructive testing technology, the leakage magnetic detection technology is also becoming an important technical means in the field of modern signal nondestructive testing. Particularly, the magnetic leakage signal is collected in a strong vibration background, and the collected signal is mostly accompanied by a large amount of noise signals. Noise reduction is particularly important for these noises.
In terms of magnetic flux leakage signal noise reduction, certain research is carried out in China, common noise reduction methods comprise wavelet transformation noise reduction, adaptive filtering noise reduction and spectral subtraction noise reduction, and particularly, the noise reduction method is basically a main method for noise reduction of the magnetic flux leakage signal along with the occurrence of wavelet transformation noise reduction, but for the magnetic flux leakage signal under a strong noise background, the noise reduction effect of single wavelet transformation is not ideal, the nonlinearity of the signal is considered, particle filtering is good for the noise reduction of the nonlinear signal, and the wavelet transformation noise reduction method and the particle filtering noise reduction method are combined to form a novel noise reduction mode.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a magnetic leakage signal noise reduction method based on the combination of wavelet transformation and particle filtering.
The wavelet transformation is used as a noise reduction method, can carry out wavelet multi-scale decomposition on noise signals and target signals in the noise signals, realizes signal separation according to the difference of two signal decomposition scales, has the characteristic of low entropy, and can better describe the non-stationarity of the signals. Particle filtering, as a novel way of handling nonlinear, non-gaussian systems, has its feasibility in nonlinear systems. The method combines the two, performs wavelet transformation on the collected particles, and can concentrate the particle filtering prior probability function, so that the variance of the importance weight is smaller, and the accuracy of the particle filtering algorithm is improved.
The technical scheme provided by the invention for solving the technical problems is as follows: a magnetic flux leakage signal noise reduction method based on the combination of wavelet transformation and particle filtering comprises the following steps:
step 1, taking the defect depth as input, taking axial magnetic flux as output, modeling a magnetic leakage signal through a least square method, and obtaining a state equation and an observation equation;
step 2, obtaining N particles through initializing sampling;
step 3, generating new particles according to the state transfer function;
step 4, performing wavelet decomposition on the new particle signals by using a Mallat algorithm to obtain wavelet coefficients of high and low frequency bands of each layer;
step 5, carrying out threshold quantization processing on wavelet coefficients of a high frequency band and a low frequency band after wavelet decomposition, and then reconstructing a state signal of wavelet decomposition, wherein the reconstructed particles are used as new particles;
step 6, calculating the importance weight of the new particle according to the state equation and the observation equation;
step 7, carrying out normalization processing on the importance weight of the new particle;
step 8, resampling the obtained particles by using a resampling method, and repeating the steps 4-7 on the resampled particles;
and 9, obtaining an estimation result of the posterior state at the moment k.
The further technical scheme is that in the step 1, a state equation of the magnetic leakage signal is obtained in the process of applying algorithm modeling to the magnetic leakage signal, and the established mathematical model is converted into an observation equation of the magnetic leakage signal.
According to a further technical scheme, in the step 4, a wavelet basis function of db3 is selected for the new particle signal, and the number of decomposition layers is 3.
The further technical scheme is that the calculation formula of the step 6 is as follows:
wherein:is an importance weight.
The further technical scheme is that the calculation formula of the step 7 is as follows:
wherein:and the importance weight is normalized.
The further technical scheme is that in the step 8, the system resampling is adopted, the particles with large weight are duplicated, the particles with small weight are removed, and the particle degradation phenomenon is reduced.
The further technical scheme is that the weight assignment principle in the step 8 is as follows:
wherein:is a weight.
The invention has the following beneficial effects: the invention is based on particle filtering commonly used for optimal estimation of nonlinear and non-Gaussian dynamic systems, and combines wavelet noise reduction to carry out noise reduction treatment on each newly generated particle set, so that the noise reduction result is more approximate to a real state value.
Drawings
FIG. 1 is a wavelet noise reduction schematic diagram;
FIG. 2 is a block diagram of a wavelet transform-particle filter algorithm implementation;
FIG. 3 is a decomposition and reconstruction of signals by the Mallat algorithm;
FIG. 4 is a diagram of raw data;
FIG. 5 is a data diagram after noise addition;
FIG. 6 is a wavelet threshold denoising data diagram;
FIG. 7 is a graph of particle filter denoising data;
FIG. 8 is a wavelet-particle filter denoising data plot;
fig. 9 is a graph showing the comparison of the magnetic flux leakage signal data before and after noise reduction.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 2, the magnetic leakage signal noise reduction method based on the combination of wavelet transformation and particle filtering of the invention comprises the following steps of.
Step 1, taking the defect depth as input, taking axial magnetic flux as output, modeling a magnetic leakage signal through a least square method, and obtaining a state equation and an observation equation;
step 2 for initial distribution P (X 0 ) Sampling to obtain N particles;
step 3, generating new particles according to the state transfer function;
step 4, selecting a db3 wavelet basis function for the generated particle signals, wherein the number of decomposition layers is 3, and performing wavelet decomposition by using a Mallat algorithm to obtain wavelet coefficients of high and low frequency bands of each layer;
step 5, carrying out threshold quantization processing on wavelet coefficients of a high frequency band and a low frequency band after wavelet decomposition, and then reconstructing a state signal of wavelet decomposition, wherein the reconstructed particles are used as new particles;
step 6, calculating the importance weight of the new particle according to the state equation and the observation equation;
step 7, carrying out normalization processing on the importance weight of the new particle;
step 8, resampling the obtained particles by using a system resampling method (namely, sampling the particles with small weight, adding new particles to keep the original number of N particles unchanged, dividing the interval [0,1] into subintervals with the same number of particles on the basis of normalization of importance weight, wherein the length of each subinterval is in direct proportion to the normalization weight of the corresponding particle;
the weight assignment principle is as follows:
step 9, obtaining an estimation result of the posterior state at the moment k;
examples
The original signal and the noise-added signal of the magnetic leakage signal are compared with the amplitudes of three noise reduction modes including wavelet threshold, particle filtering and wavelet-particle filtering, and the noise-reduced images of the single particle filtering and the wavelet-particle filtering without adding wavelet transformation of the Shan Xiaobo threshold are compared, and the signal to noise ratios before and after noise reduction of the three methods are calculated and used as the most important indexes of noise reduction performance analysis of the noise-reduced images.
Signal-to-noise ratio:
the SIGNAL-to-NOISE RATIO (SNR) is an important indicator of SIGNAL quality in an electronic device or system, and particularly has an important reference value in the process of comparing filtering algorithms. The size is expressed as:
S SNr ＝10logP s /P n
wherein P is s For signal energy, P n Is the noise energy.
By comparing fig. 5 and fig. 8, the interference of the wavelet-particle filtering to the original signal can be obviously reduced, the signal value close to the original signal can be obtained, and meanwhile, by fig. 8, the image obtained by the wavelet-particle filtering is clear compared with the other two noise reduction methods, and the obtained waveform is clearer.
Table 1 comparison of data of different methods for processing analog leakage magnetic signals
| Method | SNR/dB |
| Unprocessed data | 13.3625 |
| Wavelet threshold noise reduction | 22.1299 |
| Particle filtering | 19.1897 |
| Wavelet-particle filtering | 24.8770 |
As can be seen from table 1, the signal-to-noise ratio of the wavelet-particle noise reduction is 24.877dB, which is the largest of the three noise reduction methods, and the wavelet-particle filtering not only effectively filters out noise and improves the signal-to-noise ratio, but also more effectively saves the real signal, avoids the loss of the real signal, and the result obtained after the noise reduction is closer to the real signal.
The result shows that the noise reduction treatment of the magnetic leakage signal by adopting the wavelet-particle filtering method is an effective method, and has an important function in the noise reduction research of the magnetic leakage signal under the strong vibration background.
The present invention is not limited to the above-mentioned embodiments, but is not limited to the above-mentioned embodiments, and any person skilled in the art can make some changes or modifications to the equivalent embodiments without departing from the scope of the technical solution of the present invention, but any simple modification, equivalent changes and modifications to the above-mentioned embodiments according to the technical substance of the present invention are still within the scope of the technical solution of the present invention.
Claims (7)
1. The magnetic flux leakage signal noise reduction method based on the combination of wavelet transformation and particle filtering is characterized by comprising the following steps:
step 1, taking the defect depth as input, taking axial magnetic flux as output, modeling a magnetic leakage signal through a least square method, and obtaining a state equation and an observation equation;
step 2, obtaining N particles through initializing sampling;
step 3, generating new particles according to the state transfer function;
step 4, performing wavelet decomposition on the new particle signals by using a Mallat algorithm to obtain wavelet coefficients of high and low frequency bands of each layer;
step 5, carrying out threshold quantization processing on wavelet coefficients of a high frequency band and a low frequency band after wavelet decomposition, and then reconstructing a state signal of wavelet decomposition, wherein the reconstructed particles are used as new particles;
step 6, calculating the importance weight of the new particle according to the state equation and the observation equation;
step 7, carrying out normalization processing on the importance weight of the new particle;
step 8, resampling the obtained particles by using a system resampling method, and repeating the steps 4-7 on the resampled particles;
and 9, obtaining an estimation result of the posterior state at the moment k.
2. The method for noise reduction of magnetic leakage signals based on combination of wavelet transform and particle filtering according to claim 1, wherein in the step 1, a state equation of the magnetic leakage signals is obtained in a process of modeling the magnetic leakage signals by using an algorithm, and the established mathematical model is converted into an observation equation of the magnetic leakage signals.
3. The method for noise reduction of magnetic leakage signals based on combination of wavelet transform and particle filtering according to claim 1, wherein in the step 4, a wavelet basis function of db3 is selected for the new particle signals, and the number of decomposition layers is 3.
4. The method for noise reduction of magnetic leakage signals based on combination of wavelet transform and particle filtering according to claim 1, wherein the calculation formula in the step 6 is as follows:
wherein:is an importance weight.
5. The method for noise reduction of magnetic leakage signals based on combination of wavelet transform and particle filtering according to claim 4, wherein the calculation formula in the step 7 is as follows:
wherein:and the importance weight is normalized.
6. The method for noise reduction of magnetic leakage signals based on combination of wavelet transformation and particle filtering according to claim 1, wherein the step 8 is characterized in that the method adopts system resampling, copies particles with large weight, rejects particles with small weight, and reduces the phenomenon of particle degradation.
7. The method for noise reduction of magnetic leakage signals based on combination of wavelet transform and particle filtering according to claim 6, wherein the weight assignment in the step 8 is based on the following principle:
wherein:is a weight.
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| CN117433952A (en) * | 2023-12-21 | 2024-01-23 | 西南石油大学 | Method for rapidly measuring density of barite powder |
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