Thick-wall pressure vessel inner wall damage detection method based on low-frequency ultrasonic phase control imaging algorithm

Abstract

The invention relates to a thick-wall pressure vessel inner wall damage detection method based on a low-frequency ultrasonic phase control imaging algorithm, which comprises the following steps: calculating to obtain an ultrasonic Lamb wave frequency dispersion curve of a thick-wall plate-shaped structure, and selecting a low-frequency guided wave frequency; determining parameter configuration of array elements of the phased array according to the low-frequency guided wave frequency, and arranging the array elements on the outer wall of the pressure vessel; exciting a low-frequency Lamb wave signal, and collecting FMC data through phased array elements, including echo signal data of each array element; frequency dispersion compensation processing is carried out on the acquired FMC data, and a phase coherence factor of each imaging pixel point is calculated; and adopting a TFM (Time Frequency Modulation) algorithm based on phase coherence factor weighting to carry out delay weighted reconstruction on the compensated FMC data to obtain an imaging and positioning result of the damage defect of the inner wall of the pressure vessel. Compared with the prior art, the problems of guided wave propagation frequency dispersion effect, complex mode, low signal-to-noise ratio and the like can be solved, and high-precision imaging positioning of inner wall damage defects is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of pressure vessel inner wall damage detection, and in particular to a thick-wall pressure vessel inner wall damage detection method based on a low-frequency ultrasonic phased imaging algorithm. Background Art

[0002] Thick-walled pressure vessels are widely used in industries such as petrochemicals, energy, and metallurgy. As core pressure-bearing components, their structural safety is directly related to the stable operation of the entire system. However, over long-term service, the inner walls of pressure vessels are susceptible to the combined effects of high temperature, high pressure, corrosive media, and stress cycles, resulting in various types of damage, including cracks, corrosion, and inclusions. If not detected and addressed in a timely manner, these can easily lead to major accidents such as leaks and explosions, resulting in serious loss of life and property.

[0003] Currently, nondestructive testing technologies for pressure vessels primarily include radiographic testing, magnetic particle testing, eddy current testing, and ultrasonic testing. Ultrasonic testing, due to its advantages such as strong penetration, high positioning accuracy, and reasonable cost, has become one of the primary methods for assessing damage to the inner walls of pressure vessels. However, traditional ultrasonic testing methods, which rely heavily on direct wave signals, are often limited by the high acoustic impedance of thick-walled structures, frequent mode switching, and low signal-to-noise ratio, making it difficult to achieve high-precision imaging of inner wall defects far from the inspection surface.

[0004] In recent years, ultrasonic guided wave technology, particularly Lamb waves, has gained increasing attention in the field of structural health monitoring due to its advantages such as long propagation distance and wide coverage. However, Lamb waves exhibit significant frequency dispersion, particularly in thick-walled structures. Their multimodal and frequency dispersion are significant, which can easily lead to signal waveform distortion and affect the accuracy of defect identification and location. Furthermore, the complex geometry and boundary conditions of thick-walled containers affect the guided wave propagation path, further complicating detection.

[0005] The introduction of phased array ultrasonic technology has provided a new approach for inspecting the interior walls of thick-walled structures. By flexibly controlling the excitation beam direction and focal position, guided wave energy can be focused on the target area, effectively improving detection sensitivity and resolution. However, suppressing dispersion effects during imaging and improving defect image quality remain technical challenges.

[0006] To address these issues, researchers have begun introducing full matrix capture (FMC) and total focusing method (TFM) into guided wave phased array inspection. Combined with dispersion compensation technology and phase coherence factor weighting, these methods significantly improve imaging quality, thereby enhancing the accuracy and reliability of defect detection. However, existing research has mostly focused on thin-plate structures or single-mode excitation, lacking an integrated solution for low-frequency guided wave excitation, array element layout optimization, and high-resolution imaging of the inner walls of thick-walled pressure vessels. Summary of the Invention

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a method for detecting inner wall damage of thick-walled pressure vessels based on a low-frequency ultrasonic phased imaging algorithm. It can overcome the difficulties such as significant guided wave dispersion, complex modes and low signal-to-noise ratio in thick-walled structures, and achieve high-precision imaging and positioning of inner wall damage defects.

[0008] The object of the present invention can be achieved by the following technical solution: A method for detecting damage on the inner wall of a thick-walled pressure vessel based on a low-frequency ultrasonic phased imaging algorithm, comprising the following steps:

[0009] S1. Calculate and obtain the ultrasonic Lamb wave dispersion curve of the thick-walled plate structure and select the low-frequency guided wave frequency;

[0010] S2. Determine the parameter configuration of the phased array elements according to the low-frequency guided wave frequency, and arrange the elements on the outer wall of the pressure vessel;

[0011] S3, stimulate low-frequency Lamb wave signals and collect FMC data through phased array elements, including echo signal data of each element;

[0012] S4, performing dispersion compensation processing on the collected FMC data and calculating the phase coherence factor of each imaging pixel;

[0013] S5. Using the TFM algorithm based on phase coherence factor weighting, the compensated FMC data is reconstructed with time delay weighting to obtain the imaging and positioning results of the damage defects on the inner wall of the pressure vessel.

[0014] Furthermore, the step S1 specifically selects a frequency within the S0 modal range as the low-frequency waveguide frequency.

[0015] Furthermore, the parameter configuration of the phased array elements in step S2 includes the type, spacing and number of piezoelectric transducers.

[0016] Furthermore, the step S2 specifically arranges the phased array elements on the outer wall of the pressure vessel at equal intervals or in combination with a geometric compensation strategy.

[0017] Furthermore, the process of arranging the array elements on the outer wall of the pressure vessel in step S2 includes:

[0018] The wavelength of the guided wave is calculated using the low-frequency guided wave frequency and the speed of sound, and the array is placed at a distance of 5 to 10 times the wavelength from the detection area.

[0019] In the array arrangement area, the outer insulation layer of the pressure vessel is removed, the outer wall is polished to facilitate the coupling of the piezoelectric transducer, and the piezoelectric sheets are pasted to the outer wall of the pressure vessel in a tangential array arrangement.

[0020] Furthermore, the dispersion compensation processing in step S4 is specifically based on the propagation time and wave velocity characteristics of the waveguide signal, and a dispersion compensation algorithm is applied to correct the propagation path of the signal.

[0021] Furthermore, the dispersion compensation process in step S4 includes:

[0022] Pad the received signal g(t) with zeros; perform Fourier transform on the zero-padding received signal g(t) to obtain G(ω), which is then multiplied by the group velocity c g (ω) to obtain H(ω); divide the wave number into equal intervals Δk and interpolate H(ω) to obtain H(k); perform inverse Fourier transform on H(k) to obtain the dispersion-compensated spatial domain signal h(k).

[0023] Furthermore, the process of calculating the phase coherence factor of each imaging pixel in step S4 includes:

[0024] For the time domain signal data(i, j, t) sent by array element i and received by array element j, the corresponding PCF (Phase Coherence Factor) is obtained by the following formula:

[0025]

[0026] Where Re(x) is the real part of the complex number, Im(x) is the imaginary part of the complex number, H[data(i,j,t)] is the Hilbert transform of the time domain signal data(i,j,t), and the characterization value of the phase distribution in the original PCF calculation process is expressed as the standard deviation of the instantaneous phase of the time domain signal data(i,j,t) at each moment;

[0027] In a manner similar to the delayed summation method of the full focusing algorithm, at each imaging pixel, the phase standard deviation of all array element signals passing through the path of that point is counted, and the coherence index is constructed by the ratio of the phase standard deviation to the sum of the amplitudes. The phase coherence result PCF(x,y) at the imaging grid point (x,y) is obtained:

[0028]

[0029] Wherein, Δt is the delay of virtual focusing, and n is the number of array elements of the array transducer.

[0030] Furthermore, the TFM algorithm based on phase coherence factor weighting in step S5 specifically introduces a phase coherence factor when performing delayed summation reconstruction to weight the echo signal of each array element:

[0031] I PCF (P(x,y))=I(P(x,y))PCF(x,y)

[0032] Where P(x,y) is the point in the grid in the imaging area, I PCF (P(x,y)) is the pixel amplitude value of P(x,y) with the phase coherence factor introduced, and I(P(x,y)) is the pixel amplitude value of P(x,y) in TFM imaging.

[0033] Furthermore, in step S5, the compensated FMC data is subjected to delayed weighted reconstruction to obtain an image reconstruction result, the image reconstruction result is normalized and then logarithmically compressed, and a threshold is taken on the image to obtain an imaging result for defect identification.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] The present invention first selects an appropriate low-frequency ultrasonic Lamb wave frequency (i.e., a low-frequency guided wave frequency) based on the dispersion curve of ultrasonic Lamb waves in thick-walled plate-like structures to plan the layout of ultrasonic guided wave phased array elements on the outer wall of the pressure vessel. The collected low-frequency full-matrix capture signal (i.e., FMC data) is then combined with a phase-coherence-weighted total focusing imaging (TFM) algorithm to obtain imaging and positioning results for damage defects on the inner wall of the pressure vessel. This is the first time that the present invention utilizes an ultrasonic phased array to excite low-frequency ultrasonic guided wave signals on the outer wall of a thick-walled pressure vessel, and utilizes an improved imaging algorithm to detect damage defects on thick-walled pressure vessels. This solves the problems of guided wave propagation dispersion effect, modal complexity, and insufficient imaging accuracy in damage detection on the inner wall of thick-walled pressure vessels, and can achieve high-precision imaging and positioning of inner wall damage defects.

[0036] The present invention selects a low-frequency ultrasonic Lamb wave frequency suitable for thick-walled structures, and selects a suitable waveguide mode by analyzing the dispersion characteristic curve, which can ensure that the subsequently excited waveguide signal has good penetration ability and propagation characteristics. Among them, the selection of the Lamb wave frequency is based on the matching of the frequency-wave velocity relationship in the dispersion characteristic curve, and the frequency range with a single modal excitation characteristic and strong penetration ability is preferred to obtain a pure waveguide mode; the low-frequency waveguide selects a frequency within the S0 modal range, and enhances the sensing ability of inner wall defects through its off-plane displacement response in the inner wall direction, thereby improving the detection sensitivity and resolution.

[0037] The present invention designs and arranges ultrasonic phased array elements based on the low-frequency guided wave frequency. The element arrangement scheme incorporates the geometric shape of thick-walled pressure vessels and adopts equal spacing or curved surface compensation to ensure that the ultrasonic guided wave signal covers the entire inner wall area, which can enhance the consistency and comprehensiveness of imaging. In addition, the wavelength of the guided wave is calculated using the low-frequency guided wave frequency value and the speed of sound. The array is arranged at a position 5 to 10 times the wavelength away from the detection area, which can avoid the near-field blind spot and ensure the stability of the ultrasonic guided wave.

[0038] The present invention performs dispersion compensation on the collected FMC data, which can correct signal distortion during propagation and ensure high data quality. Dispersion compensation analyzes the propagation time and wave velocity characteristics of the guided wave signal and applies a dispersion compensation algorithm to correct the signal propagation path. This corrects the signal waveform broadening and distortion caused by the Lamb wave dispersion effect, thereby effectively addressing the dispersion effect of thick-walled structures, reducing the interference of the dispersion effect on imaging results, and ensuring the accuracy of subsequent imaging processing.

[0039] The present invention proposes a total focusing imaging (TFM) algorithm based on phase coherence factor weighting, which is used to perform delayed weighted reconstruction of dispersion-compensated data. On the one hand, at each imaging pixel point, the phase standard deviation of all array element signals passing through the path of that point is statistically calculated, and the coherence index (i.e., phase coherence factor) is constructed as the ratio of its phase standard deviation to the sum of the amplitudes to characterize the signal consistency. On the other hand, the phase coherence factor is introduced when the TFM performs delayed summation reconstruction to weight the echo signals of each array element. This strengthens the focused energy of the target defect area, while effectively suppressing background scattering noise and reducing its interference with imaging, thereby achieving high-resolution imaging and precise positioning of damage defects on the inner wall of thick-walled pressure vessels. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the method flow of the present invention;

[0041] Figure 2 Schematic diagram of the application process of the embodiment;

[0042] Figure 3 is the ultrasonic guided wave dispersion curve of the thick-walled plate structure in the embodiment;

[0043] Figure 4 Schematic diagram of the arrangement of an 8-element phased array in an embodiment;

[0044] Figure 5 The dispersion results of Lamb waves at different propagation distances in the embodiment;

[0045] Figure 6 Schematic diagram of simulation of thick plate specimen in the embodiment;

[0046] Figure 7 This is a simulated imaging diagram of a thick plate sample in the embodiment. DETAILED DESCRIPTION

[0047] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Example

[0049] like Figure 1 As shown, a method for detecting damage on the inner wall of a thick-walled pressure vessel based on a low-frequency ultrasonic phased imaging algorithm includes the following steps:

[0050] S1. Calculate and obtain the ultrasonic Lamb wave dispersion curve of the thick-walled plate structure and select the low-frequency guided wave frequency;

[0051] S2. Determine the parameter configuration of the phased array elements according to the low-frequency guided wave frequency, and arrange the elements on the outer wall of the pressure vessel;

[0052] S3, stimulate low-frequency Lamb wave signals and collect FMC data through phased array elements, including echo signal data of each element;

[0053] S4, performing dispersion compensation processing on the collected FMC data and calculating the phase coherence factor of each imaging pixel;

[0054] S5. Using the TFM algorithm based on phase coherence factor weighting, the compensated FMC data is reconstructed with time delay weighting to obtain the imaging and positioning results of the damage defects on the inner wall of the pressure vessel.

[0055] This embodiment applies the above solution, such as Figure 2 As shown, the main contents are:

[0056] Calculate and obtain the ultrasonic Lamb wave dispersion curve suitable for thick-walled plate structures, and select a low-frequency guided wave frequency with good propagation characteristics;

[0057] Based on the selected low-frequency guided wave frequency, the phased array element configuration parameters, including the type, spacing, and number of piezoelectric transducers (PZTs), are determined. The elements are then arranged on the outer wall of the pressure vessel based on the guided wave wavelength calculation results.

[0058] Stimulate low-frequency ultrasonic Lamb wave signals and collect full matrix capture (FMC) data sets, that is, collect echo signal data from each array element;

[0059] Perform dispersion compensation processing on the FMC data to eliminate signal distortion caused by the dispersion effect and calculate the phase coherence factor of each imaging pixel;

[0060] A total focusing imaging algorithm (TFM) based on phase coherence factor weighting is used to perform time-delay weighted reconstruction on the dispersion-compensated data to generate clear inner wall damage imaging results and accurately locate the damaged area, achieving high-resolution imaging and precise positioning of damage defects on the inner wall of thick-walled pressure vessels.

[0061] In practical applications, specifically:

[0062] 1. Selection of ultrasonic signal frequency and piezoelectric transducer:

[0063] Calculate and obtain the ultrasonic Lamb wave dispersion curve of thick-walled plate structure, such as Figure 3 The calculation process requires the following parameters: Young's modulus E, Poisson's ratio υ, and material thickness.

[0064] A low-frequency waveguide with good propagation characteristics is selected, and the signal frequency is selected in the region where only the S0 and A0 modes exist in the low-frequency band. The piezoelectric transducer (PZT) is determined according to the selected frequency, and the piezoelectric plate size is calculated as follows:

[0065]

[0066] Where f is the center frequency of the piezoelectric piece, N t is the thickness piezoelectric constant, and d is the diameter of the piezoelectric sheet.

[0067] 2. Array arrangement:

[0068] The wavelength of the guided wave is calculated using the known frequency value and the speed of sound, and the array is arranged at a distance of 5 to 10 times the wavelength from the detection area to avoid the near-field blind spot. The reason is that the formation of ultrasonic guided waves is a coupling process, and a stable guided wave signal is obtained only after propagating several times the wavelength. In the array layout area, the outer insulation layer of the pressure vessel is removed, the outer wall is polished to facilitate transducer coupling, and the piezoelectric sheets are attached to the outer wall in a tangential array arrangement. This embodiment takes an 8-element phased array as an example, and the array element arrangement is as follows: Figure 4 shown.

[0069] 3. Full Focusing Imaging Algorithm for Low-Frequency Ultrasonic Guided Wave Phased Array:

[0070] (1) Dispersion compensation

[0071] In practice, the phase velocity c of the Lamb wave mode is p (ω) is not a constant, but a function related to frequency, then the wave number k(ω) is usually a nonlinear function of ω, so, for example Figure 5As shown, the time domain received signal will no longer maintain the shape of the original excitation signal. This solution uses time domain-space domain mapping to achieve dispersion compensation. The specific steps are as follows: zero-fill the received signal g(t); perform Fourier transform on the zero-filled received signal g(t) to obtain G(ω), and then multiply it by the group velocity c g (ω) to obtain H(ω); divide the wave number into equal intervals Δk and interpolate H(ω) to obtain H(k); perform inverse Fourier transform on H(k) to obtain the dispersion-compensated spatial domain signal h(k).

[0072] (2) Calculation of phase coherence factor

[0073] The phase coherence factor (PCF) only performs phase coherence weighting on a single focal point. With the introduction of TFM virtual focusing, phase processing must be performed on each echo signal in the FMC dataset. The time domain signal emitted by element i and received by element j is denoted as data(i, j, t). The PCF factor for this signal is calculated using the following formula:

[0074]

[0075] In the above formula, Re(x) is the real part of the complex number, Im(x) is the imaginary part of the complex number, H[data(i,j,t)] is the Hilbert transform of the time domain signal data(i,j,t), and the phase distribution representation value during the original PCF calculation process is expressed as the standard deviation of the instantaneous phase of the time domain signal data(i,j,t) at each moment. The phase coherence result PCF(x,y) at the imaging grid point (x,y) is obtained in a delayed-sum manner similar to the full focusing algorithm:

[0076]

[0077] In the above formula, Δt is the delay of virtual focusing, and n is the number of array elements of the array transducer.

[0078] (3) Full-focus imaging algorithm

[0079] The total focusing imaging algorithm (TFM) based on the full matrix capture (FMC) data set can virtually focus on every point in the imaging area by phase shifting and superimposing the array signals in the FMC data set. The FMC data set is acquired by the full matrix capture method and is considered to contain the complete geometric features and defect information inside the target being measured. For a one-dimensional linear array transducer with a total number of N array elements, the acquisition process of the full matrix capture data set can be described as: independently excite the first array element, and let all array elements, including the excitation array element, receive and store the echo signal within the pulse repetition period, obtaining N groups of echo data S 11 ~S 1NThen the next array element is activated and the operation is repeated, and so on, until all N 2 The group echo signals are collected into a data set matrix, thereby obtaining a complete data set containing the transducer transmit and receive combinations.

[0080] Assuming the total number of array elements is N, taking any point P(x,y) in the grid of the imaging area as an example, the transit time for a given transmit-receive array element combination at point P is calculated as:

[0081]

[0082] Where c is the speed of sound in the medium, x tx and x rcv are the lateral positions of the transmitting and receiving array elements, respectively. By calculating the acoustic path difference from each transmitting array element to the virtual focal point P and then to each receiving array element, all signals in the array data set are phase-shifted and superimposed to achieve an equivalent focusing enhancement effect at point P. The corresponding amplitude of the virtual focused beam is extracted through the Hilbert transform to obtain the pixel value representing the scattering information at that point. The pixel amplitude value at point P can be expressed as:

[0083]

[0084] Where tx and rcv represent the transmitting and receiving array element numbers, respectively. The above operation is repeated for all grid pixels within the imaging area. Once the amplitude information of the pixels in the entire imaging detection area is obtained, the image of the entire target area can be reconstructed.

[0085] For a grid P(x,y) in the imaging area, after introducing the phase coherence factor PCF to perform phase coherence weighting on a single focus point, the pixel amplitude value of the focus point P is expressed as:

[0086] I PCF (P(x,y))=I(P(x,y))PCF(x,y)

[0087] (4) Normalization of imaging results and identification of defects

[0088] The image reconstruction result is normalized and logarithmically compressed using the designed imaging program, and the image threshold is taken to obtain the imaging result for defect identification. In the simulation, a 2m*2m*16mm thick plate sample ( Figure 6 The results obtained after imaging are shown as Figure 7 As shown, areas greater than -6dB in the imaging results are usually considered defects.

[0089] In summary, this solution, by introducing dispersion compensation technology, phase coherence factor weighting, and total focusing imaging, addresses the issues of guided wave propagation dispersion, modal complexity, and insufficient imaging accuracy in thick-walled pressure vessel inner wall damage detection. The goal of this solution is to provide an effective method that can both suppress dispersion effects and improve the accuracy of thick-walled pressure vessel inner wall damage detection, thereby overcoming the challenges faced by existing technologies in dealing with complex inner wall damage in thick-walled vessels.

[0090] This proposal proposes for the first time a technical solution for high-precision damage detection of the inner wall of thick-walled pressure vessels using low-frequency ultrasonic phased array guided wave signals. Through frequency dispersion compensation and phase coherence factor weighting technology, the accuracy and resolution of detection are improved.

[0091] Compared with traditional ultrasonic testing methods, this solution can effectively deal with the dispersion effect of thick-walled structures, ensure the high-quality propagation of ultrasonic guided wave signals, and overcome the propagation attenuation problem in the detection of defects on the inner wall of thick-walled containers.

[0092] In addition, the detection method proposed in this scheme has good adaptability in engineering practice and can be widely used in non-destructive testing of various thick-walled pressure vessels, such as coke towers, reactors and high-pressure heat exchangers, and has good structural adaptability and engineering feasibility.

[0093] The full-focus imaging technology provided by this solution not only improves the clarity of defect imaging, but also enhances detection capabilities in high-noise environments through phase weighting, providing a reliable basis for safety assessment and maintenance decisions in the industrial field. This solution also combines reasonable array arrangement design and signal processing mechanism to provide an efficient and feasible technical path for non-destructive testing of thick-walled structures.

[0094] It should be noted that when applying this solution, the response characteristics of typical inner wall defect types (including cracks, corrosion, inclusions, etc.) in the imaging image can also be obtained through simulation or experiments, and a damage identification standard system can be established based on the image characteristics to assist in defect type identification and intelligent evaluation.

Claims

1. A method for detecting damage on the inner wall of a thick-walled pressure vessel based on a low-frequency ultrasonic phased imaging algorithm, characterized in that: The following steps are involved: S1. Calculate and obtain the ultrasonic Lamb wave dispersion curve of the thick-walled plate structure and select the low-frequency guided wave frequency; S2. Determine the parameter configuration of the phased array elements according to the low-frequency guided wave frequency, and arrange the elements on the outer wall of the pressure vessel; S3, stimulate low-frequency Lamb wave signals and collect FMC data through phased array elements, including echo signal data of each element; S4, performing dispersion compensation processing on the collected FMC data and calculating the phase coherence factor of each imaging pixel; S5. Using the TFM algorithm based on phase coherence factor weighting, the compensated FMC data is reconstructed with time delay weighting to obtain the imaging and positioning results of the damage defects on the inner wall of the pressure vessel.

2. The method for detecting inner wall damage of thick-walled pressure vessels based on low-frequency ultrasonic phased imaging algorithm according to claim 1, characterized in that: The step S1 specifically selects a frequency within the S0 modal range as the low-frequency waveguide frequency.

3. The method for detecting inner wall damage of thick-walled pressure vessels based on low-frequency ultrasonic phased imaging algorithm according to claim 1, characterized in that: The parameter configuration of the phased array elements in step S2 includes the type, spacing and number of piezoelectric transducers.

4. The method for detecting inner wall damage of thick-walled pressure vessels based on low-frequency ultrasonic phased imaging algorithm according to claim 3, characterized in that: The step S2 specifically involves arranging the phased array elements on the outer wall of the pressure vessel at equal intervals or in combination with a geometric compensation strategy.

5. The method for detecting damage on the inner wall of a thick-walled pressure vessel based on a low-frequency ultrasonic phased imaging algorithm according to claim 4, characterized in that: The process of arranging the array elements on the outer wall of the pressure vessel in step S2 includes: The wavelength of the guided wave is calculated using the low-frequency guided wave frequency and the speed of sound, and the array is placed at a distance of 5 to 10 times the wavelength from the detection area. In the array arrangement area, the outer insulation layer of the pressure vessel is removed, the outer wall is polished to facilitate the coupling of the piezoelectric transducer, and the piezoelectric sheets are pasted to the outer wall of the pressure vessel in a tangential array arrangement.

6. The method for detecting damage on the inner wall of a thick-walled pressure vessel based on a low-frequency ultrasonic phased imaging algorithm according to claim 1, characterized in that: The dispersion compensation processing in step S4 is specifically based on the propagation time and wave velocity characteristics of the waveguide signal, and a dispersion compensation algorithm is applied to correct the propagation path of the signal.

7. The method for detecting damage on the inner wall of a thick-walled pressure vessel based on a low-frequency ultrasonic phased imaging algorithm according to claim 6, characterized in that: The dispersion compensation process in step S4 includes: Pad the received signal g(t) with zeros; perform Fourier transform on the zero-padding received signal g(t) to obtain G(ω), which is then multiplied by the group velocity c g (ω) to obtain H(ω); divide the wave number into equal intervals Δk and interpolate H(ω) to obtain h(k); perform inverse Fourier transform on h(k) to obtain the dispersion-compensated spatial domain signal h(k).

8. The method for detecting damage on the inner wall of a thick-walled pressure vessel based on a low-frequency ultrasonic phased imaging algorithm according to claim 1, characterized in that: The process of calculating the phase coherence factor of each imaging pixel in step S4 includes: For the time domain signal data(i, j, t) sent by array element i and received by array element j, the corresponding PCF is obtained by the following formula: Where Re(x) is the real part of the complex number, Im(x) is the imaginary part of the complex number, H[data(i,j,t)] is the Hilbert transform of the time domain signal data(i,j,t), and the characterization value of the phase distribution in the original PCF calculation process is expressed as the standard deviation of the instantaneous phase of the time domain signal data(i,j,t) at each moment; In a manner similar to the delayed summation method of the full focusing algorithm, at each imaging pixel, the phase standard deviation of all array element signals passing through the path of that point is counted, and the coherence index is constructed by the ratio of the phase standard deviation to the sum of the amplitudes. The phase coherence result PCF(x,y) at the imaging grid point (x,y) is obtained: Wherein, Δt is the delay of virtual focusing, and n is the number of array elements of the array transducer.

9. The method for detecting damage on the inner wall of a thick-walled pressure vessel based on a low-frequency ultrasonic phased imaging algorithm according to claim 8, characterized in that: The TFM algorithm based on phase coherence factor weighting in step S5 specifically introduces the phase coherence factor when performing delayed summation reconstruction to weight the echo signal of each array element: I PCF (P(x,y))=I(P(x,y))PCF(x,y) Where P(x,y) is the point in the grid in the imaging area, I PCF (P(x,y)) is the pixel amplitude value of P(x,y) with the phase coherence factor introduced, and I(P(x,y)) is the pixel amplitude value of P(x,y) in TFM imaging.

10. A method for detecting damage on the inner wall of a thick-walled pressure vessel based on a low-frequency ultrasonic phased imaging algorithm according to any one of claims 1 to 9, characterized in that: In step S5, the compensated FMC data is subjected to delayed weighted reconstruction to obtain an image reconstruction result. The image reconstruction result is normalized and then logarithmically compressed. After the image is thresholded, an imaging result for defect identification is obtained.

Images