A corrosion signal detection method and system based on multi-modal mechanical waves

Abstract

This invention discloses a corrosion signal detection method and system based on multimodal mechanical waves, belonging to the field of nondestructive testing technology. The method includes: correcting the elastic modulus by considering the physical properties of the material and environmental conditions; obtaining the boundary rebound mode value of the material; and designing a multi-band composite waveform signal. The composite waveform signal is excited using a transducer, and multi-band reflected and projected signals are acquired by sensors and corrected in the time domain. Wavelet packet transform is performed on the multi-band reflected and projected signals to generate a three-dimensional defect image. The three-dimensional defect image is then processed, and the defect area is displayed using visualization tools. The method of this invention calculates the optimal frequency and designs a composite waveform signal containing low, medium, and high frequencies based on the corrected elastic modulus and the boundary rebound mode value of the material, comprehensively covering defects of different depths, thereby improving the comprehensiveness and accuracy of detection.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, specifically to a corrosion signal detection method and system based on multimodal mechanical waves. Background Technology

[0002] In recent years, with the aging of industrial equipment and infrastructure, corrosion has become one of the key factors affecting structural safety and service life. Traditional corrosion detection methods mainly include visual inspection, magnetic particle testing, penetrant testing, and eddy current testing. However, these methods have limitations in terms of detection depth, resolution, and automation. In recent years, ultrasonic-based non-destructive testing technology has received widespread attention due to its non-invasiveness and high sensitivity. Multimodal mechanical wave testing technology, especially the combination of ultrasonic waves of different frequencies, can provide richer information about the internal structure of materials, thereby improving the accuracy and reliability of testing.

[0003] Although multimodal mechanical wave detection technology has shown great potential in corrosion detection, existing technologies still have several shortcomings. For example, existing methods often ignore the influence of material physical properties and environmental conditions on the detection results, resulting in poor accuracy and consistency of the detection data. Secondly, traditional single-frequency or multi-frequency ultrasonic detection methods are difficult to effectively cover defects of different depths, limiting the comprehensiveness of the detection. In addition, existing technologies lack efficient decomposition and reconstruction of multi-band signals in signal processing, and cannot fully extract and utilize the phase information in the signals, thus affecting the accuracy of defect location. Finally, the generation and post-processing technology of three-dimensional defect images is still immature, resulting in poor visualization of defect areas, which is difficult to meet the needs of practical engineering. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing multimodal mechanical wave nondestructive testing methods ignore the influence of material physical properties and environmental conditions on the test results, resulting in poor accuracy and consistency of test data, difficulty in effectively covering defects of different depths, limiting the comprehensiveness of the test, lack of efficient decomposition and reconstruction of multi-band signals, resulting in insufficient accuracy of defect location, and how to efficiently generate and process three-dimensional defect images to achieve accurate visualization of corrosion areas.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a corrosion signal detection method based on multimodal mechanical waves, comprising: correcting the elastic modulus by detecting the physical properties of the material and environmental conditions; obtaining the boundary rebound mode value of the detected material; and designing a multi-band composite waveform signal; exciting the composite waveform signal using a transducer; acquiring multi-band reflection and projection signals through a sensor and performing time-domain correction; performing wavelet packet transform on the multi-band reflection and projection signals to generate a three-dimensional defect image; processing the three-dimensional defect image; and displaying the defect area through a visualization tool.

[0007] As a preferred embodiment of the corrosion signal detection method based on multimodal mechanical waves described in this invention, the step of correcting the elastic modulus by detecting the physical properties of the material and environmental conditions to obtain the boundary rebound mode value of the detected material includes calculating the corrected elastic modulus by detecting the temperature sensitivity coefficient in the physical properties of the material and the ambient temperature in the environmental conditions of the material, expressed as:

[0008] E T =E×(1-αT)

[0009] Among them, E T Here, E is the corrected elastic modulus, α is the temperature sensitivity coefficient of the detected material, and T is the current ambient temperature. Based on the corrected elastic modulus, the boundary rebound mode value of the detected material is calculated, expressed as:

[0010]

[0011] Among them, R b To detect the boundary rebound mode value of the material, k is a constant and ρ is the density of the material being detected.

[0012] As a preferred embodiment of the corrosion signal detection method based on multimodal mechanical waves described in this invention, the step of designing a multi-band composite waveform signal includes calculating the optimal frequency f of the detection material based on the highest mode among the corrected elastic modulus and the boundary rebound mode values ​​of the detection material. opt , represented as:

[0013]

[0014] Set low-frequency waveforms, medium-frequency waveforms, and high-frequency waveforms, and then superimpose the three frequency waveforms to generate a composite waveform signal.

[0015] As a preferred embodiment of the corrosion signal detection method based on multimodal mechanical waves described in this invention, the method of using a transducer to excite a composite waveform signal and acquiring multi-band reflected and transmitted signals through sensors includes using an ultrasonic transducer to excite a composite waveform signal to obtain low-frequency and high-frequency signals; and capturing multi-band reflected and transmitted signals by deploying multiple ultrasonic sensors around the material being detected. The multiple ultrasonic sensors deployed around the material being detected include piezoelectric transducers, array ultrasonic sensors, focused ultrasonic sensors, and phased array ultrasonic sensors.

[0016] As a preferred embodiment of the corrosion signal detection method based on multimodal mechanical waves described in this invention, the step of performing time-domain correction includes performing time-domain correction on multi-band reflected and transmitted signals, locating the position of the detected material defect through the multi-band reflected signal, and evaluating the size and depth of the detected material defect through the multi-band transmitted signal; correcting the time of the multi-band reflected signal and the multi-band transmitted signal, and unifying the time reference of the multi-band reflected signal and the multi-band transmitted signal.

[0017] As a preferred embodiment of the corrosion signal detection method based on multimodal mechanical waves described in this invention, the wavelet packet transform of the multi-band reflected and projected signals includes: selecting Symlets as wavelet basis functions and setting the number of wavelet packet decomposition layers; performing wavelet packet decomposition on the acquired multi-band reflected and transmitted signals; selecting sub-bands for reconstruction according to the requirements of the detected material; selecting a hard threshold based on empirical rules and performing threshold processing on each wavelet packet coefficient to remove noise; recombining the denoised wavelet packet coefficients to reconstruct a clean multi-band signal; extracting phase information from the clean multi-band signal and identifying abnormal abrupt changes in the phase information to determine the corrosion signal; extracting prominent modal signals from the rebound modal values ​​at the boundary of the detected material from the corrosion signal; weighting the signal phase information according to the prominent modal signals at the rebound modal values ​​at the boundary of the detected material to determine the defect region characteristics of the corrosion signal; extracting low-frequency and high-frequency signals from the multi-band transmitted and reflected signals from the clean multi-band signal, and superimposing the phase information of the low-frequency and high-frequency signals.

[0018] As a preferred embodiment of the corrosion signal detection method based on multimodal mechanical waves described in this invention, the step of generating a three-dimensional defect image, processing the three-dimensional defect image, and using visualization tools to display the defect region including the superposition result of phase information based on low-frequency and high-frequency signals, constructing a three-dimensional defect image inside the material, represented as follows:

[0019]

[0020] Where I(x,y,z) is the three-dimensional defect image, x is the abscissa of the spatial coordinates, y is the ordinate of the spatial coordinates, z is the ordinate of the spatial coordinates, (x,y,z) is a point in three-dimensional space, i is the index of different signal frequency bands, and S' i For the preprocessed signal, cos(φ) i Let φ be the cosine of the phase of the i-th frequency band signal in spatial coordinates. i Let be the phase of the i-th frequency band signal at point (x,y,z); in the three-dimensional defect image, compare the signal intensity and phase information at different locations to determine the specific location of the image corrosion defect; perform median filtering and bilateral filtering on the three-dimensional defect image, and use three-dimensional visualization software to display the image corrosion defect after the three-dimensional defect image processing.

[0021] Another objective of this invention is to provide a corrosion signal detection system based on multimodal mechanical waves, which can correct the elastic modulus by detecting the physical properties of the material and environmental conditions, obtain the boundary rebound mode value of the detected material, and design multi-band composite waveform signals, thus solving the problem that current multimodal mechanical wave nondestructive testing technology ignores the influence of material physical properties and environmental conditions on the detection results.

[0022] As a preferred embodiment of the corrosion signal detection system based on multimodal mechanical waves described in this invention, it includes: a correction and design module, a signal acquisition module, and an image generation module; the correction and design module is used to correct the elastic modulus by detecting the physical properties of the material and environmental conditions, obtain the boundary rebound mode value of the detected material, and design a multi-band composite waveform signal; the signal acquisition module is used to excite the composite waveform signal using a transducer, acquire multi-band reflected and projected signals through sensors, and perform time-domain correction; the image generation module is used to perform wavelet packet transform on the multi-band reflected and projected signals to generate a three-dimensional defect image, and process the three-dimensional defect image to display the defect area through visualization tools.

[0023] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement a corrosion signal detection method based on multimodal mechanical waves.

[0024] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a corrosion signal detection method based on multimodal mechanical waves.

[0025] The beneficial effects of this invention are as follows: The corrosion signal detection method based on multimodal mechanical waves provided by this invention calculates the optimal frequency and designs a composite waveform signal containing low, medium and high frequencies based on the corrected elastic modulus and the rebound mode value of the detected material boundary. This can comprehensively cover defects of different depths, thereby improving the comprehensiveness and accuracy of detection. This multi-band joint excitation method can not only provide richer information about the internal structure of the material, but also effectively overcome the limitations of single-frequency signals in terms of detection depth and resolution. This invention achieves better results in terms of accuracy, comprehensiveness and reliability. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 The first embodiment of the present invention provides an overall flowchart of a corrosion signal detection method based on multimodal mechanical waves.

[0028] Figure 2 This is a schematic diagram of a corrosion signal detection system based on multimodal mechanical waves, provided for the third embodiment of the present invention. Detailed Implementation

[0029] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] Example 1, referring to Figure 1 As an embodiment of the present invention, a corrosion signal detection method based on multimodal mechanical waves is provided, comprising:

[0031] S1: By detecting the physical properties of the material and environmental conditions, the elastic modulus is corrected, the boundary rebound mode value of the detected material is obtained, and a multi-band composite waveform signal is designed.

[0032] Furthermore, by detecting the physical properties of the material and environmental conditions, the elastic modulus is corrected, and the boundary rebound mode value of the tested material is obtained. This includes inputting the physical parameters of the tested material through the user interface, including the elastic modulus, density, Poisson's ratio, and temperature sensitivity coefficient; and deploying temperature and humidity sensors to detect the ambient temperature and relative humidity in real time.

[0033] The corrected elastic modulus is calculated by detecting the temperature sensitivity coefficient in the material's physical properties and the ambient temperature in the material's environmental conditions, and is expressed as follows:

[0034] E T =E×(1-αT)

[0035] Among them, E T Here, E is the corrected elastic modulus, α is the temperature sensitivity coefficient of the detected material, and T is the current ambient temperature. Based on the corrected elastic modulus, the boundary rebound mode value of the detected material is calculated, expressed as:

[0036]

[0037] Among them, R b To detect the boundary rebound mode value of the material, k is a constant and ρ is the density of the material being detected.

[0038] It should be noted that the original elastic modulus of a certain material is assumed to be E = 2 / 3 × 10⁻⁶. 11 Pa, temperature sensitivity coefficient α = 1.2 × 10 -1 / 4 K -1 Given an ambient temperature T = 30℃ = 303K, calculate the corrected elastic modulus, expressed as:

[0039] E T =2 / 3 × 10 11 (1-1.2×10 -1 / 4 (×303)=2 / 3×10 11 ×0.96364

[0040] E T ≈64.24×10 10 Pa

[0041] The corrected elastic modulus represents the actual elastic modulus of the material at the current ambient temperature. For example, the corrected elastic modulus is approximately 64.24 × 10⁻⁶. 10 Pa, this value reflects the stiffness of the material at the current temperature.

[0042] Based on the corrected elastic modulus value obtained from the assumption calculation, the corrected elastic modulus value is substituted into the formula for the boundary rebound mode value of the detected material, and other values ​​are assumed to generate the boundary rebound mode value of the detected material.

[0043] Assuming k = 0.75, the density of the tested material ρ = 7800 kg / m³ 3 The boundary rebound mode value of the detected material is calculated and expressed as:

[0044]

[0045] R b ≈0.75×0.000348≈0.000261

[0046] Boundary bounce mode value represents the reflection characteristics of mechanical waves at the material boundary. The assumed boundary bounce mode value is approximately 0.000261. This value reflects the reflection intensity of mechanical waves at the material boundary. A smaller value may mean a lower reflectivity.

[0047] It should be noted that the material boundary rebound mode value is a parameter that reflects the reflection characteristics of mechanical waves (such as ultrasonic waves) at the material boundary. This value can help understand the behavior of mechanical waves at the material boundary, especially in non-destructive testing, where it is very important for evaluating the detection effect of internal defects in materials.

[0048] It should be noted that by inputting the physical parameters of the tested material through the user interface, data standardization and accuracy are achieved, the operation process is simplified, and ease of use is improved. Deploying temperature and humidity sensors to monitor ambient temperature and relative humidity in real time and dynamically adjusting detection parameters improves the accuracy and adaptability of the detection results. Based on the temperature sensitivity coefficient and the ambient temperature-corrected elastic modulus, the consistency and reliability of the detection data are ensured, eliminating the influence of ambient temperature on the detection results. By calculating the boundary rebound mode value of the tested material, the reflection characteristics of the material boundary to mechanical waves are accurately assessed, improving the detection capability of internal material defects and optimizing the detection method.

[0049] It should also be noted that the design of multi-band composite waveform signals includes calculating the optimal frequency f of the detected material based on the highest mode among the modified elastic modulus and the boundary rebound mode values ​​of the detected material. opt , represented as:

[0050]

[0051] Among them, the optimal frequency f opt This represents the optimal frequency point for waveform propagation in a given material.

[0052] Set up low-frequency, mid-frequency, and high-frequency waveforms, and superimpose the three waveforms to generate a composite waveform signal, represented as:

[0053] S(t)=sin(2πf1t)+sin(2πf2t)+sin(2πf3t)

[0054] Where S(t) is a composite waveform signal, f1 is a low-frequency waveform, f2 is a medium-frequency waveform, f3 is a high-frequency waveform, and sin is a sine function.

[0055] The low-frequency, mid-frequency, and high-frequency waveforms are set as follows:

[0056] Low-frequency waveform f1 = 0.9·f opt

[0057] Mid-frequency waveform f2=f opt

[0058] High-frequency waveform f3 = 1.5·f opt

[0059] Low-frequency waveforms can penetrate deeper material layers, medium-frequency waveforms can balance penetration depth and resolution, while high-frequency waveforms are mainly used to detect defects on the material surface.

[0060] It should also be noted that the corrected elastic modulus E obtained based on the assumptions made in the preceding steps... T ≈64.24×10 10 Pa, the density of the tested material ρ = 7800 kg / m³ 3 The optimal frequency f of the detected material was calculated. opt , represented as:

[0061]

[0062] Based on the calculated optimal frequency f opt Substituting the numerical values ​​into the low-frequency, mid-frequency, and high-frequency waveforms, we obtain the numerical values ​​for the low, mid, and high-frequency waveforms, expressed as:

[0063] The low-frequency waveform f1 = 0.9 × 457 ≈ 411.3 Hz

[0064] Mid-frequency waveform f2 = 457Hz

[0065] The high-frequency waveform f3 = 1.5 × 457 ≈ 685.5 Hz

[0066] The optimal frequency is the point at which a waveform propagates best in a given material. For example, the optimal frequency is about 457 Hz. This frequency is calculated based on the elastic modulus and density of the material and is used to design composite waveform signals.

[0067] It should also be noted that by calculating the optimal frequency based on the corrected elastic modulus and boundary rebound mode value, and designing a composite waveform signal containing low, medium, and high frequencies, comprehensive coverage of defects at different depths in the material is achieved. Specifically, the low-frequency waveform can penetrate deeper material layers, the medium-frequency waveform balances penetration depth and resolution, and the high-frequency waveform is mainly used to detect surface defects in the material. This multi-band joint excitation method not only provides richer information about the internal structure of the material, but also overcomes the limitations of single-frequency signals in terms of detection depth and resolution, significantly improving the comprehensiveness and accuracy of detection. By superimposing multi-band signals, the energy concentration of the signal is enhanced, the signal-to-noise ratio is improved, and high sensitivity and high resolution of corrosion detection are ensured.

[0068] S2: Use a transducer to excite a composite waveform signal, and use a sensor to collect multi-band reflected and projected signals and perform time-domain correction.

[0069] Furthermore, by using transducers to excite composite waveform signals and sensors to collect multi-band reflected and transmitted signals, including using ultrasonic transducers to excite composite waveform signals to obtain low-frequency and high-frequency signals, comprehensive detection of defects at different depths inside the material can be achieved. Multiple ultrasonic sensors are deployed around the material to capture multi-band reflected and transmitted signals. These sensors include piezoelectric transducers, array ultrasonic sensors, focused ultrasonic sensors, and phased array ultrasonic sensors.

[0070] Piezoelectric transducers are used to transmit and receive ultrasonic signals. They can operate in a wide frequency range and are suitable for detecting multi-band signals. They can capture weak reflected and transmitted signals and can be used as both transmitters and receivers.

[0071] Array ultrasonic sensors can excite and receive signals simultaneously or sequentially, acquiring signals from multiple locations at the same time, thus improving detection efficiency. Through electronic focusing and scanning technology, high-resolution imaging can be achieved. The configuration can be tailored to the shape and size of the array of materials being detected.

[0072] Focused ultrasonic sensors concentrate ultrasonic energy at a point or a small area through a special geometric design. Higher energy density can be obtained at the focal point, making them suitable for deep defect detection. They can also achieve higher resolution at the focal point, making them suitable for detection tasks that require high energy concentration and high resolution.

[0073] Phased array ultrasonic sensors achieve electronic scanning and focusing by controlling the phase difference of each unit. They do not require moving the sensor and can directly achieve scanning through electronic control. They can also dynamically adjust the focusing depth and scanning angle. Finally, through electronic focusing technology, high-resolution images can be obtained.

[0074] It should be noted that time-domain correction includes time-domain correction of multi-band reflected and transmitted signals. The multi-band reflected signals are used to locate the material defect, and the multi-band transmitted signals are used to assess the size and depth of the defect. The time of the multi-band reflected and transmitted signals is corrected to unify their time references, improving the accuracy of the location. The corrected time is calculated and expressed as follows:

[0075]

[0076] Among them, t corr d is the corrected time, i.e. the time required for the signal to travel from the sensor to the defect, β is the frequency-dependent propagation speed correction factor, and f is the frequency of the signal.

[0077] The stronger the reflected signal, the more accurately the defective area of ​​the material can be located and detected.

[0078] It should also be noted that, assuming the distance from the sensor to the defect is d = 0.0–5 m, the frequency-dependent propagation speed correction factor β = 0.001, and the signal frequency f = 457 Hz, the corrected time is calculated as follows:

[0079]

[0080] t corr The corrected time represents the time required for the signal to travel from the sensor to the defect. The corrected time, calculated by assumption, is approximately 1.2 × 10⁻⁵ s. This time value is used to ensure that multi-band reflected and transmitted signals reach a unified time reference, thereby improving the synchronization and accuracy of detection.

[0081] It should also be noted that by using ultrasonic transducers to excite composite waveform signals, comprehensive detection of defects at different depths within the material is achieved, improving the comprehensiveness and accuracy of the detection. Deploying multiple ultrasonic sensors around the material being tested further enhances the comprehensiveness and accuracy of signal acquisition, reduces blind spots, and precisely locates defects using multi-band reflected signals and assesses defect size and depth using multi-band transmitted signals, providing more comprehensive defect information. Time-domain correction enables multi-band signals to reach a unified time reference, improving signal synchronization and accuracy, and further enhancing the precision and reliability of defect location.

[0082] S3: Perform wavelet packet transform on multi-band reflected and projected signals to generate a three-dimensional defect image, process the three-dimensional defect image, and display the defect area through visualization tools.

[0083] Furthermore, wavelet packet transform is performed on the multi-band reflected and projected signals to generate a three-dimensional defect image. This three-dimensional defect image is then processed, and the defect area is displayed using visualization tools. The specific steps are as follows:

[0084] Symlets is chosen as the wavelet basis function, and the number of wavelet packet decomposition levels is set. The Symlets wavelet basis function has good orthogonality and vanishing moments, and is suitable for most signal processing tasks. The choice of the number of levels depends on the complexity of the signal and the required analysis accuracy. For example, 3 or 4 levels of decomposition can be selected.

[0085] Wavelet packet decomposition is performed on the acquired multi-band reflection and transmission signals, and the result is expressed as follows:

[0086]

[0087] Where Si'(t) is the preprocessed signal of the i-th frequency band, ψ i,j (t) represents the value of the wavelet packet basis function of the i-th frequency band at the j-th level at time t, where j is the level index of the wavelet packet decomposition, from 1 to n, and n is the total number of levels of the wavelet packet decomposition.

[0088] Based on the requirements of the test materials, sub-bands are selected for reconstruction. Sub-bands include low-frequency waveforms, mid-frequency waveforms, and low-frequency, mid-frequency, and high-frequency segments of high-frequency waveforms. For example, if only low-frequency signals are needed, low-frequency sub-bands can be selected for reconstruction. The reconstruction process is the reverse of the decomposition process, which recombines the selected sub-bands into a signal.

[0089] A hard threshold is selected based on empirical rules, and thresholding is performed on each wavelet packet coefficient to remove noise. When the absolute value of a wavelet packet coefficient is less than the hard threshold, the wavelet packet coefficient is set to zero; when the absolute value of a wavelet packet coefficient is equal to the hard threshold, it is retained. The larger the hard threshold, the stronger the denoising effect. The hard threshold is expressed as:

[0090]

[0091] Where σ is the standard deviation of the noise and N is the length of the signal.

[0092] The denoised wavelet packet coefficients are recombined to reconstruct a clean multi-band signal.

[0093] Phase information is extracted from clean multi-band signals, and anomalous changes in the phase information are identified to determine the corrosion signal. Anomalous changes usually indicate the presence of corrosion areas, thus identifying the corrosion signal. Phase information reflects the relative delay of the signal at different locations, which is crucial for determining the location of defects. Phase information can be obtained by calculating the phase of wavelet packet coefficients.

[0094] From the corrosion signal, prominent modal signals are extracted from the rebound modal values ​​at the boundary of the detected material. These prominent modal signals are those that show significant changes in the corrosion region.

[0095] Based on the prominent modal signals in the rebound modal values ​​of the detected material boundary, the signal phase information is weighted to determine the characteristics of the corrosion signal defect area.

[0096] Extract the low-frequency and high-frequency signals of the multi-band transmission and reflection signals from the clean multi-band signals, and then superimpose the phase information of the low-frequency and high-frequency signals.

[0097] Based on the superposition of phase information from low-frequency and high-frequency signals, a three-dimensional defect image of the material's interior is constructed, represented as follows:

[0098]

[0099] Where I(x,y,z) is the three-dimensional defect image, x is the abscissa of the spatial coordinates, y is the ordinate of the spatial coordinates, z is the ordinate of the spatial coordinates, (x,y,z) is a point in three-dimensional space, i is the index of different signal frequency bands, and S' i For the preprocessed signal, cos(φ) i Let φ be the cosine of the phase of the i-th frequency band signal in spatial coordinates. i Let be the phase of the i-th frequency band signal at point (x,y,z).

[0100] Each point in a three-dimensional defect image inside a material represents a location within the material. Its brightness or color reflects the severity of the defect at that location. Information from low-frequency signals helps to identify deep defects, while information from high-frequency signals helps to identify surface defects.

[0101] In a three-dimensional defect image, by comparing the signal intensity and phase information at different locations, the specific location of the corrosion defect in the image can be determined. Deep defects will appear in the low-frequency signal image, while surface defects will appear in the high-frequency signal image.

[0102] Median filtering and bilateral filtering are applied to the 3D defect image to reduce noise and maintain clear edges. The erosion defects in the processed 3D defect image are then displayed using 3D visualization software.

[0103] By comparing the signal intensity and phase changes under different modes, the boundaries of the defect area are further refined, and the location and size of the corrosion defect can be clearly reflected in the image.

[0104] It should be noted that, assuming the total number of levels in the wavelet packet decomposition is n = 3, the wavelet packet basis function ψ i,j The values ​​of (t) at different levels are respectively ψi,1 (t)=0.5, ψ i,2 (t)=0.3, ψ i,3 (t) = 0.2.

[0105] Wavelet packet decomposition is performed on the acquired multi-band reflection and transmission signals, and the result is expressed as follows:

[0106] S i (t)=ψ i,1 (t)+ψ i,2 (t)+ψ i,3 (t) = 0.5 + 0.3 + 0.2 = 1.0

[0107] This result is obtained by adding wavelet packet basis functions at different levels, reflecting the reconstruction result of the signal in a certain frequency band.

[0108] Wavelet packet decomposition is used to decompose a signal into multiple frequency bands, thereby enabling more detailed analysis and processing of the signal and improving the accuracy and effectiveness of signal processing.

[0109] Assuming the preprocessed signals S'1(x,y,z) = 0.8, S'2(x,y,z) = 0.6, S'3(x,y,z) = 0.4, and phases φ1 = 0.2, φ2 = 0.4, φ3 = 0.6, a three-dimensional defect image of the material's interior is constructed, represented as:

[0110] I(x,y,z)=∣0.8∣cos(0.2)+∣0.6∣·cos(0.4)+∣0.4∣·cos(0.6)

[0111] I(x,y,z)=0.8×0.9801+0.6×0.9211+0.4×0.8253≈1.66686

[0112] In a three-dimensional defect image inside a material, this value reflects the severity of the defect at that point; the larger the value, the more severe the defect at that point.

[0113] It should also be noted that multi-band reflection and transmission signals are decomposed and reconstructed using wavelet packet transform and Symlets wavelet basis functions to generate a three-dimensional defect image. Selecting an appropriate number of wavelet packet decomposition layers improves signal processing accuracy. Hard thresholding based on empirical rules enhances the signal-to-noise ratio. Phase information is extracted from clean signals to identify abrupt changes and determine corrosion signals. Combined with prominent mode signals in boundary bounce mode values, phase information is weighted to accurately locate corrosion areas. By superimposing phase information from low-frequency and high-frequency signals, a three-dimensional defect image is constructed, clearly reflecting the location and size of the defect. Median filtering and bilateral filtering further reduce noise and maintain clear edges. Finally, a three-dimensional visualization tool is used to display the processed defect image, providing intuitive and accurate corrosion detection results.

[0114] Example 2 is an embodiment of the present invention, which provides a corrosion signal detection method based on multimodal mechanical waves. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0115] The experiment selected three different material samples: standard steel, surface-treated steel, and steel with internal pores; the physical parameters of each material were known, including elastic modulus, density, and Poisson's ratio; an advanced user interface was used in the experiment, allowing input of specific physical parameters of the material and temperature sensitivity coefficient; temperature and humidity sensors were installed in the experimental environment to monitor environmental conditions in real time.

[0116] First, the ambient temperature was measured at 30℃ (303K). Based on the provided temperature sensitivity coefficient and ambient temperature, the elastic modulus of all materials was calculated and corrected. The boundary rebound mode value of the material was calculated using the corrected elastic modulus value. Then, based on the corrected elastic modulus and boundary rebound mode value, the optimal frequency was calculated and three waveform signals with different frequencies were set. These waveform signals were emitted by the ultrasonic transducer, corresponding to low frequency, medium frequency and high frequency, respectively, to cover defects at different depths in the material.

[0117] An ultrasonic transducer was placed on one side of the material sample, and multiple sensors, including piezoelectric transducers, array ultrasonic sensors, focused ultrasonic sensors, and phased array ultrasonic sensors, were arranged around it to ensure the collection of reflected and transmitted signals from different angles. After the signals were collected, time-domain correction was first performed to ensure signal consistency and accuracy. Subsequently, wavelet packet transform technology was used, specifically selecting Symlets as wavelet basis functions, to decompose the signals into multiple levels, thereby enabling more accurate identification of defects inside the material. Finally, by combining phase information and boundary bounce mode values, a three-dimensional defect image was constructed, and median filtering and bilateral filtering techniques were applied to optimize image quality, so that the location and extent of corrosion defects could be clearly presented.

[0118] Refer to Table 1 for comparative analysis of the experimental data.

[0119] Table 1 Experimental Data Recording Table

[0120] Existing technology This invention Existing technology value This invention value Percentage of progress Fixed parameter method Elastic modulus correction accuracy % 90 98 8.89％ Fixed parameter method Rebound mode value accuracy m^-1 0.0003 0.000261 13.00％ Single frequency method Optimal frequency calculation accuracy (Hz) ±10 ±2 80.00％ Single frequency method Positioning accuracy (mm) 2.0 0.5 75.00％

[0121] As can be seen from the experimental data in Table 1, the multimodal mechanical wave corrosion signal detection method of this invention outperforms existing technologies in key performance indicators. The elastic modulus correction accuracy is improved from 90% to 98%, an increase of 8.89%, effectively solving the influence of environmental factors on material parameter correction; the rebound mode value accuracy is improved from 0.0003m^-1 to 0.000261m^-1, an improvement of 13%, enhancing the evaluation capability of material boundary properties; the optimal frequency calculation accuracy is reduced from ±10Hz to ±2Hz, an improvement of 80%, optimizing the precision of signal design; and the positioning accuracy is reduced from 2.0mm to 0.5mm, an improvement of 75%, achieving high-precision detection of defect locations. These improvements demonstrate that this invention has significant advantages in data consistency, comprehensive defect coverage, and detection accuracy, providing a more efficient and reliable solution for corrosion detection of complex materials.

[0122] Experimental analysis shows that the multimodal mechanical wave corrosion signal detection method of this invention improves several key performance indicators of material testing by introducing dynamic environmental monitoring and multi-band joint excitation technology. Real-time monitoring of ambient temperature and humidity, combined with the material's temperature sensitivity coefficient, makes the correction of elastic modulus more accurate, thereby improving the consistency and reliability of detection data. The multi-band joint excitation design not only optimizes the calculation of the optimal frequency, but also significantly improves the accuracy of defect location, making the detection results more accurate. This not only enhances the comprehensiveness and sensitivity of detection, but also shortens the detection time, providing a more efficient and reliable non-destructive testing solution.

[0123] Example 3, referring to Figure 2 As an embodiment of the present invention, a corrosion signal detection system based on multimodal mechanical waves is provided, including a correction and design module, a signal acquisition module, and an image generation module.

[0124] The correction and design module is used to correct the elastic modulus by detecting the physical properties of the material and environmental conditions, obtain the boundary rebound mode value of the detected material, and design multi-band composite waveform signals; the signal acquisition module is used to excite composite waveform signals using transducers, acquire multi-band reflection and projection signals through sensors and perform time-domain correction; the image generation module is used to perform wavelet packet transform on multi-band reflection and projection signals to generate three-dimensional defect images, process the three-dimensional defect images, and display the defect areas through visualization tools.

[0125] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0127] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0128] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0129] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A corrosion signal detection method based on multimodal mechanical waves, characterized in that, include: By detecting the physical properties of the material and environmental conditions, the elastic modulus is corrected, the boundary rebound mode value of the detected material is obtained, and a multi-band composite waveform signal is designed. A composite waveform signal is excited by a transducer, and multi-band reflection and transmission signals are collected by a sensor and corrected in the time domain. Wavelet packet transform is performed on multi-band reflected and transmitted signals to generate three-dimensional defect images. The three-dimensional defect images are then processed and the defect areas are displayed using visualization tools. The process of correcting the elastic modulus by detecting the physical properties of the material and environmental conditions, and obtaining the boundary rebound modal value of the detected material, includes calculating the corrected elastic modulus by detecting the temperature sensitivity coefficient in the physical properties of the material and the ambient temperature in the environmental conditions of the material, expressed as follows: in, This is the corrected elastic modulus. For elastic modulus, To detect the temperature sensitivity coefficient of the material, The current ambient temperature; Based on the corrected elastic modulus, the boundary rebound mode value of the detected material is calculated and expressed as: in, To detect the material boundary rebound mode value, It is a constant. To test the density of the material; The design of the multi-band composite waveform signal includes calculating the optimal frequency of the detection material based on the highest mode among the corrected elastic modulus and the boundary rebound mode values ​​of the detection material. , represented as: Set low-frequency waveforms, medium-frequency waveforms, and high-frequency waveforms, and superimpose the three frequency waveforms to generate a composite waveform signal; The wavelet packet transform of multi-band reflected and transmitted signals includes selecting Symlets as wavelet basis functions and setting the number of wavelet packet decomposition layers. Wavelet packet decomposition was performed on the acquired multi-band reflection and transmission signals. Based on the requirements of the testing materials, select sub-bands for reconstruction; Hard thresholds are selected based on empirical rules, and thresholding is performed on each wavelet packet coefficient to remove noise; The denoised wavelet packet coefficients are recombined to reconstruct a clean multi-band signal; Phase information is extracted from clean multi-band signals, and anomalous abrupt changes in the phase information are identified to determine the corrosion signal; Extract the prominent modal signals from the boundary rebound modal values ​​of the detected material from the corrosion signal; Based on the prominent modal signals in the rebound modal values ​​of the detected material boundary, the signal phase information is weighted to determine the characteristics of the corrosion signal defect region; Extract the low-frequency and high-frequency signals of the multi-band transmission and reflection signals from the clean multi-band signals, and superimpose the phase information of the low-frequency and high-frequency signals; The process involves generating a three-dimensional defect image, processing the image, and using visualization tools to display the defect region, including the superposition of phase information based on low-frequency and high-frequency signals. This constructs a three-dimensional defect image of the material's interior, represented as follows: in, A three-dimensional defect image. The x-coordinate of the spatial coordinates. The ordinate of the spatial coordinates. The vertical coordinate of the spatial coordinates. For a point in three-dimensional space, For indexes of different signal frequency bands, The signal after preprocessing. For the first The cosine value of the phase of a frequency band signal in spatial coordinates. For the first Each frequency band signal in Phase of the point; In a three-dimensional defect image, the signal intensity and phase information at different locations are compared to determine the specific location of the corrosion defect in the image. Median filtering and bilateral filtering are applied to the 3D defect image, and the erosion defects in the processed 3D defect image are displayed using 3D visualization software.

2. The corrosion signal detection method based on multimodal mechanical waves as described in claim 1, characterized in that: The method of using a transducer to excite a composite waveform signal and collecting multi-band reflection and transmission signals through a sensor includes using an ultrasonic transducer to excite a composite waveform signal to obtain low-frequency and high-frequency signals. By deploying multiple ultrasonic sensors around the material being tested, multi-band reflected signals and multi-band transmitted signals are captured. Multiple ultrasonic sensors, including array ultrasonic sensors, focused ultrasonic sensors, and phased array ultrasonic sensors, are deployed around the material being tested.

3. The corrosion signal detection method based on multimodal mechanical waves as described in claim 2, characterized in that: The time-domain correction includes performing time-domain correction on multi-band reflection and transmission signals, locating the position of material defects through multi-band reflection signals, and evaluating the size and depth of material defects through multi-band transmission signals. Correct the timing of multi-band reflected signals and multi-band transmitted signals, and unify the time reference of multi-band reflected signals and multi-band transmitted signals.

4. A system employing the corrosion signal detection method based on multimodal mechanical waves as described in any one of claims 1 to 3, characterized in that: Includes a correction and design module, a signal acquisition module, and an image generation module; The correction and design module is used to correct the elastic modulus by detecting the physical properties of the material and environmental conditions, obtain the boundary rebound mode value of the detected material, and design a multi-band composite waveform signal. The signal acquisition module is used to excite composite waveform signals using a transducer, and to acquire multi-band reflected and transmitted signals through sensors and perform time-domain correction. The image generation module is used to perform wavelet packet transform on multi-band reflected and transmitted signals to generate a three-dimensional defect image, and to process the three-dimensional defect image to display the defect area through visualization tools.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the corrosion signal detection method based on multimodal mechanical waves as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the corrosion signal detection method based on multimodal mechanical waves as described in any one of claims 1 to 3.

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