Ferromagnetic material defect identification method and system based on magnetic gradient tensor measurement

Abstract

The invention discloses a ferromagnetic material defect identification method and system based on magnetic gradient tensor measurement, and relates to the technical field of nondestructive testing, and the method comprises the following steps: acquiring magnetic gradient tensor data of a to-be-tested piece; calculating a total horizontal derivative based on the magnetic gradient tensor data; calculating a z-direction partial derivative of each component based on the total horizontal derivative and calculating a vertical derivative based on the magnetic gradient tensor data; and identifying a defect boundary by using the magnetic gradient tensor fusion derivative detector and the magnetic gradient tensor anti-noise boundary detector, and outputting a defect profile diagram. According to the invention, the MGTFDED and MGTNRED detectors specially designed for industrial defect detection scenes are provided for the first time, and the problem that a geophysical method is not suitable for direct transplantation is solved. According to the method, the edges of various defects from surface cracks to deep buried holes and the like can be identified with high precision, and the accuracy, noise resistance and intelligent level of detection are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of non-destructive testing, in particular to a ferromagnetic material defect identification method and system based on magnetic gradient tensor measurement. BACKGROUND

[0002] Ferromagnetic components are widely used in industrial facilities, and their structural integrity is crucial. Currently, magnetic non-destructive testing methods such as Metal Magnetic Memory testing (MMM), Magnetic Flux Leakage testing (MFL), etc. are widely used for defect detection of such components. However, traditional methods often have limitations such as blurred boundaries, lack of sensitivity to defect types, and susceptibility to environmental noise when accurately identifying the edges of defects, especially small, deep, or complex-shaped defects.

[0003] The field of geophysical exploration has developed a series of mature techniques based on magnetic gradient tensors for boundary identification of magnetic target bodies, such as vertical derivative method, total horizontal derivative method, and analytical signal amplitude method. These methods sharpen the boundaries by enhancing the gradient changes of the field data.

[0004] Traditional magnetic gradient detection methods have significant limitations: limited detection depth, difficulty in identifying deep defects; diffuse boundaries, non-convergent; unstable detection results, slight lifting changes leading to fluctuation of results, requiring precise mechanical devices to maintain a constant lifting height; poor field applicability, difficulty in detecting curved or irregular surfaces, etc.

[0005] When these boundary identification techniques from the field of geophysical exploration are directly applied to defect detection scenarios, these problems are particularly prominent.

[0006] Therefore, there is an urgent need for a high-precision magnetic gradient tensor analysis method that is specialized for industrial defect detection scenarios and can identify the edges of various defects. SUMMARY

[0007] The present application aims to overcome the shortcomings of the prior art and provide a magnetic gradient tensor defect identification method and system that can adapt to different defect types, accurately identify edges, and has strong noise resistance, solving the problem of poor results when traditional geophysical boundary identification methods are directly applied to industrial defect detection. It is suitable for accurate identification of the edges of various defects such as cracks, holes, corrosion, and stress damage in pipelines, pressure vessels, steel structures, etc.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions: On the one hand, the present application provides a ferromagnetic material defect identification method and system based on magnetic gradient tensor measurement, comprising the following steps: The magnetic gradient tensor data of the measured object is acquired by using an AMR magnetic gradient sensor array and preprocessed; The total horizontal derivative is calculated based on the magnetic gradient tensor data; The partial derivative of each component in the z direction is calculated based on the total horizontal derivative, and the vertical derivative is calculated based on the magnetic gradient tensor data; The defect boundary is identified by using a magnetic gradient tensor fusion derivative edge detector (MGT_FDED) and a magnetic gradient tensor noise-resistant edge detector (MGT_NRED), and a defect profile map is output.

[0009] In some embodiments, the surface of the measured object is scanned by a magnetic gradient sensor array, the magnetic induction intensity components Bx, By, and Bz are acquired, and nine components of the magnetic gradient tensor (of which five are independent components) Gxx, Gxy, Gxz, Gyx, Gyy, Gyz, Gzx, Gzy, and Gzz are calculated.

[0010] In some embodiments, the calculation expression of the total horizontal derivative is: ; ; ; In the formula, 、 、 are the components of the magnetic induction intensity vector in the x, y, and z directions, respectively; is the component of the gradient tensor matrix; is the x component of the total horizontal derivative of the magnetic intensity tensor; is the y component of the total horizontal derivative of the magnetic intensity tensor; is the z component of the total horizontal derivative of the magnetic gradient tensor.

[0011] In some embodiments, the calculation expression of the partial derivative of each component in the z direction of the total horizontal derivative is: ; ; ; In the formula, is the x component of the total horizontal derivative of the magnetic intensity tensor; is the y component of the total horizontal derivative of the magnetic intensity tensor; is the z component of the total horizontal derivative of the magnetism tensor.

[0012] In some embodiments, the calculation expression of the vertical derivative is: ; ; ; wherein, is a component of the gradient tensor matrix.

[0013] In some embodiments, the magnetic gradient tensor fusion derivative detector is: ; The vertical derivative enhances the deep weak signal extraction capability, significantly improves the noise immunity, and more importantly solves the technical problem that the traditional method is sensitive to the lift-off value change, and maintains stable detection performance in a large lift-off value range. The magnetic gradient tensor noise immunity boundary detector is: ; Based on the original field component, the superficial high-frequency details are preserved, and the edge smoothing effect is avoided.

[0014] wherein, is the x component of the total horizontal derivative of the magnetism tensor; is the y component of the total horizontal derivative of the magnetism tensor; is the z component of the total horizontal derivative of the magnetism tensor; is a component of the gradient tensor matrix; is a constant.

[0015] In some embodiments, based on the defect type, different types of defects are matched with the corresponding relationship of the magnetic gradient tensor fusion derivative detector or the magnetic gradient tensor noise immunity boundary detector through a query table.

[0016] In some embodiments, surface cracks, dense pores or pitting defects are identified by the magnetic gradient tensor noise immunity boundary detector; deep buried holes, stress cracks or large area corrosion are identified by the magnetic gradient tensor fusion derivative detector.

[0017] In another aspect, the present application provides a ferromagnetic material defect identification system based on magnetic gradient tensor measurement, which is used to perform the above method, comprising: A data acquisition module: using an AMR magnetic gradient sensor array to acquire magnetic gradient tensor data of a test piece; A data processing module: calculating the total horizontal derivative based on the magnetic gradient tensor data; Calculating the partial derivative of each component in the z direction based on the total horizontal derivative and calculating the vertical derivative based on the magnetic gradient tensor data; Identify defect boundaries using magnetic gradient tensor fusion derivative detector and magnetic gradient tensor noise-resistant boundary detector; Output module: outputs and displays the processing results of the data processing module.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This paper presents the first MGT_FDED and MGT_NRED detectors specifically designed for industrial defect detection, resolving the incompatibility of geophysical methods. Optimized for weak deep-seated signals and high-frequency shallow-surface signals, respectively, the MGT_FDED and MGT_NRED detectors enable clearer and more accurate edge identification of a wide range of defects, from surface to deep layers, and from linear cracks to volumetric voids. The MGT_FDED detector effectively suppresses surface noise through the field continuation vertical derivative, significantly improving the signal-to-noise ratio and ensuring stable performance in complex industrial environments.

[0019] The present invention adopts a strategy of adaptively selecting a detector according to defect characteristics (types), thereby improving versatility and automation and reducing dependence on operator experience.

[0020] The present invention can identify the edges of various defects from surface cracks to deep buried holes with high precision, significantly improving the accuracy, noise resistance and intelligence level of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the overall process of Embodiment 1 of the present invention; Figure 2 The three-dimensional defect model used in the simulation experiment of the present invention can only be used to calculate the gradient tensor on an accurate three-dimensional model; Figure 3 Schematic diagram of the comparison between the magnetic gradient tensor noise-resistant boundary detector of Example 1 of the present invention and the prior art (surface crack at small lift-off value (MGT_NRED in the right figure is better and less affected by the geomagnetic field)); Figure 4 Schematic diagram comparing the magnetic gradient tensor fusion derivative detector of Example 1 of the present invention with the prior art (buried vias under large lift-off values ​​(MGT_FDED on the left is superior, with clear boundaries and no divergence, and is less affected by the lift-off value)); Figure 5 This is a structural diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0022] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application; based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0023] Embodiment one: Please refer to Figures 1-4 A ferromagnetic material defect identification method based on magnetic gradient tensor measurement is suitable for edge accurate identification of various defects such as cracks, holes, corrosion and stress damage in pipelines, pressure vessels, steel structures and the like. In this embodiment, a welded area on an X80 steel pipe is taken as an example, which contains a surface crack with a depth of about 1 mm and a deep buried hole with a depth of about 6 mm.

[0024] Specifically, the following steps are included: S1, acquiring magnetic gradient tensor data of the measured member by acquisition.

[0025] An AMR magnetic gradient sensor array is used to scan the weld area with a lift-off height of 1 mm and a step size of 0.5 mm. Three-component magnetic field data of the steel pipe (Bx, By, Bz) are obtained.

[0026] After data acquisition, the three-component magnetic field data of Bx, By, and Bz are filtered (filter size: ). In order to improve the signal-to-noise ratio of BDA2 to shallow defects, background field correction and other preprocessing operations can be used. The background field correction is used to separate the slowly varying background field interference from the original magnetic field data.

[0027] In one specific embodiment, the background field correction code in the Matlab code example is as follows: Estimate the background field using a large window median filter; background_Bz = medfilt2(Bz, [50, 50]); Subtract the background field from the original signal; Bz_corrected = Bz - background_Bz; Recalculate the magnetic gradient tensor and related features using the corrected Bz Nine components of the magnetic gradient tensor (5 of which are independent components) Gxx, Gxy, Gxz, Gyx, Gyy, Gyz, Gzx, Gzy, Gzz are calculated by central difference method. The grid spacing dx = dy = 0.5 mm.

[0028] S2, calculating the total horizontal derivative based on the magnetic gradient tensor data.

[0029] Computing , , characteristic quantities: ; ; ; In the formula, , , respectively, the components of the magnetic induction intensity vector in the x, y, z directions; is the component of the gradient tensor matrix; is the x component of the total horizontal derivative of the magnetic intensity tensor; is the y component of the total horizontal derivative of the magnetic intensity tensor; is the z component of the total horizontal derivative of the magnetic gradient tensor.

[0030] S3, based on the total horizontal derivative, the partial derivative of each component in the z direction is calculated by the frequency domain field continuation function, and the continuation distance dz is set to 0.01m.

[0031] ; ; ; In the formula, is the x component of the total horizontal derivative of the magnetic intensity tensor; is the y component of the total horizontal derivative of the magnetic intensity tensor; is the z component of the total horizontal derivative of the magnetic intensity tensor.

[0032] S4, based on the magnetic gradient tensor data, the vertical derivative is calculated.

[0033] ; ; ; In the formula, is the component of the gradient tensor matrix.

[0034] S5, two boundary recognition result matrices are calculated by using the magnetic gradient tensor fusion derivative detector and the magnetic gradient tensor noise-resistant boundary detector, the defect boundary is recognized, and the final adaptive boundary recognition result is imaged to provide a clear defect contour diagram for the detector.

[0035] The magnetic gradient tensor fusion derivative detector is calculated based on the vertical derivative, and the calculation expression is: ; The magnetic gradient tensor fusion derivative detector utilizes vertical derivative enhancement to improve the extraction ability of deep weak signals and slowly varying signals, and has good noise resistance. Through vertical derivative normalization, signal attenuation caused by increasing lift-off is effectively compensated, fundamentally solving the lift-off value sensitivity problem.

[0036] The vertical derivative normalization adopts a tan-1(dTHDx_dz2 | dTHDy_dz2) structure.

[0037] The code of the magnetic gradient tensor fusion derivative detector in the Matlab code example is as follows: numerator_FDED = dTHDx_dz.^2 + dTHDy_dz.^2; denominator_FDED = dGxz_dz.^3 + dGyz_dz.^3 + eps; FDED = numerator_FDED. / denominator_FDED.

[0038] The magnetic gradient tensor noise-resistant boundary detector is calculated based on the original field components, better retaining high-frequency details and spatial distribution characteristics, and the calculation expression is: ; In the formula, is the x component of the total horizontal derivative of the magnetic gradient tensor; is the y component of the total horizontal derivative of the magnetic gradient tensor; is the component of the gradient tensor matrix; is a constant, a minimum value, preventing division by zero error.

[0039] The code of the magnetic gradient tensor noise-resistant boundary detector in the Matlab code example is as follows: numerator_NRED = THDx.^2 + THDy.^2; denominator_NRED= Gxz.^3 + Gyz.^3+ eps; NRED = numerator_NRED. / denominator_NRED; Normalize FDED and NRED to zero mean and unit variance for fusion.

[0040] FDED_norm = (FDED - mean(FDED(:))) / (std(FDED(:)) + eps); NRED_norm = (NRED - mean(NRED(:))) / (std(NRED(:)) + eps).

[0041] The embodiment is based on a defect type strategy: a query table is established as follows, and the optimal method is selected according to prior knowledge or preliminary identification results.

[0042] Table 1 Defect type and detector corresponding query table

[0043] The essential difference between the magnetic gradient tensor fusion derivative detector and the magnetic gradient tensor noise-resistant boundary detector in the basic principle and the processing target is: 1. Signal feature difference: MGT_FDED is based on the vertical derivative, sensitive to deep weak signals but smooths the shallow details; MGT_NRED is based on the original field component, retains high-frequency details but has weak response to deep signals.

[0044] 2. Noise processing difference: MGT_FDED suppresses surface noise through vertical derivative, but amplifies deep noise; MGT_NRED directly processes original data, and the noise sensitivity is different.

[0045] 3. Feature space inconsistency: the boundary responses output by the two detectors are different in dimension, dynamic range and spatial distribution.

[0046] 4. Optimization target conflict: MGT_FDED pursues deep signal enhancement, MGT_NRED pursues surface detail retention, and there is a fundamental optimization conflict.

[0047] Therefore, simple weighted fusion will cause boundary blur and signal-to-noise ratio to decrease, and the most suitable detector must be selected according to the physical characteristics of the defect.

[0048] As shown in Figures 3-4 , to verify the results, ultrasonic detection (for re-inspecting internal buried holes) or magnetic powder detection (for re-inspecting surface cracks) can be used to confirm the identified defect area. Experiments show that, after using the present application, the boundary positioning error of deep defects can be less than 1 mm, and the boundary positioning error of shallow defects can be less than 0.5 mm.

[0049] In the embodiment, for a 1 mm surface crack area, the alpha_map value is close to 0, and the output result is mainly contributed by NRED, thereby retaining clear crack details. For a 6 mm deep buried hole area, the alpha_map value is about (6-3) / 5=0.6, the output result is dominated by FDED (60%) and assisted by NRED (40%), effectively enhancing the deep signal while retaining part of the structure characteristics.

[0050] Embodiment two As Figure 5 shown in the figure, a ferromagnetic material defect identification system based on magnetic gradient tensor measurement is used to perform the above method, comprising: A data acquisition module: using an AMR magnetic gradient sensor array to acquire the magnetic gradient tensor data of the measured object.

[0051] A data processing module: calculating the total horizontal derivative based on the magnetic gradient tensor data; calculating the partial derivative of each component in the z direction based on the total horizontal derivative and calculating the vertical derivative based on the magnetic gradient tensor data; Using the magnetic gradient tensor fusion derivative detector and the magnetic gradient tensor noise-resistant boundary detector to identify the defect boundary.

[0052] An output module: imaging the final adaptive boundary identification result to provide clear defect contour diagram for the detector.

[0053] The ferromagnetic material defect identification system based on magnetic gradient tensor measurement of the present application can be installed in a computer device. The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a ferromagnetic material defect identification program based on magnetic gradient tensor measurement. The memory includes at least one type of readable storage medium, including flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The processor is the control core of the electronic device, which connects all components of the computer device through various interfaces and lines, and executes or runs the program or module stored in the memory, and calls the data stored in the memory, to perform various functions and process data of the computer device.

[0054] The module of the present application refers to a series of computer program segments that can be executed by the processor of the computer device and can complete fixed functions, which are stored in the memory of the computer device.

[0055] The system provided by the embodiment of the present application has the same implementation principle, generated technical effects and the above-mentioned method embodiment. For brief description, the part not mentioned in the device embodiment can refer to the corresponding content in the above-mentioned method embodiment.

[0056] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0057] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are within the scope of the technical solution of the present invention.

Claims

1. A method for identifying defects in ferromagnetic materials based on magnetic gradient tensor measurement, characterized in that: The following steps are involved: Collect and obtain magnetic gradient tensor data of the test piece; calculating a total horizontal derivative based on the magnetic gradient tensor data; Calculating the partial derivative of each component in the z direction based on the total horizontal derivative and calculating the vertical derivative based on the magnetic gradient tensor data; The magnetic gradient tensor fusion derivative detector and the magnetic gradient tensor noise-resistant boundary detector are used to identify the defect boundary and output the defect contour map.

2. The method for identifying defects in ferromagnetic materials based on magnetic gradient tensor measurement according to claim 1, characterized in that: The surface of the test piece is scanned by a magnetic gradient sensor array to obtain the magnetic induction intensity components Bx, By, and Bz, and the nine components of the magnetic gradient tensor are calculated: Gxx, Gxy, Gxz, Gyx, Gyy, Gyz, Gzx, Gzy, and Gzz.

3. The method for identifying defects in ferromagnetic materials based on magnetic gradient tensor measurement according to claim 1, characterized in that: The calculation expression of the total horizontal derivative is: ； ； ； Where, 、 、 are the components of the magnetic induction intensity vector in the x, y, and z directions respectively; are the components of the gradient tensor matrix; is the x-component of the total horizontal derivative of the magnetic intensity tensor; is the y component of the total horizontal derivative of the magnet degree tensor; is the z component of the total horizontal derivative of the magnetic gradient tensor.

4. The method for identifying defects in ferromagnetic materials based on magnetic gradient tensor measurement according to claim 1, characterized in that: The calculation expression of the partial derivative of each component of the total horizontal derivative in the z direction is: ； ； ； Where, is the x-component of the total horizontal derivative of the magnet degree tensor; is the y component of the total horizontal derivative of the magnet degree tensor; is the z component of the total horizontal derivative of the magnet degree tensor.

5. The method for identifying defects in ferromagnetic materials based on magnetic gradient tensor measurement according to claim 1, characterized in that: The calculation expression of the vertical derivative is: ； ； ； Where, are the components of the gradient tensor matrix.

6. The method for identifying defects in ferromagnetic materials based on magnetic gradient tensor measurement according to claim 1, characterized in that: The magnetic gradient tensor fusion derivative detector is: ； The magnetic gradient tensor noise-resistant boundary detector is: ； Where, is the x-component of the total horizontal derivative of the magnetic gradient tensor; is the y component of the total horizontal derivative of the magnetic gradient tensor; are the components of the gradient tensor matrix; is a constant.

7. The method for identifying defects in ferromagnetic materials based on magnetic gradient tensor measurement according to claim 1, characterized in that: Based on the defect type, a lookup table is used to match the corresponding relationship between different types of defects and the magnetic gradient tensor fusion derivative detector or the magnetic gradient tensor anti-noise boundary detector.

8. The method for identifying defects in ferromagnetic materials based on magnetic gradient tensor measurement according to claim 7, characterized in that: Surface cracks, dense pores or pitting defects are identified by the magnetic gradient tensor noise-resistant boundary detector; deep buried holes, stress cracks or large-area corrosion are identified by the magnetic gradient tensor fusion derivative detector.

9. A ferromagnetic material defect identification system based on magnetic gradient tensor measurement, used to perform the method according to any one of claims 1 to 8, characterized in that: include: Data acquisition module: used to acquire magnetic gradient tensor data of the test piece; Data processing module: calculating the total horizontal derivative based on the magnetic gradient tensor data; Calculating the partial derivative of each component in the z direction based on the total horizontal derivative and calculating the vertical derivative based on the magnetic gradient tensor data; Identify defect boundaries using magnetic gradient tensor fusion derivative detector and magnetic gradient tensor noise-resistant boundary detector; Output module: outputs and displays the processing results of the data processing module.

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