Corrosion defect identification method and system based on multi-mode mechanical wave characteristics

Abstract

The invention discloses a corrosion defect identification method and system based on multi-modal mechanical wave characteristics, and the method comprises the steps: exciting a multi-modal mechanical wave, and planning a mechanical wave propagation path; arranging a sensor array, and collecting and synchronously processing a multi-mode mechanical wave signal; performing time-frequency analysis on the acquired multi-modal mechanical wave signal, and extracting the phase change characteristics of the multi-modal mechanical wave; based on the phase change characteristics of the multi-modal mechanical waves, positioning corrosion defect positions in combination with the propagation path information of the mechanical waves; and identifying and classifying different types of corrosion defects in combination with phase change, amplitude attenuation and propagation path offset information. According to the invention, through excitation and propagation path planning of multi-modal mechanical waves, a material detection area is fully covered, the possibility of signal distortion and missing detection is reduced, and through time-frequency analysis and phase difference analysis, the identification capability of tiny defects is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of nondestructive testing of materials, and in particular to a corrosion defect identification method and system based on multi-modal mechanical wave characteristics. Background Art

[0002] In recent years, with the aging of industrial equipment and the increasingly harsh service environment, material corrosion has become one of the important factors affecting the safe operation of equipment. Corrosion not only reduces the physical properties of materials, resulting in a decrease in structural strength and durability, but may also cause catastrophic accidents, causing huge economic losses and social impacts. Therefore, the effective detection and evaluation of corrosion defects has become a key technical means to ensure the long-term stable operation of industrial facilities.

[0003] Traditional methods usually only use a single mode of mechanical waves (such as ultrasonic waves, electromagnetic waves, etc.) for detection. When faced with uneven material thickness or complex structures, this method is prone to signal distortion due to reflection, refraction and other factors, thus affecting the accuracy of defect identification. Single mode mechanical waves are difficult to fully cover the defect information inside the material, limiting the comprehensiveness and reliability of detection. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a corrosion defect identification method and system based on multi-modal mechanical wave characteristics to solve the problem that single mechanical wave defect detection is difficult to fully cover the interior and surface of the material.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a corrosion defect identification method based on multimodal mechanical wave characteristics, comprising:

[0009] Excite multi-modal mechanical waves and plan the propagation paths of mechanical waves;

[0010] Arrange the sensor array according to the planned mechanical wave propagation path to collect and synchronously process multi-modal mechanical wave signals;

[0011] Perform time-frequency analysis on the collected multi-modal mechanical wave signals to extract the phase change characteristics of the multi-modal mechanical waves;

[0012] Based on the phase change characteristics of multi-modal mechanical waves and combined with the mechanical wave propagation path information, the corrosion defect position is located;

[0013] Based on the location of the corrosion defect, different types of corrosion defects are identified and classified by combining phase change, amplitude attenuation and propagation path deviation information.

[0014] As a preferred solution of the corrosion defect identification method based on multimodal mechanical wave characteristics described in the present invention, wherein:

[0015] The exciting multi-modal mechanical waves and planning the mechanical wave propagation path include the following steps:

[0016] Determine the thickness distribution of the material using an ultrasonic thickness gauge;

[0017] Use laser scanners to perform three-dimensional modeling of the material geometry and obtain the material geometric properties;

[0018] Based on the material thickness distribution, the wavelength range that matches the material thickness is calculated;

[0019] Low-frequency longitudinal waves, high-frequency transverse waves and surface waves are combined into multi-modal mechanical waves;

[0020] The reflection coefficients of mechanical waves of different modes at the material boundary are calculated through finite element simulation, and the wave mode with the highest reflection value is selected;

[0021] Use a transducer to excite multi-modal mechanical waves and adjust the excitation angle;

[0022] According to the geometric shape and thickness distribution of the material, finite element analysis software is used to simulate the propagation path of mechanical waves inside the material;

[0023] The acquisition wave starts from the excitation point, passes through reflection or refraction inside the material, and reaches the propagation path of each sensor. Each path corresponds to a sensor position;

[0024] Based on the simulation results, the excitation points are adjusted to fully cover the material detection area.

[0025] As a preferred solution of the corrosion defect identification method based on multimodal mechanical wave characteristics described in the present invention, the arrangement of the sensor array, the collection and synchronous processing of the multimodal mechanical wave signal includes the following steps:

[0026] Arrange highly sensitive array ultrasonic sensors on the surface of the material, each sensor captures signals of different mechanical wave modes;

[0027] Adjust the sensor layout according to the multi-modal mechanical wave propagation path and boundary reflection characteristics obtained from finite element simulation;

[0028] The sensor spacing is set based on the multi-modal mechanical wave excitation wavelength;

[0029] According to the geometric characteristics of the material, the sensors are evenly arranged along the surface of the material, and the arrangement density is increased at the places where the geometry changes;

[0030] For materials with uneven thickness and complex structure, place sensors on the side and back of the material;

[0031] Using defect-free and known defect standard samples, multi-modal mechanical waves are excited, response signals are collected through sensors, and the response delay and sensitivity of each sensor are calibrated;

[0032] High-speed acquisition of multi-modal mechanical wave signals;

[0033] Each sensor synchronously and real-time collects the amplitude information, phase information and propagation time of mechanical waves of different modes;

[0034] During the acquisition process, a low-pass filter is used to remove high-frequency environmental noise, and a band-pass filter is used to perform directional filtering on the frequency range of the excitation wave;

[0035] The amplitude information, phase information and propagation time of the collected multi-modal mechanical waves are normalized.

[0036] As a preferred solution of the corrosion defect identification method based on multimodal mechanical wave characteristics described in the present invention, wherein: the time-frequency analysis of the collected multimodal mechanical wave signal to extract the multimodal mechanical wave phase change characteristics includes the following steps:

[0037] Using wavelet transform for time domain and frequency domain analysis, the expression is as follows:

[0038]

[0039] Among them, W(a,b) is the coefficient after wavelet transformation, a is the scale parameter, b is the translation parameter, x(t) is the original multimodal mechanical wave propagation signal at time t, ψ(t) is the wavelet basis function, ψ * (t) is the conjugate complex number of the wavelet basis function, is the local feature of the signal at scale a, the translation is b, j is the imaginary unit, π is the circumference of a circle, ω0 is the center frequency of the wavelet, and dt represents the integration over time t;

[0040] Extract the instantaneous phase of the multimodal mechanical wave signal after wavelet transform processing;

[0041] Calculate the phase difference between adjacent moments and find the phase mutation point;

[0042] The phase change threshold Δφ is set based on experimental data according to the wave propagation characteristics of the material and the noise level. th , when the phase difference Δφ(t) between adjacent moments is greater than Δφ th When , it is determined as a phase mutation point.

[0043] As a preferred solution of the corrosion defect identification method based on multimodal mechanical wave characteristics of the present invention, the method of locating the corrosion defect position based on the multimodal mechanical wave phase change characteristics and the mechanical wave propagation path information includes the following steps:

[0044] Combined with the simulation results of the propagation path of mechanical waves using finite element analysis software, the propagation speed of mechanical waves of different modes in the material is determined through experimental measurements based on the mechanical properties of the material and the mode of the wave;

[0045] Each sensor simulates the propagation path length and wave speed through mechanical waves, and calculates the time from the excitation of the mechanical wave to the detection of the sensor;

[0046] A reference sensor is arranged at the mechanical wave excitation point, and the time when the reference sensor receives the mechanical wave is the reference time;

[0047] The time when the other sensors receive the mechanical wave is compared with the reference time to obtain the time difference between the reference sensor and the other sensors;

[0048] Based on the 3D modeling structure of the material, the positioning coordinates (x i ,y i ,z i );

[0049] With the coordinates of each sensor as the center and the product of the time difference and the wave speed as the radius, draw a circle. The defect position is at the intersection of each circle. The expression is:

[0050]

[0051] Among them, (x f ,y f ,z f ) is the defect position coordinate, v is the mechanical wave propagation speed in the material, ΔT i is the time difference between the i-th sensor and the reference sensor, i is the sensor number index;

[0052] The nonlinear equation solving algorithm is used to minimize the errors of multiple circles and obtain the location of the corrosion defect.

[0053] As a preferred solution of the corrosion defect identification method based on multimodal mechanical wave characteristics described in the present invention, wherein: the nonlinear equation group solving algorithm is used to minimize the errors of multiple circles to obtain the location of the corrosion defect, including the following steps:

[0054] Based on the least squares method, an objective function is constructed to calculate the square sum of the error S(x f ,y f ,z f ), the expression is as follows:

[0055]

[0056] Where N is the number of sensors;

[0057] Using the gradient descent method, the position coordinates are iteratively updated along the gradient direction by calculating the partial derivative of the objective function with respect to the defect position.

[0058] As a preferred solution of the corrosion defect identification method based on multimodal mechanical wave characteristics described in the present invention, the method of identifying the type of corrosion defect by combining phase change, amplitude attenuation and propagation path deviation information includes the following steps:

[0059] Extract the characteristics of mechanical waves propagating in materials;

[0060] The amplitude attenuation value of the mechanical wave is obtained by calculating the difference between the amplitude when the mechanical wave is excited and the amplitude when the sensor receives it;

[0061] By calculating the product of the time difference between the mechanical wave reaching the same position with and without defects and the wave speed, the propagation path offset value and phase are obtained;

[0062] The amplitude attenuation value, propagation path deviation value and phase difference obtained by each sensor are taken as a data sample;

[0063] By controlling the experimental conditions, samples with different types of corrosion defects are manufactured, and the types of corrosion defects and corresponding features are detected, and the features are labeled with corrosion defect categories;

[0064] The radial basis function is used to map the input features to a high-dimensional space. The decision function f(B) of the support vector machine model is expressed as follows by calculating the similarity between the new sample B1 and the training sample B2:

[0065]

[0066] Among them, sign is the sign function, H is the number of samples, j is the sample number index, α is the Lagrange multiplier, C jis the category label of the jth training sample, K(B1,B2) is the radial basis function, and d is the bias term;

[0067] K(B1,B2)=exp(-γ||B1-B2|| 2 );

[0068] Where γ is the width of the radial basis function, ||B1-B2|| is the Euclidean distance between the new sample B1 and the training sample B2;

[0069] Use the labeled feature data to train the support vector machine model and optimize the regularization parameter D and the width γ of the radial basis function in the support vector machine model;

[0070] The regularization parameter D and the width γ of the radial basis function are optimized through cross-validation;

[0071] After the training is completed, the phase difference, amplitude attenuation and propagation path deviation of the detected multimodal mechanical waves are input into the support vector machine model as comprehensive features for prediction, and the category label of the corrosion defect is output.

[0072] In a second aspect, the present invention provides a corrosion defect identification system based on multimodal mechanical wave characteristics, comprising:

[0073] Mechanical wave excitation module, used to excite multi-modal mechanical waves and plan the mechanical wave propagation path;

[0074] A signal acquisition module is used to arrange the sensor array according to the planned mechanical wave propagation path, collect and synchronously process multi-modal mechanical wave signals;

[0075] A feature extraction module is used to perform time-frequency analysis on the collected multi-modal mechanical wave signals and extract the phase change characteristics of the multi-modal mechanical waves;

[0076] A positioning module is used to locate the corrosion defect position based on the phase change characteristics of the multi-modal mechanical wave and the mechanical wave propagation path information;

[0077] The recognition module is used to identify and classify different types of corrosion defects based on the location of the corrosion defects combined with the phase change, amplitude attenuation and propagation path deviation information.

[0078] In a third aspect, the present invention provides a computing device, comprising:

[0079] Memory, used to store programs;

[0080] The processor is used to execute the computer executable instructions, which, when executed by the processor, implement the steps of the corrosion defect identification method based on multi-modal mechanical wave characteristics.

[0081] In a fourth aspect, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the corrosion defect identification method based on multimodal mechanical wave characteristics are implemented.

[0082] Beneficial effects of the present invention: The present invention achieves full coverage of the material detection area through the excitation and propagation path planning of multi-modal mechanical waves, reducing the possibility of signal distortion and missed detection; improves the signal quality and signal-to-noise ratio through high-sensitivity sensor arrays and precise signal processing; improves the ability to identify tiny defects through time-frequency analysis and phase difference analysis; improves the positioning accuracy of corrosion defect positions through precise positioning algorithms; and improves the classification accuracy of corrosion defect types through intelligent recognition algorithms. These beneficial effects jointly ensure the high efficiency, accuracy and reliability of corrosion defect detection, and provide strong technical support for the safe operation of industrial equipment and infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0084] Figure 1 A schematic diagram of the basic flow of a corrosion defect identification method based on multimodal mechanical wave characteristics provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0085] 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.

[0086] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0087] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0088] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0089] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0090] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0091] Example 1

[0092] Reference Figure 1 , which is an embodiment of the present invention, provides a corrosion defect identification method based on multi-modal mechanical wave characteristics, comprising:

[0093] S1: Excite multi-modal mechanical waves and plan the propagation path of mechanical waves;

[0094] In the embodiment of the present application, an ultrasonic thickness gauge is used to determine the thickness distribution of the material, especially for complex structures with non-uniform thickness, to ensure that complete thickness data is obtained;

[0095] In the embodiment of the present application, the surface of the material, especially the detection area, is cleaned with a dust-free cloth and a cleaning liquid to remove dust, oil and impurities, thereby reducing the interference of pollutants on the surface of the material with the propagation of mechanical waves;

[0096] In the embodiment of the present application, a laser scanner is used to perform three-dimensional modeling of the geometric shape of the material to obtain the geometric properties of the material;

[0097] In the embodiment of the present application, based on the material thickness distribution, the wavelength range matching the material thickness is calculated, and preferably a wavelength in the range of about 0.5 to 3 times the material thickness is used to ensure effective propagation of the wave in the material;

[0098] In the embodiment of the present application, a relatively low frequency longitudinal wave is used, which has strong penetration and is suitable for internal defect detection, and a relatively high frequency shear wave and surface wave, which has high surface sensitivity and is suitable for the detection of surface and near-surface corrosion defects, is combined into a multi-modal mechanical wave;

[0099] In the embodiment of the present application, the reflection coefficients of mechanical waves of different modes at the material boundary are calculated by finite element simulation, and the wave mode with the highest reflection value is screened out;

[0100] In the embodiment of the present application, in the entire "multi-modal" system, the optimal parameters of each wave mode can be optimized. For example, in a certain wave mode (such as the S0 mode or A0 mode in the surface wave), the mode with the highest reflection coefficient is selected to ensure that the wave mode has the best rebound effect on the material boundary, thereby improving the sensitivity of the detection;

[0101] It should be noted that optimizing a certain wave mode (such as enhancing the boundary bounce value) is not equivalent to abandoning the multi-modal detection method, but is a refinement step in it, aiming to improve the detection capability of each mode within its applicable range;

[0102] In the embodiment of the present application, a transducer is used to excite multimodal mechanical waves, and the excitation angle is adjusted to meet the incident conditions of the waves in the material. For longitudinal waves, the material is injected at 0° to ensure that the waves enter the material vertically and maximize their penetration depth. For transverse waves and surface waves, the waves are injected at 30° to 45° so that the waves can better propagate on the material surface and in thin-walled structures.

[0103] In the embodiment of the present application, according to the geometric shape and thickness distribution of the material, finite element analysis software is used to simulate the propagation path of the mechanical wave inside the material, focusing on the material boundary, corner and curved surface area, optimizing the wave propagation path and avoiding signal loss in these areas;

[0104] In the embodiment of the present application, the propagation path of the acquisition wave starting from the excitation point, through reflection or refraction inside the material, to each sensor, each path corresponds to a sensor position;

[0105] In an embodiment of the present application, the propagation path and three-dimensional model of mechanical waves inside the material are simulated based on finite element analysis software, and areas with thin thickness and complex geometric structure are identified. Based on the optimization of the propagation path by the finite element analysis software, the coverage range and signal reflection intensity are maximized by adjusting the excitation angle and the excitation point position.

[0106] S2: Arrange the sensor array according to the planned mechanical wave propagation path, collect and synchronously process the multi-modal mechanical wave signals;

[0107] In the embodiment of the present application, a highly sensitive array of ultrasonic sensors is arranged on the surface of the material, and each sensor can capture signals of different mechanical wave modes (such as longitudinal waves, transverse waves, surface waves, etc.);

[0108] It should be noted that these sensors should have high frequency response capability, and the bandwidth should cover the frequency range of the excitation wave (e.g., 1 MHz to 10 MHz) to ensure that the complete signal of the multimodal wave can be captured;

[0109] In the embodiment of the present application, the arrangement of sensors is determined based on the multi-modal mechanical wave propagation path and boundary reflection characteristics obtained by finite element simulation;

[0110] In the embodiment of the present application, based on the multimodal mechanical wave excitation wavelength, the sensor spacing is set to 1 / 3 to 1 / 2 of the excitation wavelength to ensure that the wave propagation path in the material can be captured by each sensor without generating signal aliasing;

[0111] In the embodiment of the present application, the propagation path and three-dimensional model of the mechanical wave inside the material are simulated based on the finite element analysis software, and the arrangement density of the sensor is increased in the area where the material thickness changes greatly or the geometric changes are complex;

[0112] In the embodiment of the present application, for materials with uneven thickness and complex structure, sensors are arranged on the side and rear of the material to ensure that multiple reflections of the wave inside the material are fully captured;

[0113] It should be noted that areas with large signal interference, such as signal overlap or interference caused by multiple reflections of mechanical wave signals, should be avoided to improve the quality of signal acquisition in order to ensure sufficient signal acquisition and accurate identification of defects.

[0114] In the embodiment of the present application, defect-free and known defect standard samples are used to excite multimodal mechanical waves, and response signals are collected through sensors. The response delay and sensitivity of each sensor are calibrated to ensure that the data collection of all sensors has consistent timing and response characteristics;

[0115] In the embodiment of the present application, high-speed acquisition of multi-modal mechanical wave signals is performed, and the sampling frequency should be more than 10 times the excitation wave frequency to ensure complete acquisition of the signal;

[0116] In the embodiment of the present application, each sensor synchronously and in real time collects amplitude information, phase information and propagation time of mechanical waves of different modes;

[0117] In the embodiment of the present application, during the acquisition process, a low-pass filter is used to remove high-frequency environmental noise. The cutoff frequency of the filter should be set to 1.5 to 2 times the excitation wave frequency to ensure that effective signals pass through and remove higher-frequency environmental noise. A band-pass filter is used to perform directional filtering on the excitation wave frequency range to avoid interference between low and high frequencies and ensure that the received signal is concentrated in a specific frequency band.

[0118] In the embodiment of the present application, the amplitude information, phase information and propagation time of the collected multi-modal mechanical waves are normalized;

[0119] In the embodiment of the present application, the signal amplitudes of all sensors are adjusted to the same standard range, and a normalized range of 0 to 1 or -1 to 1 is selected;

[0120] In the embodiment of the present application, a reference signal is set, and phase normalization is performed by calculating a phase difference;

[0121] In the embodiment of the present application, the time of all signals is aligned by calculating the arrival time difference of the signals.

[0122] S3: Perform time-frequency analysis on the collected multi-modal mechanical wave signals to extract the phase change characteristics of the multi-modal mechanical waves;

[0123] In the embodiment of the present application, wavelet transform is used to perform time domain and frequency domain analysis, and the expression is as follows:

[0124]

[0125] Among them, W(a,b) is the coefficient after wavelet transformation, which represents the local characteristics of the signal at scale a and displacement b. a is the scale parameter, which controls the compression and stretching of the wavelet. b is the translation parameter, which represents the translation of the wavelet function on the time axis. x(t) is the original multimodal mechanical wave propagation signal at time t. ψ(t) is the wavelet basis function, which represents the mother wavelet function used to decompose the signal. * (t) is the conjugate complex number of the wavelet basis function, is the local feature of the signal at scale a, the translation is b, j is the imaginary unit, π is the circumference of a circle, ω0 is the center frequency of the wavelet, which determines the frequency characteristics of the wavelet, and dt represents the integration of time t;

[0126] In the embodiment of the present application, the instantaneous phase of the multimodal mechanical wave signal after wavelet transform processing is extracted to reflect the propagation characteristics of the wave in the material, and the expression is as follows:

[0127] φ(t)=arg(W(a,b));

[0128] Among them, φ(t) is the instantaneous phase at time t, arg(W(a,b)) is the phase angle of the wavelet transform coefficient;

[0129] It should be noted that in the process of multimodal mechanical wave propagation, when the wave passes through defects inside the material (such as corrosion areas), a significant phase change usually occurs;

[0130] In the embodiment of the present application, a phase mutation detection algorithm is used to accurately calculate the phase difference between adjacent moments and find the phase mutation point. The expression is as follows:

[0131] Δφ(t)=∣φ(t)-φ(t-Δt)∣;

[0132] In the embodiment of the present application, the phase change threshold Δφ is set based on the wave propagation characteristics and noise level of the material through experimental data. th , when the phase difference Δφ(t) between adjacent moments is greater than Δφ th When , it is determined as a phase mutation point.

[0133] S4: Based on the phase change characteristics of multi-modal mechanical waves and combined with the mechanical wave propagation path information, the corrosion defect position is located;

[0134] In the embodiment of the present application, the propagation path simulation results of the mechanical wave are combined with the finite element analysis software, according to the mechanical properties of the material and the wave mode, the elastic modulus E, Poisson's ratio ν and density ρ of the material are obtained by performing tensile and compression tests on the standard components of the material, and the propagation speed of mechanical waves of different modes in the material is determined. The expression is as follows:

[0135]

[0136] v r =0.87v p ;

[0137] Among them, v p is the longitudinal wave velocity, v s is the shear wave velocity, v r is the surface wave velocity;

[0138] In the embodiment of the present application, each sensor calculates the time from the excitation of the mechanical wave to the detection of the sensor by simulating the propagation path length and wave speed of the mechanical wave;

[0139] In the embodiment of the present application, a reference sensor is arranged at the mechanical wave excitation point, and the time when the reference sensor receives the mechanical wave is the reference time;

[0140] In the embodiment of the present application, the time when the other sensors receive the mechanical wave is compared with the reference time to obtain the time difference between the reference sensor and the other sensors;

[0141] In the embodiment of the present application, the phase mutation point generally corresponds to the position where the wave encounters a defect in the material. The larger the time difference, the more significant the impact on the wave during propagation.

[0142] In the embodiment of the present application, when a wave signal received by a certain sensor has a significant time delay, it usually indicates that the wave encounters a defect or discontinuity (such as a corrosion area) inside the material during propagation, causing the wave propagation speed to slow down or the path to become longer;

[0143] In the embodiment of the present application, the positioning coordinates (x i ,y i ,z i );

[0144] In the embodiment of the present application, a circle is drawn with the coordinates of each sensor as the center and the product of the time difference and the wave speed as the radius. The defect position is at the intersection of each circle. The expression is as follows:

[0145]

[0146] Among them, (x f ,y f ,z f ) is the defect position coordinate, v is the mechanical wave propagation speed in the material, ΔT i is the time difference between the i-th sensor and the reference sensor, i is the sensor number index;

[0147] In the embodiment of the present application, a nonlinear equation solving algorithm is used to minimize the errors of multiple circles and obtain the location of the corrosion defect.

[0148] In the embodiment of the present application, since the time difference and wave speed measured by each sensor have certain errors, an objective function is constructed based on the least squares method to calculate the square sum of the errors S(x f ,y f ,z f ), the expression is as follows:

[0149]

[0150] Where N is the number of sensors;

[0151] In an embodiment of the present application, a gradient descent method is used to calculate the partial derivative of the objective function with respect to the defect position, and the position coordinates are iteratively updated along the gradient direction until the sum of squared errors reaches a minimum.

[0152] S5: Based on the location of the corrosion defect, different types of corrosion defects are identified and classified by combining the phase change, amplitude attenuation and propagation path deviation information.

[0153] In the embodiment of the present application, the characteristics of mechanical waves propagating in the material are extracted;

[0154] In the embodiment of the present application, the amplitude attenuation reflects the energy loss of the wave during the propagation process, especially when encountering a defect, the amplitude of the wave will be significantly reduced;

[0155] In the embodiment of the present application, the amplitude attenuation value ΔA of the mechanical wave is obtained by calculating the difference between the amplitude when the mechanical wave is excited and the amplitude when the sensor is received;

[0156] In the embodiment of the present application, defects may cause changes in the propagation path of waves, especially shear waves and surface waves, and the propagation path offset of the waves is calculated by the time difference collected by the sensor;

[0157] In the embodiment of the present application, the propagation path offset value ΔL and the phase difference Δφ(t) are obtained by calculating the product of the time difference and the wave speed when the mechanical wave reaches the same position with and without defects;

[0158] In the embodiment of the present application, the amplitude attenuation value, propagation path offset value and phase difference obtained by each sensor are taken as a data sample;

[0159] In the embodiment of the present application, samples with different types of corrosion defects are manufactured by controlling the experimental conditions, and the types of corrosion defects and corresponding features (ΔA, ΔL, Δφ(t)) are detected, and the features are labeled with corrosion defect categories (pitting, uniform corrosion, crack extension, and no defects);

[0160] For example: no defects: ΔA<0.05, ΔL<1.5, Δφ(t)<0.1π;

[0161] Pitting corrosion: (0.05<ΔA<0.1,1.5<ΔL<4.5,0.1π<Δφ(t)<0.5π);

[0162] Uniform corrosion: (0.1≤ΔA<0.3,1.5≤ΔL<15,0.5π≤Δφ(t)<π);

[0163] Crack extension: (0.3≤ΔA,15≤ΔL,π≤Δφ(t));

[0164] In the embodiment of the present application, due to the nonlinearity of defect features, radial basis functions are used to map input features to a high-dimensional space in order to handle complex nonlinear classification problems. The decision function f(B) of the support vector machine model is expressed as follows by calculating the similarity between the new sample B1 and the training sample B2:

[0165]

[0166] Among them, sign is the sign function, which converts the output of the decision function into a category label. If the result is a positive number, it is classified as a positive class, and if the result is a negative number, it is classified as a negative class. H is the number of samples, j is the sample number index, α is the Lagrange multiplier, which represents the weight of the support vector, and C j is the category label of the jth training sample, K(B1,B2) is the radial basis function, d is the bias term, and the decision boundary offset of the support vector machine model;

[0167] K(B1,B2)=exp(-γ||B1-B2|| 2 );

[0168] Among them, γ is the width of the radial basis function, which determines the similarity between two samples, and ||B1-B2|| is the Euclidean distance between the new sample B1 and the training sample B2;

[0169] In the embodiment of the present application, the support vector machine model is trained using the labeled feature data to optimize the regularization parameter D and the width γ of the radial basis function in the support vector machine model;

[0170] In the embodiment of the present application, the regularization parameter D and the width γ of the radial basis function are optimized by cross-validation to ensure that the model has good generalization ability;

[0171] In the embodiment of the present application, K-fold cross validation is used to divide the data set into K subsets, and a common value of K is 5 or 10. Each time, K-1 subsets are selected from the K subsets as training sets, and the remaining 1 subset is used as a validation set. The support vector machine model is trained and the performance of the validation set is evaluated. This process is repeated K times, so that each subset is used as a validation set exactly once;

[0172] In the embodiment of the present application, after all K training and verification are completed, the average accuracy of the support vector machine model on the verification set is calculated;

[0173] In the embodiment of the present application, a set of regularization parameters D and the width γ of the radial basis function are first defined, and a K-fold cross validation is performed on each pair of regularization parameters D and the width γ of the radial basis function, and the average accuracy of the support vector machine model is recorded;

[0174] In the embodiment of the present application, for all parameter combinations, the parameter combination with the highest cross-validation performance accuracy is selected as the final regularization parameter D and the width γ of the radial basis function;

[0175] In an embodiment of the present application, after training is completed, the detected phase difference, amplitude attenuation and propagation path deviation of the multimodal mechanical wave are input as comprehensive features into the support vector machine model for prediction, and the category label of the corrosion defect is output.

[0176] This embodiment also provides a corrosion defect identification system based on multi-modal mechanical wave characteristics, including:

[0177] Mechanical wave excitation module, used to excite multi-modal mechanical waves and plan the mechanical wave propagation path;

[0178] A signal acquisition module is used to arrange the sensor array according to the planned mechanical wave propagation path, collect and synchronously process multi-modal mechanical wave signals;

[0179] A feature extraction module is used to perform time-frequency analysis on the collected multi-modal mechanical wave signals and extract the phase change characteristics of the multi-modal mechanical waves;

[0180] A positioning module is used to locate the corrosion defect position based on the phase change characteristics of the multi-modal mechanical wave and the mechanical wave propagation path information;

[0181] The recognition module is used to identify and classify different types of corrosion defects based on the location of the corrosion defects combined with the phase change, amplitude attenuation and propagation path deviation information.

[0182] Furthermore, it also includes:

[0183] Memory, used to store programs;

[0184] A processor is used to load the program to execute the corrosion defect identification method based on multi-modal mechanical wave characteristics.

[0185] This embodiment also provides a computer-readable storage medium storing a program, and when the program is executed by a processor, the corrosion defect identification method based on multi-modal mechanical wave characteristics is implemented.

[0186] The storage medium proposed in this embodiment and the corrosion defect identification method based on multimodal mechanical wave characteristics proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0187] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0188] Example 2

[0189] Referring to Table 1, an embodiment of the present invention provides a corrosion defect identification method based on multimodal mechanical wave characteristics. In order to further verify the technical solution of the present invention, experimental simulation data of the corrosion defect identification method based on multimodal mechanical wave characteristics are given.

[0190] In order to verify the effectiveness of a corrosion defect identification method based on multi-modal mechanical wave characteristics, this experiment compared the performance differences between this method and existing single-modal ultrasonic detection technology (such as the commonly used ultrasonic phased array detection technology).

[0191] The experimental material is an aluminum alloy sample with uneven thickness and complex geometric structure. The sample size is 500mm×500mm×10mm. Three types of corrosion defects are preset on the surface: pitting, linear corrosion and regional corrosion. Two detection methods are used for comparative analysis: one is based on ultrasonic phased array technology, and the other is based on the multi-modal mechanical wave feature recognition method of the present invention.

[0192] The aluminum alloy sample was 3D modeled using a laser scanner to construct its geometric property model, and the thickness distribution of the sample was measured using an ultrasonic thickness gauge, which fluctuated between 9.8 mm and 10.2 mm.

[0193] Based on the geometric characteristics and thickness distribution, the wavelength range suitable for the material was obtained through finite element simulation calculations, and a combination of longitudinal waves, transverse waves and surface waves was used for multimodal mechanical wave excitation. The excitation frequencies were 5 MHz (longitudinal waves), 8 MHz (transverse waves) and 10 MHz (surface waves). The wave mode with the highest reflection coefficient was screened out through simulation, and the excitation angle was adjusted using a transducer to ensure that the mechanical wave could propagate throughout the material.

[0194] 25 array ultrasonic sensors (with a spacing of 20 mm) are evenly arranged on the surface of the material, and the sensor layout density is increased at the geometric changes. Each sensor synchronously collects the phase information, amplitude information and propagation time of the multi-modal mechanical wave. During the acquisition process, a bandpass filter is used to perform directional filtering on the excitation wave frequency band, and the collected signals are normalized.

[0195] The collected multimodal mechanical wave signals were analyzed in time and frequency using wavelet transform, and the instantaneous phase was extracted. The phase mutation points were identified by calculating the phase difference between adjacent moments. The position and type of corrosion defects were accurately located based on the mechanical wave propagation path and phase change characteristics, combined with the amplitude attenuation and propagation path offset information.

[0196] The same materials and defects were inspected using ultrasonic phased array technology, and the inspection results were recorded. Phased array technology mainly identifies defects through single-mode ultrasonic signals and fails to effectively capture the multi-modal wave propagation characteristics in complex geometric structures, resulting in affected inspection accuracy.

[0197] The details are shown in Table 1 below:

[0198] Table 1 Experimental data comparison table

[0199] parameter Multimodal Mechanical Wave Detection Method Ultrasonic Phased Array Method Detection success rate (%) 98 85 Positioning error(mm) 0.5 2.2 Detection time (seconds) 120 180 Noise interference impact (dB) 5 15 Accurate defect recognition rate (%) 96 82

[0200] The detection success rate of the multimodal mechanical wave method is as high as 98%, while the detection success rate of the ultrasonic phased array technology is only 85%. This shows that the multimodal mechanical wave method can cover the material inspection area more comprehensively, especially in complex geometric structures, and can more effectively identify different types of corrosion defects.

[0201] The positioning error of the multimodal mechanical wave method is less than 1mm for all defect types, with the lowest reaching 0.4mm, while the error of the ultrasonic phased array technology is larger, especially in the detection of pitting defects, with the error reaching up to 2.2mm.

[0202] The detection time of the multimodal mechanical wave method is generally shorter than that of the phased array technology. For example, when detecting linear corrosion defects, the multimodal method only takes 110 seconds, while the phased array technology takes 160 seconds.

[0203] Multimodal mechanical wave technology shows significant advantages in terms of noise interference, with a noise impact of only 5dB, while phased array technology is subject to higher noise interference of 15dB.

[0204] The multimodal mechanical wave method has an accurate defect identification rate of 96%, which is much higher than the 82% of ultrasonic phased array technology.

[0205] In summary, the corrosion defect identification method based on multimodal mechanical wave characteristics, by introducing innovative technologies such as multimodal signal detection, time-frequency analysis and path simulation, is significantly superior to the existing single-modal ultrasonic detection technology in terms of detection success rate, positioning accuracy, detection efficiency, noise resistance and defect recognition rate.

[0206] 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 defect identification method based on multimodal mechanical wave characteristics, characterized in that: include: Excite multi-modal mechanical waves and plan the propagation paths of mechanical waves; Arrange the sensor array according to the planned mechanical wave propagation path to collect and synchronously process multi-modal mechanical wave signals; Perform time-frequency analysis on the collected multi-modal mechanical wave signals to extract the phase change characteristics of the multi-modal mechanical waves; Based on the phase change characteristics of multi-modal mechanical waves and combined with the mechanical wave propagation path information, the corrosion defect position is located; Based on the location of the corrosion defect, different types of corrosion defects are identified and classified by combining phase change, amplitude attenuation and propagation path deviation information.

2. The corrosion defect identification method based on multimodal mechanical wave characteristics according to claim 1 is characterized in that: The exciting multi-modal mechanical waves and planning the mechanical wave propagation path include the following steps: Determine the thickness distribution of the material using an ultrasonic thickness gauge; Use laser scanners to perform three-dimensional modeling of the material geometry and obtain the material geometric properties; Based on the material thickness distribution, the wavelength range that matches the material thickness is calculated; Low-frequency longitudinal waves, high-frequency transverse waves and surface waves are combined into multi-modal mechanical waves; The reflection coefficients of mechanical waves of different modes at the material boundary are calculated through finite element simulation, and the wave mode with the highest reflection value is selected; Use a transducer to excite multi-modal mechanical waves and adjust the excitation angle; According to the geometric shape and thickness distribution of the material, finite element analysis software is used to simulate the propagation path of mechanical waves inside the material; The acquisition wave starts from the excitation point, passes through reflection or refraction inside the material, and reaches the propagation path of each sensor. Each path corresponds to a sensor position; Based on the simulation results, the excitation points are adjusted to fully cover the material detection area.

3. The corrosion defect identification method based on multimodal mechanical wave characteristics according to claim 1 or 2, characterized in that: The arrangement of the sensor array, collection and synchronous processing of multi-modal mechanical wave signals, includes the following steps: Arrange highly sensitive array ultrasonic sensors on the surface of the material, each sensor captures signals of different mechanical wave modes; Adjust the sensor layout according to the multi-modal mechanical wave propagation path and boundary reflection characteristics obtained from finite element simulation; The sensor spacing is set based on the multi-modal mechanical wave excitation wavelength; According to the geometric characteristics of the material, the sensors are evenly arranged along the surface of the material, and the arrangement density is increased at the places where the geometry changes; For materials with uneven thickness and complex structure, place sensors on the side and back of the material; Using defect-free and known defect standard samples, multi-modal mechanical waves are excited, response signals are collected through sensors, and the response delay and sensitivity of each sensor are calibrated; High-speed acquisition of multi-modal mechanical wave signals; Each sensor synchronously and real-time collects the amplitude information, phase information and propagation time of mechanical waves of different modes; During the acquisition process, a low-pass filter is used to remove high-frequency environmental noise, and a band-pass filter is used to perform directional filtering on the frequency range of the excitation wave; The amplitude information, phase information and propagation time of the collected multi-modal mechanical waves are normalized.

4. The corrosion defect identification method based on multimodal mechanical wave characteristics according to claim 3 is characterized in that: The time-frequency analysis of the collected multi-modal mechanical wave signals to extract the phase change characteristics of the multi-modal mechanical waves includes the following steps: Using wavelet transform for time domain and frequency domain analysis, the expression is as follows: Among them, W(a,b) is the coefficient after wavelet transformation, a is the scale parameter, b is the translation parameter, x(t) is the original multimodal mechanical wave propagation signal at time t, ψ(t) is the wavelet basis function, ψ * (t) is the conjugate complex number of the wavelet basis function, is the local feature of the signal at scale a, the translation is b, j is the imaginary unit, π is the circumference of a circle, ω0 is the center frequency of the wavelet, and dt represents the integration over time t; Extract the instantaneous phase of the multimodal mechanical wave signal after wavelet transform processing; Calculate the phase difference between adjacent moments and find the phase mutation point; The phase change threshold Δφ is set based on experimental data according to the wave propagation characteristics of the material and the noise level. th , when the phase difference Δφ(t) between adjacent moments is greater than Δφ th When , it is determined as a phase mutation point.

5. The corrosion defect identification method based on multimodal mechanical wave characteristics according to claim 4 is characterized in that: The method of locating the corrosion defect position based on the phase change characteristics of the multi-modal mechanical wave and the mechanical wave propagation path information includes the following steps: Combined with the simulation results of the propagation path of mechanical waves using finite element analysis software, the propagation speed of mechanical waves of different modes in the material is determined through experimental measurements based on the mechanical properties of the material and the mode of the wave; Each sensor simulates the propagation path length and wave speed through mechanical waves, and calculates the time from the excitation of the mechanical wave to the detection of the sensor; A reference sensor is arranged at the mechanical wave excitation point, and the time when the reference sensor receives the mechanical wave is the reference time; The time when the other sensors receive the mechanical wave is compared with the reference time to obtain the time difference between the reference sensor and the other sensors; Based on the 3D modeling structure of the material, the positioning coordinates (x i ,y i ,z i ); With the coordinates of each sensor as the center and the product of the time difference and the wave speed as the radius, draw a circle. The defect position is at the intersection of each circle. The expression is as follows: Among them, (x f ,y f ,z f ) is the defect position coordinate, v is the mechanical wave propagation speed in the material, ΔT i is the time difference between the i-th sensor and the reference sensor, i is the sensor number index; The nonlinear equation solving algorithm is used to minimize the errors of multiple circles and obtain the location of the corrosion defect.

6. The corrosion defect identification method based on multimodal mechanical wave characteristics according to claim 5 is characterized in that: The method of using a nonlinear equations solving algorithm to minimize the errors of multiple circles and obtain the location of the corrosion defect includes the following steps: Based on the least squares method, an objective function is constructed to calculate the square sum of the error S(x f ,y f ,z f ), the expression is as follows: Where N is the number of sensors; Using the gradient descent method, the position coordinates are iteratively updated along the gradient direction by calculating the partial derivative of the objective function with respect to the defect position.

7. The corrosion defect identification method based on multimodal mechanical wave characteristics according to claim 6 is characterized in that: The method of identifying the type of corrosion defect by combining phase change, amplitude attenuation and propagation path deviation information comprises the following steps: Extract the characteristics of mechanical waves propagating in materials; The amplitude attenuation value of the mechanical wave is obtained by calculating the difference between the amplitude when the mechanical wave is excited and the amplitude when the sensor receives it; By calculating the product of the time difference between the mechanical wave reaching the same position with and without defects and the wave speed, the propagation path offset value and phase are obtained; The amplitude attenuation value, propagation path deviation value and phase difference obtained by each sensor are taken as a data sample; By controlling the experimental conditions, samples with different types of corrosion defects are manufactured, and the types of corrosion defects and corresponding features are detected, and the features are labeled with corrosion defect categories; The radial basis function is used to map the input features to a high-dimensional space. The decision function f(B) of the support vector machine model is expressed as follows by calculating the similarity between the new sample B1 and the training sample B2: Among them, sign is the sign function, H is the number of samples, j is the sample number index, α is the Lagrange multiplier, C j is the category label of the jth training sample, K(B1,B2) is the radial basis function, and d is the bias term; K(B1,B2)=exp(-γ||B1-B2|| 2 ); Where γ is the width of the radial basis function, ||B1-B2|| is the Euclidean distance between the new sample B1 and the training sample B2; Use the labeled feature data to train the support vector machine model and optimize the regularization parameter D and the width γ of the radial basis function in the support vector machine model; The regularization parameter D and the width γ of the radial basis function are optimized through cross-validation; After the training is completed, the phase difference, amplitude attenuation and propagation path deviation of the detected multimodal mechanical waves are input into the support vector machine model as comprehensive features for prediction, and the category label of the corrosion defect is output.

8. A system based on the corrosion defect identification method based on multimodal mechanical wave characteristics according to claim 1, characterized in that: Mechanical wave excitation module, used to excite multi-modal mechanical waves and plan the mechanical wave propagation path; A signal acquisition module is used to arrange the sensor array according to the planned mechanical wave propagation path, collect and synchronously process multi-modal mechanical wave signals; A feature extraction module is used to perform time-frequency analysis on the collected multi-modal mechanical wave signals and extract the phase change characteristics of the multi-modal mechanical waves; A positioning module is used to locate the corrosion defect position based on the phase change characteristics of the multi-modal mechanical wave and the mechanical wave propagation path information; The recognition module is used to identify and classify different types of corrosion defects based on the location of the corrosion defects combined with the phase change, amplitude attenuation and propagation path deviation information.

9. A computing device, characterized in that include: Memory, used to store programs; A processor is used to load the program to execute the steps of the corrosion defect identification method based on multimodal mechanical wave characteristics as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the steps of the corrosion defect identification method based on multimodal mechanical wave characteristics as described in any one of claims 1 to 7 are implemented.

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