An ultrasonic thickness extraction method based on wavelet compression energy estimation

Abstract

The application discloses a kind of ultrasonic thickness extraction methods based on wavelet compression energy estimation, it is related to ultrasonic thickness measurement technical field, comprising the following steps: S1, the first ultrasonic signal of time domain ultrasonic wave is obtained, and its power spectral density is calculated;S2, the total energy of first ultrasonic signal and the cumulative energy distribution under different cut-off frequencies are calculated, and the filter band is found out;S3, wavelet change is carried out, and the first time-frequency diagram is established;S4, according to the filter band, the first time-frequency diagram is filtered, and the second time-frequency diagram is obtained, the first ultrasonic signal is reconstructed, and the second ultrasonic signal is obtained;S5, the second time-frequency diagram is segmented according to energy, and the position of ultrasonic echo is calculated, and finally the thickness is calculated.The present application can quickly find the center frequency and effectively process filtering when facing larger wave packet, through wave packet energy cutting, find small specific envelope, combined with autocorrelation or same single wave packet cross-correlation, solve the problem of wave packet dispersion caused by the roughness change of reflecting surface.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic thickness measurement technology, and specifically to an ultrasonic thickness extraction method based on wavelet compressed energy estimation. Background Technology

[0002] Ultrasonic thickness measurement, as an important method for monitoring metal corrosion, is widely used in industry, especially with the increasing lifespan of industrial equipment, making its corrosion monitoring significance. Traditional ultrasonic thickness measurement equipment absorbs and attenuates ultrasonic oscillations through a backing, which significantly accelerates the attenuation of ultrasonic pulse packets and reduces the packet width. However, in recent years, to reduce sensor thickness, the monitoring unit has been placed under the paint layer, which has significantly reduced the packet attenuation rate and significantly increased the packet width.

[0003] The existing waveform diagram of roughness reduction caused by corrosion of the reflective surface is as follows: Figure 2 As shown, during long-term monitoring, the corrosion of the reflective surface will cause a decrease in roughness as the coupling state changes. Existing technology shows that if the roughness of the reflective surface changes, it will cause wave packet divergence. Wave packet divergence will cause a deterioration in signal quality and make it difficult to extract thickness. Therefore, how to combine waveforms to achieve accurate thickness extraction has become a difficult problem. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an ultrasonic thickness extraction method based on wavelet compressed energy estimation.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] This invention discloses an ultrasonic thickness extraction method based on wavelet compressed energy estimation, comprising the following steps:

[0007] S1. Acquire the first ultrasonic signal of the time-domain ultrasound and calculate the power spectral density of the first ultrasonic signal;

[0008] S2. Calculate the total energy of the first ultrasonic signal, then calculate the cumulative energy distribution at different cutoff frequencies, and find the filter band.

[0009] S3. Perform wavelet transform on the first ultrasonic signal to establish the first time-frequency diagram;

[0010] S4. Filter the first time-frequency diagram according to the filtering frequency band to obtain the second time-frequency diagram, and reconstruct the first ultrasonic signal based on the second time-frequency diagram to obtain the second ultrasonic signal;

[0011] S5. The second time-frequency map is segmented according to energy to find the time periods of different echoes. The location of the ultrasonic echo is calculated by autocorrelation or cross-correlation with the same single wave packet, and finally the thickness is calculated.

[0012] Preferably, step S1 specifically includes: acquiring a first ultrasonic signal x[n] with a total length of N, and then dividing the first ultrasonic signal x[n] into... K Each segment is of length L, and there are M overlapping points between adjacent segments. Then the value of the nth point in the i-th segment is , ,in, , Then, through the formula Calculate power spectral density ,in The factor for calculating the power spectral density is represented by f, where f represents the frequency and j represents the imaginary number. This represents the value of the nth point in the j'th segment.

[0013] Preferably, step S2 specifically includes: using the formula Calculate the total energy of the first ultrasound signal ;in The frequency resolution is expressed by the following formula: , This represents the sampling rate; then, it is expressed using the formula... Calculate the cumulative energy distribution at different cutoff frequencies Where m corresponds to different frequencies, as shown by the formula and Determine the energy percentage minimum frequency band Energy percentage The calculation formula is: .

[0014] Preferably, step S3 specifically includes: selecting a discrete wavelet basis. Perform wavelet transform on the first ultrasonic signal x[n] to obtain the first time-frequency diagram. Where v represents the scale parameter and k represents the time-domain translation parameter, This represents the mother wavelet.

[0015] Preferably, step S4 specifically includes: based on the minimum frequency band obtained in step S2 For the first time-frequency diagram Filtering is performed, setting values ​​outside the frequency band to 0, while retaining the original amplitude and phase of values ​​within the frequency band, to obtain the second time-frequency diagram. Based on the second time-frequency diagram The first ultrasonic signal x[n] is reconstructed to obtain the second ultrasonic signal x. ” [n], The second ultrasonic signal x ”[n] Segment by window and calculate the average energy, find 5 consecutive segments with energy below the average energy, and then perform wave packet segmentation.

[0016] Preferably, step S5 includes the following steps:

[0017] S501, transfer the second ultrasonic signal x ” The first envelope in [n] is deleted, generating the third ultrasound signal x. ’ [n], through the formula Calculate the third ultrasound signal x ’ The autocorrelation of [n] is calculated to obtain the autocorrelation result. ;

[0018] S502, Autocorrelation Results Envelope extraction is performed to construct the envelope curve a(m), and a moving average filter is applied to the envelope. ; The envelope curve after smoothing filtering;

[0019] S503. Calculate the envelope curve after smoothing filtering. The first peak position P is finally determined by the formula. Calculate the thickness D, where v' represents the speed of sound.

[0020] Preferably, step S5 further includes the following steps:

[0021] S511, transfer the second ultrasonic signal x ” The first envelope in [n] is deleted, generating the third ultrasound signal x. ’ [n], extract the third ultrasound signal x ’ The second wave packet of [n] Its position, n2, and half-wave width t2 are used to extract the third ultrasonic signal x. ’ The position n3 of the third wave packet of [n] and the half-wave width t3;

[0022] S512, transfer the third ultrasonic signal x ’ [n] and the second wave packet Perform cross-correlation, Obtain cross-correlation results Then, the envelope of the cross-correlation results is extracted to construct the envelope curve a(m), and a moving average filter is applied to the envelope. ; The envelope curve after smoothing filtering;

[0023] S513, Envelope Curve Based on Smoothing Filtering In the first range Find the first maximum value point In the second range Find the second maximum point Calculate the first maximum point Second maximum point The positional difference P' is finally determined by the formula. Calculate the thickness D, where v' represents the speed of sound.

[0024] The beneficial effects of this invention are:

[0025] 1) When faced with a large wave packet, this application can quickly find the center frequency and effectively process the filtering, thereby enabling accurate thickness measurement under conditions of uncertain center frequency, large excitation wave packet, dispersion and high roughness of the reflecting surface.

[0026] 2) This application identifies small, specific envelopes by cutting wave packet energy, and then solves the problem of wave packet dispersion due to changes in the roughness of the reflecting surface by combining autocorrelation or cross-correlation with a single wave packet. Attached Figure Description

[0027] Figure 1 Waveform diagram of existing technology;

[0028] Figure 2 Waveform diagram showing the roughness reduction caused by corrosion of the reflective surface in existing technology;

[0029] Figure 3 This is a schematic diagram illustrating the steps of directly performing data autocorrelation in an ultrasonic thickness extraction method based on wavelet compressed energy estimation according to an embodiment of the present invention.

[0030] Figure 4 This is a schematic diagram illustrating the steps of cross-correlation of a single wave packet in an ultrasonic thickness extraction method based on wavelet compressed energy estimation according to an embodiment of the present invention. Detailed Implementation

[0031] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] This application discloses an ultrasonic thickness extraction method based on wavelet compressed energy estimation. In some ultrasonic monitoring scenarios, center frequency shifts may occur (e.g., dispersion in wireless coupling systems, changes in the resonant frequency of piezoelectric ceramics and multiple induction coils, and dispersion during ultrasonic transmission), leading to uncertainty in the center frequency. For fast-attenuating single-pulse ultrasonic detection, this effect does not need to be considered. However, for cases with large wave packets, finding the center frequency and effectively filtering it is crucial. Existing waveform diagrams are as follows... Figure 1As shown. This application, through a specific algorithm, can quickly find the center frequency and effectively process the filtering when dealing with large wave packets, thereby achieving accurate thickness measurement under conditions of uncertain center frequency, large excitation wave packets, dispersion, and high roughness of the reflecting surface.

[0033] In this embodiment, after obtaining the ultrasonic thickness measurement waveform, data autocorrelation is directly performed during the data extraction process. Although this involves a larger computational load, it offers higher reliability. A schematic diagram of the steps for directly performing data autocorrelation is shown below. Figure 3 As shown, the specific steps include:

[0034] S1. Acquire the first ultrasonic signal of the time-domain ultrasound and calculate the power spectral density of the first ultrasonic signal;

[0035] S2. Calculate the total energy of the first ultrasonic signal, then calculate the cumulative energy distribution at different cutoff frequencies, and find the filter band.

[0036] S3. Perform wavelet transform on the first ultrasonic signal to establish the first time-frequency diagram;

[0037] S4. Filter the first time-frequency diagram according to the filtering frequency band to obtain the second time-frequency diagram, and reconstruct the first ultrasonic signal based on the second time-frequency diagram to obtain the second ultrasonic signal;

[0038] S5. The second time-frequency map is segmented according to energy to find the time periods of different echoes. The location of the ultrasonic echo is calculated by autocorrelation, and finally the thickness is calculated.

[0039] For example, step S1 specifically includes: acquiring a first ultrasound signal x[n] with a total length of N, and then dividing the first ultrasound signal x[n] into... K Each segment is of length L, and there are M overlapping points between adjacent segments. Then the value of the nth point in the i-th segment is , ,in, , Then, through the formula Calculate power spectral density ,in The factor for calculating the power spectral density is represented by f, where f represents the frequency and j represents the imaginary number. This represents the value of the nth point in the j'th segment.

[0040] For example, step S2 specifically includes: using the formula Calculate the total energy of the first ultrasound signal ;in The frequency resolution is expressed by the following formula: , This represents the sampling rate; then, it is expressed using the formula... Calculate the cumulative energy distribution at different cutoff frequencies Where m corresponds to different frequencies, as shown by the formula and Determine the energy percentage minimum frequency band Energy percentage The calculation formula is: Energy percentage The value is generally between 0.4 and 0.95.

[0041] For example, step S3 specifically includes: selecting a discrete wavelet basis. Perform wavelet transform on the first ultrasonic signal x[n] to obtain the first time-frequency diagram. Where v represents the scale parameter and k represents the time-domain translation parameter, Representing the mother wavelet, such as the Haar wavelet, when When = 1, 0≤t<1 / 2); when When t = -1, 1 / 2 ≤ t < 1.

[0042] For example, step S4 specifically includes: based on the minimum frequency band obtained in step S2 For the first time-frequency diagram Filtering is performed, setting values ​​outside the frequency band to 0, while retaining the original amplitude and phase of values ​​within the frequency band, to obtain the second time-frequency diagram. Based on the second time-frequency diagram The first ultrasonic signal x[n] is reconstructed to obtain the second ultrasonic signal x. ” [n], The second ultrasonic signal x ” [n] Segment by window and calculate the average energy, find 5 consecutive segments with energy below the average energy, and then perform wave packet segmentation.

[0043] For example, step S5 includes the following steps:

[0044] S501, transfer the second ultrasonic signal x ” The first envelope in [n] is deleted, generating the third ultrasound signal x. ’ [n], through the formula Calculate the third ultrasound signal x ’ The autocorrelation of [n] is calculated to obtain the autocorrelation result. ;

[0045] S502, Autocorrelation Results Envelope extraction is performed to construct the envelope curve a(m), and a moving average filter is applied to the envelope. ; The envelope curve after smoothing filtering;

[0046] S503. Calculate the envelope curve after smoothing filtering. The first peak position P is finally determined by the formula. Calculate the thickness D, where v' represents the speed of sound.

[0047] When reducing computational load is required, this embodiment directly extracts individual wave packets for cross-correlation during the data extraction process after obtaining the ultrasonic thickness measurement waveform. However, in cases of high noise, accurate extraction of individual wave packets may be incorrect, leading to significant errors in thickness calculation. A schematic diagram of the steps for cross-correlation of individual wave packets is shown below. Figure 4 As shown, the specific steps include:

[0048] S1. Acquire the first ultrasonic signal of the time-domain ultrasound and calculate the power spectral density of the first ultrasonic signal;

[0049] S2. Calculate the total energy of the first ultrasonic signal, then calculate the cumulative energy distribution at different cutoff frequencies, and find the filter band.

[0050] S3. Perform wavelet transform on the first ultrasonic signal to establish the first time-frequency diagram;

[0051] S4. Filter the first time-frequency diagram according to the filtering frequency band to obtain the second time-frequency diagram, and reconstruct the first ultrasonic signal based on the second time-frequency diagram to obtain the second ultrasonic signal;

[0052] S5. The second time-frequency diagram is segmented according to energy to find the time periods of different echoes. The position of the ultrasonic echo is calculated by cross-correlation of a single wave packet, and finally the thickness is calculated.

[0053] For example, step S1 specifically includes: acquiring a first ultrasound signal x[n] with a total length of N, and then dividing the first ultrasound signal x[n] into... K Each segment is of length L, and there are M overlapping points between adjacent segments. Then the value of the nth point in the i-th segment is , ,in, , Then, through the formula Calculate power spectral density ,in The factor for calculating the power spectral density is represented by f, where f represents the frequency and j represents the imaginary number. This represents the value of the nth point in the j'th segment.

[0054] For example, step S2 specifically includes: using the formula Calculate the total energy of the first ultrasound signal ;in The frequency resolution is expressed by the following formula: , This represents the sampling rate; then, it is expressed using the formula... Calculate the cumulative energy distribution at different cutoff frequencies Where m corresponds to different frequencies, as shown by the formula and Determine the energy percentage minimum frequency band Energy percentage The calculation formula is: Energy percentage The value is generally between 0.4 and 0.95.

[0055] For example, step S3 specifically includes: selecting a discrete wavelet basis. Perform wavelet transform on the first ultrasonic signal x[n] to obtain the first time-frequency diagram. Where v represents the scale parameter and k represents the time-domain translation parameter, This represents the mother wavelet.

[0056] For example, step S4 specifically includes: based on the minimum frequency band obtained in step S2 For the first time-frequency diagram Filtering is performed, setting values ​​outside the frequency band to 0, while retaining the original amplitude and phase of values ​​within the frequency band, to obtain the second time-frequency diagram. Based on the second time-frequency diagram The first ultrasonic signal x[n] is reconstructed to obtain the second ultrasonic signal x. ” [n], The second ultrasonic signal x ” [n] Segment by window and calculate the average energy, find 5 consecutive segments with energy below the average energy, and then perform wave packet segmentation.

[0057] For example, step S5 further includes the following steps:

[0058] S511, transfer the second ultrasonic signal x ” The first envelope in [n] is deleted, generating the third ultrasound signal x. ’ [n], extract the third ultrasound signal x ’ The second wave packet of [n] Its position, n2, and half-wave width t2 are used to extract the third ultrasonic signal x. ’ The position n3 of the third wave packet of [n] and the half-wave width t3;

[0059] S512, transfer the third ultrasonic signal x ’ [n] and the second wave packet Perform cross-correlation, Obtain cross-correlation results Then, the envelope of the cross-correlation results is extracted to construct the envelope curve a(m), and a moving average filter is applied to the envelope. ; The envelope curve after smoothing filtering;

[0060] S513, Envelope Curve Based on Smoothing Filtering In the first range Find the first maximum value point In the second range Find the second maximum point Calculate the first maximum point Second maximum point The positional difference P' is finally determined by the formula. Calculate the thickness D, where v' represents the speed of sound.

[0061] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for ultrasonic thickness extraction based on wavelet compressed energy estimation, characterized in that, Includes the following steps: S1. Acquire the first ultrasonic signal of the time-domain ultrasound and calculate the power spectral density of the first ultrasonic signal; S2. Calculate the total energy of the first ultrasonic signal, then calculate the cumulative energy distribution at different cutoff frequencies, and find the filter band. S3. Perform wavelet transform on the first ultrasonic signal to establish the first time-frequency diagram; S4. Filter the first time-frequency diagram according to the filtering frequency band to obtain the second time-frequency diagram, and reconstruct the first ultrasonic signal based on the second time-frequency diagram to obtain the second ultrasonic signal; S5. The second time-frequency map is segmented according to energy to find the time periods of different echoes. The location of the ultrasonic echo is calculated by autocorrelation or cross-correlation with the same single wave packet, and finally the thickness is calculated.

2. The ultrasonic thickness extraction method based on wavelet compressed energy estimation according to claim 1, characterized in that, Step S1 specifically includes: acquiring a first ultrasound signal x[n] with a total length of N, and then dividing the first ultrasound signal x[n] into... K Each segment is of length L, and there are M overlapping points between adjacent segments. Then the value of the nth point in the i-th segment is , ,in, , Then, through the formula Calculate power spectral density ,in The factor for calculating the power spectral density is represented by f, where f represents the frequency and j represents the imaginary number. This represents the value of the nth point in the j'th segment.

3. The ultrasonic thickness extraction method based on wavelet compressed energy estimation according to claim 2, characterized in that, Step S2 specifically includes: using the formula Calculate the total energy of the first ultrasound signal ;in The frequency resolution is expressed by the following formula: , This represents the sampling rate; then, it is expressed using the formula... Calculate the cumulative energy distribution at different cutoff frequencies Where m corresponds to different frequencies, as shown by the formula and Determine the energy percentage minimum frequency band Energy percentage The calculation formula is .

4. The ultrasonic thickness extraction method based on wavelet compressed energy estimation according to claim 3, characterized in that, Step S3 specifically includes: selecting a discrete wavelet basis. Perform wavelet transform on the first ultrasonic signal x[n] to obtain the first time-frequency diagram. Where v represents the scale parameter and k represents the time-domain translation parameter, This represents the mother wavelet.

5. The ultrasonic thickness extraction method based on wavelet compressed energy estimation according to claim 4, characterized in that, Step S4 specifically includes: based on the minimum frequency band obtained in step S2 For the first time-frequency diagram Filtering is performed, setting values ​​outside the frequency band to 0, while retaining the original amplitude and phase of values ​​within the frequency band, to obtain the second time-frequency diagram. Based on the second time-frequency diagram The first ultrasonic signal x[n] is reconstructed to obtain the second ultrasonic signal x. ” [n], The second ultrasonic signal x ” [n] Segment by window and calculate the average energy, find 5 consecutive segments with energy below the average energy, and then perform wave packet segmentation.

6. The ultrasonic thickness extraction method based on wavelet compressed energy estimation according to claim 5, characterized in that, Step S5 includes the following steps: S501, transfer the second ultrasonic signal x ” The first envelope in [n] is deleted, generating the third ultrasound signal x. ’ [n], through the formula Calculate the third ultrasound signal x ’ The autocorrelation of [n] is calculated to obtain the autocorrelation result. ; S502, Autocorrelation Results Envelope extraction is performed to construct the envelope curve a(m), and a moving average filter is applied to the envelope. ; The envelope curve after smoothing filtering; S503. Calculate the envelope curve after smoothing filtering. The first peak position P is finally determined by the formula. Calculate the thickness D, where v' represents the speed of sound.

7. The ultrasonic thickness extraction method based on wavelet compressed energy estimation according to claim 5, characterized in that, Step S5 also includes the following steps: S511, transfer the second ultrasonic signal x ” The first envelope in [n] is deleted, generating the third ultrasound signal x. ’ [n], extract the third ultrasound signal x ’ The second wave packet of [n] Its position, n2, and half-wave width t2 are used to extract the third ultrasonic signal x. ’ The position n3 of the third wave packet of [n] and the half-wave width t3; S512, transfer the third ultrasonic signal x ’ [n] and the second wave packet Perform cross-correlation, Obtain cross-correlation results Then, the envelope of the cross-correlation results is extracted to construct the envelope curve a(m), and a moving average filter is applied to the envelope. ; The envelope curve after smoothing filtering; S513, Envelope Curve Based on Smoothing Filtering In the first range Find the first maximum value point In the second range Find the second maximum point Calculate the first maximum point Second maximum point The positional difference P' is finally determined by the formula. Calculate the thickness D, where v' represents the speed of sound.

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