Nonlinear ultrasonic guided wave detection method for intergranular corrosion of pressure vessel

Abstract

The invention relates to the technical field of pressure vessel intergranular corrosion detection, in particular to a nonlinear ultrasonic guided wave detection method for pressure vessel intergranular corrosion. The system comprises a signal generation and emission module, a signal acquisition and preprocessing module, a feature extraction and analysis module, a damage positioning and evaluation module, an optimization and calibration module and a data storage and visualization module. According to the invention, through cooperative work of the multi-band excitation signal and the distributed sensor array, the detection sensitivity and resolution are significantly improved; a nonlinear characteristic matrix and a multi-level decomposition capture material nonlinear effect are utilized to realize accurate positioning and evaluation; an adaptive parameter adjustment mechanism simplifies the operation process and reduces the detection cost; and a cloud database and a graphical interface are convenient for result storage and analysis. The intergranular corrosion can be efficiently and reliably detected, and a scientific basis is provided for the industrial field.

Description

Technical Field

[0001] This invention belongs to the field of nondestructive testing technology, specifically a nonlinear ultrasonic guided wave detection method for intergranular corrosion of pressure vessels. Background Technology

[0002] With the widespread application of pressure vessels in industry, their safety and reliability have become a key research focus. Intergranular corrosion is a common form of material failure, posing a serious threat to the long-term operation of pressure vessels, especially under high temperature and pressure environments. Existing detection methods have certain limitations in addressing intergranular corrosion, making it difficult to achieve efficient and accurate nonlinear ultrasonic guided wave detection.

[0003] A search revealed a carrier-modulated nonlinear ultrasonic guided wave damage detection method, publication number CN110108802B, published on May 18, 2021. This patent combines high and low frequency components via carrier modulation, utilizing the nonlinear modulation effect to detect damage in materials, and extracts nonlinear components through empirical mode decomposition and signal reconstruction to assess the degree of damage. However, this technical solution mainly targets general material damage detection and does not specifically consider the unique characteristics of intergranular corrosion in pressure vessels. Since intergranular corrosion typically occurs at the microstructural level of materials and its nonlinear characteristics may be weak, existing methods may have insufficient detection sensitivity and resolution, making it difficult to accurately identify early signs of intergranular corrosion. Furthermore, this solution relies heavily on signal processing, which may complicate the detection process and affect efficiency in practical applications.

[0004] A search revealed a method for quantitative detection of ultrasonic guided wave defects and its application, with publication number CN114740093B, published on May 7, 2024. This patent obtains propagation velocity and reflection time parameters through ultrasonic guided wave detection and processes the signal using the ICEEMDAN method to achieve quantitative detection of defect length. This method boasts high detection accuracy and efficiency, and is suitable for defect assessment in various scenarios. However, this technical solution primarily targets defect detection in specific structures such as anchor bolts, and does not fully consider the complex environment and variable characteristics of intergranular corrosion in pressure vessels. Intergranular corrosion is typically accompanied by the propagation of microcracks within the material and localized stress concentration; existing methods may not be able to fully capture these subtle changes, leading to inaccurate detection results. Furthermore, this solution requires high precision in equipment calibration and parameter settings in practical applications, potentially increasing operational difficulty and cost.

[0005] The aforementioned problems indicate that existing nonlinear ultrasonic guided wave detection methods still have certain shortcomings in terms of detection sensitivity, resolution, applicability, and ease of operation when dealing with intergranular corrosion in pressure vessels. Therefore, this invention provides a nonlinear ultrasonic guided wave detection method for intergranular corrosion in pressure vessels, aiming to optimize the detection process, improve the ability to identify intergranular corrosion, simplify operation steps, and reduce overall detection costs, thereby meeting the industrial demand for efficient and reliable intergranular corrosion detection. Summary of the Invention

[0006] This invention provides a nonlinear ultrasonic guided wave detection method for intergranular corrosion in pressure vessels, addressing the problems of insufficient detection sensitivity, low resolution, limited applicability, and complex operation in existing technologies for dealing with intergranular corrosion. Existing nonlinear ultrasonic guided wave detection methods struggle to comprehensively capture the microscopic changes in intergranular corrosion and require sophisticated equipment calibration and parameter settings, thus limiting their efficiency and reliability in practical applications.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a nonlinear ultrasonic guided wave detection method for intergranular corrosion of pressure vessels, comprising the following modules: The signal generation and transmission module generates a composite signal containing low-frequency modulation components and high-frequency carrier components through a multi-band excitation signal generator, and inputs the signal to a piezoelectric transducer. The piezoelectric transducer is installed on the surface of the pressure vessel and achieves tight adhesion to the vessel surface through a coupling agent to ensure the stability of signal transmission. The frequency range of the composite signal is dynamically adjusted according to the elastic modulus and grain size of the pressure vessel material to adapt to the testing requirements of pressure vessels made of different materials. The signal acquisition and preprocessing module uses a distributed sensor array to receive reflected signals and records the time difference of signal propagation through a time synchronization mechanism. The distributed sensor array consists of multiple miniature piezoelectric sensors, each of which is mounted on the surface of the pressure vessel by a magnetic fixing device, forming a uniformly distributed detection network. After the received signal is filtered by a bandpass filter to remove noise interference, the time-frequency characteristics of the signal are extracted using a short-time Fourier transform. The feature extraction and analysis module constructs a nonlinear feature matrix based on the extracted time-frequency features and performs multi-level decomposition of the signal using a wavelet packet decomposition algorithm. The nonlinear feature matrix is ​​generated by calculating the ratio of the higher-order harmonic components to the fundamental component of the signal, and is used to characterize the nonlinear effects of the material caused by intergranular corrosion. The multi-level decomposed signal is further analyzed through an energy distribution map to identify the energy concentration points in the intergranular corrosion region. The damage localization and assessment module determines the specific location of intergranular corrosion based on the signal propagation time difference and energy distribution map, combined with a geometric localization algorithm. The geometric localization algorithm generates damage localization coordinates in three-dimensional space by calculating the relative distance between each sensor and the signal arrival time difference. At the same time, based on the intensity and distribution range of the energy concentration point, a fuzzy logic reasoning model is used to assess the severity of intergranular corrosion. The optimization and calibration module dynamically calibrates the excitation signal frequency and sensor sensitivity of the detection system through an adaptive parameter adjustment mechanism. The adaptive parameter adjustment mechanism is based on feedback control of real-time acquired signal quality indicators, including signal-to-noise ratio and signal amplitude fluctuation range. When the signal quality indicators are lower than a preset threshold, the system automatically adjusts the frequency range of the excitation signal or the gain parameters of the sensor until the detection requirements are met. The data storage and visualization module stores the detection results in a cloud database and displays the location, severity, and historical trend of intergranular corrosion through a graphical interface. The cloud database adopts a distributed storage architecture, supporting concurrent access by multiple users. The graphical interface presents the energy distribution of intergranular corrosion in the form of a heat map and provides interactive operation functions, allowing users to adjust display parameters to obtain more detailed analysis results.

[0008] The technical solution of this invention significantly improves the sensitivity and resolution of intergranular corrosion detection through the coordinated operation of a multi-band excitation signal generator and a distributed sensor array. The dynamic adjustment mechanism of the composite signal in the signal generation and emission module can adapt to pressure vessels of different materials, enhancing the applicability of the detection method. The construction method of the nonlinear feature matrix in the feature extraction and analysis module effectively captures the nonlinear effects of materials caused by intergranular corrosion, while the combination of multi-level decomposition and energy distribution maps further improves the accuracy of the detection results.

[0009] The application of geometric positioning algorithms in the damage localization and assessment module enables precise location of intergranular corrosion, while the introduction of fuzzy logic reasoning models provides a scientific basis for assessing corrosion severity. The optimization and calibration module addresses the complexity of equipment calibration in existing technologies through an adaptive parameter adjustment mechanism, simplifying the operation process and reducing testing costs. The data storage and visualization module not only facilitates long-term storage and analysis of test results but also enhances the user experience through a graphical interface.

[0010] In summary, this invention, through a series of innovative technical means, overcomes the shortcomings of existing technologies in intergranular corrosion detection, and provides an efficient and reliable detection method for the industrial field. Attached Figure Description

[0011] Figure 1This is a schematic diagram of the system structure of the nonlinear ultrasonic guided wave detection method for intergranular corrosion of pressure vessels according to the present invention. It shows the connection relationship between the signal generation and transmission module, the signal acquisition and preprocessing module, the feature extraction and analysis module, the damage location and assessment module, the optimization and calibration module, and the data storage and visualization module.

[0012] Figure 2 This is a schematic diagram of the arrangement of a distributed sensor array on the surface of a pressure vessel, showing the installation method in which multiple miniature piezoelectric sensors are evenly distributed on the surface of the vessel by magnetic fixing devices, as well as the basic principle of signal propagation path and time difference recording.

[0013] Figure 3 This is a schematic diagram of the visualization interface for intergranular corrosion detection results, showing the energy distribution in the form of a heat map, and marking the location of the corrosion area and the severity assessment results.

[0014] The attached figures are labeled as follows: 1. Signal generation and transmission module; 2. Signal acquisition and preprocessing module; 3. Feature extraction and analysis module; 4. Damage localization and assessment module; 5. Optimization and calibration module; 6. Data storage and visualization module; 7. Distributed sensor array; 8. Pressure vessel; 9. Heat map; 10. Corrosion area. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Specific implementation examples are given below.

[0017] The nonlinear ultrasonic guided wave detection method for intergranular corrosion in pressure vessels of the present invention achieves efficient detection of intergranular corrosion through the coordinated operation of multiple modules. Its specific implementation method is described in conjunction with the appendix. Figure 1 To be continued Figure 3 Detailed explanation follows. (Attached) Figure 1 The diagram illustrates the overall system structure, including the connections between the signal generation and transmission module 1, signal acquisition and preprocessing module 2, feature extraction and analysis module 3, damage localization and assessment module 4, optimization and calibration module 5, and data storage and visualization module 6. Figure 2 The specific arrangement of the distributed sensor array 7 on the surface of the pressure vessel 8 is shown, while the attached... Figure 3 The system then presents a visual interface of the detection results, including a heat map (9) and annotations for the corrosion area (10).

[0018] The signal generation and transmission module 1 is one of the core components of the entire system, mainly consisting of a multi-band excitation signal generator and a piezoelectric transducer. The multi-band excitation signal generator produces a composite signal containing low-frequency modulation and high-frequency carrier components. The frequency range of this signal is dynamically adjusted according to the elastic modulus and grain size of the pressure vessel material. For example, when inspecting a stainless steel pressure vessel, the low-frequency modulation component of the composite signal is set to 10kHz to 50kHz, and the high-frequency carrier component is set to 1MHz to 5MHz. The piezoelectric transducer is tightly bonded to the surface of the pressure vessel 8 using a coupling agent, ensuring stable signal transmission to the interior of the vessel. The selection of the coupling agent must consider its viscosity and acoustic impedance matching characteristics to reduce energy loss during signal propagation. The piezoelectric transducer is typically positioned in the central region of the pressure vessel 8 or near potentially corroded areas to ensure the signal covers the largest possible detection range.

[0019] The signal acquisition and preprocessing module 2 receives the reflected signal through the distributed sensor array 7 and records the time difference of signal propagation. (See attached diagram) Figure 2 As shown, the distributed sensor array 7 consists of multiple miniature piezoelectric sensors, which are uniformly distributed on the surface of the pressure vessel 8 via magnetic fixing devices. The spacing between each sensor is determined based on the geometry and material properties of the pressure vessel 8, typically set between 5cm and 10cm to ensure uniformity and integrity of signal coverage. The magnetic fixing device design allows for quick installation and removal of the sensors while ensuring good contact with the vessel surface. After noise interference is removed by a bandpass filter, the received signal is subjected to short-time Fourier transform to extract the time-frequency characteristics of the signal. The cutoff frequency range of the bandpass filter is dynamically adjusted according to the frequency range of the composite signal; for example, in the detection of stainless steel material, the low cutoff frequency of the bandpass filter is set to 5kHz, and the high cutoff frequency is set to 10MHz. The time window length of the short-time Fourier transform is set to 1ms to balance time resolution and frequency resolution.

[0020] The feature extraction and analysis module 3 constructs a nonlinear feature matrix based on the extracted time-frequency features and performs multi-level decomposition of the signal using a wavelet packet decomposition algorithm. The nonlinear feature matrix is ​​generated by calculating the ratio of higher-order harmonic components to the fundamental component, where the higher-order harmonic components include the second and third harmonics. For example, in a detection instance, if the amplitude of the fundamental component is 1V, the amplitude of the second harmonic component is 0.1V, and the amplitude of the third harmonic component is 0.05V, then the corresponding nonlinear feature matrix elements are 0.1 and 0.05, respectively. The wavelet packet decomposition algorithm is set to four decomposition levels. The signal after each decomposition is further analyzed using an energy distribution map to identify the energy concentration points in the intergranular corrosion region. The horizontal axis of the energy distribution map represents time, the vertical axis represents frequency, and the color intensity represents energy intensity, thus visually displaying the energy distribution of the signal.

[0021] The damage localization and assessment module 4 determines the specific location of intergranular corrosion based on the signal propagation time difference and energy distribution map, combined with a geometric localization algorithm. The geometric localization algorithm generates three-dimensional damage localization coordinates by calculating the relative distances between sensors and the signal arrival time difference. For example, in a detection instance, the distance between sensor A and sensor B is 10 cm, the signal arrives at sensor A in 1 ms, and at sensor B in 1.2 ms. Based on the signal propagation speed, the corrosion area is calculated to be located at a specific position between sensor A and sensor B. A fuzzy logic reasoning model is used to assess the severity of intergranular corrosion. Its input variables include the intensity and distribution range of the energy concentration point, and the output variable is the corrosion level. The rule base of the fuzzy logic reasoning model contains multiple rules; for example, when the intensity of the energy concentration point is greater than 0.8 and the distribution range is greater than 5 cm², the corrosion level is determined to be severe.

[0022] The optimization and calibration module 5 dynamically calibrates the excitation signal frequency and sensor sensitivity of the detection system through an adaptive parameter adjustment mechanism. This mechanism uses feedback control based on real-time acquired signal quality indicators, including the signal-to-noise ratio (SNR) and signal amplitude fluctuation range. For example, during a detection process, if the SNR falls below 20 dB or the signal amplitude fluctuation range exceeds ±10%, the system will automatically adjust the frequency range of the excitation signal or the sensor's gain parameter. The frequency range adjustment step is set to 1 kHz, and the sensor gain parameter adjustment step is set to 1 dB, until the signal quality indicators meet the preset threshold requirements.

[0023] The data storage and visualization module 6 stores the detection results in a cloud database and displays the location, severity, and historical trends of intergranular corrosion through a graphical interface. The cloud database employs a distributed storage architecture, supporting concurrent access by multiple users. Its data table structure includes fields such as detection time, corrosion location coordinates, and corrosion level. The graphical interface presents the energy distribution of intergranular corrosion in the form of a heatmap (Module 9), with the color gradient from blue to red indicating increasing energy intensity. Users can adjust display parameters through interactive functions, such as selecting different time ranges or corrosion level thresholds, to obtain more detailed analysis results. (Appendix) Figure 3 The heat map 9 clearly marks the location of the corrosion area 10 and the severity assessment results, making it easy for users to intuitively understand the detection situation.

[0024] The entire system operates as follows: First, the signal generation and transmission module 1 generates a composite signal and transmits it into the pressure vessel 8 via a piezoelectric transducer. During propagation inside the vessel, the signal is reflected when it encounters intergranular corrosion regions. The reflected signal is received by the distributed sensor array 7 and transmitted to the signal acquisition and preprocessing module 2. The signal acquisition and preprocessing module 2 filters and extracts time-frequency features from the received signal, then transmits the processed signal to the feature extraction and analysis module 3. The feature extraction and analysis module 3 generates an energy distribution map by constructing a nonlinear feature matrix and multi-level decomposition, and transmits the analysis results to the damage location and assessment module 4. The damage location and assessment module 4 uses a geometric location algorithm and a fuzzy logic reasoning model to determine the specific location and severity of the corrosion region, and transmits the results to the optimization and calibration module 5. The optimization and calibration module 5 dynamically adjusts the system parameters based on signal quality indicators to ensure the accuracy and reliability of the detection results. Finally, the data storage and visualization module 6 stores the detection results in a cloud database and displays them to the user through a graphical interface.

[0025] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0026] In an industrial setting, intergranular corrosion detection is required on a stainless steel pressure vessel. The pressure vessel has a diameter of 1.5 meters and a height of 3 meters, and its surface may contain potential intergranular corrosion zones. The operator first evenly distributes a distributed sensor array 7 on the outer surface of the pressure vessel 8, as shown in the attached diagram. Figure 2 As shown, each miniature piezoelectric sensor is mounted on the container surface using a magnetic mounting device. The spacing between the sensors is set to 8 cm to ensure uniform and complete signal coverage. The magnetic mounting device design allows for quick installation and removal of the sensors while ensuring good contact with the container surface.

[0027] Subsequently, the operator activates signal generation and transmission module 1. The multi-band excitation signal generator produces a composite signal, with the low-frequency modulation component set to 30kHz and the high-frequency carrier component set to 3MHz. This composite signal is transmitted into the pressure vessel 8 via a piezoelectric transducer. The piezoelectric transducer is tightly attached to the vessel surface using a coupling agent. The coupling agent is chosen based on its viscosity and acoustic impedance matching characteristics to minimize energy loss during signal propagation. The piezoelectric transducer is positioned in the central region of the pressure vessel 8 to ensure the signal covers the largest possible detection range.

[0028] When the composite signal propagates inside the pressure vessel 8, it is reflected when it encounters intergranular corrosion regions. The distributed sensor array 7 receives these reflected signals and transmits them to the signal acquisition and preprocessing module 2. The signal acquisition and preprocessing module 2 performs bandpass filtering on the received signal, with the low cutoff frequency set to 5kHz and the high cutoff frequency set to 10MHz to remove noise interference. The filtered signal is then subjected to short-time Fourier transform to extract time-frequency features, with a time window length set to 1 millisecond to balance time resolution and frequency resolution.

[0029] The extracted time-frequency features are transmitted to feature extraction and analysis module 3, which calculates the ratio of higher-order harmonic components to the fundamental component of the signal based on a nonlinear feature matrix. For example, in a certain detection instance, the amplitude of the fundamental component is 1 volt, the amplitude of the second harmonic component is 0.1 volt, and the amplitude of the third harmonic component is 0.05 volt; the corresponding nonlinear feature matrix elements are 0.1 and 0.05, respectively. Subsequently, the wavelet packet decomposition algorithm performs four-level decomposition on the signal. The signal after each level of decomposition is further analyzed using an energy distribution map to identify the energy concentration points in the intergranular corrosion region. The horizontal axis of the energy distribution map represents time, the vertical axis represents frequency, and the color intensity represents energy intensity, thus visually displaying the energy distribution of the signal.

[0030] Damage localization and assessment module 4 determines the specific location of intergranular corrosion based on the signal propagation time difference and energy distribution map, combined with a geometric localization algorithm. For example, in a certain detection instance, the distance between sensor A and sensor B is 10 cm, the signal arrives at sensor A in 1 ms and at sensor B in 1.2 ms, then the corrosion area is calculated to be located at a specific position between sensor A and sensor B based on the signal propagation speed. A fuzzy logic reasoning model is used to assess the severity of intergranular corrosion, and its input variables include the intensity and distribution range of the energy concentration point. For example, when the intensity of the energy concentration point is greater than 0.8 and the distribution range is greater than 5 square centimeters, the corrosion level is determined to be severe.

[0031] The optimization and calibration module 5 dynamically calibrates the excitation signal frequency and sensor sensitivity of the detection system through an adaptive parameter adjustment mechanism. For example, during a certain detection process, if the signal-to-noise ratio is lower than 20dB or the signal amplitude fluctuation exceeds ±10%, the system will automatically adjust the frequency range of the excitation signal or the sensor gain parameter. The frequency range adjustment step size of the excitation signal is set to 1kHz, and the sensor gain parameter adjustment step size is set to 1dB, until the signal quality indicators meet the preset threshold requirements.

[0032] Finally, the data storage and visualization module 6 stores the detection results in a cloud database and displays the location, severity, and historical trends of intergranular corrosion through a graphical interface. The cloud database employs a distributed storage architecture, supporting concurrent access by multiple users. Its data table structure includes fields such as detection time, corrosion location coordinates, and corrosion level. The graphical interface presents the energy distribution of intergranular corrosion in the form of a heatmap (9), with the color gradient from blue to red indicating increasing energy intensity. Users can adjust display parameters through interactive functions, such as selecting different time ranges or corrosion level thresholds, to obtain more detailed analysis results. (Appendix) Figure 3 The heat map 9 clearly marks the location of the corrosion area 10 and the severity assessment results, making it easy for users to intuitively understand the detection situation.

[0033] Through the above steps, this invention achieves efficient detection of intergranular corrosion in pressure vessels. The signal generation and transmission module 1 ensures stable transmission of the composite signal to the vessel interior; the signal acquisition and preprocessing module 2 extracts the time-frequency characteristics of the signal using a distributed sensor array 7 and short-time Fourier transform; the feature extraction and analysis module 3 generates an energy distribution map using a nonlinear feature matrix and multi-level decomposition; the damage location and assessment module 4 determines the specific location and severity of the corrosion area using a geometric location algorithm and a fuzzy logic reasoning model; the optimization and calibration module 5 ensures the accuracy and reliability of the detection results through an adaptive parameter adjustment mechanism; and the data storage and visualization module 6 enhances the user experience through a graphical interface. The entire system operates smoothly and efficiently, significantly improving the sensitivity, resolution, and applicability of intergranular corrosion detection, while simplifying the operation process and reducing detection costs.

[0034] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intergranular corrosion nonlinear ultrasonic guided wave detection of a pressure vessel, the method comprising: The system comprises the following modules A signal generation and transmission module (1) generates a composite signal containing a low-frequency modulation component and a high-frequency carrier component through a multi-frequency excitation signal generator, and inputs the signal to a piezoelectric transducer; the piezoelectric transducer is installed on the surface of a pressure vessel (8) and is tightly attached to the surface of the vessel through a coupling agent; the frequency range of the composite signal is dynamically adjusted according to the elastic modulus and grain size of the pressure vessel material; A signal acquisition and preprocessing module (2) receives reflected signals using a distributed sensor array (7) and records the time difference of signal propagation through a time synchronization mechanism; the distributed sensor array (7) is composed of multiple miniature piezoelectric sensors, each sensor is installed on the surface of the pressure vessel (8) through a magnetic attraction fixing device; after the received signal is filtered by a band-pass filter to remove noise interference, the short-time Fourier transform is used to extract the time-frequency characteristics of the signal; A feature extraction and analysis module (3) constructs a nonlinear feature matrix based on the extracted time-frequency characteristics and performs multi-level decomposition of the signal using a wavelet packet decomposition algorithm; the nonlinear feature matrix is generated by calculating the ratio of high-order harmonic components to fundamental components of the signal; the energy distribution diagram of the multi-level decomposed signal is further analyzed to identify the energy concentration point of the intergranular corrosion area; A damage positioning and evaluation module (4) determines the specific location of intergranular corrosion based on the time difference of signal propagation and the energy distribution diagram, combined with a geometric positioning algorithm; the geometric positioning algorithm generates damage positioning coordinates in a three-dimensional space by calculating the relative distance between sensors and the time difference of signal arrival; at the same time, based on the intensity and distribution range of the energy concentration point, a fuzzy logic reasoning model is used to evaluate the severity of intergranular corrosion; An optimization and calibration module (5) dynamically calibrates the excitation signal frequency and sensor sensitivity of the detection system through an adaptive parameter adjustment mechanism; the adaptive parameter adjustment mechanism performs feedback control based on real-time acquired signal quality indicators, including signal-to-noise ratio and signal amplitude fluctuation range; when the signal quality indicators are below the preset threshold, the system automatically adjusts the frequency range of the excitation signal or the gain parameters of the sensor; A data storage and visualization module (6) stores the detection results to a cloud database and displays the location, severity and historical trend of intergranular corrosion through a graphical interface; the cloud database uses a distributed storage architecture to support multiple user concurrent access; the graphical interface presents the energy distribution of intergranular corrosion in the form of a heat map (9).

2. The method of claim 1, wherein: The low-frequency modulation component of the composite signal is set to 10 kHz to 50 kHz, and the high-frequency carrier component is set to 1 MHz to 5 MHz.

3. The method of claim 1, wherein: The distance between each sensor in the distributed sensor array (7) is set to 5 cm to 10 cm.

4. The method of claim 1, wherein: The low cutoff frequency of the band-pass filter is set to 5 kHz, and the high cutoff frequency is set to 10 MHz.

5. The method of claim 1, wherein: The decomposition level of the wavelet packet decomposition algorithm is set to 4 levels.

6. The method of claim 1, wherein: The geometric positioning algorithm generates damage positioning coordinates in a three-dimensional space by calculating the relative distance between sensors and the time difference of signal arrival.

7. The method of claim 1, wherein: The input variable of the fuzzy logic inference model includes the intensity and distribution range of the energy concentration point, and the output variable is the corrosion grade.

8. The method of claim 1, wherein: In the adaptive parameter adjustment mechanism, the frequency range adjustment step of the excitation signal is set to 1 kHz, and the gain parameter adjustment step of the sensor is set to 1 dB.

9. The method of claim 1, wherein: The data table structure of the cloud database includes detection time, corrosion position coordinates and corrosion grade fields.

10. The method of claim 1, wherein: The graphical interface presents the energy distribution of intergranular corrosion in the form of a heat map (9), and the color gradient of the heat map indicates that the energy intensity is from low to high from blue to red.

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