CN113792675A - Metal material analysis method and device - Google Patents
Metal material analysis method and device Download PDFInfo
- Publication number
- CN113792675A CN113792675A CN202111091065.8A CN202111091065A CN113792675A CN 113792675 A CN113792675 A CN 113792675A CN 202111091065 A CN202111091065 A CN 202111091065A CN 113792675 A CN113792675 A CN 113792675A
- Authority
- CN
- China
- Prior art keywords
- metal material
- characteristic
- detected
- signal
- discrete
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000007769 metal material Substances 0.000 title claims abstract description 322
- 238000004458 analytical method Methods 0.000 title claims abstract description 95
- 239000000463 material Substances 0.000 claims abstract description 169
- 230000004044 response Effects 0.000 claims abstract description 40
- 238000000034 method Methods 0.000 claims abstract description 24
- 230000009467 reduction Effects 0.000 claims description 23
- 238000001914 filtration Methods 0.000 claims description 22
- 238000004364 calculation method Methods 0.000 claims description 19
- 238000000354 decomposition reaction Methods 0.000 claims description 17
- 238000005457 optimization Methods 0.000 claims description 16
- 238000000605 extraction Methods 0.000 claims description 15
- 238000013139 quantization Methods 0.000 claims description 6
- 230000035945 sensitivity Effects 0.000 abstract description 3
- 230000005284 excitation Effects 0.000 description 6
- 238000005070 sampling Methods 0.000 description 6
- 230000003321 amplification Effects 0.000 description 4
- 238000003199 nucleic acid amplification method Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000004611 spectroscopical analysis Methods 0.000 description 2
- 238000004448 titration Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 108010076504 Protein Sorting Signals Proteins 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
- G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Abstract
The embodiment of the invention discloses a metal material analysis method and a device thereof. The metal material analysis method comprises the following steps: the method comprises the steps of obtaining eddy current response signals of a metal material to be detected, obtaining material comparison characteristics of the metal material to be detected from the eddy current response signals of the metal material to be detected based on a discrete wavelet transform model, wherein the material comparison characteristics comprise detail characteristics, approximate characteristics, noise-reduced characteristics and discrete analysis characteristics, and when the material comparison characteristics of the metal material to be detected are matched with the material comparison characteristics of sample metal materials in a metal material characteristic library, determining the material of the metal material to be detected as the material of the corresponding sample metal material, so that the material determination of the metal material to be detected with the advantages of no damage, high sensitivity, high accuracy and high efficiency is realized.
Description
Technical Field
The embodiment of the invention relates to the technical field of metal material analysis, in particular to a metal material analysis method and a metal material analysis device.
Background
The high consistency of metal materials is one of the key quality factors in high-end manufacturing industry, military industry and aerospace industry. In the prior art, the composition of an incoming metal material is generally determined by an off-line detection method such as a titration method or a spectroscopy method to determine the material. However, off-line detection methods such as titration and spectroscopy inevitably cause some damage to the metal material.
Disclosure of Invention
The embodiment of the invention provides a metal material analysis method and a device thereof, which can determine the material of a metal material in a lossless, high-sensitivity, high-precision and high-efficiency manner.
In a first aspect, an embodiment of the present invention provides a metal material analysis method, where the metal material analysis method includes:
acquiring an eddy current response signal of a metal material to be detected;
acquiring material comparison characteristics of the metal material to be detected from an eddy current response signal of the metal material to be detected based on a discrete wavelet transform model, wherein the material comparison characteristics comprise detail characteristics, approximate characteristics, noise-reduced characteristics and discrete analysis characteristics;
and when the material comparison characteristic of the metal material to be detected is matched with the material comparison characteristic of the sample metal material in the metal material characteristic library, determining that the material of the metal material to be detected is the corresponding material of the sample metal material.
Optionally, the method further includes: and when the material comparison characteristic of the metal material to be detected is not matched with the material comparison characteristic of the sample metal material characteristic in the metal material characteristic library, storing the material comparison characteristic of the metal material to be detected into the metal material characteristic library so as to take the metal material to be detected as the sample metal material.
Optionally, when the material comparison characteristic of the to-be-detected metal material is matched with the material comparison characteristic of the sample metal material in the metal material characteristic library, determining that the material of the to-be-detected metal material is corresponding to the material of the sample metal material includes:
calculating a material comparison characteristic weighted average value of the metal material to be detected according to the detail characteristic, the approximate characteristic, the noise-reduced characteristic, the discrete analysis characteristic and the corresponding preset weight of the metal material to be detected;
calculating a material comparison characteristic weighted average value of the sample metal material in the metal material characteristic library according to the detail characteristic, the approximate characteristic, the noise-reduced characteristic, the discrete analysis characteristic and the preset weight of the sample metal material in the metal material characteristic library;
and comparing the material comparison characteristic weighted average value of the metal material to be detected with the material comparison characteristic weighted average value of the sample metal material in the metal material characteristic library.
Optionally, the material comparison characteristics of the metal material to be detected are obtained from the eddy current response signal of the metal material to be detected based on a discrete wavelet transform model, and the material comparison characteristics include detail characteristics, approximate characteristics, post-noise reduction characteristics and discrete analysis characteristics:
obtaining a discrete input signal from the eddy current response signal;
respectively carrying out high-pass filtering calculation and low-pass filtering calculation on the discrete input signals to obtain high-frequency signals and low-frequency signals;
the detail features are determined from the high frequency signal and the approximation features are determined from the low frequency signal.
Optionally, after determining the detail feature according to the high-frequency signal and determining the approximate feature according to the low-frequency signal, the method further includes:
decomposing the low-frequency signal by an empirical mode decomposition algorithm to obtain a low-frequency content modal component and a high-frequency content modal component;
filtering the high-frequency connotation modal components based on a wavelet decomposition algorithm and a wavelet reconstruction algorithm;
and performing signal reconstruction on the low-frequency content modal component and the filtered high-frequency content modal component to obtain a noise reduction signal, and determining the noise-reduced characteristics according to the noise reduction signal.
Optionally, performing signal reconstruction on the low-frequency content modal component and the filtered high-frequency content modal component to obtain a noise reduction signal, and determining the noise-reduced feature according to the noise reduction signal further includes:
reconstructing the noise reduction signal and the high-frequency signal to obtain a discrete data signal;
restoring an eddy current secondary optimization signal curve according to the discrete data signal;
determining a discrete signal characteristic extraction interval of the eddy current secondary optimization signal curve;
and carrying out discrete analysis on the discrete signal feature extraction interval to obtain the discrete analysis features.
Optionally, the filtering the high-frequency content modal component based on a wavelet decomposition algorithm and a wavelet reconstruction algorithm includes:
performing wavelet decomposition on the high-frequency connotation modal component to obtain a low-frequency coefficient and a high-frequency coefficient;
performing threshold quantization on the high-frequency coefficient;
and performing wavelet reconstruction on the low-frequency coefficient and the high-frequency coefficient after threshold quantization to finish filtering the high-frequency connotation modal component.
Optionally, the restoring the eddy current quadratic optimization signal curve according to the discrete data signal includes:
determining a non-linear fit function from the discrete data signal;
based on the nonlinear fitting function, reducing the eddy current secondary optimization signal curve by a cubic spline interpolation method;
performing discrete analysis on the discrete signal feature extraction interval to obtain the discrete analysis features comprises:
and carrying out deviation analysis calculation and convolution analysis calculation on the discrete signal feature extraction interval to obtain the discrete analysis features.
In a second aspect, an embodiment of the present invention further provides a metal material analysis apparatus, where the metal material analysis apparatus includes:
the signal acquisition module is used for acquiring an eddy current response signal of the metal material to be detected;
the material comparison characteristic acquisition module is used for acquiring the material comparison characteristics of the metal material to be detected from the eddy current response signal of the metal material to be detected based on a discrete wavelet transform model, and the material comparison characteristics comprise detail characteristics, approximate characteristics, noise-reduced characteristics and discrete analysis characteristics;
and the characteristic analysis module is used for determining that the material of the metal material to be detected is the corresponding material of the sample metal material when the material comparison characteristic of the metal material to be detected is matched with the material comparison characteristic of the sample metal material in the metal material characteristic library.
Optionally, the characteristic analysis module is further configured to store the material comparison characteristic of the metal material to be detected in the metal material characteristic library when the material comparison characteristic of the metal material to be detected is not matched with the material comparison characteristic of the sample metal material characteristic in the metal material characteristic library, so as to use the metal material to be detected as the sample metal material.
According to the metal material analysis method and device provided by the embodiment of the invention, the nondestructive and high-sensitivity eddy current response signal of the metal material to be detected is obtained, the material comparison characteristic of the metal material to be detected is obtained from the eddy current response signal of the metal material to be detected with high accuracy and high efficiency based on the discrete wavelet transform model, the material comparison characteristic comprises a detail characteristic, an approximate characteristic, a noise-reduced characteristic and a discrete analysis characteristic, and when the material comparison characteristic of the metal material to be detected is matched with the material comparison characteristic of the sample metal material in the metal material characteristic library, the material of the metal material to be detected is determined to be the material of the corresponding sample metal material, so that the nondestructive, high-sensitivity, high-accuracy and high-efficiency material determination of the metal material to be detected is realized.
Drawings
FIG. 1 is a flow chart of a method for analyzing a metal material according to an embodiment of the present invention;
FIG. 2 is a flow chart of another method for analyzing metal material according to an embodiment of the present invention;
FIG. 3 is a flow chart of another method for analyzing metal material according to an embodiment of the present invention;
FIG. 4 is a flow chart of another method for analyzing metal material according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a metal material analysis apparatus according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
An embodiment of the present invention provides a metal material analysis method, and fig. 1 is a flowchart of the metal material analysis method provided in the embodiment of the present invention. Referring to fig. 1, the metal material analysis method includes:
and S10, acquiring an eddy current response signal of the metal material to be detected.
Specifically, the sensitivity of the eddy current response signal of the metal material is high, and the metal material is basically not damaged by acquiring the eddy current response signal of the metal material. The eddy current response signal of the metal material to be measured can be obtained through the eddy current sensor or the pulse eddy current sensor. For example, the eddy current sensor or the pulsed eddy current sensor sends an excitation signal to the material to be measured, the metal material to be measured generates an eddy current under the excitation of the excitation signal, and then the eddy current sensor or the pulsed eddy current sensor acquires an eddy current response signal corresponding to the eddy current in the metal material to be measured.
Before the eddy current sensor or the pulsed eddy current sensor sends the excitation signal to the material to be measured, the eddy current sensor or the pulsed eddy current sensor may be set with parameters for generating the excitation signal, where the parameters include, but are not limited to, at least one of coarse slope adjustment, fine slope adjustment, measurement period, pulse width, sampling point number, and amplification reference. For example, among parameters corresponding to the excitation signal, the signal voltage reference value VREF is set to 0.805664mV, the slope coarse adjustment is 2 times (512VREF) (the slope coarse adjustment parameter range is 0-8, "2 times 512 VREF" is equal to 2 times 512 times VREF 825mV), the slope fine adjustment is 400 times (VREF) (the slope fine bar parameter range is 0-512, "400 times VREF" is equal to 400 times VREF — 322.265625mV), the measurement period is 1000us, the pulse width is 8us, the number of sampling points is 39 data points, and the amplification reference is 186 times (VREF) (the amplification reference parameter range is 0-8000, "186 times VREF" is equal to 186 times VREF 135.3515625 mV). In addition, at least one of the slope rough adjustment, the slope fine adjustment, the measurement period, the pulse width, the sampling point number and the amplification reference is adjusted to adjust the distortion degree of the adjustable eddy current response signal, so that the eddy current response signal distortion is avoided.
S11, obtaining material comparison characteristics of the metal material to be detected from the eddy current response signal of the metal material to be detected based on the discrete wavelet transform model, wherein the material comparison characteristics comprise detail characteristics, approximate characteristics, noise-reduced characteristics and discrete analysis characteristics.
Specifically, the discrete wavelet transformation model has strong wavelet detail feature resolution capability, does not depend on an amplifier or human eyes, is favorable for identifying detail differences among metal materials by adopting the discrete wavelet transformation model, and has high identification accuracy. In this embodiment, the material of the metal material to be measured may be determined by using a three-order discrete wavelet transform model. The material comparison characteristic of the metal material to be detected is a characteristic capable of representing the material of the metal material to be detected, such as the profile, the details, the components and the like of the sampling point of the metal material to be detected. The detail characteristic, the approximate characteristic, the noise-reduced characteristic and the discrete analysis characteristic respectively represent the material of the metal material to be measured in different dimensions. The metal material is analyzed based on the discrete analysis characteristics, so that the material of the metal material can be determined efficiently.
And S12, when the material comparison characteristics of the metal material to be detected are matched with the material comparison characteristics of the sample metal material in the metal material characteristic library, determining that the material of the metal material to be detected is the material of the corresponding sample metal material.
Specifically, the detailed characteristics, the approximate characteristics, the noise-reduced characteristics and the discrete analysis characteristics of the sample metal material respectively represent the material of the sample metal material in different dimensions. The metal material characteristic library is pre-stored with material comparison characteristics of a plurality of sample metal materials. For example, the material comparison characteristics of the metal material to be detected are simultaneously and respectively compared with the material comparison characteristics of each sample metal material in the metal material characteristic library, or the comparison characteristics of the metal material to be detected are sequentially compared with the material comparison characteristics of a plurality of sample metal materials in the metal material characteristic library; and when the material comparison characteristic of the metal material to be detected is matched with the material comparison characteristic of a certain sample metal material in the metal material characteristic library, determining that the material of the metal material to be detected is the material of the corresponding sample metal material.
The metal material analysis method provided in this embodiment may be implemented to nondestructively obtain a high-sensitivity eddy current response signal of a metal material to be detected, and further study characteristics of the eddy current response signal of the metal material to be detected, that is, to obtain a detailed characteristic, an approximate characteristic, a denoised characteristic, and a discrete analysis characteristic of the metal material to be detected from the eddy current response signal of the metal material to be detected with high accuracy and high efficiency based on a discrete wavelet transform model, and finally compare the material comparison characteristic of the metal material to be detected with the material comparison characteristic of a sample metal material in a metal material characteristic library based on a pre-established metal material characteristic library, and determine that the material of the metal material to be detected is the material of a corresponding sample metal material when the material comparison characteristic of the metal material to be detected is matched with the material comparison characteristic of a sample metal material in the metal material characteristic library, therefore, the material determination of the metal material to be detected with no damage, high sensitivity, high precision and high efficiency is realized. By adopting the online metal material analysis method of the embodiment, the material of the metal material to be detected can be determined within about 1 second.
On the basis of the foregoing embodiments, optionally, the metal material analysis method provided in this embodiment further includes: and when the material comparison characteristic of the metal material to be detected is not matched with the material comparison characteristic of the sample metal material characteristic in the metal material characteristic library, storing the material comparison characteristic of the metal material to be detected into the metal material characteristic library so as to take the metal material to be detected as the sample metal material.
Fig. 2 is a flowchart of another metal material analysis method according to an embodiment of the present invention. Referring to fig. 2, the metal material analysis method includes:
and S20, acquiring an eddy current response signal of the metal material to be detected.
S21, obtaining material comparison characteristics of the metal material to be detected from the eddy current response signal of the metal material to be detected based on the discrete wavelet transform model, wherein the material comparison characteristics comprise detail characteristics, approximate characteristics, noise-reduced characteristics and discrete analysis characteristics.
S22, comparing the material comparison characteristics of the metal material to be detected with the material comparison characteristics of the sample metal material in the metal material characteristic library; when the material comparison characteristic of the metal material to be detected is matched with the material comparison characteristic of the sample metal material in the metal material characteristic library, executing step S23; when the material comparison characteristic of the metal material to be detected does not match the material comparison characteristic of the sample metal material characteristic in the metal material characteristic library, step S24 is executed.
And S23, determining the material of the metal material to be detected as the material of the corresponding sample metal material.
And S24, storing the material comparison characteristics of the metal material to be detected into a metal material characteristic library so as to take the metal material to be detected as a sample metal material.
Specifically, when the material comparison characteristic of the metal material to be detected is not matched with the material comparison characteristic of the sample metal material characteristic in the metal material characteristic library, it is indicated that the sample metal material with the same material as the metal material to be detected is not stored in the metal material characteristic library, so that the material comparison characteristic of the metal material to be detected can be stored in the metal material characteristic library to use the metal material to be detected as the sample metal material, the covering of the metal material characteristic library on the sample metal material is enriched and perfected, and the determination of the material of the next metal material to be detected is facilitated.
In this embodiment, the detailed feature, the approximate feature, the noise-reduced feature and the discrete analysis feature all include a plurality of sub-features, and when the material comparison feature of the metal material to be detected is compared with the material comparison feature of the sample metal material in the metal material feature library, the plurality of sub-features of the metal material to be detected and the plurality of sub-features of the sample metal material may be compared one by one, or compared in a one-to-one correspondence manner.
Optionally, when the material comparison characteristic of the metal material to be measured is matched with the material comparison characteristic of the sample metal material in the metal material characteristic library, determining that the material of the metal material to be measured is the material of the corresponding sample metal material includes: calculating a material comparison characteristic weighted average value of the metal material to be detected according to the detail characteristic, the approximate characteristic, the noise-reduced characteristic, the discrete analysis characteristic and the corresponding preset weight of the metal material to be detected; calculating a material comparison characteristic weighted average value of the sample metal material in the metal material characteristic library according to the detail characteristic, the approximate characteristic, the noise-reduced characteristic, the discrete analysis characteristic and the preset weight of the sample metal material in the metal material characteristic library; and comparing the material comparison characteristic weighted average value of the metal material to be detected with the material comparison characteristic weighted average value of the sample metal material in the metal material characteristic library.
Fig. 3 is a flowchart of another metal material analysis method according to an embodiment of the present invention. Referring to fig. 3, the metal material analysis method includes:
and S30, acquiring an eddy current response signal of the metal material to be detected.
S31, obtaining material comparison characteristics of the metal material to be detected from the eddy current response signal of the metal material to be detected based on the discrete wavelet transform model, wherein the material comparison characteristics comprise detail characteristics, approximate characteristics, noise-reduced characteristics and discrete analysis characteristics.
And S32, calculating a material comparison characteristic weighted average value of the metal material to be detected according to the detail characteristic, the approximate characteristic, the noise-reduced characteristic, the discrete analysis characteristic and the corresponding preset weight of the metal material to be detected.
Specifically, the preset weight may be weights corresponding to the detail feature, the approximate feature, the noise-reduced feature, and the discrete analysis feature, or weights corresponding to sub-features in the detail feature, the approximate feature, the noise-reduced feature, and the discrete analysis feature, and the preset weight may be set as needed.
S33, calculating a material comparison characteristic weighted average value of the sample metal material in the metal material characteristic library according to the detail characteristic, the approximate characteristic, the noise-reduced characteristic, the discrete analysis characteristic and the preset weight of the sample metal material in the metal material characteristic library.
Specifically, the weight on which the material comparison characteristic weighted average of the sample metal material depends is the same as the weight on which the material comparison characteristic weighted average of the metal material to be detected depends, so that the comparability of the material comparison characteristic of the sample metal material and the material comparison characteristic of the metal material to be detected is ensured.
S34, comparing the material comparison characteristic weighted average value of the metal material to be detected with the material comparison characteristic weighted average value of the sample metal material in the metal material characteristic library; when the material comparison characteristic weighted average of the metal material to be detected is matched with the material comparison characteristic weighted average of the sample metal material in the metal material characteristic library, executing the step S35; when the material comparison characteristic weighted average of the metal material to be measured does not match the material comparison characteristic weighted average of the sample metal material in the metal material characteristic library, step S36 is executed.
Specifically, the method of comparing the weighted average of the material comparison characteristics may be used as an evaluation model for evaluating the material of the metal material to be measured, and the weighted average of the material comparison characteristics may be used as an evaluation index. The comparison efficiency can be improved by adopting the material comparison characteristic weighted average value for comparison, and the reliability of comparison is improved due to the setting of the preset weight. In addition, in this embodiment, a plurality of sets of preset weights may be set corresponding to a plurality of sample metal materials in the metal material feature library, and the preset weights of the metal material to be measured and the preset weights of the sample metal materials compared with the preset weights are set to be the same.
And S35, determining the material of the metal material to be detected as the material of the corresponding sample metal material.
And S36, storing the material comparison characteristics of the metal material to be detected into a metal material characteristic library so as to take the metal material to be detected as a sample metal material.
Optionally, the material comparison characteristics of the metal material to be detected are obtained from the eddy current response signal of the metal material to be detected based on the discrete wavelet transform model, and the material comparison characteristics include detail characteristics, approximate characteristics, noise-reduced characteristics and discrete analysis characteristics: obtaining a discrete input signal from the eddy current response signal; respectively carrying out high-pass filtering calculation and low-pass filtering calculation on the discrete input signals to obtain high-frequency signals and low-frequency signals; detail features are determined from the high frequency signals and approximation features are determined from the low frequency signals.
Optionally, after determining the detail feature according to the high-frequency signal and determining the approximate feature according to the low-frequency signal, the method further includes: decomposing the low-frequency signal by an empirical mode decomposition algorithm to obtain a low-frequency content modal component and a high-frequency content modal component; filtering the high-frequency connotation modal components based on a wavelet decomposition algorithm and a wavelet reconstruction algorithm; and performing signal reconstruction on the low-frequency content modal component and the filtered high-frequency content modal component to obtain a noise reduction signal, and determining the noise reduction characteristics according to the noise reduction signal.
Optionally, signal reconstruction is performed on the low-frequency content modal component and the filtered high-frequency content modal component to obtain a noise reduction signal, and the method further includes, after determining the noise reduction characteristics according to the noise reduction signal: reconstructing the noise reduction signal and the high-frequency signal to obtain a discrete data signal; restoring an eddy current secondary optimization signal curve according to the discrete data signal; determining a discrete signal characteristic extraction interval of the eddy current secondary optimization signal curve; and carrying out discrete analysis on the discrete signal feature extraction interval to obtain discrete analysis features.
Fig. 4 is a flowchart of another metal material analysis method according to an embodiment of the present invention. Referring to fig. 4, the metal material analysis method includes:
and S40, acquiring an eddy current response signal of the metal material to be detected.
And S41, acquiring a discrete input signal from the eddy current response signal.
Specifically, the discrete input signal may be a valid signal selected from the digitized eddy current response signals after the eddy current response signals are digitized.
And S42, respectively carrying out high-pass filtering calculation and low-pass filtering calculation on the discrete input signals to obtain high-frequency signals and low-frequency signals. In other words, in the process of performing the high-pass filtering calculation and the low-pass filtering calculation on the discrete input signal, the low-frequency signal is output by using the low-pass filtering calculation model, and the high-frequency signal is output by using the high-pass filtering calculation model.
And S43, determining detail characteristics according to the high-frequency signals, and determining approximate characteristics according to the low-frequency signals.
And S44, decomposing the low-frequency signal through an empirical mode decomposition algorithm to obtain a low-frequency content modal component and a high-frequency content modal component.
Specifically, the Empirical Mode Decomposition (EMD) performs signal Decomposition according to the time scale features of the data itself without setting any basis function in advance. Therefore, the method has obvious advantages in processing non-stationary and non-linear data, is suitable for analyzing non-linear and non-stationary signal sequences, and has high signal-to-noise ratio and adaptability.
The empirical mode decomposition algorithm comprises the following processes: for a given signal x (t), e.g. a low frequency signal in this embodiment; finding all extreme points of x (t); forming a lower envelope (e) for the minimum value points and an upper envelope (e) for the maximum values by an interpolation method; calculating the mean m (t) ((int) (t) + emax (t))/2; and (d), (t) x (t) -m (t) is extracted, and then the low-frequency content modal component and the high-frequency content modal component are obtained.
And S45, filtering the high-frequency connotation modal components based on the wavelet decomposition algorithm and the wavelet reconstruction algorithm.
Optionally, the filtering the high-frequency content modal component based on the wavelet decomposition algorithm and the wavelet reconstruction algorithm includes: performing wavelet decomposition on the high-frequency connotation modal component to obtain a low-frequency coefficient and a high-frequency coefficient; performing threshold quantization on the high-frequency coefficient; and performing wavelet reconstruction on the low-frequency coefficient and the high-frequency coefficient after threshold quantization to complete filtering of the high-frequency connotation modal component.
And S46, performing signal reconstruction on the low-frequency content modal component and the filtered high-frequency content modal component to obtain a noise reduction signal, and determining the noise reduction characteristics according to the noise reduction signal.
And S47, reconstructing the noise reduction signal and the high-frequency signal to obtain a discrete data signal.
And S48, restoring an eddy current quadratic optimization signal curve according to the discrete data signal.
Optionally, the restoring the eddy current quadratic optimization signal curve according to the discrete data signal includes: determining a non-linear fit function from the discrete data signal; and based on the nonlinear fitting function, reducing the eddy current secondary optimization signal curve by a cubic spline interpolation method. Specifically, discrete data signals are adopted to model and solve a nonlinear fitting function expression in an MATLAB fitting toolbox; after the fitting residual is optimized and reduced, determining the goodness of fit; and restoring the eddy current secondary optimization signal curve by a cubic spline interpolation method.
And S49, determining a discrete signal characteristic extraction interval of the eddy current quadratic optimization signal curve.
Specifically, an interval with severe change of the characteristic signal in the eddy current quadratic optimization signal curve is determined as a discrete signal characteristic extraction interval, wherein the interval with severe change is defined as the interval with the total standard deviation of the characteristic signal change exceeding 10%, and the standard deviation value is calculated according to 10 sampling signals.
And S410, carrying out discrete analysis on the discrete signal feature extraction interval to obtain discrete analysis features.
Optionally, the discrete analysis of the discrete signal feature extraction interval to obtain the discrete analysis feature includes: and carrying out deviation analysis calculation and convolution analysis calculation on the discrete signal feature extraction interval to obtain discrete analysis features. In particular, the discrete analysis may include standard deviation analysis, discrete signal convolution and analysis. In this embodiment, a deviation analysis algorithm and a convolution and algorithm are used to complete discrete analysis, that is, an upper limit threshold and a lower limit threshold of a distribution curve are set, SD, CV, variance and other standard deviation coefficients are solved according to a deviation calculation formula, linear convolution and analysis of an input signal and a system unit impulse response are performed, and noise is eliminated and signal characteristics are enhanced by performing weighted average sum on a signal sampling point and other surrounding points.
S411, when the material comparison characteristics of the metal material to be detected are matched with the material comparison characteristics of the sample metal material in the metal material characteristic library, determining that the material of the metal material to be detected is the material of the corresponding sample metal material.
The embodiment of the invention also provides a metal material analysis device. Fig. 5 is a schematic structural diagram of a metal material analysis apparatus according to an embodiment of the present invention. Referring to fig. 5, the metal material analyzing apparatus includes: and the signal acquisition module 10 is used for acquiring an eddy current response signal of the metal material to be detected. The material comparison characteristic obtaining module 20 is configured to obtain a material comparison characteristic of the metal material to be detected from the eddy current response signal of the metal material to be detected based on the discrete wavelet transform model, where the material comparison characteristic includes a detail characteristic, an approximate characteristic, a post-noise-reduction characteristic, and a discrete analysis characteristic. The characteristic analysis module 30 is configured to determine that the material of the metal material to be detected is the material of the corresponding sample metal material when the material comparison characteristic of the metal material to be detected is matched with the material comparison characteristic of the sample metal material in the metal material characteristic library.
Optionally, the characteristic analysis module 30 is further configured to store the material comparison characteristic of the metal material to be detected in the metal material characteristic library when the material comparison characteristic of the metal material to be detected is not matched with the material comparison characteristic of the sample metal material characteristic in the metal material characteristic library, so as to use the metal material to be detected as the sample metal material.
The metal material analyzing device and the metal material analyzing method provided by the embodiment of the invention belong to the same invention concept, can realize the same technical effect, and repeated contents are not repeated here.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for analyzing a metal material, comprising:
acquiring an eddy current response signal of a metal material to be detected;
acquiring material comparison characteristics of the metal material to be detected from an eddy current response signal of the metal material to be detected based on a discrete wavelet transform model, wherein the material comparison characteristics comprise detail characteristics, approximate characteristics, noise-reduced characteristics and discrete analysis characteristics;
and when the material comparison characteristic of the metal material to be detected is matched with the material comparison characteristic of the sample metal material in the metal material characteristic library, determining that the material of the metal material to be detected is the corresponding material of the sample metal material.
2. The method for analyzing a metal material according to claim 1, further comprising: and when the material comparison characteristic of the metal material to be detected is not matched with the material comparison characteristic of the sample metal material characteristic in the metal material characteristic library, storing the material comparison characteristic of the metal material to be detected into the metal material characteristic library so as to take the metal material to be detected as the sample metal material.
3. The metal material analysis method of claim 1, wherein when the material comparison feature of the to-be-detected metal material matches the material comparison feature of the sample metal material in the metal material feature library, determining that the material of the to-be-detected metal material is the corresponding material of the sample metal material comprises:
calculating a material comparison characteristic weighted average value of the metal material to be detected according to the detail characteristic, the approximate characteristic, the noise-reduced characteristic, the discrete analysis characteristic and the corresponding preset weight of the metal material to be detected;
calculating a material comparison characteristic weighted average value of the sample metal material in the metal material characteristic library according to the detail characteristic, the approximate characteristic, the noise-reduced characteristic, the discrete analysis characteristic and the preset weight of the sample metal material in the metal material characteristic library;
and comparing the material comparison characteristic weighted average value of the metal material to be detected with the material comparison characteristic weighted average value of the sample metal material in the metal material characteristic library.
4. The metal material analysis method according to claim 1, wherein the obtaining of the material comparison characteristics of the metal material to be detected from the eddy current response signal of the metal material to be detected based on a discrete wavelet transform model comprises:
obtaining a discrete input signal from the eddy current response signal;
respectively carrying out high-pass filtering calculation and low-pass filtering calculation on the discrete input signals to obtain high-frequency signals and low-frequency signals;
the detail features are determined from the high frequency signal and the approximation features are determined from the low frequency signal.
5. The method of claim 4, wherein determining the detail feature from the high frequency signal and determining the approximate feature from the low frequency signal further comprises:
decomposing the low-frequency signal by an empirical mode decomposition algorithm to obtain a low-frequency content modal component and a high-frequency content modal component;
filtering the high-frequency connotation modal components based on a wavelet decomposition algorithm and a wavelet reconstruction algorithm;
and performing signal reconstruction on the low-frequency content modal component and the filtered high-frequency content modal component to obtain a noise reduction signal, and determining the noise-reduced characteristics according to the noise reduction signal.
6. The metal material analysis method according to claim 5, wherein the signal reconstruction of the low-frequency content modal component and the filtered high-frequency content modal component is performed to obtain a noise reduction signal, and the method further comprises, after determining the noise reduction characteristic according to the noise reduction signal:
reconstructing the noise reduction signal and the high-frequency signal to obtain a discrete data signal;
restoring an eddy current secondary optimization signal curve according to the discrete data signal;
determining a discrete signal characteristic extraction interval of the eddy current secondary optimization signal curve;
and carrying out discrete analysis on the discrete signal feature extraction interval to obtain the discrete analysis features.
7. The metal material analysis method of claim 5, wherein filtering the high-frequency content modal components based on a wavelet decomposition algorithm and a wavelet reconstruction algorithm comprises:
performing wavelet decomposition on the high-frequency connotation modal component to obtain a low-frequency coefficient and a high-frequency coefficient;
performing threshold quantization on the high-frequency coefficient;
and performing wavelet reconstruction on the low-frequency coefficient and the high-frequency coefficient after threshold quantization to finish filtering the high-frequency connotation modal component.
8. The method of claim 6, wherein the step of reconstructing an eddy current quadratic optimization signal curve from the discrete data signal comprises:
determining a non-linear fit function from the discrete data signal;
based on the nonlinear fitting function, reducing the eddy current secondary optimization signal curve by a cubic spline interpolation method;
performing discrete analysis on the discrete signal feature extraction interval to obtain the discrete analysis features comprises:
and carrying out deviation analysis calculation and convolution analysis calculation on the discrete signal feature extraction interval to obtain the discrete analysis features.
9. A metal material analyzing apparatus, comprising:
the signal acquisition module is used for acquiring an eddy current response signal of the metal material to be detected;
the material comparison characteristic acquisition module is used for acquiring the material comparison characteristics of the metal material to be detected from the eddy current response signal of the metal material to be detected based on a discrete wavelet transform model, and the material comparison characteristics comprise detail characteristics, approximate characteristics, noise-reduced characteristics and discrete analysis characteristics;
and the characteristic analysis module is used for determining that the material of the metal material to be detected is the corresponding material of the sample metal material when the material comparison characteristic of the metal material to be detected is matched with the material comparison characteristic of the sample metal material in the metal material characteristic library.
10. The metal material analysis device of claim 9, wherein the characteristic analysis module is further configured to store the material comparison characteristic of the metal material to be detected in the metal material characteristic library to use the metal material to be detected as the sample metal material when the material comparison characteristic of the metal material to be detected is not matched with the material comparison characteristic of the sample metal material characteristic in the metal material characteristic library.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111091065.8A CN113792675A (en) | 2021-09-17 | 2021-09-17 | Metal material analysis method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111091065.8A CN113792675A (en) | 2021-09-17 | 2021-09-17 | Metal material analysis method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113792675A true CN113792675A (en) | 2021-12-14 |
Family
ID=79183878
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111091065.8A Pending CN113792675A (en) | 2021-09-17 | 2021-09-17 | Metal material analysis method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113792675A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117607248A (en) * | 2024-01-23 | 2024-02-27 | 电子科技大学 | Metal defect detection imaging method based on digital lock-in amplifier |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030193331A1 (en) * | 2002-04-15 | 2003-10-16 | General Electric Company | Method for in-situ eddy current inspection of coated components in turbine engines |
| WO2012113041A1 (en) * | 2011-02-25 | 2012-08-30 | Technical University Of Gabrovo | Method of detecting and identifying substances or mixtures and determining their characteristics |
| WO2015072365A1 (en) * | 2013-11-18 | 2015-05-21 | Ntn株式会社 | Eddy current flaw detection device |
| US20160274230A1 (en) * | 2015-03-16 | 2016-09-22 | Shenzhen University | Wireless network based method for detecting presence of metal |
| WO2017162107A1 (en) * | 2016-03-25 | 2017-09-28 | 东莞市华盾电子科技有限公司 | Discrimination method and test method for detection of metal classification and system thereof |
| CN109557167A (en) * | 2018-10-26 | 2019-04-02 | 东莞市华盾电子科技有限公司 | A kind of scaling method and system of sorting metals detection |
| CN110376289A (en) * | 2019-07-11 | 2019-10-25 | 南京航空航天大学 | A kind of composite fiber braiding layer damnification recognition method based on sound emission means |
| CN110702801A (en) * | 2019-08-26 | 2020-01-17 | 天津大学 | Plate-shaped structure fatigue crack positioning system and method based on same-side ultrasonic frequency mixing wavelet |
| CN111351422A (en) * | 2020-01-17 | 2020-06-30 | 华中科技大学 | Metal component pulse eddy current thickness measuring method and equipment based on characteristic frequency |
| CN113221666A (en) * | 2021-04-20 | 2021-08-06 | 武汉工程大学 | Intelligent identification method for seam defects of metal plate |
-
2021
- 2021-09-17 CN CN202111091065.8A patent/CN113792675A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030193331A1 (en) * | 2002-04-15 | 2003-10-16 | General Electric Company | Method for in-situ eddy current inspection of coated components in turbine engines |
| WO2012113041A1 (en) * | 2011-02-25 | 2012-08-30 | Technical University Of Gabrovo | Method of detecting and identifying substances or mixtures and determining their characteristics |
| WO2015072365A1 (en) * | 2013-11-18 | 2015-05-21 | Ntn株式会社 | Eddy current flaw detection device |
| US20160274230A1 (en) * | 2015-03-16 | 2016-09-22 | Shenzhen University | Wireless network based method for detecting presence of metal |
| WO2017162107A1 (en) * | 2016-03-25 | 2017-09-28 | 东莞市华盾电子科技有限公司 | Discrimination method and test method for detection of metal classification and system thereof |
| CN109557167A (en) * | 2018-10-26 | 2019-04-02 | 东莞市华盾电子科技有限公司 | A kind of scaling method and system of sorting metals detection |
| CN110376289A (en) * | 2019-07-11 | 2019-10-25 | 南京航空航天大学 | A kind of composite fiber braiding layer damnification recognition method based on sound emission means |
| CN110702801A (en) * | 2019-08-26 | 2020-01-17 | 天津大学 | Plate-shaped structure fatigue crack positioning system and method based on same-side ultrasonic frequency mixing wavelet |
| CN111351422A (en) * | 2020-01-17 | 2020-06-30 | 华中科技大学 | Metal component pulse eddy current thickness measuring method and equipment based on characteristic frequency |
| CN113221666A (en) * | 2021-04-20 | 2021-08-06 | 武汉工程大学 | Intelligent identification method for seam defects of metal plate |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117607248A (en) * | 2024-01-23 | 2024-02-27 | 电子科技大学 | Metal defect detection imaging method based on digital lock-in amplifier |
| CN117607248B (en) * | 2024-01-23 | 2024-04-02 | 电子科技大学 | Metal defect detection imaging method based on digital lock-in amplifier |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101436190B1 (en) | A method for detection and automatic identification of damage to rolling bearings | |
| KR910015089A (en) | Digital signal processing method and apparatus. Power system signal processing device and its control device | |
| RU2488815C2 (en) | Method and apparatus for classifying sound-generating processes | |
| CN109142514B (en) | Defect detection device and method based on pulse eddy current array | |
| CN113792675A (en) | Metal material analysis method and device | |
| CN111638474B (en) | Vehicle electromagnetic exposure measurement method and device and computer equipment | |
| US5136529A (en) | Digital signal weighting processing apparatus and method | |
| CN117171516B (en) | Data optimization correction method for X-ray thickness gauge | |
| KR100772840B1 (en) | Apparatus and method for testing linearity character of analog to digital converter | |
| CN107610055B (en) | Fourier transform spectrometer interferogram noise detection and suppression method | |
| CN111879709B (en) | Lake water body spectral reflectivity inspection method and device | |
| US6301550B1 (en) | Phase delay correction system | |
| CN117473263A (en) | Automatic recognition method and system for frequency and damping ratio of bridge vibration monitoring | |
| CN115797335B (en) | Euler movement amplification effect evaluation and optimization method for bridge vibration measurement | |
| CN116884381A (en) | Circuit system for microphone noise suppression | |
| Poliarus et al. | Influence of measurements uncertainty on uncertainty of Gilbert-Huang transform modes | |
| CN113655534B (en) | Nuclear magnetic resonance FID signal noise suppression method based on multi-linear singular value tensor decomposition | |
| CN115078190A (en) | Suspension body on-site laser granularity data processing method and device | |
| Zhang et al. | A new method of defects identification for wire rope based on three-dimensional magnetic flux leakage | |
| CN111198348B (en) | Calibration method for noise test system of magnetic sensor | |
| CN111289800B (en) | Small-resistance vibration monitoring method based on generalized regression neural network | |
| RU2331893C1 (en) | Method of discrete component separation in signal spectre and device for its implementation | |
| JP4076797B2 (en) | Noise removal method and noise removal filter | |
| CN107506779B (en) | Estimation method and system for water content of plant stems | |
| Klevtsov | Using the Method of Normalized Amplitude for Assessing the Quality of the Calibration Tests of the Pressure Sensor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |