Pipeline corrosion monitoring method and system based on ultrasonic guided waves

Abstract

The invention discloses a pipeline corrosion monitoring method and system based on ultrasonic guided waves, relates to the technical field of pipeline detection, solves the problem of signal confusion caused by multi-mode coupling from the source by screening an optimal detection mode and establishing a mapping relation between defect parameters and guided wave signals, and lays a precise foundation for damage detection. Damage characteristic signals are extracted by means of modal order separation, and common-source synthetic focusing imaging is combined, so that chemical complex environment noise is effectively inhibited, the signal-to-noise ratio of long-distance transmission signals is improved, and high-precision positioning of defects is realized. A corrosion dynamic monitoring model is constructed, quantitative evaluation of corrosion area loss and corrosion rate is completed, limitation of traditional single damage detection is broken through, full-period dynamic monitoring, prediction and early warning of pipeline corrosion are achieved, monitoring accuracy, stability and intelligent level are greatly improved, reliable technical support is provided for safe production of chemical pipelines, and the method is suitable for popularization and application. The leakage risk is reduced.

Description

Technical Field

[0001] This application relates to the field of pipeline inspection technology, specifically to a pipeline corrosion monitoring method and system based on ultrasonic guided waves. Background Technology

[0002] Pipelines are a vital national infrastructure, indispensable in fields such as energy and chemical engineering. Shandong Province, as a major pipeline province and the largest chemical province, faces higher demands for the safety and intelligence of pipeline transportation due to the rapid development of its chemical industry. Chlor-alkali chemical production is a pillar of basic chemical industry, and its core raw material, chlorine, is a highly toxic gas. Leaks are characterized by their suddenness and significant hazards. Currently, the consumption of chlorine is surging, but the development of supporting safety protection and monitoring technologies is lagging behind, resulting in a persistently high risk of pipeline leaks. Establishing an efficient pipeline damage monitoring system has become an urgent need for safe production in the chemical industry.

[0003] Pipelines are susceptible to corrosion and wall thinning due to various factors during long-term service, which can lead to leaks, loss of life and property, and ecological pollution. The state has listed pipeline leak monitoring and location, as well as integrity assessment, as key technological support capabilities that urgently need to be improved. Research on pipeline health monitoring technology has significant scientific and engineering practical value. Ultrasonic guided wave detection technology has become the preferred technology for pipeline health monitoring due to its advantages such as non-destructive testing, high detection efficiency, and wide monitoring range. Among them, piezoelectric ultrasonic guided waves have outstanding comprehensive performance and are suitable for long-term online monitoring, while axisymmetric mode guided waves have characteristics such as low non-dispersion attenuation, which are of great significance for long-distance pipeline corrosion monitoring.

[0004] The research and application of ultrasonic guided wave testing technology has become a hot topic in the field of pipeline damage monitoring both domestically and internationally. Foreign technologies are mature, with many testing instruments commercialized, and research on guided wave propagation characteristics and attenuation laws is relatively in-depth. While domestic research has made some breakthroughs, with universities achieving numerous results in the development of detection systems and damage identification methods, the overall development is lagging behind. There are still shortcomings in multimodal signal processing, adaptability to complex working conditions, long-distance signal processing and imaging, and existing research mainly focuses on damage detection; the combined application of guided wave damage characterization and historical monitoring data still needs further development.

[0005] While current ultrasonic guided wave-based pipeline monitoring technology has proven feasible, it still faces three major technical bottlenecks in its application to corrosion monitoring of long-distance chemical pipelines: First, the multimodal coupling propagation of ultrasonic guided waves easily leads to signal confusion, making it difficult to accurately extract damage characteristic information. Second, guided waves experience significant energy attenuation during long-distance transmission, resulting in a decrease in the signal-to-noise ratio of the received signal and affecting the accuracy of damage identification. Third, the complex environmental noise in chemical settings creates strong interference, further reducing the accuracy and stability of detection. Furthermore, existing research mostly focuses on single-instance damage detection and has not yet achieved an effective combination of guided wave damage characterization and historical pipeline monitoring data, failing to meet the practical needs of long-term health status tracking and monitoring of chemical pipelines. Summary of the Invention

[0006] In order to solve the above-mentioned technical problems, this application proposes the following technical solution: In a first aspect, embodiments of this application provide a pipeline corrosion monitoring method based on ultrasonic guided waves, including: Determine the propagation law of guided waves and select the optimal detection mode, and establish the mapping relationship between pipeline defect parameters and guided wave signals; Damage feature signals are extracted by modal order separation, and damage imaging is achieved by combining common-source synthetic focusing, thereby improving resolution and noise suppression capabilities to determine the location of defects in the pipeline. A dynamic corrosion monitoring model is constructed to achieve quantitative assessment of corrosion area loss and corrosion rate at the defect locations and to provide pipeline prediction and early warning.

[0007] In one possible implementation, determining the guided wave propagation law and selecting the optimal detection mode, and establishing the mapping relationship between pipeline defect parameters and guided wave signals, includes: Using an infinitely long isotropic hollow circular tube as the object, we completed the waveguide classification and clarified the displacement field properties of each mode; Analyze the waveguide propagation characteristics and determine the optimal excitation mode and the best excitation frequency band based on the four mode selection criteria; The propagation of guided waves in a pipe with typical defects was simulated. The circumferential, radial and axial parameters of the defects were changed, and the corresponding echo signals were collected. The quantitative mapping relationship between the geometric parameters of the defects and the guided wave signals was analyzed and established to realize the inversion of the axial defect length.

[0008] In one possible implementation, the process of classifying waveguides and defining the displacement field properties of each mode, using an infinitely long isotropic hollow circular tube as the object, includes: A complete dispersion equation describing axisymmetric and non-axisymmetric modal guided waves has been established: In the formula , It is the Lame constant. It is a displacement vector. For density, For time, For Hamiltonian operators, For the Laplace operator, For displacement divergence, The gradient of volumetric strain, It is the acceleration of a point mass; displacement vector It can be decomposed into an expansion scalar potential function. and isochoric vector potential function : in: F Coordinate vector and time The function, the and satisfy: in: For longitudinal wave velocity, , For transverse wave velocity, ; The solution to the above equation can be expressed as: Where: integers Indicates the circumferential order of the guided wave. For radial coordinates in a cylindrical coordinate system, Angular coordinates in a cylindrical coordinate system For the axial coordinates of the cylindrical coordinate system. The radial component of the magnetic field in cylindrical coordinates. The angular component of the magnetic field in cylindrical coordinates. This represents the axial component of the magnetic field in cylindrical coordinates. Angular frequency, The axial wave number, , , and It is a radially undetermined function, only related to... Related; Based on the direction of propagation, guided waves are classified into circumferential guided waves and cylindrical guided waves. Based on modal characteristics, they are classified into three categories: longitudinal, torsional, and bending, and the displacement field characteristics of each mode are clearly defined.

[0009] In one possible implementation, the analysis of guided wave propagation characteristics, determining the optimal excitation mode and optimal excitation frequency band based on four mode selection criteria, includes: Solve the guided wave dispersion curve of the pipeline and analyze the influence of dispersion characteristics and multi-mode characteristics on the detection. Based on the criteria of modal separability, non-dispersion, axisymmetry, and damage-sensitive mode selection, the optimal excitation mode is selected as torsion from the dispersion curve; By combining propagation distance and damage resolution, the optimal excitation frequency band was determined through simulation and experimental verification.

[0010] In one possible implementation, the step of extracting damage feature signals through modal order separation and combining it with common-source synthetic focusing to achieve damage imaging, thereby improving resolution and noise suppression capabilities to determine the location of defects in the pipeline, includes: A sensor array is arranged around the pipe to collect response signals, separating axisymmetric and non-axisymmetric modes, extracting the signal envelope and determining the wave packet arrival time, and combining the group velocities of the two types of modes to calculate the axial distance from the axisymmetric and non-axisymmetric defects to the sensor ring. A single mode is excited, and the defect reflection echo is received through an array sensor. The echo signal is decomposed into modal components of each order by a two-dimensional Fourier transform, thus completing mode separation. The wavenumbers of each mode are obtained from the dispersion curve, and a phase shift is applied to the mode components to simulate the reverse propagation of the guided wave signal and compensate for the diffusion and dispersion effects during propagation. The phase-shifted signal is restored to the spatial domain by performing a two-dimensional inverse Fourier transform. The spatial image of the defect is generated by the integral superposition method or the reflection coefficient distribution is extracted by the deconvolution algorithm to restore the location, size and shape of the defect.

[0011] In one possible implementation, the step of circumferentially arranging a sensor array in the pipe to acquire response signals, separating axisymmetric and non-axisymmetric modes, extracting the signal envelope and determining the wave packet arrival time, and calculating the axial distance from the axisymmetric and non-axisymmetric defects to the sensor ring by combining the group velocities of the two types of modes, includes: Based on mode separation technology, axisymmetric mode signals are synthesized by a circumferentially uniformly arranged sensor array to suppress non-axisymmetric interference; The bending mode components are separated from the original signal, and the circumferential distribution characteristics of the defects are constructed by combining the array phase difference information: In the formula, 、 These are the extracted torsional mode and nth-order bending mode signals, respectively. It is the collected response signal, the It is collected by M piezoelectric sensors on the piezoelectric sensor ring. 、 The expression is as follows: in:, For the first Signals collected by a sensor ; The extracted T-mode and F-mode signal envelopes are used to determine the location of axisymmetric feature structures, and the F-mode signal envelope determines the location of non-axisymmetric simulated damage. Based on the T-mode group velocity and the F-mode group velocity, the axial distances of axisymmetric damage and non-axisymmetric damage between the pipe structure damage distance sensor rings are obtained: in: The center time of the wave packet. For the T-mode group velocity, For the F-mode group velocity, The axial distance of the axisymmetric damage. This represents the axial distance of non-axisymmetric damage.

[0012] In one possible implementation, the excitation single mode receives defect reflection echoes via an array sensor, and decomposes the echo signal into modal components using a two-dimensional Fourier transform, thus completing mode separation, including: In cylindrical coordinates, the axial position is z, and the circumferential angle is... Pipeline guided wave signal It can be decomposed into a linear superposition of multiple modes: in: and Representing the first Clan The amplitude and wavenumber of the first-order modal components both vary with the angular frequency. change; When excited by a single T-mode, the reflected echo of the guided wave encountering damage contains only the T-mode and F-mode, and the above equation can be simplified to: in: and These represent the amplitude and wavenumber of the nth modal component, respectively. Performing a two-dimensional Fourier transform on the signal can extract the modal components of the guided wave: in, express The expression in the two-dimensional frequency domain, and Representing the first First mode at angular frequency The amplitude and phase below, superscript This serves as the variable identifier for the corresponding parameter during the integration process.

[0013] In one possible implementation, the step of performing a two-dimensional inverse Fourier transform on the phase-shifted signal to restore it to the spatial domain, generating a spatial image of the defect using an integral superposition method or extracting the reflection coefficient distribution using a deconvolution algorithm, and restoring the defect location, size, and shape includes: In axial position Signal measured at Its expression in the order-frequency domain is obtained by two-dimensional Fourier transform: ; right Apply phase shift to obtain axial position The expression of the guided wave signal in the order-frequency domain: Will Axial position is obtained by two-dimensional inverse Fourier transform. Guided wave signal at the location ; The axial position z= Guided wave signals collected at location After synthesized focused backpropagation, z= in the direction of the echo source is obtained. The acoustic signal at that location is expressed as follows: in, and These represent the two-dimensional Fourier transform and the two-dimensional inverse Fourier transform, respectively. By changing... The value is used to obtain the distribution information of acoustic signals within a certain axial range; Only a certain axial interval is extracted from each time step. The guided wave signal is then extracted, and the absolute value of the extracted guided wave signal is integrated along the time axis to obtain information about the spatial coordinates. Image of the distributed defects. Where: and These represent the shear wave velocity and the shear wave wavelength at the center frequency, respectively. The number of cycles of the excitation signal: in: The center excitation frequency of the incident wave; In axial position Signal measured at In the order-frequency domain, it is represented as Applying a phase shift to it yields... The expression of the guided wave in the order-frequency domain: Will Obtained through two-dimensional inverse Fourier transform waveguide signal ; By superimposing along the axis, we can obtain information about the spatial coordinates. Image of distributed defects; Deconvolution image reconstruction involves calculating the acoustic signal distribution of the incident and reflected guided waves at the target location, then using the incident guided wave signal as a reference, performing deconvolution on the reflected guided wave signal to extract the reflection coefficient at the target location. The final reflection coefficient distribution map will reflect the location, size, and shape of the defect.

[0014] In one possible implementation, the construction of a corrosion dynamic monitoring model to achieve quantitative assessment and pipeline prediction and early warning of corrosion area loss and corrosion rate at the defect location includes: At a designated location on the pipeline, chemical corrosion is used to quantitatively and progressively increase damage. After each corrosion, a monitoring system is activated to collect guided wave data, and ultrasonic testing is used to measure the area loss of the corrosion zone and record the number of corrosion cycles. The area loss and corrosion rate of each corrosion were calculated based on the measured data, and the data were organized into a corrosion monitoring dataset in chronological order. The corrosion dynamic monitoring model is constructed by selecting a basic model based on the characteristics of corrosion monitoring data, and the corrosion dynamic monitoring model is trained, optimized and verified using corrosion monitoring data. By continuously integrating newly added corrosion monitoring data into the dataset and iteratively optimizing model parameters, long-term dynamic monitoring and early warning of pipeline corrosion status can be achieved.

[0015] In one possible implementation, the step of selecting a base model based on the characteristics of corrosion monitoring data to construct the corrosion dynamic monitoring model, and then training, optimizing, and validating the corrosion dynamic monitoring model using corrosion monitoring data, includes: By analyzing the corrosion loss characterized by corrosion monitoring data, a predictive model is established to predict the future trend of corrosion loss. Suppose the original non-negative data sequence It can be represented as: make For sequence The cumulative generation, i.e.: but A single accumulation generates a sequence It can be represented as: Let the nearest neighbor mean sequence of the original sequence be... : The formula for calculating its background value is: The expression for the first-order differential equation model of the prediction model is: In the formula: , There are two parameters to be estimated. For development coefficient, The degree of gray effect, The gray derivative, This is the value for whitening the background; like The gray derivative corresponds to , This is the whitening background value, corresponding to The whitening equation corresponding to the differential equation is: To estimate the parameters, the discrete equations are rewritten in the following form: set up Let be the vector of parameters to be estimated, then: Solving using the least squares method, we have: in: , ; After solving the matrix Substituting the values ​​and solving the differential equation, we obtain the solution to the prediction model equation: In the formula: for The predicted value of the sequence is generated by accumulating the values ​​at each time step; After performing cumulative subtraction and restoration on the above formula, the predicted value of the original data sequence is obtained: Pick Substituting into the above formula yields the original data column. The restoring response: The residual and posterior difference tests are performed by subtracting the above-mentioned subtracted and restored sequence from the original sequence. If both the residual test and the posterior error test result in a satisfactory prediction, the grey prediction model can be used for prediction; otherwise, the model needs to be improved to enhance its prediction accuracy.

[0016] Secondly, embodiments of this application provide a pipeline corrosion monitoring system based on ultrasonic guided waves, comprising: The mapping relationship establishment module is used to determine the propagation law of guided waves and select the optimal detection mode, and establish the mapping relationship between pipeline defect parameters and guided wave signals; The defect location module is used to extract damage feature signals through modal order separation, and combine it with common-source synthetic focusing to achieve damage imaging, thereby improving resolution and noise suppression capabilities to determine the location of defects in the pipeline. The early warning and monitoring module is used to construct a dynamic corrosion monitoring model to achieve quantitative assessment of corrosion area loss and corrosion rate at the defect location and to predict and warn pipelines.

[0017] In this embodiment, by selecting the optimal detection mode and establishing a mapping relationship between defect parameters and guided wave signals, the signal confusion problem caused by multimodal coupling is fundamentally solved, laying a precise foundation for damage detection. Damage feature signals are extracted by modal order separation and combined with common-source synthetic focusing imaging, effectively suppressing noise in complex chemical environments and improving the signal-to-noise ratio of long-distance transmission signals, achieving high-precision defect localization. A dynamic corrosion monitoring model is constructed to quantitatively assess corrosion area loss and corrosion rate, breaking through the limitations of traditional single-shot damage detection. This enables full-cycle dynamic monitoring and predictive early warning of pipeline corrosion, significantly improving the accuracy, stability, and intelligence of monitoring, providing reliable technical support for safe production of chemical pipelines, and reducing leakage risks. Attached Figure Description

[0018] Figure 1 A schematic flowchart of a pipeline corrosion monitoring method based on ultrasonic guided waves provided in this application embodiment; Figure 2 A schematic diagram of a free-boundary circular tube provided in an embodiment of this application; Figure 3 This is a schematic diagram of the pipeline dispersion curve provided in an embodiment of this application; Figure 4 This is a schematic diagram of co-source synthesis focusing provided in an embodiment of this application; Figure 5 A schematic diagram of the experimental system platform provided in the embodiments of this application; Figure 6 This is a schematic diagram of the model building process provided in the embodiments of this application; Figure 7 A field diagram of the installed sensors provided for an embodiment of this application; Figure 8 The on-site debugging effect diagram provided for the embodiments of this application; Figure 9 This is a schematic diagram of an 80kHz signal and detection results provided in an embodiment of this application; Figure 10 This is a schematic diagram of the automatic monitoring task results provided in an embodiment of this application; Figure 11 This is a schematic diagram of the wall thickness monitoring signal and calculation results provided in the embodiments of this application; Figure 12 This is a schematic diagram of a pipeline corrosion monitoring system based on ultrasonic guided waves, provided as an embodiment of this application. Detailed Implementation

[0019] The present solution will now be described in conjunction with the accompanying drawings and specific embodiments.

[0020] See Figure 1 The pipeline corrosion monitoring method based on ultrasonic guided waves provided in this embodiment includes: S101, determine the propagation law of guided waves and select the optimal detection mode, and establish the mapping relationship between pipeline defect parameters and guided wave signals.

[0021] In-service pipelines are susceptible to structural damage due to factors such as media corrosion and external loads. Furthermore, pipeline systems include typical geometric features such as welds, flanges, and bends. When ultrasonic guided waves propagate in pipelines, scattering and reflection occur at structural discontinuities, and the echo characteristics contain crucial information about the location, type, and severity of defects. By constructing pipeline models under different damage states and combining signal comparison and feature extraction techniques, the mapping relationship between guided wave propagation patterns and damage parameters can be revealed. This clarifies the detection threshold and quantification capability of guided waves for corrosion defects, providing theoretical support for pipeline health assessment under complex operating conditions.

[0022] First, a general solution to the problem of three-dimensional elastic wave propagation in an infinitely long, isotropic hollow circular tube is proposed, and a complete dispersion equation describing axisymmetric and non-axisymmetric modal guided waves is established. Consider, for example... Figure 2 The elastic wave propagating in the isotropic circular tube shown above, according to elasticity mechanics, satisfies the equilibrium Navier equations for the particle displacement: In the formula , It is the Lame constant. It is a displacement vector. For density, For time, For Hamiltonian operators, For the Laplace operator, For displacement divergence, The gradient of volumetric strain, It represents the acceleration of a point mass.

[0023] Using Helmholtz decomposition, the displacement vector is... It can be decomposed into an expansion scalar potential function. and isochoric vector potential function : in: F Coordinate vector and time The function, the and satisfy: in: For longitudinal wave velocity, , For transverse wave velocity, .

[0024] The solution to the above equation can be expressed as: Where: integers Indicates the circumferential order of the guided wave. For radial coordinates in a cylindrical coordinate system, Angular coordinates in a cylindrical coordinate system For the axial coordinates of the cylindrical coordinate system. The radial component of the magnetic field in cylindrical coordinates. The angular component of the magnetic field in cylindrical coordinates. This represents the axial component of the magnetic field in cylindrical coordinates. Angular frequency, The axial wave number, , , and It is a radially undetermined function, only related to... Related.

[0025] Based on the propagation of guided waves in the circumferential and axial directions, guided waves in pipelines are classified into circumferential guided waves and cylindrical guided waves. When propagating along the circumferential direction, the boundary condition for guided waves is that the stress on both the inner and outer surfaces in the circumferential direction is zero. In this case, the length coordinate z is neglected; therefore, their free boundary conditions are expressed as follows: For any pipe, when the pipe's dimensions are constant, there are multiple guided wave propagation modes, each with a corresponding solution to a characteristic frequency equation. The modes propagating along the Z-axis can generally be divided into three types: ① Longitudinal mode: The longitudinal mode is an axisymmetric mode, generally denoted as L(0, m), and expressed in the displacement field as... , , ② Torsional mode: The torsional mode is also an axisymmetric mode, generally denoted as T(0, m), and the displacement field is expressed as... , , ③ Bending mode: The bending mode is a non-axisymmetric mode, generally denoted as F(n, m), and the displacement field is expressed as... , , The circumferential order n represents the number of harmonic variations of the mode along the pipe wall, with values ​​of 1, 2, 3...n. The module m represents the type of harmonic motion passing through the pipe wall, with values ​​of 1, 2, 3...m. When m=1, it is the fundamental mode, which can propagate at zero frequency.

[0026] Dispersion characteristics and multimode characteristics are the most important propagation characteristics of guided waves. In actual testing, multimode coupling can cause intermodal interference, leading to a decrease in signal-to-noise ratio and wave packet aliasing, which seriously affects damage feature extraction. Dispersion is manifested as the characteristic of guided wave propagation velocity changing with frequency, and can be divided into physical dispersion dominated by material properties and geometric dispersion dominated by structural dimensions. When the object to be tested is determined, the guided wave mode and frequency jointly determine the dispersion characteristics: the same mode has different propagation velocities at different frequencies, and different modes at the same frequency also have different velocities. Excessive dispersion will lead to signal waveform distortion and amplitude attenuation, directly affecting the accuracy of damage feature identification. The dispersion curve, as a quantitative representation of dispersion characteristics, is a key parameter for guided wave detection. The dispersion curve of the pipeline guided wave is solved using the Pcdisp software package in Matlab, and the results are as follows. Figure 3 As shown.

[0027] Ultrasonic guided wave testing technology is based on the characteristic that stress waves generate reflected wave packets when they encounter damage in pipelines. It enables rapid long-distance pipeline inspection through single-point excitation, offering advantages such as high inspection efficiency and ease of operation. The selection of the guided wave mode directly affects the inspection performance; an ideal mode should meet the following characteristics: Modal separability: The excitation method must be engineering-operable, and the selected mode should have a significant difference in displacement field distribution from other modes to facilitate signal decoupling.

[0028] Non-dispersive characteristics: Except for the T (0,1) mode, the characteristics of group velocity and phase velocity of other modes with frequency change will cause wave packet broadening, which will affect feature extraction. Therefore, non-dispersive modes are preferred.

[0029] Axisymmetry: Axisymmetric modes (such as longitudinal L(0,n) and torsional T(0,n) modes) are easier to excite than non-axisymmetric modes and have a more uniform energy distribution.

[0030] Damage sensitivity: The selected mode must have a broad spectrum of response capability to different types of defects (cracks, corrosion, etc.) and maintain energy stability during propagation.

[0031] according to Figure 3 Dispersion curve analysis shows that the longitudinal L(0,2) mode has the highest propagation speed in the 200kHz frequency range, but it has significant limitations: L(0,1) mode interference needs to be suppressed, and the radial displacement component is prone to energy leakage, resulting in limited propagation distance. In contrast, the torsional T(0,1) mode exhibits unique advantages: it is completely non-dispersive in this frequency band, avoiding wave packet broadening. Because liquids cannot withstand shear stress, this mode only propagates in solids, effectively avoiding the influence of liquid media on energy attenuation; at the same time, only a single torsional mode exists in this frequency band, simplifying transducer design. Considering the requirements for multi-damage type detection, this embodiment selects the T(0,1) mode as the excitation source.

[0032] The selection of the excitation frequency requires a trade-off between propagation distance and damage resolution. Low-frequency guided waves have less attenuation, making them suitable for long-distance monitoring, but their sensitivity to minute defects is reduced; high-frequency guided waves are more sensitive to surface defects, but their attenuation is aggravated. Through system simulation and experimental verification, the optimal excitation frequency band for this study was determined to be 20-100kHz. This range can ensure the non-dispersion characteristics of the T(0,1) mode while balancing the requirements of propagation distance and damage detection accuracy.

[0033] Based on simple damage forms such as pipe cracks and perforations, this paper uses ABAQUS finite element software to simulate the propagation and damage detection of ultrasonic guided waves in seamless carbon steel pipes. The system analyzes the mapping relationship between the geometric parameters of pipe defects (circumferential length, radial depth, and axial dimension) and the guided wave echo signal. When the circumferential length of the defect changes linearly, the guided wave echo reflection coefficient shows a significant linear positive correlation with the circumferential dimension of the defect. This phenomenon stems from the synchronous increase in the effective reflection area of ​​the stress wave as the circumferential defect expands. Furthermore, when the circumferential and axial dimensions of the defect remain constant, the echo signal amplitude exhibits a nonlinear trend with the radial depth of the defect. This nonlinear characteristic is mainly caused by the change in stress wave scattering mode induced by the increase in defect depth. By establishing a quantitative relationship between the echo reflection coefficient and the axial dimension of the defect, accurate inversion of the axial defect length can be achieved.

[0034] S102 extracts damage feature signals through modal order separation and combines them with common-source synthetic focusing to achieve damage imaging, thereby improving resolution and noise suppression capabilities to determine the location of defects in the pipeline.

[0035] When ultrasonic guided waves propagate in a pipeline, they exhibit axisymmetric mode reflection at axisymmetric interfaces such as welds and flanges; however, they undergo mode conversion upon encountering non-axisymmetric defects such as corrosion and pits, exciting higher-order non-axisymmetric modes. The scattering behavior of the axisymmetric T(0,1) mode at non-axisymmetric defects can serve as a basis for defect identification. According to the dispersion curve, when the excitation frequency satisfies a wavelength much greater than the pipe wall thickness, the higher-order bending mode F(n,2) approaches the propagation characteristics of the torsional mode; therefore, this frequency band is typically chosen for detection to simplify signal analysis.

[0036] Based on modal separation technology, axisymmetric modal signals are synthesized using a circumferentially uniformly arranged sensor array to suppress non-axisymmetric interference. Time-frequency analysis is employed to separate bending modal components from the original signal. The circumferential distribution characteristics of the defect are constructed by combining array phase difference information. This method can effectively identify non-axisymmetric defects and quantify their characteristic parameters. According to the ultrasonic wave characteristics, the signal extraction methods for the torsional mode and the nth-order bending mode are as follows: In the formula, 、 These are the extracted torsional mode and nth-order bending mode signals, respectively. It is the collected response signal, the It is collected by M piezoelectric sensors on the piezoelectric sensor ring. 、 The expression is as follows: in:, For the first Signals collected by a sensor .

[0037] In the formula, 、 These are the extracted torsional mode and nth-order bending mode signals, respectively. It is the collected response signal, the It is collected by M piezoelectric sensors on the piezoelectric sensor ring. 、 The expression is as follows: in: For the first Signals collected by a sensor ; The propagation time of the response signal reflected from pipeline structural damage can be extracted based on the Hilbert transform. First, the signal envelope is obtained, with the wave packet center time being the arrival time. For the extracted T-mode and F-mode signals, their signal envelopes are calculated to obtain the signal propagation time. The T-mode signal envelope can determine the location of axisymmetric features, such as welds; the F-mode signal envelope determines the location of non-axisymmetric simulated damage. Based on the T(0,1) mode group velocity and the F(n,2) mode group velocity, the axial distances between the pipeline structural damage and the sensor rings are obtained as follows: in: The center time of the wave packet. For the T-mode group velocity, For the F-mode group velocity, The axial distance of the axisymmetric damage. This represents the axial distance of non-axisymmetric damage.

[0038] Ultrasonic guided wave-based pipeline inspection technology enables large-scale, efficient inspection and can detect areas inaccessible by conventional methods, thus holding broad application prospects in the field of in-service inspection of industrial pipelines. Similar to conventional ultrasonic testing technology, the center excitation frequency of the ultrasonic wave has a significant impact on the guided wave inspection results: on the one hand, higher frequencies result in shorter wavelengths, higher resolution, and higher sensitivity for detecting minute defects; on the other hand, the propagation attenuation of the guided wave in the pipeline increases with increasing frequency, thus reducing the detectable range. Therefore, operators need to balance the detectable distance and detection resolution when selecting the excitation frequency. Guided wave focusing technology can improve the detectable distance at higher excitation frequencies through energy focusing, achieving a balance between detection range and detection resolution.

[0039] Common-source synthetic focusing technology does not rely on expensive multi-channel phased array excitation sources, but rather on an array signal post-processing algorithm. Therefore, compared to active focusing, common-source synthetic focusing offers greater cost control and is simpler to operate. The detection steps of common-source synthetic focusing technology include: (1) An axisymmetric mode is excited in the pipe; (2) The guided wave signal is received by an array of sensors arranged along the circumference of the pipe; (3) Process the acquired array signals to generate defect images.

[0040] The array signal post-processing, as the core step in synthesis and focusing, can be further subdivided into: (1) Separate the modes corresponding to different circumferential orders, such as Figure 4 As shown; (2) Apply phase shift to each modal component to complete the “backpropagation” of the signal; (3) Convert the signal after "backpropagation" into a defect image. In cylindrical coordinates, the axial position is z, and the circumferential angle is... Pipeline guided wave signal It can be decomposed into a linear superposition of multiple modes: in: and Representing the first Clan The amplitude and wavenumber of the first-order modal components both vary with the angular frequency. Changes. This will describe the relationship between wavenumber and frequency. When plotted in the frequency-wavenumber domain, this becomes the dispersion curve.

[0041] When excited by a single T(0,1) mode, the reflected echo of the guided wave encountering damage contains only the T(0,1) and F(n,2) modes. The above equation can be simplified to: in: and These represent the amplitude and wavenumber of the nth modal component, respectively; a two-dimensional Fourier transform of the signal can extract the modal components of the guided wave: in: express The expression in the two-dimensional frequency domain, and Representing the first First mode at angular frequency The amplitude and phase below, superscript This serves as the variable identifier for the corresponding parameter during the integration process.

[0042] and The dispersion curve, which relates wavenumber to angular frequency, can be obtained through theoretical formulas and numerical solutions given the relevant pipe parameters. It can be seen that the influence of the axial position z on the guided wave signal lies in applying a certain phase shift to each mode. Therefore, given... Under certain conditions, a phase shift can be artificially applied during signal post-processing. This is to simulate the process of guided waves propagating "forward" or "backward" over a certain axial distance Δz.

[0043] For defect echoes, if the initial signal of the reflected guided wave at its source (i.e., the defect location) can be obtained through "reverse propagation," the diffusion effect of the defect echo during propagation can be compensated, achieving the convergence of acoustic energy in the spatial domain. In guided wave detection, different angular frequencies... In this case, the artificially applied phase shift can be adjusted according to the modal dispersion curves, thus enabling synthetic focusing to compensate for the dispersion effect during guided wave propagation and achieve the convergence of acoustic energy in the time domain. The specific operational steps for pipe guided wave synthetic focusing can be divided into: In axial position Signal measured at Its expression in the order-frequency domain is obtained by two-dimensional Fourier transform: ; right Apply phase shift to obtain axial position The expression of the guided wave signal in the order-frequency domain: Will Axial position is obtained by two-dimensional inverse Fourier transform. Guided wave signal at the location .

[0044] Wherein, if the axial position z is relative to the original signal receiving position "Forward propagation" simulates the direction of sound wave propagation along the direction of sound wave propagation by calculating the situation after the sound wave continues to propagate forward a certain distance; if the axial position is relative to the original signal receiving position... "Reverse propagation," which is the backward direction along the direction of sound wave propagation, simulates the distribution of the sound signal when the sound wave is in another axial position before reaching the current receiving position. Generally, "forward propagation" is required when calculating the incident wave signal at the target position, while "reverse propagation" is required when calculating the initial reflected wave signal at the defect position.

[0045] The axial position z= Guided wave signals collected at location After synthesized focused backpropagation, z= in the direction of the echo source is obtained. The acoustic signal at that location is expressed as follows: in, and These represent the two-dimensional Fourier transform and the two-dimensional inverse Fourier transform, respectively. By changing... The value is used to obtain the distribution information of acoustic signals within a certain axial range.

[0046] To further obtain defect images, an integral superposition method is used: only a certain axial interval is extracted from each time step. The guided wave signal is then extracted, and the absolute value of the extracted guided wave signal is integrated along the time axis to obtain information about the spatial coordinates. Image of the distributed defects. Where: and These represent the shear wave velocity and the shear wave wavelength at the center frequency, respectively. The number of cycles of the excitation signal: in: Let be the center excitation frequency of the incident wave. The above is based on a width of... The rectangular window edge is averaged along the z-axis, and the axial resolution of the final image will increase with... The value decreases as it increases. Here... This refers to the time-domain envelope broadening of the excitation signal.

[0047] Therefore, in axial position Signal measured at In the order-frequency domain, it is represented as Applying a phase shift to it yields... The expression of the guided wave in the order-frequency domain: Will Obtained through two-dimensional inverse Fourier transform waveguide signal By superimposing them along the axis, we can obtain information about the spatial coordinates. Image of the distribution of defects.

[0048] The general idea of ​​the deconvolution image reconstruction algorithm is as follows: calculate the acoustic signal distribution of the incident guided wave and the reflected guided wave at the target position, and then use the incident guided wave signal as a reference to perform deconvolution operation on the reflected guided wave signal to extract the reflection coefficient at the target position. The final reflection coefficient distribution map will reflect the location, size and shape of the defect.

[0049] S103, Construct a dynamic corrosion monitoring model to achieve quantitative assessment of corrosion area loss and corrosion rate at the defect location and to provide pipeline prediction and early warning.

[0050] In this embodiment, to meet the needs of system testing and data analysis, a piezoelectric shear transducer ring is attached to a seamless carbon steel pipe in a non-destructive state to build a long-distance pipeline ultrasonic guided wave monitoring system. This system mainly consists of an oscilloscope, DC power supply, PC, pipeline piezoelectric ultrasonic guided wave monitoring instrument, coaxial shielded cable, piezoelectric sensor ring, and the pipeline to be tested. Figure 5 As shown.

[0051] In the experimental system platform, the piezoelectric sensor ring excites ultrasonic guided waves under the excitation of the long-distance pipeline monitoring system instrument. The guided wave signal propagates in the pipeline structure under test, and is received by the sensor after encountering simulated damage and weld reflection. The waveform data is transmitted to the host computer for analysis via gigabit Ethernet.

[0052] The selection of excitation signal parameters is based on the pipe size and dispersion curve characteristics. Common excitation signal choices include tip pulses, square wave pulses, and window function modulation signals. Comparative analysis shows that while tip pulses offer a simple excitation circuit and high signal-to-noise ratio, their wide spectrum leads to severe dispersion; square wave pulses improve the spectral width but still exhibit significant dispersion; window function modulation signals suppress dispersion by optimizing sidelobe energy. Through comparative verification, the Hanning window modulation signal was ultimately chosen, as its concentrated main lobe energy and narrow bandwidth effectively reduce dispersion effects during guided wave propagation.

[0053] Secondly, chemical corrosion is used at selected locations on the pipeline to quantitatively and sequentially induce damage propagation. After each corrosion cycle, the system is activated to monitor pipeline corrosion damage, record and save monitoring data, and use ultrasonic testing to detect the actual loss of the corroded area, as well as record the number of corrosion cycles.

[0054] Finally, the corrosion loss area and corrosion loss rate were calculated. Combined with historical monitoring datasets, the evolution trend of corrosion characteristics was studied. Since both corrosion area and corrosion rate conform to a univariate model, the least squares method was used to construct an area loss monitoring model and a corrosion rate monitoring model. The models were then updated and iterated according to the increase in monitoring tasks.

[0055] To address the characteristics of limited data types, small quantities, and monotonous trends in ultrasonic guided wave monitoring results for long-distance pipelines, this paper utilizes grey system theory to establish a systematic grey model by analyzing corrosion loss characteristics represented by historical monitoring data, thereby predicting future trends in corrosion loss.

[0056] Grey prediction model GM(1,1) modeling: Suppose the original non-negative data sequence It can be represented as: make For sequence The cumulative generation, i.e.: but A single accumulation generates a sequence It can be represented as: Let the nearest neighbor mean sequence of the original sequence be... : The formula for calculating its background value is: The expression for the first-order differential equation model of the grey prediction model is: In the formula: , There are two parameters to be estimated. For development coefficient, For gray level, The gray derivative, This is the value for whitening the background.

[0057] Albinotype of GM(1,1): like The gray derivative corresponds to , This is the whitening background value, corresponding to The whitening equation corresponding to the differential equation is: To estimate the parameters, the discrete equations are rewritten in the following form: set up Let be the vector of parameters to be estimated, then: Solving using the least squares method, we have: in: , ; After solving the matrix Substituting the values ​​and solving the differential equation, we obtain the solution to the prediction model equation: In the formula: for The predicted value of the sequence is generated by accumulating the values ​​at each time step; After performing cumulative subtraction and restoration on the above formula, the predicted value of the original data sequence is obtained: Pick Substituting into the above formula yields the original data column. The restoring response: .

[0058] Model accuracy verification: Grey prediction tests generally include residual and posterior error tests. The residual test is performed by restoring the sequence after the cumulative subtraction. With the original sequence The difference is processed as follows: Residual: Relative error: when At that time, it is considered that the general requirements can be met, while when If the accuracy is high, then it is considered to be high.

[0059] The posterior difference test is based on the ratio of posterior differences. C and small error probability P To verify the accuracy of the model.

[0060] The posterior difference ratio is: The small probability error is: in: and These are the variances of the original data column and the residuals, respectively. It is the average value of the residuals.

[0061] If both the residual test and the posterior error test show that the predicted results are satisfactory, then this prediction model can be used for prediction; otherwise, the model needs to be improved to enhance its prediction accuracy. The modeling flowchart is as follows: Figure 6 As shown.

[0062] The residual correction method mainly uses the difference between the original data and the fitted value as the residual sequence, and uses this as the original sequence to build a GM(1,1) model to fit the residual sequence. The residual correction model built in this way can effectively improve the prediction accuracy.

[0063] When the original data exhibits nonlinearity and volatility, the GM(1,1) model performs poorly in predicting the system's subsequent trends and lifetime values. However, by improving the smoothness and background values ​​of the original data and then applying the GM(1,2) model based on the correction effect of the factor sequence, it can better reflect the volatility trend of the system data sequence, and its overall accuracy is further improved compared to the GM(1,1) model. Finally, the GM(1,2) model established through residual correction can achieve high prediction accuracy and meet the requirements for predicting the changing trends and subsequent lifetime values ​​of lifetime data.

[0064] Finally, the data from the corrosion propagation experiment were divided into two parts according to time sequence: the earlier part was historical data, and the later part was data to be predicted. The historical data was analyzed to establish a grey prediction model, which was then used to predict the data to be predicted, and the model's effectiveness was verified.

[0065] To realize the application of the above-mentioned pipeline corrosion monitoring method based on ultrasonic guided waves, considering the actual conditions of the two selected pipeline sites and the specific requirements for signal acquisition and transmission, and to ensure the safe and reliable operation of the monitoring system, the installation and commissioning of the monitoring system must be carried out in three steps: preliminary planning, actual installation, and subsequent commissioning.

[0066] To meet the system's requirement for real-time monitoring of corrosion in the pipeline, a ring-shaped piezoelectric sensor was installed using adhesive bonding at the midpoint of the pipeline. Utilizing the bidirectional propagation characteristics of ultrasonic guided waves, online monitoring of corrosion was conducted on a long, bidirectional section of the liquid chlorine pipeline.

[0067] For the pre-determined sensor installation section, first remove the outermost pipe insulation layer to expose the pipe at the installation location. Further remove any surface paint and other materials from the exposed area to ensure the pipe surface in direct contact with the sensor is clean and flat. Select a flat area within the exposed pipe section to plan the sensor installation location. Use a caulking gun to apply an appropriate amount of DP490 adhesive to each sensor unit location, then attach the sensor to the designated installation position on the pipe and tighten the bolts. Use polyurethane sealant to seal the sensor probe edges and cable connectors for moisture and water protection. Use stainless steel clamps to firmly secure the sensor edges and cable connectors to the pipe. Figure 7 The sensor has been installed.

[0068] After installing the sensors in their designated locations and completing cable connections and securing them, fix the monitoring instruments on-site and connect them to the sensors. Place the host computer with the monitoring system software installed in the on-site server rack, connect the gigabit network cable from the equipment, and install a monitor. The sensor monitoring information will be displayed on the monitor after the monitoring task is completed. At this point, the on-site equipment installation is complete.

[0069] After the equipment is installed on site, the monitoring system needs to be debugged to ensure its smooth operation. The specific debugging steps are as follows: (1) Turn on the air switch and power supply of the on-site testing equipment to power on the testing equipment; (2) Modify the monitoring task parameter file, start the host computer, and run the monitoring system software; (3) Click "Start" on the host computer monitoring page to start the monitoring system; (4) Observe the real-time signal display on the monitoring page and check whether the signal is updated according to the monitoring task parameters.

[0070] After the above debugging process, it was found that the host computer could normally control the detection equipment to perform pipeline corrosion monitoring tasks, and the collected signals and processing results could be displayed and updated in real time. The on-site display is as follows: Figure 8As shown, this indicates that the system debugging work has been completed and the monitoring system is now operating normally.

[0071] After the equipment is installed on-site, the epoxy resin coupling between the sensor and the pipeline is cured by heating. After curing, manual commissioning tests are performed, and automatic test tasks are set up for subsequent long-term pipeline corrosion monitoring.

[0072] After curing is completed, the system functions are debugged by adjusting parameters such as ultrasonic guided wave excitation frequency, amplitude, and acquisition amplification.

[0073] Five excitation frequencies of 40kHz, 60kHz, 80kHz, 100kHz, and 120kHz were selected for testing. Analysis of the acquired signals showed that the echo signals in the 40kHz and 60kHz test signals were not obvious, while the 80kHz, 100kHz, and 120kHz test signals showed relatively obvious echo patterns, with the 80kHz signal exhibiting the most prominent echo characteristics and the highest signal-to-noise ratio. Figure 9 The 80kHz signal and detection results are shown, where (a) is the response signal excited by P5; (b) is the detection result; (c) is the position magnification of the echo signal; and (d) is the position magnification of the echo packet.

[0074] An 80kHz center frequency, 90V excitation, and 40dB acquisition gain were selected as the parameters for subsequent automatic testing. Monitoring data at 4 AM daily was chosen to minimize the impact of variables. Figure 10 Partial test results are shown. It can be seen that although the signal noise levels are different (which is related to the ambient noise and the working status of the pipeline and the equipment near the pipeline), the positions of the torsional mode (blue line) and bending mode (red line) of the signal echo remain basically unchanged. This indicates that the source of the echo signal reflection is stable, and no significant signal changes occurred during the monitoring process, indicating that the pipeline health status is stable.

[0075] Because long-distance pipeline ultrasonic guided wave monitoring systems have a near-field blind zone at the sensor ring location, local wall thickness measurement is used to compensate for this deficiency in order to meet the requirements of full-area monitoring. Based on the original pipe wall thickness, the excitation signal frequency for wall thickness monitoring is set to 405 kHz. Figure 11 The signal displayed during a measurement in the monitoring process showed a large amplitude and the spectrum was as expected, indicating that the wall thickness was calculated accurately.

[0076] Corresponding to the pipeline corrosion monitoring method based on ultrasonic guided waves provided in the above embodiments, this application also provides an embodiment of a pipeline corrosion monitoring system based on ultrasonic guided waves.

[0077] See Figure 12 A pipeline corrosion monitoring system 20 based on ultrasonic guided waves includes: The mapping relationship establishment module 201 is used to determine the propagation law of guided waves and screen the optimal detection mode, and establish the mapping relationship between pipeline defect parameters and guided wave signals; The defect location module 202 is used to extract damage feature signals through modal order separation, and combine them with common-source synthetic focusing to achieve damage imaging, thereby improving resolution and noise suppression capabilities to determine the defect location in the pipeline. The early warning monitoring module 203 is used to construct a corrosion dynamic monitoring model to realize the quantitative assessment of corrosion area loss and corrosion rate at the defect location and to predict and warn pipelines.

[0078] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects have an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0079] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A pipeline corrosion monitoring method based on ultrasonic guided waves, characterized in that, include: Determine the propagation law of guided waves and select the optimal detection mode, and establish the mapping relationship between pipeline defect parameters and guided wave signals; Damage feature signals are extracted by modal order separation, and damage imaging is achieved by combining common-source synthetic focusing, thereby improving resolution and noise suppression capabilities to determine the location of defects in the pipeline. A dynamic corrosion monitoring model is constructed to achieve quantitative assessment of corrosion area loss and corrosion rate at the defect locations and to provide pipeline prediction and early warning.

2. The pipeline corrosion monitoring method based on ultrasonic guided waves according to claim 1, characterized in that, The process of determining the guided wave propagation law and selecting the optimal detection mode, and establishing the mapping relationship between pipeline defect parameters and guided wave signals, includes: Using an infinitely long isotropic hollow circular tube as the object, we completed the waveguide classification and clarified the displacement field properties of each mode; Analyze the waveguide propagation characteristics and determine the optimal excitation mode and the best excitation frequency band based on the four mode selection criteria; The propagation of guided waves in a pipe with typical defects was simulated. The circumferential, radial and axial parameters of the defects were changed, and the corresponding echo signals were collected. The quantitative mapping relationship between the geometric parameters of the defects and the guided wave signals was analyzed and established to realize the inversion of the axial defect length.

3. The pipeline corrosion monitoring method based on ultrasonic guided waves according to claim 2, characterized in that, The process of classifying waveguides and defining the displacement field properties of each mode, using an infinitely long isotropic hollow circular tube as the object, includes: A complete dispersion equation describing axisymmetric and non-axisymmetric modal guided waves has been established: In the formula , It is the Lame constant. It is a displacement vector. For density, For time, For Hamiltonian operators, For the Laplace operator, For displacement divergence, The gradient of volumetric strain, It is the acceleration of a point mass; displacement vector It can be decomposed into an expansion scalar potential function. and isochoric vector potential function : in: F Coordinate vector and time The function, the and satisfy: in: For longitudinal wave velocity, , For transverse wave velocity, ; The solution to the above equation can be expressed as: Where: integers Indicates the circumferential order of the guided wave. For radial coordinates in a cylindrical coordinate system, Angular coordinates in a cylindrical coordinate system For the axial coordinates of the cylindrical coordinate system. The radial component of the magnetic field in cylindrical coordinates. The angular component of the magnetic field in cylindrical coordinates. This represents the axial component of the magnetic field in cylindrical coordinates. Angular frequency, The axial wave number, , , and It is a radially undetermined function, only related to... Related; Based on the direction of propagation, guided waves are classified into circumferential guided waves and cylindrical guided waves. Based on modal characteristics, they are classified into three categories: longitudinal, torsional, and bending, and the displacement field characteristics of each mode are clearly defined.

4. The pipeline corrosion monitoring method based on ultrasonic guided waves according to claim 3, characterized in that, The analysis of guided wave propagation characteristics determines the optimal excitation mode and the best excitation frequency band based on four mode selection criteria, including: Solve the guided wave dispersion curve of the pipeline and analyze the influence of dispersion characteristics and multi-mode characteristics on the detection. Based on the criteria of modal separability, non-dispersion, axisymmetry, and damage-sensitive mode selection, the optimal excitation mode is selected as torsion from the dispersion curve; By combining propagation distance and damage resolution, the optimal excitation frequency band was determined through simulation and experimental verification.

5. The pipeline corrosion monitoring method based on ultrasonic guided waves according to claim 1, characterized in that, The method of extracting damage feature signals through modal order separation and combining them with common-source synthetic focusing to achieve damage imaging, thereby improving resolution and noise suppression capabilities, and determining the location of defects in the pipeline includes: A sensor array is arranged around the pipe to collect response signals, separating axisymmetric and non-axisymmetric modes, extracting the signal envelope and determining the wave packet arrival time, and combining the group velocities of the two types of modes to calculate the axial distance from the axisymmetric and non-axisymmetric defects to the sensor ring. A single mode is excited, and the defect reflection echo is received through an array sensor. The echo signal is decomposed into modal components of each order by a two-dimensional Fourier transform, thus completing mode separation. The wavenumbers of each mode are obtained from the dispersion curve, and a phase shift is applied to the mode components to simulate the reverse propagation of the guided wave signal and compensate for the diffusion and dispersion effects during propagation. The phase-shifted signal is restored to the spatial domain by performing a two-dimensional inverse Fourier transform. The spatial image of the defect is generated by the integral superposition method or the reflection coefficient distribution is extracted by the deconvolution algorithm to restore the location, size and shape of the defect.

6. The pipeline corrosion monitoring method based on ultrasonic guided waves according to claim 5, characterized in that, The process of arranging a sensor array around the pipe to collect response signals, separating axisymmetric and non-axisymmetric modes, extracting the signal envelope and determining the wave packet arrival time, and calculating the axial distance from the axisymmetric and non-axisymmetric defects to the sensor ring by combining the group velocities of the two types of modes includes: Based on mode separation technology, axisymmetric mode signals are synthesized by a circumferentially uniformly arranged sensor array to suppress non-axisymmetric interference; The bending mode components are separated from the original signal, and the circumferential distribution characteristics of the defects are constructed by combining the array phase difference information: In the formula, 、 These are the extracted torsional mode and nth-order bending mode signals, respectively. It is the collected response signal, the It is collected by M piezoelectric sensors on the piezoelectric sensor ring. 、 The expression is as follows: in: For the first Signals collected by a sensor ; The extracted T-mode and F-mode signal envelopes are used to determine the location of axisymmetric feature structures, and the F-mode signal envelope determines the location of non-axisymmetric simulated damage. Based on the T-mode group velocity and the F-mode group velocity, the axial distances of axisymmetric damage and non-axisymmetric damage between the pipe structure damage distance sensor rings are obtained: in: The center time of the wave packet. For the T-mode group velocity, For the F-mode group velocity, The axial distance of the axisymmetric damage. This represents the axial distance of non-axisymmetric damage.

7. The pipeline corrosion monitoring method based on ultrasonic guided waves according to claim 6, characterized in that, The excitation single mode receives defect reflection echoes through an array sensor, and performs a two-dimensional Fourier transform on the echo signal to decompose it into modal components of various orders, thus completing mode separation, including: In cylindrical coordinates, the axial position is z, and the circumferential angle is... Pipeline guided wave signal It can be decomposed into a linear superposition of multiple modes: in: and Representing the first Clan The amplitude and wavenumber of the first-order modal components both vary with the angular frequency. change; When excited by a single T-mode, the reflected echo of the guided wave encountering damage contains only the T-mode and F-mode, and the above equation can be simplified to: in: and These represent the amplitude and wavenumber of the nth modal component, respectively. Performing a two-dimensional Fourier transform on the signal can extract the modal components of the guided wave: in, express The expression in the two-dimensional frequency domain, and Representing the first First mode at angular frequency The amplitude and phase below, superscript This serves as the variable identifier for the corresponding parameter during the integration process.

8. The pipeline corrosion monitoring method based on ultrasonic guided waves according to claim 7, characterized in that, The process of performing a two-dimensional inverse Fourier transform on the phase-shifted signal to restore it to the spatial domain, generating a spatial image of the defect using the integral superposition method or extracting the reflection coefficient distribution using the deconvolution algorithm, and restoring the location, size, and shape of the defect includes: In axial position Signal measured at Its expression in the order-frequency domain is obtained by two-dimensional Fourier transform: ; right Apply phase shift to obtain axial position The expression of the guided wave signal in the order-frequency domain: Will Axial position is obtained by two-dimensional inverse Fourier transform. Guided wave signal at the location ; The axial position z= Guided wave signals collected at location After synthesized focused backpropagation, z= in the direction of the echo source is obtained. The acoustic signal at that location is expressed as follows: in, and These represent the two-dimensional Fourier transform and the two-dimensional inverse Fourier transform, respectively, by changing... The value is used to obtain the distribution information of acoustic signals within a certain axial range; Only a certain axial interval is extracted from each time step. The guided wave signal is then extracted, and the absolute value of the extracted guided wave signal is integrated along the time axis to obtain information about the spatial coordinates. Image of the distributed defects. Where: and These represent the shear wave velocity and the shear wave wavelength at the center frequency, respectively. The number of cycles of the excitation signal: in: The center excitation frequency of the incident wave; In axial position Signal measured at In the order-frequency domain, it is represented as Applying a phase shift to it yields The expression of the guided wave in the order-frequency domain: Will Obtained through two-dimensional inverse Fourier transform waveguide signal ; By superimposing along the axis, we can obtain information about the spatial coordinates. Image of the distribution of defects; Deconvolution image reconstruction involves calculating the acoustic signal distribution of the incident and reflected guided waves at the target location, then using the incident guided wave signal as a reference, performing deconvolution on the reflected guided wave signal to extract the reflection coefficient at the target location. The final reflection coefficient distribution map will reflect the location, size, and shape of the defect.

9. The pipeline corrosion monitoring method based on ultrasonic guided waves according to claim 1, characterized in that, The construction of the corrosion dynamic monitoring model, which enables quantitative assessment of corrosion area loss and corrosion rate at the defect location and provides pipeline prediction and early warning, includes: At a designated location on the pipeline, chemical corrosion is used to quantitatively and progressively increase damage. After each corrosion, a monitoring system is activated to collect guided wave data, and ultrasonic testing is used to measure the area loss of the corrosion zone and record the number of corrosion cycles. The area loss and corrosion rate of each corrosion were calculated based on the measured data, and the data were organized into a corrosion monitoring dataset in chronological order. The corrosion dynamic monitoring model is constructed by selecting a basic model based on the characteristics of corrosion monitoring data, and the corrosion dynamic monitoring model is trained, optimized and verified using corrosion monitoring data. By continuously integrating newly added corrosion monitoring data into the dataset and iteratively optimizing model parameters, long-term dynamic monitoring and early warning of pipeline corrosion status can be achieved.

10. The pipeline corrosion monitoring method based on ultrasonic guided waves according to claim 9, characterized in that, The process of selecting a basic model based on the characteristics of corrosion monitoring data to construct the corrosion dynamic monitoring model, and then training, optimizing, and validating the corrosion dynamic monitoring model using corrosion monitoring data, includes: By analyzing the corrosion loss characterized by corrosion monitoring data, a predictive model is established to predict the future trend of corrosion loss. Suppose the original non-negative data sequence It can be represented as: make For sequence The cumulative generation, i.e.: but A single accumulation generates a sequence It can be represented as: Let the nearest neighbor mean sequence of the original sequence be... : The formula for calculating its background value is: The expression for the first-order differential equation model of the prediction model is: In the formula: , There are two parameters to be estimated. For development coefficient, The degree of gray effect, The gray derivative, This is the value for whitening the background; like The gray derivative corresponds to , This is the whitening background value, corresponding to The whitening equation corresponding to the differential equation is: To estimate the parameters, the discrete equations are rewritten in the following form: set up Let be the vector of parameters to be estimated, then: Solving using the least squares method, we have: in: , ; After solving the matrix Substituting the values ​​and solving the differential equation, we obtain the solution to the prediction model equation: In the formula: for The predicted value of the sequence is generated by accumulating the values ​​at each time step; After performing cumulative subtraction and restoration on the above formula, the predicted value of the original data sequence is obtained: Pick Substituting into the above formula yields the original data column. The restoring response: The residual and posterior difference tests are performed by subtracting the above-mentioned subtracted and restored sequence from the original sequence. If both the residual test and the posterior error test result in a satisfactory prediction, then the prediction model can be used for prediction; otherwise, the model needs to be improved to enhance its prediction accuracy.

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