L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control

Abstract

The invention provides an L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control, and relates to the field of electromagnetic eddy current detection.The method comprises the steps that defect imaging is carried out on collected eddy current data through an array eddy current sensor by means of the L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control; defect position information of the metal material in a damaged state is theoretically obtained, and quantitative evaluation is carried out on defects. According to the imaging method disclosed by the invention, defect space distribution after multiple times of iterative optimization is highly matched with actually measured data, the defect characteristics of the metal component in a damaged state can be effectively positioned and evaluated, the non-contact detection characteristic eliminates the dependence on a mechanical scanning device, and the implementation is convenient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electromagnetic eddy current testing, and particularly relates to an L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control. BACKGROUND

[0002] The array eddy current sensor combined with the imaging technology can intuitively represent the two-dimensional or three-dimensional spatial state of the conductor defects, facilitate the detection personnel to quickly and accurately evaluate the shape, size, depth and distribution of the defects in the material, and timely infer the occurrence and growth trend of the defects. However, the traditional scanning imaging technology needs to use a scanning device to accurately control the spatial position of the sensor, combine the impedance change with the spatial position information, and obtain the image of the detected region. SUMMARY

[0003] The present application aims to at least solve one of the technical problems in the related art to some extent.

[0004] To this end, a first object of the present application is to provide an L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control, which can obtain the internal surface defect information of the metal component through calculation imaging, and facilitate implementation.

[0005] A second object of the present application is to provide a computer device.

[0006] A third object of the present application is to provide a non-transitory computer readable storage medium.

[0007] To achieve the above objects, a first aspect of the present application provides an L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control, comprising:

[0008] Step S1: a planar plate array eddy current sensor is built, and a vortex response signal of a region to be imaged is measured by a successive excitation-successive reception mode;

[0009] Step S2: a planar array sensor sensitivity matrix is calculated, and an L1 regularization reconstruction algorithm is used to image the conductivity distribution of the region to be reconstructed;

[0010] Step S3: according to the reconstruction result of the L1 regularization reconstruction algorithm, the conductivity σ1 of the metal sample and the defect conductivity value σ0 are set, and the normalized processing is used to convert the relative value of the reconstructed conductivity into the real conductivity distribution;

[0011] Step S4: the real conductivity distribution of the reconstructed region is input into the Dirichlet-Neumann mapping and used as the eddy current feedback signal, and the array eddy current sensor response signal under the current conductivity distribution is obtained;

[0012] Step S5: difference processing is performed on the array eddy current sensor measurement signal and the eddy current feedback signal, and the difference signal is input to a fractional order PID controller to obtain a control signal;

[0013] Step S6: the control signal is input to an L1 regularization reconstruction algorithm to update the conductivity distribution of the region to be imaged;

[0014] Step S7: steps S4-S6 are repeatedly executed, and whether the defect reconstruction converges is judged according to whether the deviation of the reconstruction results before and after the iteration process is less than a threshold value;

[0015] Step S8: output the final defect imaging result.

[0016] To achieve the above purpose, the second aspect of the present application provides a computer device, comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, when the processor executes the computer program, the above-mentioned L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control is realized.

[0017] In order to achieve the above-mentioned purpose, the third aspect of the present application provides a non-transitory computer readable storage medium, when the instructions in the storage medium are executed by the processor, the L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control can be executed.

[0018] The L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control of the embodiment of the present application realizes defect imaging based on the L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control through the array eddy current sensor to the collected eddy current data, which can theoretically obtain the defect position information under the damage state of the metal material, and can quantitatively evaluate the defect.

[0019] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter in the description of the application. BRIEF DESCRIPTION OF DRAWINGS

[0020] The above-mentioned and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0021] Figure 1 A flowchart of the L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control provided by the first embodiment of the present application;

[0022] Figure 2 The whole closed-loop control L1 regularization reconstruction algorithm diagram of the embodiment of the present application;

[0023] Figure 3A three-dimensional imaging result diagram of a defect of an embodiment of the present application. DETAILED DESCRIPTION

[0024] Embodiments of the present application are described in detail below with reference to examples shown in the accompanying drawings, in which the same or similar numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0025] A L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control of an embodiment of the present application is described below with reference to the accompanying drawings.

[0026] Figure 1 A flowchart of a L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control provided by Embodiment One of the present application.

[0027] As shown in Figure 1 , the L1 regularization eddy current defect imaging method based on closed-loop fractional order PID control includes the following steps:

[0028] Step S1: Build a planar flat array eddy current sensor, and obtain eddy current response signals of the region to be imaged by measuring in a sequential excitation-sequential reception mode;

[0029] Specifically, a 3x3 planar array eddy current sensor is designed. The excitation frequency is set to 1 kHz, the outer diameter of the coil is 5 mm, the height of the coil is 3 mm, the lift-off distance of the array sensor is kept at 0.5 mm, the spacing of the coils is 10 mm, the sequential excitation-sequential reception mode is used to obtain the eddy current response signals of the region to be imaged, and a total of 36 measurement data are accumulated. The imaging area is 30 mm x 30 mm.

[0030] Step S2: Calculate the sensitivity matrix of the planar array sensor, and use the L1 regularization reconstruction algorithm to image the conductivity distribution of the region to be reconstructed;

[0031] Specifically, step S2 includes:

[0032] Step S2.1: Calculate the sensitivity matrix using the field vector method, calculate the electric field parameters of each sub-element under the condition of single coil excitation by dividing the field, and the conductivity sensitivity matrix can be calculated as:

[0033] S = -ω 2 A A A B

[0034] In the formula, and are the vector magnetic potentials of each sub-element under the conditions of coil i and j excitation, respectively.

[0035] Specifically, the region to be imaged is divided at intervals of 1 mm, the vector magnetic potential of each point is calculated by Comsol, and the strength of the sensitive field of each point is calculated according to the formula of the conductivity sensitivity matrix.

[0036] Step S2.2: Defining the electromagnetic parameter distribution σ in eddy current tomography, the array eddy current sensor induced voltage is U, and the sensitivity matrix is S. The L1 regularization defect imaging can be expressed as:

[0037] f(G)=min{‖SG-U‖ 2 +α‖LG‖1}

[0038] Step S2.3: There is an L1 norm in the objective function, and the SplitBregman solving algorithm is used, and the iterative formula can be expressed as:

[0039]

[0040] In the formula, b k+1 and d k+1 can be calculated respectively as:

[0041] b k+1 =b k +G k+1 -d k+1

[0042] d k+1 =Shrink(LG k+1 +b k ,α / β)

[0043] The vector-valued Shrink operator is as follows:

[0044]

[0045] In the formula, τ is a constant, which can be estimated by simulation data and is set to 0.1. The regularization matrix L uses a unit matrix.

[0046] Step S3: According to the reconstruction result of the L1 regularization reconstruction algorithm, the conductivity σ1 of the metal test piece and the defect conductivity value σ0 are set, and the normalized processing is used to convert the relative value of the reconstructed conductivity into the real conductivity distribution;

[0047] Specifically, step S3 includes:

[0048] Step S3.1: The metal component containing defects is image reconstructed, and the result can be regarded as a binary image. The non-defect metal region is a high conductivity region, and the conductivity σ1 is set, while the defect region is a low conductivity region, and the conductivity σ0 is set.

[0049] Step S3.2: The reconstructed conductivity range relative value range is controlled to 0 to 1 by using a Sigmoid function, and the reconstructed conductivity distribution is smoothed;

[0050] Step S3.3: The reconstructed conductivity distribution is linearly stretched by formula normalization processing to the conductivity range, and is mapped to a real value.

[0051]

[0052] where G B (i) and G N (i) are the reconstructed conductivity distribution before and after normalization. a is the coefficient of the Sigmoid function.

[0053] Specifically, the test piece is a copper plate, the conductivity of which is 58 MS / m, the conductivity of the defect area is 0 S / m, the coefficient of the Sigmoid function is obtained by numerical simulation, and the optimal value is set to 0.5.

[0054] Step S4: The real conductivity distribution of the reconstructed area is input into the Dirichlet-Neumann mapping and used as the eddy current feedback signal to obtain the array eddy current sensor response signal under the current conductivity distribution;

[0055] Specifically, when the conductivity distribution of the sensing area is σ D (Z), the Dirichlet-Neumann mapping can be mapped to the coil feedback matrix M fb .

[0056]

[0057] where z=x+yi is the coordinate of point (x, y). is the boundary of the sensing area, is the unit normal vector of the area boundary. The difference signal Δm=M m -M fb is the input of the fractional order PID controller, and the Dirichlet-Neumann mapping can be conveniently and accurately calculated by using finite element numerical software.

[0058] Step S5: The difference between the array eddy current sensor measurement signal and the eddy current feedback signal is processed, and the difference signal is input into the fractional order PID controller to obtain the control signal;

[0059] Specifically, step S5 includes:

[0060] Step S5.1: Calculate the difference p between the measurement mutual inductance matrix M m and the feedback mutual inductance matrix M fb .​N×N (i), and input it as the input of the fractional order PID controller u(i).

[0061] P N×N (i) = M m -M fb

[0062] Step S5.2: input the difference signal P N×N (i) into the fractional order PID controller u(i) to obtain the control signal at the current time, and input it into the L1 regularization imaging algorithm again:

[0063]

[0064] is the binomial coefficient, which can be expressed as:

[0065]

[0066] where Γ(λ) is the Gamma function, and is expressed as:

[0067]

[0068] Specifically, K p , K1, K D in the PID controller are set to 1, 100 and 0.1 respectively, and λ and μ are set to 1.3 and 0.9 respectively.

[0069] Step S6: input the control signal into the L1 regularization reconstruction algorithm to update the conductivity distribution of the region to be imaged;

[0070] Specifically, the control signal u(t) is input into the L1 regularization reconstruction algorithm to update the conductivity distribution reconstruction result, and the whole closed-loop control L1 regularization reconstruction algorithm is shown in Figure 2 .

[0071] Step S7: repeatedly execute steps S4-S6, and judge whether the defect reconstruction converges according to whether the deviation of the reconstruction results before and after the iteration process is less than a threshold value;

[0072] Step S8: output the final defect imaging result.

[0073] Specifically, the final defect imaging result is output to obtain the defect position, size, number and other information, and the reconstruction result of the defect is shown in Figure 3 .

[0074] The L1 regularization eddy current defect imaging method based on the closed-loop fractional order PID control of the embodiment of the application realizes adaptive iterative correction of defect imaging through dynamic optimization of control parameters. The algorithm is based on the principle of electromagnetic induction, combines the multi-scale feature extraction of eddy current detection signals based on the characteristics of fractional calculus, and constructs a three-dimensional visual model of internal defects of a metal component under the L1 sparse constraint framework. The defect spatial distribution of the imaging method proposed in the embodiment after multiple iterations is highly consistent with the measured data, which can effectively locate and evaluate the defect characteristics of the metal component in the damaged state, and the non-contact detection characteristics eliminate the dependence on mechanical scanning devices, which has significant application potential in nondestructive testing in the fields of aerospace, rail transportation and the like.

[0075] In order to realize the above-mentioned embodiments, the application further provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the above-mentioned embodiments when executing the computer program.

[0076] In order to realize the above-mentioned embodiments, the application further provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the method of the above-mentioned embodiments.

[0077] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0078] In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0079] Any processes or methods described in the flowcharts or otherwise described herein can be understood as representing modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions (or steps) and / or can be implemented entirely in hardware. The various embodiments of the application can include additional or fewer steps or methods as desired for a given implementation, and the order of steps or methods can be changed from those depicted in the figures or described herein. Additionally, the steps or methods can be performed in parallel or with reverse order, as appropriate, with the appropriate changes to the description of the steps or methods.

[0080] Logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be embodied in computer-readable instructions, segments, or portions of code, which can be transferred from the storage medium to an instruction execution structure or other computer processing unit (CPU) for execution. The resultant sequence of instructions, code segments or portions carried out by a computer will produce the steps necessary to implement the functions specified in the particular implementation. Alternatively, the steps or methods can be implemented using specially designed hardware based on the requirements of a given application. Accordingly, those skilled in the art will appreciate that the systems and methods described herein can be embodied in a variety of forms including, but not limited to, a data carrier, a program, a computer-readable medium, a computer program product, a physical

[0081] It should be understood that aspects of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware which is stored in memory and executed by a suitable instruction execution system. If desired, such software or firmware can be implemented using any one or combination of the following technologies, which are well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0082] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiment method can be completed by programs instructing related hardware, and the programs can be stored in a computer readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0083] In addition, each functional unit in each embodiment of the present application can be integrated into one processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0084] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. An L1 regularized eddy current defect imaging method based on closed-loop fractional-order PID control, characterized in that: include: Step S1: constructing a planar flat-plate array eddy current sensor, and obtaining the eddy current response signal of the area to be imaged by measuring in a successive excitation-successive reception mode; Step S2: Calculate the sensitivity matrix of the planar array sensor and use the L1 regularized reconstruction algorithm to image the conductivity distribution of the area to be reconstructed; Step S3: According to the reconstruction result inverted by the L1 regularization reconstruction algorithm, the conductivity of the metal specimen σ1 and the defect conductivity value σ0 are set, and the reconstructed conductivity relative value is converted into the real conductivity distribution by normalization processing; Step S4: inputting the true conductivity distribution of the reconstructed area into the Dirichlet-Neumann mapping and using it as the eddy current feedback signal to obtain the array eddy current sensor response signal under the current conductivity distribution; Step S5: performing differential processing on the measurement signal of the array eddy current sensor and the eddy current feedback signal, and inputting the differential signal into the fractional-order PID controller to obtain a control signal; Step S6: inputting the control signal into the L1 regularized reconstruction algorithm to update the conductivity distribution of the imaging area; Step S7: Repeat steps S4-S6, and determine whether the defect reconstruction has converged based on whether the deviation between the two reconstruction results before and after the iterative process is less than a threshold; Step S8: Output the final defect imaging result.

2. The method according to claim 1, wherein The step S2 specifically includes: The sensitivity matrix is ​​calculated using the field vector method. By dividing the field domain, the electric field parameters of each subdivided unit are calculated when a single coil is excited. The conductivity sensitivity matrix is ​​expressed as: S=-ω 2 A A A B Where ω is the excitation frequency, A A is the vector magnetic potential when coil A is excited, A B is the vector magnetic potential when coil B is excited; The electromagnetic parameter distribution σ in eddy current tomography is defined as U, the induced voltage of the array eddy current sensor is defined as U, and the L1 regularized defect imaging objective function is defined as: f(G)=min{‖SG-U‖ 2 +α‖LG‖1} Among them, G is the defect distribution result, L is the regularization matrix, and α is the regularization coefficient; There is an L1 norm in the objective function, and the SplitBregman solution algorithm is used. Its iterative formula can be expressed as: Among them, β is the inversion coefficient, b k+1 and d k+1 for: b k+1 =b k +G k+1 -d k+1 d k+1 =Shrink(LG k+1 +b k ,α / β) The vector-valued shrink operator is: τ is a constant estimated from simulation data.

3. The method according to claim 1, wherein The step S3 specifically includes: Image reconstruction is performed on metal components with defects. The result can be regarded as a binary image. The non-defective metal area is a high conductivity area with a conductivity of σ1, and the defective area is a low conductivity area with a conductivity of σ0. The reconstructed conductivity is normalized using the Sigmoid function, the relative value range of the reconstructed conductivity is controlled to 0 to 1, and the reconstructed conductivity distribution is smoothed; The conductivity range is linearly stretched by formula normalization to map the reconstructed conductivity distribution to the true value.

4. The method according to claim 3, wherein The reconstructed conductivity is normalized using the Sigmoid function, which is expressed as: Among them, G B (i) G N (i) is the reconstructed conductivity distribution before and after normalization, i is the pixel number, and a is the coefficient of the Sigmoid function.

5. The method according to claim 1, wherein The step S4 specifically includes: When the conductivity distribution in the sensing area is σ D (Z), the Dirichlet-Neumann map is the coil feedback matrix M fb , expressed as: Where D is the current conductivity distribution, is the feedback signal, Z=x+yi is the coordinate of point (x,y), is the boundary of the sensing area, is the unit normal vector to the region boundary.

6. The method according to claim 1, wherein The step S5 specifically includes: Calculate the measurement mutual inductance matrix M m and feedback mutual inductance matrix M fb The difference between N×N (i), and use it as the input of the fractional-order PID controller, expressed as: P N×N (i)=M m -M fb Where i is the number of iterations, N×N is the number of pixels in the reconstructed area; The differential signal P N×N (i) is input to the fractional-order PID controller u(i) to obtain the control signal u(t) at the current moment, which is expressed as: in, is the binomial coefficient, expressed as: Γ(λ) is the Gamma function, expressed as: K p is the controller proportional coefficient, K1 is the controller integral coefficient, K D is the controller differential coefficient, is the binomial coefficient, h is the time step, λ is the order of integration, and μ is the order of differentiation.

7. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

8. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

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