A defect detection method and system based on observation window adaptive screening

Abstract

The application discloses a kind of based on observation window adaptive screening defect detection method and system, the method includes: constructing open ultrasonic propagation link, on the basis of initial estimated defect depth, set scanning interval to carry out stepwise interval scanning;Extract adjacent multiple groups of depth inversion results to construct sliding analysis window and calculate its dispersion;In the case where defect true depth is unknown, with data stability as reliability criterion, the interval with minimum dispersion is adaptively screened as the best observation window, and the final defect depth is calculated and output in the window.The system includes: open discrete ultrasonic hardware end and data processing terminal.The application uses algorithm model to compensate hardware defects, and converts the probe interval selection process relying on artificial experience into data-driven objective screening, which improves the stability and reliability of measurement results under open low-cost ultrasonic detection conditions.The application can be widely applied in the field of nondestructive testing.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing, and in particular to a defect detection method and system based on adaptive screening of observation windows. Background Technology

[0002] Time-of-flight ultrasonic diffraction (TOFD) is an ultrasonic non-destructive testing technique based on Huygens' principle. It inverts the defect depth by receiving the time difference of diffracted waves generated at the defect endpoint. Existing related testing equipment mostly adopts highly integrated industrial-grade closed systems. The probe spacing is usually preset according to empirical formulas before testing, and the measurement accuracy is guaranteed by the high signal-to-noise ratio provided by expensive, high-performance dedicated hardware.

[0003] However, in experimental teaching, mechanism verification, or cost-constrained testing scenarios, low-cost discrete ultrasound hardware is often used to construct open propagation links. These systems generally suffer from large probe beam divergence angles, significant signal energy attenuation, and low signal-to-noise ratios. If traditional fixed probe spacing is still used, the system is easily affected by near-field blind zones or far-field attenuation, causing weak diffraction wave characteristics to be submerged by noise and difficult to reliably identify. This results in measurement results with a single spacing exhibiting significant randomness and systematic errors. Summary of the Invention

[0004] In view of this, in order to solve the technical problem that most existing detection methods based on Huygens' principle adopt a fixed probe spacing, resulting in low detection accuracy, the present invention proposes a defect detection method based on adaptive screening of the observation window. This method includes the following steps: An open discrete ultrasonic propagation link is adopted, with the transmitting and receiving probes symmetrically placed on the surface of the medium under test. Detection is performed at the initial spacing, and a preliminary estimate of the defect depth is calculated based on Huygens' principle. Using this estimate as a reference, the probe spacing is gradually changed for scanning, and the defect depth at each spacing is obtained through inversion. Based on these inversion results, a sliding analysis window is constructed, and dispersion evaluation is carried out to obtain the dispersion index of each window. The probe center spacing range corresponding to the window with the smallest dispersion is selected as the adaptive observation window. Finally, multiple sets of defect depth inversion results are extracted within this window and processed to obtain the final defect detection result.

[0005] In some embodiments, the open discrete ultrasonic propagation link includes a physically discrete pulse signal generator, a piezoelectric ceramic driver, a single pair of ultrasonic probes, and a digital oscilloscope; the output signal of the pulse signal generator is amplified by the piezoelectric ceramic driver to excite the transmitting probe, and the signal received by the receiving probe is directly connected to the digital oscilloscope for the acquisition and visualization of the raw time domain signal.

[0006] In addition to the above method, in a second aspect, the present invention also proposes a defect detection system based on adaptive screening of observation windows, the system comprising: The open, discrete ultrasound hardware includes an independently configured pulse signal generator, drive module, transmitting probe, receiving probe, and visual signal acquisition module. A data processing terminal is communicatively connected to the visualization signal acquisition module. The data processing terminal is equipped with a processor and a memory. The memory stores a computer program. When the processor executes the computer program, it implements the steps of the method described above.

[0007] Based on the above scheme, this invention provides a defect detection method and system based on adaptive screening of observation window. For the physical defect of severe beam divergence in low-cost discrete ultrasonic systems, an evaluation model combining spatial step scanning and sliding window discreteness analysis is introduced. This method transforms the traditional probe spacing selection process that relies on manual experience into a data-driven objective screening algorithm, suppressing the extremely high system error of low-cost devices when blindly setting the spacing to an extremely low level. Thus, under simple hardware conditions, a relatively stable defect depth inversion calculation is completed, improving the stability and accuracy of the detection results. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating the steps of a defect detection method based on adaptive filtering of observation windows according to the present invention. Figure 2 A schematic diagram of the hardware structure of an open discrete ultrasonic propagation link provided in an embodiment of the present invention; Figure 3 A schematic diagram of the ultrasonic diffraction time difference geometric model based on Huygens' principle provided in an embodiment of the present invention; Figure 4 The distribution curve of the measurement error of a defect test block with an actual depth of 6.5 mm as a function of probe spacing, provided for an embodiment of the present invention; Figure 5 The distribution curve of the measurement error of the defect test block with an actual depth of 15.5 mm provided in the embodiment of the present invention as a function of the probe spacing. Detailed Implementation

[0009] Currently, there is a lack of an adaptive measurement method for open, low-cost ultrasonic testing systems, an adaptive calibration method that uses algorithmic models to compensate for hardware defects and dynamically find the optimal measurement position to improve the stability and reliability of defect depth measurement results; at the same time, there is a lack of an experimental method that can combine changes in macroscopic propagation paths to intuitively and quantitatively verify the ultrasonic diffraction propagation mechanism.

[0010] This invention aims to solve the technical problem of large randomness and systematic error in measurement results at a single spacing in low-cost discrete ultrasonic testing systems due to large probe beam divergence angle, significant signal energy attenuation, and low system signal-to-noise ratio; at the same time, through multi-path consistency analysis, it enables intuitive and quantitative verification of the ultrasonic diffraction propagation mechanism.

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] It should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0013] It should be understood that the terms "system," "apparatus," "unit," and / or "module" used in this application are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0014] In the description of the embodiments of this application, "a plurality of" refers to two or more. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0015] Furthermore, flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, the steps can be processed in reverse order or simultaneously. Additionally, other operations can be added to these processes, or one or more steps can be removed from them.

[0016] Reference Figure 1 This is a flowchart illustrating an optional example of the defect detection method based on adaptive filtering of observation windows proposed in this invention. This method can be applied to computer devices, and the detection method proposed in this embodiment may include, but is not limited to, the following steps: Step S1: Construct an open discrete ultrasonic propagation link by symmetrically arranging the transmitting probe and receiving probe on the surface of the medium being measured. Step S2: Based on the transmitting probe and the receiving probe, perform detection at the initial distance, and calculate the initial estimated defect depth based on Huygens' principle; Step S3: Using the initial estimated defect depth as a reference, perform step-by-step spacing scanning and calculate the corresponding defect depth inversion results respectively; Step S4: Construct a sliding analysis window based on the defect depth inversion results and perform discreteness analysis to obtain the corresponding discreteness calculation results; Step S5: Based on the dispersion calculation results, filter the probe center spacing interval corresponding to the sliding analysis window with the smallest dispersion as the adaptive observation window; Step S6: Based on the adaptive observation window, extract the corresponding multiple sets of defect depth inversion results and perform calculation processing to obtain the defect detection results.

[0017] In some feasible embodiments, step S1 specifically includes: like Figure 2 As shown, the traditional integrated black-box flaw detector is decoupled, and an experimental platform is built using physically discrete components. Specifically, a 7.4V lithium battery pack powers a PWM pulse signal generator, which outputs a pulse signal of a specific frequency (0-100KHz). After being amplified by a piezoelectric ceramic driver (output voltage of 130V), the signal excites the transmitting probe to generate ultrasonic waves. The transmitting and receiving probes are symmetrically arranged on the surface of a prefabricated standard test block with defects. The two probes can move synchronously and symmetrically along the surface of the test block to adjust the center-to-center distance between the probes. The receiving probe receives the ultrasonic signal propagating inside the test block and directly connects it to a digital oscilloscope (sampling rate not less than 1GS / s) for the acquisition and visualization of the raw time-domain signal. The data processing terminal is connected to the digital oscilloscope to perform subsequent signal processing, depth inversion calculations, and observation window selection algorithms.

[0018] Preferably, to ensure the acoustic coupling effect, water is applied as an acoustic coupling agent between the contact surfaces of the transmitting probe, the receiving probe, and the test block to displace air from the contact surfaces, reduce air resistance to ultrasonic wave transmission, and improve the clarity of the ultrasonic image.

[0019] In some feasible embodiments, step S2 specifically includes: With an arbitrarily set initial probe center-to-center distance (2S), the transmitting probe is excited to generate ultrasonic waves. The ultrasonic waves propagate within the test block, and when they encounter a defect endpoint, according to the Huygens-Fresnel principle, the defect endpoint acts as a new secondary wave source, generating diffracted waves. A digital oscilloscope simultaneously acquires the original time-domain signal containing the direct wave, the diffracted waves from the upper and lower endpoints of the defect.

[0020] To improve the signal-to-noise ratio of diffracted waves, the acquired ultrasonic time-domain signal needs to be filtered before time difference extraction. The raw signal is imported into the data processing terminal, and the data processing software performs bandpass filtering based on the center frequency of the ultrasonic probe to remove environmental noise and out-of-band interference components that may be introduced by the discrete hardware structure, thereby improving the accuracy of diffracted wave peak identification.

[0021] Subsequently, the time it takes for the direct wave to reach the receiving probe is extracted. And the time it takes for the diffracted wave from the defect endpoint to reach the receiving probe. Calculate the time difference between the two. .

[0022] like Figure 3 As shown, a geometric model of ultrasonic diffraction time difference is established based on Huygens' principle. The acoustic path equation is constructed using the geometric Pythagorean theorem, combined with the propagation speed of ultrasound in the measured medium. and half of the probe center spacing The defect depth was calculated by inversion. The specific calculation formula is as follows: .in, The speed at which ultrasound propagates in the measured medium is denoted as . The time difference between the direct wave and the diffracted wave at the defect endpoint. It is half the center distance between the transmitting probe and the receiving probe. This serves as the initial estimate for setting the subsequent scan range.

[0023] In some feasible embodiments, step S3 specifically includes: Due to the acoustic beam diffusion and energy attenuation characteristics of low-cost probes, the measurement results at a single spacing exhibit significant randomness and systematic errors. Therefore, this invention uses the initially estimated defect depth as a benchmark to define the scanning interval.

[0024] Specifically, the starting and ending distances of the scan are determined based on the initial estimated depth, so that the ratio of the probe center distance (2S) to the defect depth covers a range of 1.5 to 3.0. Within this scanning range, a step-by-step scan is performed according to a preset step size.

[0025] In this embodiment, the preset step size is 2mm (a fixed step size or an adaptive step size within the range of 1mm to 5mm can also be used). During the scanning process, the center distance between the transmitting probe and the receiving probe is changed synchronously and symmetrically. At each probe distance, the signal acquisition, filtering and time difference extraction process in step S2 is repeated to calculate the defect depth inversion result at the corresponding distance.

[0026] In some feasible embodiments, step S4 specifically includes: In practical ultrasonic blind testing environments, the true depth of defects is unknown, making it impossible for the system to directly calculate the "measurement error" to find the optimal probe spacing. Therefore, this invention innovatively introduces "dispersion" as a core objective indicator for measuring data reliability. Its physical basis lies in the geometric consistency principle of ultrasonic diffraction: when the probe spacing is within the optimal observation range, interference from near-field blind zones, direct-pass wave masking, and far-field beam diffusion is minimized. At this point, the depth inversion results under multiple consecutive spacings should be highly consistent, i.e., the fluctuation (dispersion) is minimized.

[0027] The specific operation is as follows: Arrange the multiple sets of defect depth inversion results obtained in step S3 in ascending order of probe spacing. Extract several adjacent sets (e.g., three consecutive sets) of depth inversion results to construct a "sliding analysis window". As the probe center spacing increases, this analysis window slides backward point by point.

[0028] Within each sliding analysis window, the dispersion of the multiple depth inversion results is calculated. Dispersion can be calculated using standard deviation, variance, the range of multiple results, or the deviation rate relative to the initial estimated depth. By traversing and comparing the dispersion of different sliding windows, the dispersion distribution characteristics of the depth calculation results as a function of the probe center-to-center distance can be obtained. This characteristic curve typically exhibits a "U-shaped" curve with a distinct region of minimum values.

[0029] Through this preferred embodiment, the present invention abandons the traditional single-point extremum search method. In blind testing environments where the true depth of the defect is unknown, it innovatively uses "dispersion" as the core indicator for evaluating the reliability of measurement results, employing a continuous interval sliding window dispersion screening strategy. This strategy can effectively filter out accidental extrema caused by environmental noise, near-field blind zones, or poor local coupling, accurately locking the observation interval where the diffraction signal is most stable and least affected by far-field attenuation, greatly enhancing the robustness of the algorithm and the reliability of the detection results.

[0030] In some feasible embodiments, step S5 specifically includes: The data processing terminal performs a global comparison of the dispersion calculation results of each sliding analysis window, and selects the probe center spacing interval corresponding to the sliding window with the smallest dispersion as the adaptive observation window.

[0031] Within the propagation path corresponding to this interval, the diffracted wave signal exhibits the highest signal-to-noise ratio, and the depth inversion results show minimal fluctuation. Compared to traditional methods that rely solely on single-point signal amplitude or manual experience to select the spacing, this invention effectively filters out accidental jump points caused by environmental noise or poor local coupling through the "discretion" screening of continuous intervals. This significantly enhances the system's adaptability and detection robustness in situations with unknown defect depths.

[0032] In some feasible embodiments, step S6 specifically includes: Within the selected adaptive observation window, the arithmetic mean of the multiple depth inversion results contained in the window is calculated as the final defect depth output, thereby effectively suppressing random errors in low-cost systems.

[0033] Meanwhile, when the calculated defect depth results under different probe center-to-center spacing conditions remain highly consistent (i.e., extremely small dispersion), it is proven from a geometric kinematic perspective that the beams received by the receiving probe at different positions all precisely converge at the same physical spatial point (i.e., the defect endpoint). This, at the macroscopic experimental level, intuitively and quantitatively verifies the physical mechanism of Huygens' principle regarding "the edge of an obstacle as a secondary wave source radiating spherical waves outward."

[0034] Based on the overall process of the above method, the present invention also provides relevant verification data: Although Figure 4 and Figure 5 The visual representation shows how the absolute measurement error changes with the probe spacing. However, in actual blind testing, the "sliding window dispersion" curve calculated by the system backend shows a high positive correlation with the absolute error curve. That is, the trough of the smallest dispersion is precisely the interval with the smallest absolute error.

[0035] To further illustrate the effectiveness of the adaptive screening method of this invention, the following detailed explanation is provided in conjunction with specific experimental test data. In this embodiment, the measurement error is calculated as follows: .

[0036] Tests were conducted on a defect specimen with an actual depth of 6.5 mm: After the initial rough measurement in step S2, the system sets the scanning step size to 2mm and performs scanning within a range of 8mm to 24mm between the probe center-to-center distance (2S). It is important to emphasize that in the actual blind testing process, the system does not know the true depth of the defect beforehand, but directly performs depth inversion calculation on the signals collected at each distance.

[0037] Subsequently, the system executes the sliding window dispersion analysis algorithm in steps S4 and S5, extracting three consecutive measurement points as an analysis window. The calculation shows that within the sliding analysis window consisting of probe spacings of 12mm, 14mm, and 16mm, the three depth measurements retrieved by the system are 6.951mm, 6.812mm, and 6.965mm, respectively. The system calculates the dispersion (measured by range) of the data within this window to be only 0.153mm, reaching the lowest globally. Since the interval with the most stable data (smallest dispersion) in the ultrasonic diffraction geometry model is least affected by near-field and far-field interference, the system objectively locks [12mm, 16mm] as the optimal observation window and calculates the arithmetic mean of the three depth results within this window (i.e., 6.9093mm) as the final output.

[0038] Post-hoc verification and error analysis: To verify the accuracy of the above discrete-time screening algorithm, we compared the measured values ​​at each interval with the actual depth of the test block (6.5 mm), and plotted the results as follows: Figure 4 The absolute error distribution curve shown illustrates this. It can be clearly seen that when the probe spacing is small (e.g., 8mm) or too large (e.g., 24mm), the absolute errors reach as high as 1.65mm and 2.22mm respectively, exhibiting an overall "U-shaped" characteristic. However, the system automatically locks onto the [12mm, 16mm] interval based on dispersion, precisely hitting the trough region with the smallest absolute error (absolute error strictly controlled at 0.4093mm, relative error only about 6.30%). This fully demonstrates the scientific validity of using dispersion as an objective evaluation indicator.

[0039] Tests were conducted on a defect specimen with an actual depth of 15.5 mm: Similarly, the system performs step scans and depth inversion within the range of 24mm to 46mm. The dispersion analysis algorithm shows that when the sliding analysis window moves to the [30mm, 34mm] interval (i.e., probe spacing of 30mm, 32mm, and 34mm), the three depth measurements retrieved by the system are 15.952mm, 15.765mm, and 15.781mm, respectively. The system calculates that the dispersion (measured by range) of the data within this window drops sharply to 0.187mm, the lowest globally. The system automatically locks this interval with the smallest dispersion as the optimal observation window and calculates the final depth output value to be 15.8327mm.

[0040] Post-event combination Figure 5 Verification of the error curves shows that the absolute error is as high as 1.13mm to 3.85mm in the intervals of 24-28mm and 38-46mm; while the absolute error of the [30mm, 34mm] window that is automatically locked by the system is only 0.3327mm (the relative error is as low as about 2.15%).

[0041] The two sets of real experimental data for defects at different depths fully demonstrate that, when the actual depth is unknown, the discrete filtering algorithm of this invention can accurately avoid high-error-prone areas and achieve high-precision measurement in a low-cost system. The adaptive observation window filtering method proposed in this invention effectively overcomes the physical defects of low-cost discrete ultrasonic hardware, such as large beam divergence angle and low signal-to-noise ratio. This method transforms the original "manual optimization" process, which relied on the operator's rich engineering experience, into an "objective algorithmic locking" based on a discrete mathematical model. This not only improves the accuracy and stability of the measurement but also endows this low-cost open system with extremely high universality and engineering / educational application value.

[0042] Furthermore, this invention constructs an open physical link, breaking through the "black box" limitation of traditional flaw detection equipment. By changing the probe center-to-center distance within an adaptive observation window, altering the macroscopic geometric propagation path, and verifying the high consistency of depth inversion results, this invention experimentally verifies the location of the diffraction source from a geometric kinematics perspective. This closed-loop logic intuitively and quantitatively verifies the correctness of Huygens' principle at the macroscopic experimental level, providing an achievable experimental method for studying the propagation mechanism of ultrasonic diffraction, and endowing this method with extremely high value for physics teaching demonstration and basic scientific research verification.

[0043] A system for implementing the above method includes an open discrete ultrasound hardware terminal and a data processing terminal.

[0044] The open-type discrete ultrasound hardware includes: an independently configured PWM pulse signal generator (YK-PWM1041 model), a drive module (YK-HA130D model), a transmitting probe, a receiving probe (both are custom broadband longitudinal wave probes with a center frequency of 5MHz), and a digital oscilloscope (RIGOL DS1104Z Plus model) as a visualization signal acquisition module. The transmitting and receiving probes are symmetrically arranged, and the center-to-center distance can be adjusted in steps via a slide rail or manually.

[0045] A data processing terminal (such as a computer with algorithm software like MATLAB or Python installed) is connected to a digital oscilloscope. The data processing terminal is equipped with a processor and memory. The memory contains pre-installed modules for bandpass filtering algorithms, Huygens geometric model calculations, and sliding window discreteness analysis. When the processor executes these modules, it automatically reads the oscilloscope waveform, extracts the time difference, performs step-scan data recording, calculates the discreteness of each sliding window, and finally selects the optimal observation window and outputs a high-precision defect depth, thus completing the quantitative verification of the ultrasonic diffraction mechanism.

[0046] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0047] A storage medium storing processor-executable instructions, which, when executed by a processor, are used to implement a defect detection method based on adaptive screening of an observation window as described above.

[0048] The content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0049] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A defect detection method based on adaptive filtering of observation windows, characterized in that, Includes the following steps: An open, discrete ultrasonic propagation link is constructed, with the transmitting and receiving probes symmetrically arranged on the surface of the medium being measured; Based on the transmitting probe and the receiving probe, detection is performed at the initial distance, and the initial estimated defect depth is calculated based on Huygens' principle; Based on the initial estimated defect depth, a step-by-step spacing scan is performed, and the corresponding defect depth inversion results are calculated respectively. Based on the defect depth inversion results, a sliding analysis window is constructed, and a discreteness analysis is performed to obtain the corresponding discreteness calculation results. Based on the dispersion calculation results, the probe center spacing interval corresponding to the sliding analysis window with the smallest dispersion is selected as the adaptive observation window. Based on the adaptive observation window, multiple sets of defect depth inversion results corresponding to the window are extracted and processed to obtain defect detection results.

2. The defect detection method based on adaptive filtering of observation window according to claim 1, characterized in that, Following the step of performing a step-by-step interval scan based on the initial estimated defect depth and calculating the corresponding defect depth inversion results, the method further includes: The acquired ultrasonic time-domain signal is bandpass filtered based on the center frequency of the ultrasonic probe in the open discrete ultrasonic propagation link.

3. The defect detection method based on adaptive screening of observation window according to claim 1, characterized in that, The step of performing a step-by-step interval scan based on the initial estimated defect depth and calculating the corresponding defect depth inversion results specifically includes: Based on the initial estimated defect depth, the scanning range and preset step size are set; Based on the scanning interval and the preset step size, the center distance between the transmitting probe and the receiving probe is changed synchronously and symmetrically, and step scanning is performed to obtain multiple sets of ultrasonic diffraction signals under different probe center distances. The corresponding defect depth inversion results are calculated based on the ultrasonic diffraction signals.

4. The defect detection method based on adaptive filtering of observation window according to claim 1, characterized in that, The formula for calculating defect depth is: in, The value S represents the defect depth, and S is half the distance between the centers of the transmitting and receiving probes. This indicates the speed at which ultrasound waves propagate in the measured medium. Indicates time difference, This indicates the time it takes for the diffracted wave from the defect endpoint to reach the receiving probe. This indicates the time it takes for the direct wave to reach the receiving probe.

5. The defect detection method based on adaptive filtering of observation window according to claim 1, characterized in that, The indicators for the dispersion analysis include the standard deviation of each group of depth results within the sliding analysis window, the deviation rate of each group of depth results within the sliding analysis window relative to the initial estimated defect depth, and the range of multiple groups of results within the sliding analysis window.

6. The defect detection method based on adaptive filtering of observation window according to claim 3, characterized in that, The constraints of the scan interval include: The starting and ending intervals of the scan are defined by the multiple relationship of the initially estimated defect depth; The scanning interval covers a range greater than the preset ratio of probe center spacing to defect depth.

7. The defect detection method based on adaptive filtering of observation window according to claim 1, characterized in that, The step of extracting and processing multiple sets of defect depth inversion results within the adaptive observation window to obtain the defect detection result specifically involves using the arithmetic mean of multiple sets of depth inversion results within the adaptive observation window as the final defect depth detection result.

8. A defect detection system based on adaptive filtering using an observation window, characterized in that, The defect detection method based on adaptive filtering of observation windows as described in any one of claims 1-7 includes: The open, discrete ultrasound hardware includes an independently configured pulse signal generator, drive module, transmitting probe, receiving probe, and visual signal acquisition module. A data processing terminal is communicatively connected to the visualization signal acquisition module. The data processing terminal is equipped with a processor and a memory. The memory stores a computer program. When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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