An Eddy Current Detection Sensor with Inverse Gaussian Resonant Regulation Distribution and a Detection Method

Abstract

The present invention provides an eddy current detection sensor with an inverse Gaussian resonance regulation distribution and a detection method, which relates to the technical field of eddy current detection, and includes: an excitation coil layer and a detection coil layer stacked; the excitation coil layer includes a first PCB board, on which two rectangular spiral excitation coils arranged side by side are provided, the two spiral excitation coils are connected in parallel, and the current directions in the two spiral excitation coils are opposite; when an excitation signal is applied to the two spiral excitation coils, a differential magnetic field is formed; the detection coil layer includes a second PCB board, on which a plurality of rectangular spiral detection coils and a plurality of capacitors are provided, the spiral detection coils and the capacitors are connected in parallel one by one, and a low-pass filter is formed by the parallel capacitors; the number of turns of the plurality of spiral detection coils is distributed in an inverse Gaussian distribution according to the arrangement order; the spiral excitation coils and the spiral detection coils are arranged at intervals, and the spiral excitation coils and the spiral detection coils are coupled interactively in the vertical direction.

Description

Technical Field

[0001] The present invention relates to the technical field of eddy current testing, and particularly to an eddy current testing sensor with an inverse Gaussian resonance regulation distribution and a testing method therefor. Background Art

[0002] During long-term operation, basic components such as pipelines and aerospace components will inevitably lead to problems such as aging, wear, and blockage. To prevent these problems from accelerating and causing more serious social hazards, it is crucial to conduct regular inspections, cleaning, and maintenance. Nondestructive testing techniques are widely used in the industrial field due to their effectiveness in ensuring the integrity of workpieces and operational efficiency. Among them, ultrasonic testing can effectively detect and quantify internal and external corrosion defects in thick-walled pipelines. However, it requires the use of an indispensable coupling agent, which limits its application. Magnetic flux leakage testing is one of the most widely used techniques, especially in the detection of long-distance oil and gas pipelines, and can effectively detect metal loss, cracks, and weld defects, etc. However, due to the weight of the permanent magnet and the complex structure, it faces challenges when passing through small-diameter pipelines with low pressure and 1.5D elbows. In contrast, eddy current testing is a promising method suitable for small-diameter pipeline detection, with the advantages of small size, light weight, and low cost. However, under random working conditions, the effects of liftoff and background noise will prevent a detectable electromotive force from being induced in the secondary coil. This effect is particularly obvious when using sinusoidal excitation, because the sensitivity decreases significantly after a distance of more than 5 mm. To sum up, balancing sensitivity and resolution is crucial for better identifying and quantifying defects, and this principle applies not only to eddy current systems but also to other application scenarios.

[0003] To address the above challenges, array probe packaged sensors based on GMR and TMR can provide high sensitivity and a small effective induction area for detecting eddy current signals. However, these sensors are not superior to coil-based sensors when detecting surface features at high frequencies. In addition, since these sensors directly detect the magnetic field distribution only through devices such as giant magnetoresistance, their sensitivity depends entirely on the excitation magnetic field, while ignoring the relationship between sensitivity and the received field. In the current field of eddy current testing, balancing sensitivity and resolution remains a key issue. In practice, to improve the spatial resolution of the sensor array while maintaining sufficient sensitivity, coils with smaller sizes and more turns are usually used. Although the inductance value of a single coil can be increased by adding an iron core in a cylindrical coil, this method requires more space. In addition, as the inductance increases, the resistance also rises significantly, resulting in higher eddy current losses. However, in the case of limited space, to increase the inductance of the coil and the sensitivity of the probe, a printing technique for multilayer coils needs to be adopted, but this will increase the cost. Summary of the Invention

[0004] In order to solve the technical problems that it is difficult to balance the sensitivity and resolution of current eddy current detection technology, and the detection equipment requires more space and has a high manufacturing cost, the present invention provides an eddy current detection sensor and detection method with inverse Gaussian resonance regulation distribution.

[0005] The technical solutions provided by the embodiments of the present invention are as follows:

[0006] An eddy current detection sensor with inverse Gaussian resonance regulation distribution provided by an embodiment of the present invention includes: an excitation coil layer and a detection coil layer stacked;

[0007] The excitation coil layer includes a first PCB board, on which two rectangular spiral excitation coils are arranged side by side, the two spiral excitation coils are connected in parallel, and the current directions in the two spiral excitation coils are opposite;

[0008] When an excitation signal is applied to the two spiral excitation coils, a differential magnetic field is formed between the two spiral excitation coils;

[0009] The detection coil layer includes a second PCB board, on which a plurality of rectangular spiral detection coils and a plurality of capacitors are arranged, the spiral detection coils are connected in parallel with the capacitors one by one, and a low-pass filter is formed by connecting the capacitors in parallel;

[0010] The number of turns of the plurality of spiral detection coils is distributed in an inverse Gaussian distribution according to the arrangement order;

[0011] The spiral excitation coil and the spiral detection coil are arranged at intervals, and the spiral excitation coil and the spiral detection coil are coupled interactively in the vertical direction.

[0012] A detection method provided by an embodiment of the present invention is applied to the above-mentioned eddy current detection sensor with inverse Gaussian resonance regulation distribution, and the detection method specifically includes:

[0013] S1: Set the spiral detection coil between the workpiece to be detected and the spiral excitation coil;

[0014] S2: Input an excitation signal to the two spiral excitation coils to form a differential magnetic field between the two spiral excitation coils;

[0015] S3: Obtain eddy current signals through the plurality of spiral detection coils respectively;

[0016] S4: Perform defect detection on the workpiece to be detected according to the eddy current signal.

[0017] The beneficial effects brought by the technical solutions provided by the embodiments of the present invention at least include:

[0018] (1) In the present invention, the form of the detection coil turns is in an inverse Gaussian distribution, so that the magnetic field received by the detection signal in the non-uniform magnetic field has high consistency, each detection coil has similar sensitivity, and through the arrangement of multiple coils, the detection resolution is increased.

[0019] (2) In the present invention, the spiral excitation coil and the spiral detection coil are arranged at intervals, the spiral excitation coil and the spiral detection coil are cross-coupled in the vertical direction, and the spiral detection coil array is in the middle of the spiral excitation coil and the test piece in space. On the one hand, the direct coupling energy between the excitation coil and the detection coil is reduced, and on the other hand, the coupling path between the test piece and the detection coil array is reduced, thereby increasing the voltage intensity related to the test piece in the receiving coil. For the excitation coil and the detection coil, when the space size is limited and the receiving inductance is limited, and through the cross-coupling method in the vertical direction, the magnetic field generated by the excitation coil can first store a part of the energy through the receiving coil and then perform inductive coupling with the test piece, thereby improving the coupling ability of small inductance and weak signals, enabling the detection coil to receive more test piece information, and thus improving the detection sensitivity.

[0020] (3) In the present invention, the spiral detection coil forms a low-pass filter by connecting the capacitors in parallel, which improves the eddy current signal intensity under defect perturbation. By adjusting and arranging different turns and capacitances, it helps to adjust the resonance frequency of each coil and optimize the resonance effect of the array to adapt to different working conditions. Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a schematic structural diagram of an eddy current detection sensor with an inverse Gaussian resonance regulation distribution provided by an embodiment of the present invention;

[0023] Figure 2 It is a schematic structural diagram of an excitation coil layer provided by an embodiment of the present invention;

[0024] Figure 3 It is a schematic structural diagram of a detection coil layer provided by an embodiment of the present invention;

[0025] Figure 4 It is a detection result diagram of pipes with different defects provided by an embodiment of the present invention.

[0026] [Reference Signs]

[0027] 1. First PCB board; 2. Spiral excitation coil; 3. Spiral detection coil; 4. Capacitor. Specific implementation manner

[0028] The technical solutions in the present invention will be described below with reference to the accompanying drawings.

[0029] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0030] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0031] Refer to the attached specification Figure 1 , which shows a structural schematic diagram of an eddy current detection sensor with an inverse Gaussian resonance regulation distribution provided by an embodiment of the present invention.

[0032] Refer to the attached specification Figure 2 , which shows a structural schematic diagram of an excitation coil layer provided by an embodiment of the present invention.

[0033] Refer to the attached specification Figure 3 , which shows a structural schematic diagram of a detection coil layer provided by an embodiment of the present invention.

[0034] An embodiment of the present invention provides an eddy current detection sensor with an inverse Gaussian resonance regulation distribution, including: an excitation coil layer and a detection coil layer stacked.

[0035] The excitation coil layer includes a first PCB (Printed Circuit Board) board 1, on which two rectangular spiral excitation coils 2 arranged side by side are provided. The two spiral excitation coils 2 are connected in parallel, and the current directions in the two spiral excitation coils 2 are opposite.

[0036] When an excitation signal is applied to the two spiral excitation coils 2, a differential magnetic field is formed between the two spiral excitation coils 2. The differential magnetic field has unique characteristics: due to the opposite current directions, their magnetic fields cancel each other out in some regions and strengthen each other in other regions. This differential magnetic field can effectively reduce external noise interference and enhance the magnetic field intensity in local regions, making the eddy current detection sensor have higher sensitivity and accuracy when performing defect detection.

[0037] The detection coil layer includes a second PCB board, on which a plurality of rectangular spiral detection coils 3 and a plurality of capacitors are arranged. The spiral detection coils 3 and the capacitors are connected in parallel one by one, and a low-pass filter is formed by connecting the capacitors in parallel.

[0038] It should be noted that the low-pass filter can effectively filter out the high-frequency noise in the eddy current signal and retain the effective signal in the low-frequency part. This can reduce the influence of environmental interference and irrelevant signals, and improve the quality and stability of the signal.

[0039] Among them, the capacitance value of the capacitor can be adjusted according to actual needs.

[0040] The number of turns of the plurality of spiral detection coils 3 is distributed in an inverse Gaussian distribution according to the arrangement order.

[0041] Among them, the inverse Gaussian distribution is a continuous probability distribution, which is usually used to describe a random variable with positive skewness, and its probability density function PDF presents a unimodal and long right-tail shape.

[0042] Furthermore, the reason for distributing the number of turns of the plurality of spiral detection coils 3 in an inverse Gaussian distribution according to the arrangement order is that in the research process, it is found that the eddy current distribution / magnetic flux distribution of the differential magnetic field formed between the two spiral excitation coils 2 presents a Gaussian distribution state in the whole region. Therefore, it is also determined that the number of turns of the plurality of spiral detection coils 3 adopts an inverse Gaussian distribution, so that the magnetic field received by the detection signal in the non-uniform magnetic field has high consistency, each detection coil has similar sensitivity, and the detection resolution is increased through the arrangement of multiple coils.

[0043] The spiral excitation coil 2 and the spiral detection coil 3 are arranged at intervals, and the spiral excitation coil 2 and the spiral detection coil 3 are mutually coupled in the vertical direction.

[0044] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0045] (1) In the present invention, by adopting the form that the number of turns of the detection coil is distributed in an inverse Gaussian distribution, the magnetic field received by the detection signal in the non-uniform magnetic field has high consistency, each detection coil has similar sensitivity, and the detection resolution is increased through the arrangement of multiple coils.

[0046] (2) In the present invention, the spiral excitation coil and the spiral detection coil are arranged at intervals, and the spiral excitation coil and the spiral detection coil are inductively coupled with each other in the vertical direction. The spiral detection coil array is located between the spiral excitation coil and the test piece in space. On the one hand, it reduces the direct coupling energy between the excitation coil and the detection coil, and on the other hand, it reduces the coupling path between the test piece and the detection coil array, thereby increasing the voltage intensity related to the test piece in the receiving coil. For the excitation coil and the detection coil, in a limited space size and with a limited receiving inductance, and moreover, through the inductive coupling method in the vertical direction, the magnetic field generated by the excitation coil can first store a part of the energy through the receiving coil and then be inductively coupled with the test piece, thereby enhancing the coupling ability of small inductance and weak signals, enabling the detection coil to receive more information of the test piece, and thus improving the detection sensitivity.

[0047] (3) In the present invention, the spiral detection coil forms a low-pass filter by connecting the capacitors in parallel, which improves the eddy current signal intensity under defect perturbation. Through the adjustment and layout of different turns and capacitances, it helps to adjust the resonance frequency of each coil and optimize the resonance effect of the array to adapt to different working conditions.

[0048] Among them, those skilled in the art can set the number of the spiral detection coils 3 according to the actual situation, and the present invention does not make any limitations.

[0049] For example, the number of the spiral detection coils 3 is 9, and the number of turns of the 9 spiral detection coils 3 is distributed in an inverse Gaussian distribution according to the arrangement order.

[0050] Further, the number of turns of the 9 spiral detection coils 3 are 39, 34, 29, 25, 19, 25, 29, 34, and 39 respectively.

[0051] It should be noted that the number of turns being 39, 34, 29, 25, 19, 25, 29, 34, and 39 respectively is a typical inverse Gaussian distribution scheme.

[0052] In a possible implementation manner, the length and width dimensions of the spiral detection coil 3 are the same.

[0053] In the present invention, the spiral coils with the same dimensions can ensure that the electromagnetic field is evenly distributed in each coil area, thereby improving the sensitivity and uniformity of the detection area and helping to detect small defects.

[0054] In a possible implementation manner, the symmetry center formed by the two spiral excitation coils 2 coincides with the symmetry center formed by the multiple spiral detection coils 3.

[0055] In the present invention, when the symmetry centers of the excitation coil and the detection coil coincide, the magnetic field generated by the excitation coil can be evenly distributed within the detection coil region, thereby improving the symmetry and stability of the magnetic field and contributing to more accurate defect detection.

[0056] In a possible implementation manner, based on the Seagull Optimization Algorithm (SOA), the structural parameters of the eddy current detection sensor are optimized.

[0057] Among them, the Seagull Optimization Algorithm (SOA) is a nature-inspired optimization algorithm that simulates the foraging behavior of seagulls.

[0058] Optionally, the structural parameters include: the wire diameter, number of turns, number of layers, distance between each layer of coils, distance between the two coils, number of spiral detection coils 3, wire diameter, number of turns, number of layers, distance between each layer of coils, distance between each coil, and capacitance value of the capacitor.

[0059] Specifically, the specific manner of optimizing the structural parameters of the eddy current detection sensor based on the Seagull Optimization Algorithm includes:

[0060] Setting the structural optimization fitness function of the Seagull Optimization Algorithm with the goal of enhancing the intensity of the received eddy current signal, magnetic field uniformity, and reducing the signal-to-noise ratio of the eddy current signal:

[0061] ;

[0062] Among them, f represents the structural optimization fitness function, J k represents the eddy current signal intensity of the k-th spiral detection coil, B k represents the magnetic field intensity received by the k-th spiral detection coil, represents the average value of the magnetic field intensities received by multiple spiral detection coils, SNR k represents the signal-to-noise ratio of the eddy current signal of the k-th spiral detection coil, K represents the total number of spiral detection coils, λ 1 represents the weight coefficient of the eddy current signal intensity, λ 2 represents the weight coefficient of the magnetic field uniformity, λ 3 represents the weight coefficient of the signal-to-noise ratio of the eddy current signal.

[0063] Among them, those skilled in the art can set the magnitudes of the weight coefficient λ 1 of the eddy current signal intensity, the weight coefficient λ 2 of the magnetic field uniformity, and the weight coefficient λ 3 of the signal-to-noise ratio of the eddy current signal according to the actual situation, and the present invention does not make any limitations.

[0064] In the present invention, by setting a structural optimization fitness function based on the seagull optimization algorithm, aiming to enhance the eddy current signal intensity, optimize the magnetic field uniformity, and reduce the signal-to-noise ratio, the overall performance of the eddy current detection sensor can be significantly improved. This fitness function can comprehensively optimize the design parameters of the sensor by considering the weighted sum of the three key indicators of eddy current signal intensity, magnetic field uniformity, and signal-to-noise ratio, ensuring a stronger and more stable signal while reducing noise interference and improving the detection accuracy. By balancing these three objectives, the optimized sensor not only improves the sensitivity and resolution but also can maintain good performance in different working environments, thereby enhancing the detection ability of small object defects.

[0065] Initialize seagull individuals, where each seagull individual represents a set of feasible structural parameters. Each seagull individual consists of multiple dimensional components, and each component represents a structural parameter.

[0066] In the global search stage, avoid collisions and move towards the optimal individual:

[0067] ;

[0068] ;

[0069] ;

[0070] Where, represents the position of the i-th seagull individual after the global search stage at the t-th iteration, represents the position of the i-th seagull individual after anti-collision processing at the t-th iteration, A represents the control factor, represents the position of the i-th seagull individual at the t-th iteration, represents the displacement of the i-th seagull individual moving towards the optimal individual at the t-th iteration, B represents the search balance factor, represents the position of the optimal individual at the t-th iteration.

[0071] In the present invention, in the global search stage, the strategy of avoiding collisions and moving towards the optimal individual helps to improve the search efficiency and stability of the seagull optimization algorithm. Through the control factor and the search balance factor, the algorithm can effectively guide each seagull individual to avoid collisions with other individuals in the search space while ensuring that the individual moves towards the optimal solution. This process not only accelerates the convergence speed but also guarantees the exploration ability in the global search stage, enabling the algorithm to widely explore the solution space and avoid falling into local optimal solutions, thereby improving the quality of the final solution and the diversity of global search.

[0072] ;

[0073] where \(t\) represents the current iteration number, \(T\) represents the maximum iteration number, and \(f\) c represents the linearly decreasing frequency.

[0074] In the present invention, as the number of iterations increases, the control factor gradually decreases. This means that in the initial stage of the search, the search range of individuals is large, enabling extensive exploration; while as the iteration progresses, the search range gradually narrows, and more energy is concentrated near the optimal solution, which helps to accelerate convergence.

[0075] ;

[0076] where \(r\) 1 represents a random number between 0 and 1.

[0077] In the present invention, as the iteration progresses, the change of the search balance factor can gradually guide individuals to converge to the global optimal solution, thereby improving the global search ability and accuracy of the optimization algorithm.

[0078] In the local search stage, a random number \(r\) 2 is generated. According to the random number \(r\) 2 , a parallel selection is made between the spiral search strategy and the surrounding strategy, and displacement is carried out in the form of spiral motion:

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] where represents the position of the \(i\)-th seagull individual after spiral motion at the \(t\)-th iteration, \(x\) represents the spiral flight coefficient in the \(x\) direction, \(y\) represents the spiral flight coefficient in the \(y\) direction, \(z\) represents the spiral flight coefficient in the \(z\) direction, \(r\) represents the radius of the spiral flight trajectory, \(\theta\) represents a random number between 0 and \(2\pi\), \(u\), \(v\) represent spiral constants, and \(e\) represents the natural constant.

[0085] In the present invention, by generating a random number to select between the spiral search strategy and the surrounding strategy and combining spiral motion for displacement, the diversity and flexibility of the search process can be significantly enhanced. When , the spiral search strategy is adopted, enabling the individual to make a spiral-shaped motion in the solution space, thereby widely exploring the surrounding area; when When using the surrounding strategy, it enables individuals to approach the optimal solution set more precisely. The algorithm can maintain strong exploration ability during local search and converge to the optimal solution efficiently, thereby improving the accuracy and convergence speed of the optimization process. In addition, the introduction of the spiral motion trajectory enhances the randomness and directionality of the search process, making the algorithm less likely to fall into local optima and ensuring a higher probability of finding the global optimal solution.

[0086] Perform mutation operations on each seagull individual:

[0087] ;

[0088] Among them, represents the position of the i-th seagull individual after mutation at the t-th iteration, P r represents a random individual, and ω represents an adaptive scaling factor.

[0089] In the present invention, performing mutation operations can increase the diversity of the search and the global exploration ability. The mutation operation helps individuals jump out of local optimal solutions and avoid the algorithm converging to suboptimal solutions prematurely by introducing the difference between a random individual and the current optimal individual.

[0090] ;

[0091] Among them, ω max represents the maximum scaling factor, and ω min represents the minimum scaling factor.

[0092] In the present invention, as the number of iterations increases, the scaling factor gradually decreases, starting from a larger value and finally tending to a smaller value. This helps to maintain a larger search range in the initial stage of optimization, encourages extensive exploration of the solution space, and when gradually converging in the later stage of optimization, by reducing the scaling factor, it strengthens the local search accuracy, enabling the algorithm to approach the global optimal solution more precisely. This method can effectively balance the exploration and exploitation capabilities, avoid premature convergence, and improve the quality of the final solution.

[0093] Judge whether the fitness value of the position after mutation is greater than the fitness value of the position before mutation. If so, replace the position before mutation with the position after mutation. Otherwise, keep the position before mutation unchanged.

[0094] Update the fitness values of each seagull individual and the global optimal individual.

[0095] Judge whether the current iteration number has reached the maximum iteration number. If so, output the set of structural parameters represented by the seagull individual with the highest current fitness. Otherwise, return to continue the iteration.

[0096] In the present invention, by optimizing the structural parameters of the eddy current detection sensor (such as the number of turns, size, spacing, etc. of the coil), the sensitivity, resolution, and signal strength of the sensor can be improved, thereby achieving higher-precision defect detection. Especially in the detection of micro-defects, the optimized sensor can provide better performance.

[0097] An embodiment of the present invention provides a detection method, which is applied to the eddy current detection sensor with the above-mentioned inverse Gaussian resonance regulation distribution. The detection method specifically includes:

[0098] S1: Set the spiral detection coil 3 between the workpiece to be detected and the spiral excitation coil 2.

[0099] S2: Input an excitation signal to the two spiral excitation coils 2 to form a differential magnetic field between the two spiral excitation coils 2.

[0100] S3: Obtain eddy current signals through the multiple spiral detection coils 3 respectively.

[0101] S4: Perform defect detection on the workpiece to be detected according to the eddy current signals.

[0102] In the present invention, by setting the spiral detection coil between the workpiece to be detected and the spiral excitation coil and using the differential magnetic field method, the sensitivity and accuracy for detecting defects in small objects can be effectively improved. The differential magnetic field reduces the interference of external noise, making the detection signal clearer. The parallel operation of multiple spiral detection coils can obtain eddy current signals from different positions, further enhancing the comprehensiveness and reliability of the detection. Through this method, micro-defects in the workpiece to be detected can be accurately detected, improving the performance and accuracy of eddy current detection.

[0103] In a possible implementation manner, the S4 specifically includes sub-steps S401 to S410:

[0104] S401: Perform registration processing on the eddy current signals obtained by the multiple spiral detection coils 3.

[0105] Specifically, the registration can be performed by identifying features in the signal such as peaks and local extrema, and then aligning these feature points.

[0106] It should be noted that through the registration processing, the data of different detection coils can be correctly aligned in space, enhancing the defect detection ability, especially for micro or complex-shaped defects, and improving the overall detection accuracy and sensitivity.

[0107] S402: Extract the signal feature maps of the respective eddy current signals through continuous wavelet transform.

[0108] Among them, the Continuous Wavelet Transform (CWT) is a mathematical tool for signal analysis that can decompose a signal into components with different frequencies and time resolutions.

[0109] S403: For the eddy current signal of a single spiral detection coil 3 and the corresponding signal feature map, feature extraction is performed on the one-dimensional eddy current signal through the first convolution channel to obtain a first feature map.

[0110] The first convolution channel contains 10 consecutive one-dimensional convolution layers, and the output features of each one-dimensional convolution layer are specifically: FConv i FConv i represents the output of the i-th one-dimensional convolution layer. .

[0111] S404: Perform skip connections on the output features of each one-dimensional convolution layer to form multiple one-dimensional intermediate features:

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] Among them, represents the j-th one-dimensional intermediate feature for the eddy current data of the u-th spiral detection coil. up represents the upsampling operation, which is used to ensure the consistency of the feature map size. represents the element-wise addition operation.

[0117] In the present invention, a skip connection mechanism is innovatively adopted. The skip connection combines feature information at different levels, enabling the network to retain more details and context information. Especially when dealing with deep networks, it can avoid information loss, improve the representation ability of the model, enable the network to better capture the eddy current signal features at different scales, and thus enhance the accuracy and sensitivity of defect detection.

[0118] S405: Perform feature extraction on the two-dimensional signal feature map through the second convolution channel to obtain a second feature map.

[0119] The second convolution channel contains 10 consecutive two-dimensional convolution layers, and the output features of each two-dimensional convolution layer are specifically: SConv i SConv i represents the output of the i-th one-dimensional convolution layer. 。

[0120] S406: Perform skip connections on the output features of each two-dimensional convolutional layer to form multiple two-dimensional intermediate features:

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] Among them, represents the j-th two-dimensional intermediate feature of the eddy current data of the u-th helical detection coil. up represents the upsampling operation, which is used to ensure the consistency of the feature map size. represents the element-wise addition operation.

[0126] S407: Concatenate and fuse the one-dimensional intermediate feature and the two-dimensional intermediate feature to obtain a fused feature map:

[0127] ;

[0128] Among them, represents the j-th fused feature map of the eddy current data of the u-th helical detection coil. flatten represents the flattening operation.

[0129] In the present invention, the one-dimensional feature and the two-dimensional feature represent the information of the eddy current signal in different dimensions. By concatenating and fusing the one-dimensional intermediate feature and the two-dimensional intermediate feature to obtain a fused feature map, the feature information of different scales can be effectively combined, enhancing the performance of the model, thereby improving the accuracy and robustness of defect detection.

[0130] S408: Perform defect detection on the workpiece to be detected according to the fused feature map:

[0131] ;

[0132] Among them, represents the defect detection probability set of the j-th fused feature map of the eddy current data of the u-th helical detection coil. represents the probability value that the j-th fused feature map of the eddy current data of the u-th helical detection coil belongs to the k-th defect category. K represents the total number of defect categories. Softmax represents the Softmax activation function. W j represents the weight matrix for the j-th fused feature map. b j represents the bias value for the j-th fused feature map.

[0133] In the present invention, by using the Softmax function to perform defect detection on the fused feature map, the extracted features can be effectively mapped to different defect categories. The Softmax function converts the output of each feature map into probability values for each defect category, which helps to accurately classify the eddy current signals and ensure more reliable identification of different types of defects.

[0134] S409: Summarize the defect detection results of the eddy current data of each spiral detection coil:

[0135] ;

[0136] where p k represents the probability value belonging to the k-th defect category, and U represents the total number of spiral detection coils.

[0137] In the present invention, by summarizing the defect detection results of the eddy current data of each spiral detection coil, the outputs of multiple detection coils can be comprehensively considered, improving the accuracy and stability of defect classification. By averaging the defect detection probabilities of each spiral detection coil, the balanced contribution of data from different coils is ensured, making the final defect category probabilities more reliable. Such a method can reduce the noise impact that a single coil may bring, and through the information fusion of multiple coils, enhance the overall sensitivity and robustness of the system, thereby improving the accuracy of eddy current detection, especially when dealing with complex or tiny defects.

[0138] S410: Take the defect category with the maximum probability value as the final detection result of the eddy current detection.

[0139] In the present invention, one-dimensional and two-dimensional convolution channels are used to extract deep features from the signal respectively, and multi-level features are fused through skip connections and upsampling operations, which helps to enhance the signal information at different scales. By fusing multi-scale features and combining the data of multiple spiral detection coils, the accuracy and reliability of the detection are improved.

[0140] Refer to the attached Figure 4 illustrates the detection result diagram of a pipeline with different defects provided by an embodiment of the present invention. The detection results of the traction pipe with different defects. By arranging and imaging the multi-channel time-series signals, it can be clearly seen that each channel responds to the circumferential weld and spiral weld regions, enabling a clear distinction between the two. In addition, the abnormal signals detected between the welds are identified as defects. After comparison with the defect list, it is confirmed that all defects in the pipeline are effectively detected.

[0141] As described above, this is only the specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

[0142] The following points need to be explained:

[0143] (1) The drawings of the embodiments of the present invention only relate to the structures involved in the embodiments of the present invention, and other structures can refer to the general design.

[0144] (2) For clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is enlarged or reduced, that is, these drawings are not drawn according to the actual scale. It can be understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element can be "directly" on or under the other element or there can be intermediate elements.

[0145] (3) Without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0146] As mentioned above, this is only the specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An eddy current detection sensor with inverse Gaussian resonance control distribution, characterized in that: include: An excitation coil layer and a detection coil layer are stacked; The excitation coil layer comprises a first PCB board (1), on which two rectangular spiral excitation coils (2) are arranged side by side, the two spiral excitation coils (2) are connected in parallel, and the current directions in the two spiral excitation coils (2) are opposite; When an excitation signal is introduced into the two spiral excitation coils (2), a differential magnetic field is formed between the two spiral excitation coils (2); The detection coil layer comprises a second PCB board, on which a plurality of rectangular spiral detection coils (3) and a plurality of capacitors (4) are arranged, the spiral detection coils (3) and the capacitors (4) being connected in parallel in a one-to-one correspondence, and a low-pass filter is formed by connecting the capacitors (4) in parallel; The number of turns of the plurality of spiral detection coils (3) is inversely Gaussian distributed according to the arrangement order, so that each of the spiral detection coils (3) has similar sensitivity in the differential magnetic field; The spiral excitation coil (2) and the spiral detection coil (3) are arranged at intervals, and the spiral excitation coil (2) and the spiral detection coil (3) are interactively coupled in a vertical direction.

2. The eddy current detection sensor with inverse Gaussian resonance control distribution according to claim 1 is characterized in that: The number of the spiral detection coils (3) is 9, and the number of turns of the 9 spiral detection coils (3) is inverse Gaussian distribution according to the arrangement order.

3. The eddy current detection sensor with inverse Gaussian resonance control distribution according to claim 2 is characterized in that: The numbers of turns of the nine spiral detection coils (3) are 39, 34, 29, 25, 19, 25, 29, 34, and 39 respectively.

4. The eddy current detection sensor with inverse Gaussian resonance control distribution according to claim 1, characterized in that: The length and width of the spiral detection coil (3) are consistent.

5. The eddy current detection sensor with inverse Gaussian resonance control distribution according to claim 1, characterized in that: The symmetry center formed by the two spiral excitation coils (2) coincides with the symmetry center formed by the plurality of spiral detection coils (3).

6. The eddy current detection sensor with inverse Gaussian resonance control distribution according to claim 1, characterized in that: Based on the Seagull optimization algorithm, the structural parameters of the eddy current detection sensor are optimized.

7. The eddy current detection sensor with inverse Gaussian resonance control distribution according to claim 6, characterized in that: The structural parameters include: The wire diameter, number of turns, number of layers, distance between each layer of coils, and distance between two coils of the spiral excitation coil (2); The number, wire diameter, number of turns, number of layers, distance between each layer of coils, and distance between each coil of the spiral detection coil (3); The capacitance of capacitor (4).

8. The eddy current detection sensor with inverse Gaussian resonance control distribution according to claim 6, characterized in that: The structural optimization fitness function of the Seagull optimization algorithm is set with the goal of improving the received eddy current signal strength, magnetic field uniformity, and reducing the eddy current signal-to-noise ratio: Among them, f represents the structural optimization fitness function, J k represents the eddy current signal strength of the kth spiral detection coil, B k represents the magnetic field strength received by the k-th spiral detection coil, Represents the average magnetic field strength received by multiple spiral detection coils, SNR k represents the signal-to-noise ratio of the eddy current signal of the kth spiral detection coil, K represents the total number of spiral detection coils, λ1 represents the weight coefficient of the eddy current signal intensity, λ2 represents the weight coefficient of the magnetic field uniformity, and λ3 represents the weight coefficient of the eddy current signal signal-to-noise ratio.

9. A detection method, characterized in that: The eddy current detection sensor with inverse Gaussian resonance control distribution applied to any one of claims 1 to 8, wherein the detection method specifically comprises: S1: arranging the spiral detection coil (3) between the object to be detected and the spiral excitation coil (2); S2: inputting an excitation signal to the two spiral excitation coils (2) to form a differential magnetic field between the two spiral excitation coils (2); S3: acquiring eddy current signals respectively through the plurality of spiral detection coils (3); S4: performing defect detection on the part to be detected according to the eddy current signal.

10. The detection method according to claim 9, characterized in that: The S4 specifically includes: S401: performing registration processing on eddy current signals acquired by the plurality of spiral detection coils (3); S402: extracting a signal characteristic graph of each eddy current signal through continuous wavelet transform; S403: for the eddy current signal of the single spiral detection coil (3) and the corresponding signal characteristic graph, extract the characteristics of the one-dimensional eddy current signal through a first convolution channel to obtain a first characteristic graph; S404: Perform jump connections on the output features of each one-dimensional convolutional layer to form multiple one-dimensional intermediate features; S405: extracting features from the two-dimensional signal feature map through a second convolution channel to obtain a second feature map; S406: Perform jump connections on the output features of each two-dimensional convolutional layer to form multiple two-dimensional intermediate features; S407: Concatenate and fuse the one-dimensional intermediate feature and the two-dimensional intermediate feature to obtain a fused feature map; S408: performing defect detection on the part to be inspected according to the fused feature map; S409: Summarizing defect detection results of eddy current data of each spiral detection coil; S410: The defect category with the largest probability value is taken as the final detection result of the eddy current detection.

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