Corrosion signal detection method and system based on multi-mode mechanical waves

Abstract

The invention discloses a corrosion signal detection method and system based on multi-mode mechanical waves, and relates to the technical field of nondestructive testing, and the method comprises the steps: correcting an elastic modulus through the physical characteristics and environmental conditions of a detection material, obtaining a boundary rebound mode value of the detection material, and designing a multi-band composite waveform signal; exciting a composite waveform signal by using a transducer, collecting a multi-band reflection and projection signal through a sensor, and carrying out time domain correction; and performing wavelet packet transformation on the multi-band reflection signal and the projection signal to generate a three-dimensional defect image, processing the three-dimensional defect image, and displaying a defect area through a visual tool. According to the method, on the basis of the corrected elastic modulus and the boundary rebound modal value of the detection material, the optimal frequency is calculated, the composite waveform signal containing the low frequency, the intermediate frequency and the high frequency is designed, defects of different depths are comprehensively covered, and therefore the comprehensiveness and accuracy of detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of nondestructive testing, and in particular to a corrosion signal detection method and system based on multi-modal mechanical waves. Background Art

[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 inspection, penetration inspection and eddy current inspection. However, these methods have limitations in detection depth, resolution and degree of automation. In recent years, ultrasonic-based nondestructive testing technology has attracted widespread attention due to its non-invasiveness and high sensitivity. Multimodal mechanical wave detection technology, especially the combination of ultrasonic waves of different frequencies, can provide richer internal information of materials, thereby improving the accuracy and reliability of detection.

[0003] Although multimodal mechanical wave detection technology has shown great potential in corrosion detection, the existing technology still has several shortcomings. For example, the existing methods often ignore the impact 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 at different depths, limiting the comprehensiveness of the detection. In addition, the existing technology lacks efficient decomposition and reconstruction of multi-band signals in signal processing, and cannot fully extract and utilize the phase information in the signal, which in turn affects the accuracy of defect positioning. Finally, the generation and post-processing technology of three-dimensional defect images is not yet mature, resulting in poor visualization of defect areas and difficulty in meeting actual engineering needs. Summary of the invention

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

[0005] Therefore, the technical problem solved by the present invention is: the existing multimodal mechanical wave nondestructive testing method ignores the influence of material physical properties and environmental conditions on the test results, resulting in poor accuracy and consistency of the test data, making it difficult to effectively cover defects at different depths, limiting the comprehensiveness of the test, lacking efficient decomposition and reconstruction of multi-band signals, resulting in insufficient accuracy in defect positioning, and the problem of how to efficiently generate and process three-dimensional defect images to achieve accurate visualization of corrosion areas.

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

[0007] As a preferred solution of the corrosion signal detection method based on multimodal mechanical waves described in the present invention, wherein: the physical properties and environmental conditions of the detection material are used to correct the elastic modulus and obtain the boundary rebound modal value of the detection material, including calculating the corrected elastic modulus through the temperature sensitivity coefficient in the physical properties of the detection material and the ambient temperature in the environmental conditions of the detection material, expressed as:

[0008] E T =E×(1-αT)

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

[0010]

[0011] Among them, R b is the boundary rebound modal value of the test material, k is a constant, and ρ is the density of the test material.

[0012] As a preferred solution of the corrosion signal detection method based on multimodal mechanical waves described in the present invention, the multi-band composite waveform signal is designed to include calculating the optimal frequency f of the detection material based on the highest mode in the modified elastic modulus and the boundary rebound mode value of the detection material. opt , expressed as:

[0013]

[0014] A low-frequency waveform, a medium-frequency waveform and a high-frequency waveform are set, and the three frequency waveforms are superimposed to generate a composite waveform signal.

[0015] As a preferred solution of the corrosion signal detection method based on multimodal mechanical waves described in the present invention, wherein: the use of a transducer to excite a composite waveform signal and the collection of multi-band reflection and projection signals through a sensor includes using an ultrasonic transducer to excite a composite waveform signal to obtain a low-band signal and a high-band signal; by deploying multiple ultrasonic sensors around the detection material, the multi-band reflection signal and the multi-band transmission signal are captured; and multiple ultrasonic sensors including piezoelectric transducers, array ultrasonic sensors, focused ultrasonic sensors and phased array ultrasonic sensors are deployed around the detection material.

[0016] As a preferred solution of the corrosion signal detection method based on multimodal mechanical waves described in the present invention, the time domain correction includes time domain correction of multi-band reflection and projection signals, locating the position of the defect of the detection material through the multi-band reflection signal, and evaluating the size and depth of the defect of the detection material through the multi-band transmission signal; correcting the time of the multi-band reflection signal and the multi-band transmission signal, and unifying the time base of the multi-band reflection signal and the multi-band transmission signal.

[0017] As a preferred solution of the corrosion signal detection method based on multimodal mechanical waves described in the present invention, the wavelet packet transform of the multi-band reflection signal and the projection signal includes: selecting Symlets as the wavelet basis function and setting the number of wavelet packet decomposition layers; performing wavelet packet decomposition on the collected multi-band reflection signal and multi-band transmission signal; selecting sub-bands for reconstruction according to the requirements of the detection material; selecting a hard threshold based on the empirical rule, 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 mutations in the phase information to determine the corrosion signal; extracting the prominent modal signal in the boundary rebound modal value of the detection material from the corrosion signal; weighting the signal phase information according to the prominent modal signal in the boundary rebound modal value of the detection material to determine the defect area characteristics of the corrosion signal; extracting the low-frequency band signal and the high-frequency band signal of the multi-band transmission signal and the multi-band reflection signal from the clean multi-band signal, and superimposing the phase information of the low-frequency band signal and the high-frequency band signal.

[0018] As a preferred solution of the corrosion signal detection method based on multimodal mechanical waves described in the present invention, wherein: the three-dimensional defect image is generated and processed, and the defect area is displayed by a visualization tool including the phase information superposition result based on the low-frequency band signal and the high-frequency band signal, and the three-dimensional defect image inside the material is constructed, which is expressed as:

[0019]

[0020] Among them, I(x,y,z) is a three-dimensional defect image, x is the horizontal coordinate of the spatial coordinate, y is the vertical coordinate of the spatial coordinate, z is the vertical coordinate of the spatial coordinate, (x,y,z) is a point in the three-dimensional space, i is the index of different signal frequency bands, S' i is the preprocessed signal, cos(φ i ) is the cosine value of the phase of the signal in the i-th frequency band in the spatial coordinate, φ i is the phase of the ith frequency band signal at the point (x, y, z); in the three-dimensional defect image, the signal strength and phase information at different positions are compared to determine the specific location of the image corrosion defect; the three-dimensional defect image is subjected to median filtering and bilateral filtering, and the image corrosion defect after the three-dimensional defect image is processed is displayed using three-dimensional visualization software.

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

[0022] As a preferred solution of the corrosion signal detection system based on multimodal mechanical waves described in the present 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, obtain the boundary rebound modal value of the detection material, and design a multi-band composite waveform signal by detecting the physical properties and environmental conditions of the material; the signal acquisition module is used to use a transducer to excite the composite waveform signal, collect multi-band reflection and projection signals through a sensor and perform time domain correction; the image generation module is used to perform wavelet packet transform on the multi-band reflection signal and projection signal to generate a three-dimensional defect image, and process the three-dimensional defect image to display the defect area through a visualization tool.

[0023] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a corrosion signal detection method based on multi-modal mechanical waves.

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

[0025] Beneficial effects of the present invention: The corrosion signal detection method based on multimodal mechanical waves provided by the present invention calculates the optimal frequency and designs a composite waveform signal including low frequency, medium frequency and high frequency based on the corrected elastic modulus and the boundary rebound modal value of the detection material. It can comprehensively cover defects at different depths, thereby improving the comprehensiveness and accuracy of detection. This multi-band joint excitation method can not only provide richer internal information of the material, but also effectively overcome the limitations of single frequency signals in detection depth and resolution. The present invention achieves better results in terms of accuracy, comprehensiveness and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0027] Figure 1 An overall flow chart of a corrosion signal detection method based on multimodal mechanical waves provided for the first embodiment of the present invention.

[0028] Figure 2 A schematic diagram of a module of a corrosion signal detection system based on multimodal mechanical waves provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0030] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a corrosion signal detection method based on multimodal mechanical waves, comprising:

[0031] S1: By testing the physical properties and environmental conditions of the material, correct the elastic modulus, obtain the boundary rebound modal value of the test material, and design a multi-band composite waveform signal.

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

[0033] By testing the temperature sensitivity coefficient in the physical properties of the material and the ambient temperature in the environmental conditions of the material, the modified elastic modulus is calculated and expressed as:

[0034] E T =E×(1-αT)

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

[0036]

[0037] Among them, R b is the boundary rebound modal value of the test material, k is a constant, and ρ is the density of the test material.

[0038] It should be noted that, assuming that the original elastic modulus of a material is E = 2 / 3 × 10 11 Pa, temperature sensitivity coefficient α = 1.2 × 10 -1 / 4 K -1 , the current ambient temperature T = 30 ° C = 303 K, 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. By example, the corrected elastic modulus is about 64.24×10 10 Pa, this value reflects the stiffness of the material at the current temperature.

[0042] Based on the corrected elastic modulus value calculated based on the assumption, the corrected elastic modulus value is substituted into the formula for the boundary rebound modal value of the detection material, and other values ​​are assumed to generate the boundary rebound modal value of the detection material.

[0043] Assume k = 0.75, test material density ρ = 7800 kg / m 3 , calculate the boundary rebound modal value of the test material, expressed as:

[0044]

[0045] R b ≈0.75×0.000348≈0.000261

[0046] The boundary rebound modal value represents the reflection characteristics of the material boundary to the mechanical wave. The assumed boundary rebound modal value is approximately 0.000261. This value reflects the reflection intensity of the material boundary to the mechanical wave. A smaller value may mean a lower reflectivity.

[0047] It should be noted that the rebound modal value of the material boundary is a parameter that reflects the reflection characteristics of the material boundary to mechanical waves (such as ultrasound). This value can help understand the behavior of mechanical waves at the material boundary, especially in non-destructive testing. 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 material under test through the user interface, data standardization and accuracy are achieved, the operation process is simplified, and ease of use is improved; temperature and humidity sensors are deployed to monitor ambient temperature and relative humidity in real time, and the detection parameters are dynamically adjusted to improve the accuracy and adaptability of the detection results; the elastic modulus is corrected based on the temperature sensitivity coefficient and ambient temperature to ensure the consistency and reliability of the detection data and eliminate the influence of ambient temperature on the detection results; by calculating the boundary rebound modal value of the detection material, the reflection characteristics of the material boundary to mechanical waves are accurately evaluated, thereby improving the detection capability of internal defects in the material and optimizing the detection method.

[0049] It should also be noted that the design of the multi-band composite waveform signal includes calculating the optimal frequency f of the test material based on the corrected elastic modulus and the highest mode in the boundary rebound mode value of the test material. opt , expressed as:

[0050]

[0051] Among them, the optimal frequency f opt Represents the frequency point at which a waveform propagates best in a given material.

[0052] Set a low-frequency waveform, a medium-frequency waveform, and a high-frequency waveform, and superimpose the three frequency waveforms to generate a composite waveform signal, which is expressed as:

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

[0054] Among them, 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 waveform, medium frequency waveform and high frequency waveform are set as follows:

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

[0057] Medium 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, and high-frequency waveforms are mainly used to detect surface defects of materials.

[0060] It should also be noted that the modified elastic modulus E obtained based on the assumptions in the previous step is T ≈64.24×10 10 Pa, test material density ρ = 7800 kg / m 3 , calculate the optimal frequency f of the test material opt , expressed as:

[0061]

[0062] According to the calculated optimal frequency f opt , substitute the values ​​into the low-frequency waveform, medium-frequency waveform and high-frequency waveform to obtain the values ​​of the low, medium and high-frequency waveforms, expressed as:

[0063] Low frequency waveform f1=0.9×457≈411.3Hz

[0064] Medium frequency waveform f2 = 457 Hz

[0065] High frequency waveform f3 = 1.5 × 457 ≈ 685.5 Hz

[0066] The calculated optimal frequency represents the best frequency point for waveform propagation in a given material. It can be seen that the optimal frequency obtained in the example is about 457Hz. 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 modal value, and designing a composite waveform signal including low frequency, medium frequency and high frequency, comprehensive coverage of defects at different depths of the material is achieved. Specifically, the low-frequency waveform can penetrate deeper material layers, the medium-frequency waveform balances the penetration depth and resolution, and the high-frequency waveform is mainly used to detect surface defects of the material. This multi-band joint excitation method not only provides richer internal information of the material, but also overcomes the limitations of single-frequency signals in detection depth and resolution, significantly improving the comprehensiveness and accuracy of detection. Through the superposition of multi-band signals, the energy concentration of the signal is enhanced, the signal-to-noise ratio is improved, and the high sensitivity and high resolution of corrosion detection are ensured.

[0068] S2: Use the transducer to excite the composite waveform signal, collect the multi-band reflection and projection signals through the sensor and perform time domain correction.

[0069] Furthermore, the transducer is used to excite the composite waveform signal, and the multi-band reflection and projection signals are collected by the sensor, including the use of an ultrasonic transducer to excite the composite waveform signal to obtain low-frequency band signals and high-frequency band signals, thereby realizing comprehensive detection of defects at different depths inside the inspection material; by deploying multiple ultrasonic sensors around the inspection material, the multi-band reflection signals and multi-band transmission signals are captured; and multiple ultrasonic sensors including piezoelectric transducers, array ultrasonic sensors, focused ultrasonic sensors and phased array ultrasonic sensors are deployed around the inspection material.

[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 a transmitter and a receiver.

[0071] Array ultrasonic sensors can excite and receive signals simultaneously or sequentially, and can obtain signals from multiple positions at the same time to improve detection efficiency. Through electronic focusing and scanning technology, high-resolution imaging can be achieved. The configuration requirements can be configured according to the shape and size of the detection material array.

[0072] The focused ultrasonic sensor uses a special geometric design to concentrate the ultrasonic energy at one point or a small area. It can obtain a higher energy density at the focus and is suitable for deep defect detection. It can obtain a higher resolution at the focus and is suitable for detection tasks that require high energy concentration and high resolution.

[0073] The phased array ultrasonic sensor achieves electronic scanning and focusing by controlling the phase difference of each unit. It does not require a mobile sensor, can scan directly through electronic control, and can dynamically adjust the focus depth and scanning angle. Finally, through electronic focusing technology, high-resolution images can be obtained.

[0074] It should be noted that the time domain correction includes time domain correction of multi-band reflection and projection signals, locating the defect position of the detection material through the multi-band reflection signal, and evaluating the size and depth of the defect of the detection material through the multi-band transmission signal; correcting the time of the multi-band reflection signal and the multi-band transmission signal, unifying the time base of the multi-band reflection signal and the multi-band transmission signal, improving the accuracy of positioning, and calculating the corrected time, which is expressed as:

[0075]

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

[0077] The stronger the reflected signal, the more accurate the positioning and detection of material defect areas.

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

[0079]

[0080] t corr is the corrected time, which means the time required for the signal to travel from the sensor to the defect. The corrected time calculated by assumption is approximately 1.2×10-5s. This time value is used to ensure that the multi-band reflection signal and transmission signal reach a unified time base, 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 inside the material is achieved, improving the comprehensiveness and accuracy of detection. Multiple ultrasonic sensors are deployed around the test material to improve the comprehensiveness and accuracy of signal acquisition, reduce blind spots, and accurately locate the defect position through multi-band reflection signals. Multi-band transmission signals are used to evaluate the defect size and depth, providing more comprehensive defect information. Time domain correction enables multi-band signals to reach a unified time base, improves the synchronization and accuracy of signals, and further enhances the accuracy and reliability of defect location.

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

[0083] Furthermore, the multi-band reflection signal and the projection signal are transformed by wavelet packet to generate a three-dimensional defect image, and the three-dimensional defect image is processed to display the defect area through a visualization tool. The specific steps are as follows:

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

[0085] The collected multi-band reflection signal and multi-band transmission signal are decomposed by wavelet packet, which is expressed as:

[0086]

[0087] Among them, Si'(t) is the signal of the i-th frequency band after preprocessing, ψ i,j (t) is the value of the wavelet packet basis function of the i-th frequency band at the j-th level at time t, 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] According to the requirements of the detection material, sub-bands are selected for reconstruction. The sub-bands include low-frequency waveforms, medium-frequency waveforms and low-frequency bands, medium-frequency bands and high-frequency bands in 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 inverse process of the decomposition process, and the selected sub-bands are recombined into signals.

[0089] The hard threshold is selected based on the empirical rule, and each wavelet packet coefficient is thresholded to remove noise. When the absolute value of the wavelet packet coefficient is less than the hard threshold, the wavelet packet coefficient is reset to zero. When the absolute value of the wavelet packet coefficient is equal to the hard threshold, the wavelet packet coefficient is retained and reset to zero. 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 abnormal mutations in the phase information are identified to determine the corrosion signal. Abnormal mutations usually indicate the presence of a corrosion area, thereby determining the corrosion signal. The phase information reflects the relative delay of the signal at different positions, which is crucial for determining the location of the defect. The phase information can be obtained by calculating the phase of the wavelet packet coefficients.

[0094] From the corrosion signal, the prominent modal signals in the rebound modal values ​​of the inspection material boundary are extracted. These prominent modal signals are signals that show obvious changes in the corrosion area.

[0095] According to the prominent modal signals in the rebound modal values ​​of the detected material boundaries, the signal phase information is weighted and processed to determine the characteristics of the corrosion signal defect area.

[0096] The low-band signal and the high-band signal of the multi-band transmission signal and the multi-band reflection signal are extracted from the clean multi-band signal, and the phase information of the low-band signal and the high-band signal are superimposed.

[0097] Based on the phase information superposition results of low-frequency signals and high-frequency signals, a three-dimensional defect image inside the material is constructed, which is expressed as:

[0098]

[0099] Among them, I(x,y,z) is a three-dimensional defect image, x is the horizontal coordinate of the spatial coordinate, y is the vertical coordinate of the spatial coordinate, z is the vertical coordinate of the spatial coordinate, (x,y,z) is a point in the three-dimensional space, i is the index of different signal frequency bands, S' i is the preprocessed signal, cos(φ i ) is the cosine value of the phase of the signal in the i-th frequency band in the spatial coordinate, φ i is the phase of the signal in the i-th frequency band at the point (x, y, z).

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

[0101] In the three-dimensional defect image, the signal intensity and phase information at different positions are compared to determine the specific location of the image corrosion defect. Deep defects will appear in the image of low-frequency signals, while surface defects will appear in the image of high-frequency signals.

[0102] Median filtering and bilateral filtering are performed on the three-dimensional defect image to reduce noise and keep the edges clear, and the corrosion defects of the image after three-dimensional defect image processing are displayed using three-dimensional visualization software.

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

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

[0105] The collected multi-band reflection signal and multi-band transmission signal are decomposed by wavelet packet, which is expressed as:

[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 to decompose the signal into multiple frequency bands, so as to analyze and process the signal more carefully and improve the accuracy and effect of signal processing.

[0109] Assuming that the preprocessed signal S'1(x, y, z) = 0.8, S'2(x, y, z) = 0.6, S'3(x, y, z) = 0.4, phase φ1 = 0.2, φ2 = 0.4, φ3 = 0.6, construct a three-dimensional defect image inside the material, expressed 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 the three-dimensional defect image inside the material, this value reflects the severity of the defect at that point. The larger the value, the more serious the defect at that point.

[0113] It should also be noted that through wavelet packet transform and Symlets wavelet basis function, multi-band reflection and transmission signals are decomposed and reconstructed to generate three-dimensional defect images. The appropriate number of wavelet packet decomposition layers is selected to improve the accuracy of signal processing. The hard threshold is selected for denoising based on the rule of thumb to improve the signal-to-noise ratio of the signal. Phase information is extracted from the clean signal, abnormal mutations are identified, and corrosion signals are determined. The phase information is weighted and processed in combination with the prominent modal signals in the boundary rebound modal values ​​to accurately locate the corrosion area. The three-dimensional defect image is constructed by superimposing the phase information of low-frequency and high-frequency signals to clearly reflect the position and size of the defect. Median filtering and bilateral filtering further reduce noise and keep the edges clear. Finally, the processed defect image is displayed using a three-dimensional visualization tool to provide 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] Three different material samples were selected for the experiment: 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 the input of specific physical parameters of the material as well as the 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°C (303K); the elastic modulus of all materials was calculated and corrected based on the provided temperature sensitivity coefficient and ambient temperature; the boundary rebound modal value of the material was calculated using the corrected elastic modulus value, and then, based on the corrected elastic modulus and boundary rebound modal 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 of the material.

[0117] An ultrasonic transducer is placed on one side of the material sample, and multiple sensors are arranged around it, including piezoelectric transducers, array ultrasonic sensors, focused ultrasonic sensors, and phased array ultrasonic sensors, to ensure that reflected and transmitted signals are collected from different angles. After the signal is collected, it is first corrected in the time domain to ensure the consistency and accuracy of the signal. Subsequently, the wavelet packet transform technology is used, and Symlets are selected as the wavelet basis function to perform multi-level decomposition of the signal, so that the defects inside the material can be identified more accurately. Finally, a three-dimensional defect image is constructed by combining phase information and boundary rebound modal values, and median filtering and bilateral filtering techniques are applied to optimize the image quality, so that the location and range of the corrosion defects can be clearly presented.

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

[0119] Table 1 Experimental data record table

[0120] Prior art The present invention Current technology value The value of the present invention Improvement percentage Fixed parameter method Elastic modulus correction accuracy % 90 98 8.89％ Fixed parameter method Rebound modal 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] It can be seen from the experimental data in Table 1 that the corrosion signal detection method of the multimodal mechanical wave of the present invention is superior to the prior art in key performance indicators. The accuracy of elastic modulus correction is improved from 90% to 98%, an improvement of 8.89%, which effectively solves the influence of environmental factors on material parameter correction; the accuracy of rebound modal value is improved from 0.0003m^-1 to 0.000261m^-1, an improvement of 13%, which enhances the ability to evaluate material boundary characteristics; the accuracy of optimal frequency calculation is reduced from ±10Hz to ±2Hz, and the accuracy is improved by 80%, which optimizes the accuracy of signal design; the positioning accuracy is reduced from 2.0mm to 0.5mm, an improvement of 75%, and high-precision detection of defect positions is achieved. These improvements show that the present invention has obvious advantages in data consistency, comprehensive defect coverage and detection accuracy, and provides a more efficient and reliable solution for corrosion detection of complex materials.

[0122] It can be seen from the experimental analysis results that the corrosion signal detection method of the multimodal mechanical wave of the present invention improves multiple key performance indicators of material detection by introducing dynamic environmental monitoring and multi-band joint excitation technology, real-time monitoring of ambient temperature and humidity, and combining the temperature sensitivity coefficient of the material to make the correction of the elastic modulus more accurate, thereby improving the consistency and reliability of the detection data. The multi-band joint excitation design not only optimizes the calculation of the optimal frequency, but also greatly improves the accuracy of defect positioning, making the detection results more accurate, which not only enhances the comprehensiveness and sensitivity of detection, but also shortens the detection time, providing a more efficient and reliable non-destructive detection solution.

[0123] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a corrosion signal detection system based on multimodal mechanical waves, 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, obtain the boundary rebound modal value of the test material, and design a multi-band composite waveform signal by testing the physical properties and environmental conditions of the material; the signal acquisition module is used to use the transducer to excite the composite waveform signal, collect the multi-band reflection and projection signals through the sensor and perform time domain correction; the image generation module is used to perform wavelet packet transform on the multi-band reflection signal and projection signal to generate a three-dimensional defect image, and process the three-dimensional defect image to display the defect area through visualization tools.

[0125] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

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

[0127] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0128] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in 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 rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in 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 testing the physical properties and environmental conditions of the material, the elastic modulus is corrected, the boundary rebound modal value of the test material is obtained, and a multi-band composite waveform signal is designed; The transducer is used to excite the composite waveform signal, and the multi-band reflection and projection signals are collected through the sensor and corrected in the time domain; The multi-band reflection signal and projection signal are transformed by wavelet packet to generate a three-dimensional defect image, which is then processed to display the defect area through visualization tools.

2. The corrosion signal detection method based on multimodal mechanical waves according to claim 1, characterized in that: The method of correcting the elastic modulus by detecting the physical properties and environmental conditions of the material and obtaining the boundary rebound modal value of the 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, which is expressed as: And T =E×(1-αT) Among them, E T is the corrected elastic modulus, E is the elastic modulus, α is the temperature sensitivity coefficient of the test material, and T is the current ambient temperature; Based on the corrected elastic modulus, the boundary rebound modal value of the test material is calculated and expressed as: Among them, R b is the boundary rebound modal value of the test material, k is a constant, and ρ is the density of the test material.

3. The corrosion signal detection method based on multimodal mechanical waves according to claim 2, characterized in that: The design of the multi-band composite waveform signal includes calculating the optimal frequency f of the test material based on the corrected elastic modulus and the highest mode in the boundary rebound mode value of the test material. opt , expressed as: A low-frequency waveform, a medium-frequency waveform and a high-frequency waveform are set, and the three frequency waveforms are superimposed to generate a composite waveform signal.

4. The corrosion signal detection method based on multimodal mechanical waves according to claim 3, characterized in that: The method of using a transducer to excite a composite waveform signal and collecting multi-band reflection and projection signals through a sensor includes using an ultrasonic transducer to excite a composite waveform signal to obtain a low-frequency band signal and a high-frequency band signal; By deploying multiple ultrasonic sensors around the test material, multi-band reflection signals and multi-band transmission signals are captured; Multiple ultrasonic sensors including piezoelectric transducers, array ultrasonic sensors, focused ultrasonic sensors and phased array ultrasonic sensors are deployed around the detection material.

5. The corrosion signal detection method based on multimodal mechanical waves according to claim 4, characterized in that: The time domain correction includes performing time domain correction on the multi-band reflection and projection signals, locating the position of the defect of the inspection material through the multi-band reflection signal, and evaluating the size and depth of the defect of the inspection material through the multi-band transmission signal; Correct the time of multi-band reflection signal and multi-band transmission signal, and unify the time base of multi-band reflection signal and multi-band transmission signal.

6. The corrosion signal detection method based on multimodal mechanical waves according to claim 5, characterized in that: The wavelet packet transforming of the multi-band reflected signal and the projected signal includes selecting Symlets as wavelet basis functions and setting the number of wavelet packet decomposition layers; Perform wavelet packet decomposition on the collected multi-band reflection signals and multi-band transmission signals; Select sub-bands for reconstruction based on the test material requirements; A hard threshold is selected based on empirical rules, and each wavelet packet coefficient is thresholded to remove noise; The denoised wavelet packet coefficients are recombined to reconstruct a clean multi-band signal; Extract phase information from clean multi-band signals and identify abnormal mutations in the phase information to determine corrosion signals; Extract the prominent modal signals in the boundary rebound modal values ​​of the inspection material from the corrosion signal; According to the prominent modal signal in the rebound modal value of the detected material boundary, the signal phase information is weighted to determine the characteristics of the corrosion signal defect area; The low-band signal and the high-band signal of the multi-band transmission signal and the multi-band reflection signal are extracted from the clean multi-band signal, and the phase information of the low-band signal and the high-band signal are superimposed.

7. The corrosion signal detection method based on multimodal mechanical waves according to claim 6, characterized in that: The three-dimensional defect image is generated and processed, and the defect area is displayed through a visualization tool, including the phase information superposition result based on the low-frequency band signal and the high-frequency band signal, to construct a three-dimensional defect image inside the material, which is expressed as: Among them, I(x,y,z) is a three-dimensional defect image, x is the horizontal coordinate of the spatial coordinate, y is the vertical coordinate of the spatial coordinate, z is the vertical coordinate of the spatial coordinate, (x,y,z) is a point in the three-dimensional space, i is the index of different signal frequency bands, S' i is the preprocessed signal, cos(φ i ) is the cosine value of the phase of the signal in the i-th frequency band in the spatial coordinate, φ i is the phase of the i-th frequency band signal at point (x, y, z); In the three-dimensional defect image, the signal intensity and phase information at different positions are compared to determine the specific position of the image corrosion defect; The three-dimensional defect image is subjected to median filtering and bilateral filtering, and the corrosion defects of the image after the three-dimensional defect image processing are displayed using three-dimensional visualization software.

8. A system using the corrosion signal detection method based on multimodal mechanical waves as claimed in any one of claims 1 to 7, characterized in that: It includes correction and design module, signal acquisition module and image generation module; The correction and design module is used to correct the elastic modulus, obtain the boundary rebound modal value of the test material, and design a multi-band composite waveform signal by testing the physical properties and environmental conditions of the material; The signal acquisition module is used to use the transducer to excite the composite waveform signal, collect multi-band reflection and projection signals through the sensor and perform time domain correction; The image generation module is used to perform wavelet packet transform on the multi-band reflection signal and the projection signal to generate a three-dimensional defect image, and process the three-dimensional defect image to display the defect area through a visualization tool.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the corrosion signal detection method based on multimodal mechanical waves described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the corrosion signal detection method based on multimodal mechanical waves described in any one of claims 1 to 7 are implemented.

Images