Universal quantitative method for detecting defects by pull-free magnetic flux leakage

Abstract

This invention discloses a universal quantification method for non-traction magnetic flux leakage detection defects, combining initial quantification based on a magnetic flux leakage physical model with parameter correction based on minimal excavation verification. First, engineering magnetic flux leakage data is directly read in, and all defects are initially quantified without samples using the physical model. Then, at least two defects are selected for excavation testing, and the measured data are compared with the quantification results of the physical model to evaluate accuracy. If the accuracy meets the standard, the process ends; if not, the key parameters of the physical model are corrected using these two excavation data, and all defects are re-quantified, thus achieving high accuracy and high versatility in defect quantification at low cost.

Description

Technical Field

[0001] This invention belongs to the field of nondestructive testing technology, specifically relating to a general quantitative method for detecting defects in magnetic flux leakage without tension. Background Technology

[0002] Magnetic flux leakage (MFL) testing is currently the most common technique for in-service inspection of ferromagnetic components such as oil and gas pipelines to detect defects such as metal loss, cracks, and corrosion. The accuracy of quantifying defect dimensions (length, width, and depth) based on MFL signals is a core indicator for evaluating the comprehensive effectiveness of key factors such as the performance of testing equipment, the quality of testing data, and the accuracy of the quantification model.

[0003] Current mainstream methods for quantifying defects in magnetic flux leakage detection heavily rely on "tension experiments." The process involves creating 30-100 sample tubes, each representing different pipe materials, operating conditions, and defect types (mostly regular artificial defects), for specific testing equipment before formal testing. Signals from these samples are obtained through tension experiments to establish or calibrate a quantitative model. This model is then used to quantify defect signals in actual engineering projects. However, existing technologies suffer from the following limitations and drawbacks: High cost and low efficiency: The process of preparing and collecting traction samples is cumbersome and costly, and requires the management of a large number of samples, which greatly increases the cost and management difficulty of engineering testing.

[0004] There is a fundamental error: the pulled samples are mostly regular artificial defects (such as square grooves and round holes), which are quite different from the complex natural defects in actual engineering, resulting in approximation errors in the quantification results.

[0005] Poor adaptability to operating conditions: The sampling conditions of the traction experiment (such as magnetization intensity, running speed, and pipe wall condition) are difficult to match the actual detection conditions, resulting in unstable accuracy of the sample-based quantification model in practical applications.

[0006] Errors are difficult to correct: The samples themselves may have manufacturing errors, and the quantization methods based on the samples lack effective error correction mechanisms, making it difficult to correct and optimize the quantization results.

[0007] Therefore, how to solve or avoid the above problems is crucial for magnetic flux leakage detection. Summary of the Invention

[0008] Given that existing technologies rely excessively on costly and inherently flawed traction samples, this invention aims to propose a novel, universal defect quantification method for magnetic flux leakage detection that does not depend on traction samples. This method fundamentally eliminates the dependence on samples by embedding the physical principles of magnetic flux leakage detection, and utilizes a very small amount (two) of excavation verification data for closed-loop calibration, achieving universal and high-precision defect quantification applicable to different equipment and operating conditions.

[0009] To achieve the above objectives, the present invention provides the following technical solution: A general quantitative method for detecting defects without traction magnetic flux leakage includes the following steps: Data preprocessing: The raw detection data is read in, high-frequency noise is removed by a low-pass filter, and the signal is baseline-calibrated to align the data.

[0010] Initial quantification: Based on the variation characteristics of the leakage magnetic signal in the defect area, signals whose radial component peak and valley values ​​and whose axial component peak values ​​exceed 5% of the average value of the axial component are identified and marked as suspected defects.

[0011] Length quantization: For each defect, extract its radial component signal and find the positions of the maximum peak and minimum trough. Defect length = (trough position index - peak position index) × distance interval between two adjacent data points along the axis.

[0012] Width quantization: For each defect, extract the amplitude of its zero-crossing circumferential tangential component across all channels, and find the channel numbers containing the maximum positive value and the minimum negative value. Defect width = (peak channel number - valley channel number) × distance interval between two adjacent circumferential channels.

[0013] Depth quantization: Extract the average axial signal value of the defect-free region near the defect. Internal wall thickness-direction magnetic flux = average axial signal value × wall thickness × permeability of the measured material relative to air. Integrate (or sum and multiply by the sampling interval) the axial signal of the defect region to obtain the magnetic potential drop. Apply the preset defect depth quantization physical model formula, substituting the aforementioned data and the already quantized defect length and width into the physical model formula to calculate the defect depth, completing the initial quantization of the defect.

[0014] Excavation verification: From the defect list output by S2, excavation verification is carried out in accordance with national and industry standards.

[0015] Accuracy judgment: According to industry standards, such as the existing GB / T-27699-2023, the accuracy of the initial quantification of defects is statistically determined. For example, for commonly used high-definition detectors in the industry, if the depth error of more than 90% of excavation defects is less than the allowable value of the pipe wall thickness, the accuracy is considered to meet the standard.

[0016] Process End: If the initial defect quantification accuracy meets industry standard requirements, the defect quantification ends and the final defect report is output.

[0017] Model Correction and Secondary Quantization: If the defect depth error does not meet industry standard requirements, select at least two defects with good signal-to-noise ratios: one outer wall defect and one inner wall defect. Using the measured data from these two defects, calculate and fix the key parameters in the defect depth quantization physical model. In subsequent defect depth quantization calculations, incorporate these key parameters into the depth quantization physical model to calculate the depth of all other defects. This process involves secondary quantization of all defects, resulting in a final report.

[0018] Compared with the prior art, the present invention has the following beneficial effects: Eliminates reliance on stretch samples: Completely eliminates the cumbersome process of producing and managing large numbers of stretch samples, significantly reducing the start-up cost and management complexity of engineering testing.

[0019] High versatility: The physical model-based method does not depend on specific equipment or working conditions, and can be compatible with data generated by testing equipment from different manufacturers and models.

[0020] Accuracy is guaranteed and verifiable: A closed-loop mechanism of "small-scale excavation verification - accuracy judgment - model correction" is introduced, which ensures that the quantitative accuracy can meet industry standards at extremely low cost and solves the problem of working condition adaptability deviation that may exist in pure physical models.

[0021] Error correction capability: Compared with the traditional "one-time calibration, batch use" sample-dependent mode, this invention corrects the model through real excavation data, forming error feedback and error correction capabilities. Attached Figure Description

[0022] Figure 1 This is a flowchart of the general quantitative method for detecting defects without traction magnetic flux leakage, as presented in this invention.

[0023] Figure 2 This is an example diagram illustrating the calculation of defect length based on the radial component of the magnetic field according to the present invention.

[0024] Figure 3 This is an example diagram illustrating the calculation of defect width based on the circumferential component of the magnetic field according to the present invention.

[0025] Figure 4 This is an example diagram illustrating the calculation of defect depth based on the continuity of the magnetic field according to the present invention. Detailed Implementation

[0026] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0027] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods have been described herein, any methods similar or equivalent to those described herein may be used in the implementation or testing of this invention.

[0028] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be obvious to those skilled in the art. This specification and embodiments are merely exemplary.

[0029] The data source for this invention is a magnetic flux leakage detector. Taking a certain type of triaxial high-resolution magnetic flux leakage detector as an example, its main parameters are as follows: Axial sampling interval: 1mm (triggered by the odometer wheel encoder); Number of circumferential sensors: 128, circumferential spacing: 3.3mm; Sensor type: Hall element, range ±2000Gs, sensitivity 0.5Gs; Detector operating speed: 1~5m / s, speed fluctuation <10%; Material of the test pipe section: Q235; Data recording format: 1000 sampling points are recorded per meter, and each sampling point contains leakage magnetic field data in three components: radial, axial, and circumferential. Before testing, the detector is calibrated on a standard pipe section to ensure consistent sensitivity across all channels. During testing, operating parameters such as speed, temperature, and pressure are recorded in real time. After testing, the raw data is exported to a common format (such as CSV or binary) and imported into analysis software.

[0030] 1. Defect length quantification method under sample-less conditions: The defect length is estimated based on the peak-to-valley distance of the radial (normal) component of the leakage magnetic signal. Specifically, the positions of the peaks and troughs are identified on the signal curve along the scanning direction, and the distance between them is multiplied by the data sampling interval to obtain the defect length.

[0031] like Figure 2 Example shown: Defect length = (114 (valley position) - 75 (peak position)) × 1mm (data axial spacing) = 39mm.

[0032] 2. Defect width quantification method under sample-less conditions: The defect width is estimated based on the peak and valley positions of the circumferential leakage magnetic signal (the tangential component perpendicular to the scanning direction). Specifically, on the channel distribution perpendicular to the scanning direction, the sensor channels where the peaks and valleys of the circumferential signal are located are identified, and the difference in the number of channels multiplied by the circumferential spacing of the sensors gives the defect width.

[0033] like Figure 3 Example shown: Defect width = [(29 (peak channel) - 21 (valley channel))] × 3.3 mm (sensor channel spacing) = 26.4 mm.

[0034] 3. Defect Depth Quantification Method under Sample-Free Conditions The calculation of defect depth based on the magnetic field continuity between the non-defect region and the defect region includes the following sub-steps: 3.1 Calculate the internal magnetic field: Extract the average value of the axial component of the leakage magnetic signal in the defect-free area, and combine it with the wall thickness of the object under inspection to calculate the magnetic field flux inside the object under inspection.

[0035] 3.2 Calculation of magnetic potential drop: Based on the continuity of the magnetic field between the non-defect area and the defect area inside the component, the magnetic potential drop (magnetomotive force loss) of the magnetic field in the defect area is calculated.

[0036] 3.3 Calculating the Defect Depth: Based on the continuous relationship of the magnetic field at the defect interface, the obtained magnetic potential drop is linked with the leakage magnetic field at the defect interface to calculate the depth of the defect interface, which is then used as the depth of the defect.

[0037] The calculation of defect depth based on the magnetic field continuity between the non-defect region and the defect region includes the following sub-steps: (1) Calculate the internal magnetic field flux: Extract the average axial signal of the defect-free region near the defect (e.g., 50 sampling points before and after the defect), denoted as H_avg. Internal magnetic field flux Φ_internal = H_avg × wall thickness × magnetic permeability of the material under test relative to air.

[0038] (2) Calculate the magnetic potential drop: Integrate the axial signal of the defect region (sum and multiply by the sampling interval) to obtain the magnetic potential drop MMF_drop.

[0039] (3) Based on the physical model relationship, the defect length, defect width, and leakage flux triaxial components are substituted into the physical model relationship to calculate the defect depth d_opt.

[0040] like Figure 4 Example shown: (1) The average value of the axial component of the defect-free region is 389.648 Gs, the wall thickness is 10 mm, and the internal magnetic field flux is 389.648 Gs × 10 mm × 1500 (relative magnetic permeability of the material) = 5844720 Gs·mm.

[0041] (2) Calculation of the axial component of the defect region in relation to its length. Defect length = 40 mm. Magnetic potential drop in the defect region = 477.969 Gs × 40 mm = 19118.76 Gs·mm.

[0042] (3) Using the above physical model defect depth quantification method, the defect depth is approximately 5.2 mm.

[0043] Example 1 (no corrections required, accuracy meets standards): Scenario: A natural gas pipeline with a wall thickness of 12.7 mm, axial spacing of detectors of 1 mm, circumferential spacing of 3.3 mm, and a pipeline length of 100 km.

[0044] Data acquisition: The detector runs at a speed of 1.5 m / s to acquire raw data. After denoising and baseline correction, 15,000 defect signals are identified.

[0045] Initial quantification: For each defect, quantify it according to the length, width, and depth methods described above.

[0046] Taking one of the defects, #05, as an example: radial component peak-valley index difference = 32, length = 32mm; circumferential component peak channel - valley channel = 8, width = 26.4mm; axial defect-free area average value = 412Gs, magnetic potential drop integral value = 21000Gs·mm, and defect depth quantized based on physical model = 4.8mm.

[0047] Excavation verification: Defects with a depth greater than 30% of the wall thickness were selected for excavation and measurement, such as inner wall defect #03 and outer wall defect #11. The measured depths were 4.2 mm and 5.6 mm, respectively, and the initial quantified depths were 4.1 mm and 5.8 mm, respectively. The depth errors were 0.1 mm (0.8% of the wall thickness) and 0.2 mm (1.6% of the wall thickness), respectively.

[0048] Accuracy Judgment: According to GB / T 27699-2023, the depth error of high-definition detectors should be ≤10% of the wall thickness (i.e., 1.27mm) for general, pit-shaped, and circumferential trench defects, and ≤20% of the wall thickness for axial trench defects. If the quantification accuracy of more than 90% of all excavation defects meets the above standard requirements, the accuracy is considered satisfactory.

[0049] Process complete: Output a defect list and quantitative report; no corrections required.

[0050] Example 2 (requires correction, accuracy is not up to standard) Scenario: A gathering and transportation pipeline in an oil field, with a wall thickness of 18mm. The detector parameters are the same as in Example 1, and the detection length is the same as in Example 1.

[0051] Data acquisition: Due to the low magnetization of the detector, some signal amplitudes are weak.

[0052] Initial quantification: A total of 22,000 defects were identified. Taking defects #09 (inner wall) and #17 (outer wall) as examples: Excavation Verification: Defects with a depth greater than 30% of the wall thickness were selected for excavation and measurement, totaling 30 locations. The quantitative error of all excavation points was calculated. For example, for defect #09: initial quantitative depth = 6.5mm, actual measured excavation depth = 4.2mm, error 2.3mm (12.8% wall thickness). For defect #17: initial quantitative depth = 11.2mm, actual measured excavation depth = 8.5mm, error 2.7mm (15% wall thickness).

[0053] Accuracy Judgment: According to GB / T 27699-2023, the depth error requirement for high-definition detectors is ≤10% of the wall thickness (i.e., 1.8mm) for general, pit-shaped, and circumferential trench defects, and ≤20% of the wall thickness for axial trench defects. If the percentage of defects meeting the above standards for quantitative accuracy is less than 90% for all excavation defects, the accuracy is considered substandard. Statistics show that only 83.3% of all excavation defects (only 25 out of 30 met the standard), below the 90% threshold, therefore the accuracy is considered substandard.

[0054] Model Correction: Select two defects with good signal-to-noise ratios from the excavation defects. Using these two excavation points, correct the magnetization coefficient matrix [k] in the depth quantization model. Let the depth model be D = [k] × D_init, where D_init is the initial quantization depth (based on the default k = 1). Solve the system of equations: 4.2 = k × 6.5 → |k1| = 0.646 8.5 = k × 11.2 → |k²| = 0.759 The average value is |k| = (0.646 + 0.759) / 2 = 0.7025.

[0055] Secondary quantization: The correction factor |k|=0.7385 was applied to all 22,000 defects, and the depth was recalculated. After correction, the depth of defect #09 was 6.5×0.7025≈4.57mm, with an error of 0.37mm (2.1% of the wall thickness) compared to the measured 4.2mm, meeting the accuracy requirements. The depth of defect #17 was 11.2×0.7025≈7.87mm, with an error of 0.63mm (3.5% of the wall thickness) compared to the measured 8.5mm, also meeting the accuracy requirements. The quantization accuracy of the remaining defects was also significantly improved, with an overall compliance rate exceeding 95%. The final report was then output.

[0056] Example 3 (Special operating condition: sensor saturation) Scenario: A pipeline has a deep corrosion defect (depth > 80% of the wall thickness), which causes the sensor signal to saturate.

[0057] Data acquisition: The radial component signal shows a flat top, and the peak and valley values ​​cannot be accurately extracted.

[0058] Handling method: During S1 data preprocessing, a saturation signal was detected, and the defect was automatically marked as "signal saturation, quantization results are for reference only".

[0059] During the initial quantization of S2, the length and width are estimated using unsaturated axial and circumferential components (the depth cannot be quantized and is marked as >80% of the wall thickness).

[0060] During the S3 excavation verification, the saturated defect should be selected for excavation first to obtain the true dimensions.

[0061] The actual excavation depth was 9.2 mm (wall thickness 10 mm), and the initial quantification of length and width errors was small. Since the depth could not be quantified, this defect was skipped during accuracy determination, and the actual depth of this defect was not used to correct the model parameters (similar to Example 2).

[0062] The final report should note the specificity of the defect and provide reliable quantitative results.

[0063] Boundary and Exception Handling: Signal saturation: When the defect signal is too strong, causing sensor saturation, the peak and valley values ​​in S2 become inaccurate. Handling method: Mark the defect as "signal saturation, quantization results are for reference only," and prioritize excavation verification of this type of defect in S3.

[0064] Weak signal / no signal: For minor defects or areas with no signal at all, the quantization process is not triggered.

[0065] Data Missing: Data from a certain sensor channel is completely lost. Handling Method: During S2 width quantization, interpolate to fill the area with missing channels, or abandon width quantization for that area and mark it as "Channel Missing, Low Reliability of Width Estimation".

[0066] Multiple defect coupling: When two defects are too close together, their signals overlap. Solution: Add a signal separation algorithm before S2; if separation is not possible, quantize it as a composite defect and note this in the report.

[0067] Non-convergence correction: In S42, if model correction based on two samples causes parameters to exceed reasonable ranges (e.g., the magnetization coefficient becomes negative), the solution is to abandon the correction, maintain the original physical model results, and output the warning "Model correction failed, it is recommended to add excavation points".

[0068] Optional implementation methods and variations: Variant A (Extended Correction Parameters): The corrected physical model parameters are not limited to corrections to magnetization, wall thickness, detection lift-off, detection speed, etc., but may also include sensor sensitivity, pipe wall permeability, stress state of the measured component, etc., to cope with more complex working conditions.

[0069] Variant B (Iterative Correction): If the accuracy still does not meet the requirements after the second quantization, two new defects can be selected again for excavation and verification based on the second quantization results, forming an iterative correction process until the accuracy meets the standard or the preset upper limit of the number of iterations is reached.

[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A universal quantitative method for detecting defects using non-traction magnetic flux leakage, characterized in that, Includes the following steps: S1: Data preprocessing: Read in the leakage magnetic field detection project data, and perform quality analysis and preprocessing on the project data; S2: Initial Quantization of Physical Model Without Samples: Based on the correspondence between the feature values ​​of the leakage magnetic signal and the length and width of the defect, and the physical model based on the feature values ​​of the leakage magnetic signal and the depth of the defect, all defects in the engineering data are quantified, and a preliminary list of defects and quantization results are output. S3: Excavation Verification: Based on the defect list and quantification results output by S2, select at least two representative defects for excavation measurement, and use the actual excavation measurement data as verification data; compare the verification data with the quantification results given by S2 to obtain the defect quantification accuracy of this detection. S4: Accuracy Judgment: Determine whether the quantization accuracy given in S3 meets the standard requirements; S41: Process End: If the quantification accuracy meets the requirements, the defect quantification is complete; S42: Model Correction and Secondary Quantization: If the quantization accuracy does not meet the requirements, based on the two verification data obtained in S3, the key parameters in the magnetic flux leakage detection physical model are corrected, and the corrected physical model is used to perform a second quantization on all defects.

2. The universal quantitative method for detecting defects without traction leakage magnetic flux according to claim 1, characterized in that, Step S2 includes the following steps: S21: The length of the defect is calculated based on the peak-valley distance of the radial component of the leakage flux; S22: The width of the defect is calculated based on the peak and valley positions of the leakage magnetic field in the circumferential direction; S23: Based on the continuity of the magnetic fields between the non-defect region and the defect region, the depth of the defect is calculated.

3. The general quantitative method for detecting defects without traction leakage magnetic flux according to claim 2, characterized in that, In step S23, the method for quantifying the defect depth is as follows: based on the magnetic field continuity between the non-defect region and the defect region, the depth is estimated by calculating the internal magnetic field flux and the magnetic potential drop in the defect region.

4. The universal quantitative method for detecting defects without traction leakage magnetic flux according to claim 3, characterized in that, The specific calculation process for the defect depth is as follows: The average value of the axial component of the magnetic flux leakage signal in the defect-free area is extracted, and the internal magnetic field flux is estimated by combining it with the wall thickness of the object under inspection. Based on the continuity of the magnetic field between the non-defect region and the defect region inside the component, the magnetic potential drop of the magnetic field in the defect region is calculated. Based on the continuous relationship between magnetic potential drop and leakage magnetic field at the defect interface, the defect depth is directly calculated using a physical model formula.

5. The universal quantitative method for detecting defects without traction leakage magnetic flux according to claim 1, characterized in that, The at least two defects mentioned in step S3 include at least one outer wall defect and one inner wall defect.

6. The universal quantitative method for detecting defects without traction leakage magnetic flux according to claim 1, characterized in that, The key parameters in the corrected magnetic flux leakage detection physical model described in step S42 specifically include: using the actual depth of two excavation defects and the initial quantization depth, constructing a set of equations based on the defect depth quantization physical model to solve the correction coefficient matrix k, and substituting the solved k into the defect depth quantization physical model for secondary quantization of all subsequent defects.

7. The general quantitative method for detecting defects without traction leakage magnetic flux according to claim 6, characterized in that, It also includes boundary and anomaly handling steps: for cases of signal saturation, weak signal, missing data, multiple defect coupling, and non-convergence of correction, corresponding marking, interpolation, separation, or abandonment of correction operations are performed respectively.

8. A memory, characterized in that, The storage device stores instructions and data to implement a general quantitative method for non-traction magnetic flux leakage detection of defects as described in any one of claims 1-7.

9. A universal quantitative system for detecting defects using non-traction magnetic flux leakage, characterized in that, The device includes the storage device and the processor, wherein the storage device stores a computer program, and when the program is executed by the processor, it implements the general quantitative method for non-traction magnetic flux leakage detection of defects as described in any one of claims 1-7.

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