Turbine blade defect detection method based on multi-frequency eddy current

Abstract

The invention provides a turbine blade defect detection method based on multi-frequency eddy current. The method comprises the steps that eddy current signals of at least two frequencies are generated through a multi-frequency eddy current detection device; acquiring a detection response signal under each frequency on the surface of the turbine blade to be detected; frequency mixing is carried out on the detection response signals, and edge effect signals in the detection response signals are eliminated; extracting defect response information contained in the frequency mixing signal after the edge effect signal is eliminated; and positioning the defect of the turbine blade according to the defect response information. Sine wave signals with multiple frequencies are used for excitation, frequency mixing processing is carried out on the received signals, interference of edge effect signals is restrained, accordingly, defect signals are accurately extracted, the detection accuracy and sensitivity of crack defects on the surface and near the surface of the turbine blade are remarkably improved, and the omission ratio is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of eddy current detection, and in particular to a turbine blade defect detection method based on multi-frequency eddy current. Background Art

[0002] Turbine blades are important components of aircraft engines, so the quality of the blades has a great impact on the safety performance of the aircraft. Since turbine blades are in a harsh working environment and are easily affected by many factors, such as centrifugal loads, aerodynamic loads and corrosion during high-speed rotation, it is very easy to produce cracks and other types of defects.

[0003] The special working environment of turbine blades and the influence of many factors make them the parts with the highest failure rate in the operation of aircraft engines, thus affecting the performance of aircraft engines and even leading to safety accidents. Due to the importance of turbine blades in aircraft engines and their high failure rate, in order to ensure the safe use of aircraft engines, regular quality inspections of turbine blades are required.

[0004] At present, the main detection methods for turbine blade defects include non-destructive testing methods such as X-ray, penetration, ultrasound, and eddy current. Eddy current detection technology is mainly suitable for defect detection on the surface and near the surface of conductive materials. It has the advantages of non-contact detection, no need for coupling agents, and high detection sensitivity for defective surfaces and near-surface defects. Therefore, the use of eddy current detection technology for quality inspection of turbine blades has a good detection effect.

[0005] In the process of using single-frequency conventional eddy current to detect turbine blades, it was found that if defects such as cracks are located at the edge of the blade, the defect signal and the edge effect signal cannot be well distinguished, and the defect signal is easily submerged in the edge effect signal, resulting in missed detection.

[0006] Beneficial effect: The present invention provides a turbine blade defect detection method based on multi-frequency eddy currents. The method uses sinusoidal wave signals of multiple frequencies for excitation and performs mixing processing on the received signals to suppress the interference of edge effect signals, thereby accurately extracting defect signals, significantly improving the detection accuracy and sensitivity of turbine blade surface and near-surface crack defects, and reducing the missed detection rate. Summary of the invention

[0007] In a first aspect of the present disclosure, a method for detecting defects in turbine blades based on multi-frequency eddy currents is provided, the method comprising:

[0008] Generate eddy current signals of at least two frequencies by a multi-frequency eddy current detection device;

[0009] Acquiring detection response signals at various frequencies on the surface of the turbine blade to be detected;

[0010] Mixing the detection response signals and eliminating edge effect signals therein;

[0011] Extracting defect response information contained in the mixed signal after eliminating the edge effect signal;

[0012] The defects of turbine blades are located based on the defect response information.

[0013] In combination with the first aspect, the at least two frequencies include a first frequency f1 and a second frequency f2, the first frequency f1 is also used to test the response frequency band of defects on the surface of the turbine blade, and the second frequency f2 is smaller than the first frequency f1.

[0014] In combination with the first aspect, acquiring the detection response signal at each frequency on the surface of the turbine blade to be detected includes:

[0015] Acquiring a response frequency band of defects on the surface of the turbine blade;

[0016] A detection response signal of the turbine blade surface is acquired based on the response frequency band.

[0017] In combination with the first aspect, the step of obtaining the response frequency band of the defects on the surface of the turbine blade includes:

[0018] A first defect is scratched at a preset position on the surface of the turbine blade away from the edge direction, and a second defect, a third defect and a fourth defect are scratched on the surface of the turbine blade at preset positions close to different edge directions and parallel to the corresponding edge, wherein the direction of the second defect is parallel to the fourth defect;

[0019] Respectively acquiring detection response signals of the first defect, the second defect, the third defect and the fourth defect at the first frequency f1;

[0020] The response frequency band of the defect on the surface of the turbine blade is determined according to the detection response signals of the first defect, the second defect, the third defect and the fourth defect at the first frequency f1.

[0021] In combination with the first aspect, mixing the detection response signals and eliminating the edge effect signals therein includes:

[0022] Selecting the detection response signal at the second frequency f2, rotating and scaling it so that it has the same amplitude and phase as the detection response signal at the first frequency f1;

[0023] The processed second frequency f2 signal is subtracted from the first frequency f1 signal by the following formula to cancel the edge effect signal and retain the defect signal:

[0024] C1=A1(S)+B1(N),

[0025] C2=A2(S)+B2(N),

[0026] Among them, C1 and C2 are the detection signals obtained at frequencies f1 and f2 respectively, A1(S) and A2(S) are the responses of the defect at frequencies f1 and f2 respectively; B1(N) and B2(N) are the responses of the interference source at frequencies f1 and f2 respectively;

[0027]

[0028] in, is the regulating factor,

[0029]

[0030] In combination with the first aspect, the extracting of defect response information contained in the mixed signal after the edge effect signal is eliminated includes performing frequency domain analysis on the mixed signal to extract amplitude and phase characteristics of the mixed signal.

[0031] In combination with the first aspect, locating the defect of the turbine blade according to the defect response information includes:

[0032] Using the extracted amplitude and phase characteristics of the mixing signal to construct a defect distribution model and establishing a spatial coordinate mapping relationship in combination with geometric parameters of the turbine blade;

[0033] The defect area is determined based on the local abnormal changes in signal amplitude and phase, and the size and direction of the defect are determined.

[0034] A second aspect of the present disclosure provides a turbine blade defect detection device based on multi-frequency eddy current, which is used to implement the turbine blade defect detection method based on multi-frequency eddy current, comprising:

[0035] A multi-frequency eddy current signal generating module, used for generating eddy current signals of at least two frequencies and outputting them to the detection probe;

[0036] A detection probe for sensing eddy current signal responses at various frequencies on the surface of the turbine blade;

[0037] A signal acquisition module, used to receive the response signal sensed by the detection probe and convert it into a processable digital signal;

[0038] A signal processing module is used to mix the collected response signals, suppress edge effects, and extract defect response information;

[0039] The defect analysis module is used to construct a defect distribution model and locate the defect based on the extracted defect response information and the geometric parameters of the turbine blades;

[0040] The display module is used to output the inspection results in the form of graphics or text, including the location, size and direction of the defects.

[0041] According to a third aspect of the present disclosure, an electronic device is provided, including:

[0042] one or more processors;

[0043] A storage unit is used to store one or more programs, which, when executed by the one or more processors, can enable the one or more processors to implement any one of the turbine blade defect detection methods based on multi-frequency eddy currents.

[0044] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the turbine blade defect detection methods based on multi-frequency eddy currents can be implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic flow chart of a turbine blade defect detection method based on multi-frequency eddy current according to an embodiment of the present disclosure;

[0046] Figure 2 A defect setting diagram for determining a response frequency band of defects on the surface of the turbine blade according to an embodiment of the present disclosure;

[0047] Figure 3 It is a structural schematic diagram of a turbine blade defect detection device based on multi-frequency eddy current according to an embodiment of the present disclosure;

[0048] Figure 4 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0049] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the embodiments of the present disclosure.

[0050] The terms used in the disclosed embodiments are only for the purpose of describing specific embodiments and are not intended to limit the disclosed embodiments. The singular forms of "a", "said" and "the" used in the disclosed embodiments and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0051] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the disclosed embodiments, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the disclosed embodiments, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0052] like Figure 1 FIG. 1 is a flow chart of a method for detecting defects in turbine blades based on multi-frequency eddy currents according to an embodiment of the present disclosure. The method comprises:

[0053] S101: generating eddy current signals of at least two frequencies by a multi-frequency eddy current detection device;

[0054] Preferably, the at least two frequencies include a first frequency f1 and a second frequency f2, the first frequency f1 is also used to test the response frequency band of defects on the surface of the turbine blade, and the second frequency f2 is smaller than the first frequency f1.

[0055] Exemplarily, the eddy current detection and impedance analysis method used in the present invention can provide more than two excitation frequencies and can generate impedance diagrams for the two frequencies.

[0056] The multi-frequency eddy current detection function is placed in a dual-frequency working mode, wherein the first frequency uses the basic frequency f1, and the second frequency is another frequency different from the first frequency, for example, it can be selected between 0.2f1-0.6f1.

[0057] Furthermore, obtaining the detection response signal at each frequency on the surface of the turbine blade to be detected includes:

[0058] Acquiring a response frequency band of defects on the surface of the turbine blade;

[0059] A detection response signal of the turbine blade surface is acquired based on the response frequency band.

[0060] Specifically, before obtaining the detection response signals at various frequencies on the surface of the turbine blade to be detected, it is necessary to obtain the response frequency band of the defects on the surface of the turbine blade, which specifically includes:

[0061] A first defect is scratched at a preset position on the surface of the turbine blade away from the edge direction, and a second defect, a third defect and a fourth defect are scratched on the surface of the turbine blade at preset positions close to different edge directions and parallel to the corresponding edge, wherein the direction of the second defect is parallel to the fourth defect;

[0062] Respectively acquiring detection response signals of the first defect, the second defect, the third defect and the fourth defect at the first frequency f1;

[0063] The response frequency band of the defect on the surface of the turbine blade is determined according to the detection response signals of the first defect, the second defect, the third defect and the fourth defect at the first frequency f1.

[0064] For details, see Figure 2 An artificial crack defect is made on a turbine blade as a debugging specimen required for eddy current testing to determine the response frequency band of the defect on the surface of the turbine blade.

[0065] There are four artificial crack defects on the blade, namely the first defect A, the second defect B, the third defect C and the fourth defect D (not marked), among which defect A is a crack defect in the middle of the blade, and B, C and D are crack defects parallel to and close to the edge of the blade. The information of each defect is shown in Table 1.

[0066] Table 1

[0067]

[0068] It should be noted that the first defect engraved at the preset position away from the edge direction can be set according to the critical position where no mixing signal interference occurs between the edge and the defect in actual working conditions. The second defect, the third defect and the fourth defect are also set in the same principle, and this embodiment does not impose any restrictions.

[0069] In addition, among the artificial defects set in this embodiment, the first defect is in the blade, that is, there will be no mixing signal interference between the edge and the defect, and the other three defects are respectively located on the three edges of the blade, that is, there will be mixing signal interference between the edge and the defect. Based on this principle, if the blade has multiple edges, multiple corresponding defects can also be set.

[0070] At the same time, the number of defects can also be set based on the above principle and geometric multiples of the number of edges of the blade to reduce errors, which is not limited in this embodiment.

[0071] S102: Acquire detection response signals at various frequencies on the surface of the turbine blade to be detected.

[0072] Specifically, a pencil probe is selected as the detection probe, and the artificial defects are first detected using a single frequency, and the frequency, gain and phase parameters of the instrument are adjusted.

[0073] Use a pencil probe to vertically scan defect A to obtain a separate crack defect signal. Parameters such as frequency and gain are selected to make the instrument have appropriate sensitivity to artificial cracks.

[0074] And the frequency parameter is determined as the basic frequency f1 of eddy current detection.

[0075] The horizontal track is the signal of the lift-off effect caused by the shaking of the probe. The oblique track is the signal of the defect formation.

[0076] Use the comparison specimen to perform multi-frequency eddy current testing and adjust appropriate instrument parameters such as frequency and internal algorithm parameters.

[0077] Use a pencil probe to gradually move from the middle of the blade toward the edge of the blade on the comparison test block in a direction perpendicular to the blade edge without passing through the defect area, and obtain two edge effect signals corresponding to the two detection frequencies.

[0078] Avoid excessive shaking of the probe during operation to avoid introducing too much lift-off effect into the edge effect signal.

[0079] S103: Mix the detection response signals and eliminate the edge effect signals therein.

[0080] Specifically, a detection response signal at the second frequency f2 is selected, and is rotated and scaled so that it has the same amplitude and phase as the detection response signal at the first frequency f1;

[0081] The processed second frequency f2 signal is subtracted from the first frequency f1 signal by the following formula to cancel the edge effect signal and retain the defect signal:

[0082] C1=A1(S)+B1(N),

[0083] C2=A2(S)+B2(N),

[0084] Among them, C1 and C2 are the detection signals obtained at frequencies f1 and f2 respectively, A1(S) and A2(S) are the responses of the defect at frequencies f1 and f2 respectively; B1(N) and B2(N) are the responses of the interference source at frequencies f1 and f2 respectively;

[0085]

[0086] in, is the regulating factor,

[0087]

[0088] After the edge effect signal is mixed, the edge effect residual signal is very small. If the observed residual signal is not small enough, the f2 frequency can be adjusted, re-detected, and then the mixing calculation can be performed again until a relatively small edge signal residual signal is obtained.

[0089] S104: extracting defect response information contained in the mixed signal after eliminating the edge effect signal;

[0090] Specifically, extracting the defect response information contained in the mixed signal after eliminating the edge effect signal includes performing frequency domain analysis on the mixed signal to extract amplitude and phase characteristics of the mixed signal.

[0091] Through frequency domain analysis, it is possible to effectively distinguish signals related to defects from interference signals unrelated to edge effects, noise, etc. Amplitude and phase characteristics can more intuitively reflect the shape, size and depth of defects, making defect positioning more accurate.

[0092] In the inspection of complex workpieces such as turbine blades, the distribution of defects is usually discrete due to their complex shapes. By extracting amplitude and phase features and combining them with machine learning algorithms such as support vector machines (SVM) or neural networks, the ability to identify and classify complex defects can be further improved.

[0093] Different types of defects (such as cracks, corrosion, impact marks, etc.) usually show different amplitude and phase characteristics in the frequency domain. For example, cracks may cause amplitude changes in high-frequency signals, while corrosion may affect the phase shift of low-frequency signals. By analyzing these characteristics, the inspection system can not only identify the existence of defects, but also determine the type and characteristics of defects.

[0094] S105: Locate the defect of the turbine blade according to the defect response information.

[0095] Specifically, the extracted amplitude and phase characteristics of the mixing signal are used to construct a defect distribution model and a spatial coordinate mapping relationship is established in combination with geometric parameters of the turbine blade;

[0096] The defect area is determined based on the local abnormal changes in signal amplitude and phase, and the size and direction of the defect are determined.

[0097] According to the amplitude and phase characteristics of the mixed signal extracted in step S104, potential defective signals can be firstly identified in the signal space. At this time, the defective information is usually manifested as a local increase in amplitude or an abnormal change in phase.

[0098] In order to convert these signal information into defect locations on the turbine blade surface, the system needs to build a defect distribution model, which can be optimized by comparing experimental data, simulation data, and actual measurements.

[0099] The construction of defect distribution models usually involves the spatial mapping of signal data and the application of classification algorithms, such as machine learning models (such as support vector machines (SVMs) or neural networks) to optimize defect detection and location judgment.

[0100] The geometric structure of turbine blades (such as blade shape, size and surface features) directly affects the propagation and reflection of eddy current signals. Therefore, when establishing the spatial coordinate mapping relationship, it is necessary to combine the specific geometric parameters of the blades.

[0101] By matching the geometric data obtained in the experiment (such as the edge shape, curvature, surface contour, etc. of the blade) with the spatial position of the detection signal (amplitude, phase and other characteristics), the detected signal can be accurately mapped to the actual physical position.

[0102] This mapping relationship can be achieved by calculating the geometric transformation between the geometric model and the signal response, or by utilizing the spatial coordinate system of the sensor.

[0103] In frequency domain analysis, defects usually cause significant changes in the amplitude or phase of local signals. For example, a crack or corrosion area may appear as an abnormal increase in the local amplitude or a significant shift in the phase.

[0104] The system determines the location of defects by analyzing these local abnormal changes. Specifically, by setting a certain threshold, the system can detect abnormal fluctuations in amplitude or phase, and further match these abnormal signals with the geometric position of the blade.

[0105] By comparing the stability of signals in adjacent areas, the boundary of the defect can be determined. For example, the starting and ending points of the defect can be identified by the range of amplitude and phase changes.

[0106] Once the defect area is located, the next task is to determine the size and orientation of the defect.

[0107] By analyzing the degree of signal change in the defect area, the size of the defect can be estimated. For example, the range of amplitude change and phase shift is usually proportional to the depth, width and length of the defect.

[0108] The direction of the defect is usually closely related to the curvature of the blade surface, the cause of the defect (such as cracks, corrosion, etc.) and the propagation direction of the eddy current. The system can determine the directionality of the defect by comparing the signal response in different directions.

[0109] like Figure 3 FIG. 1 is a schematic diagram of a structure of a turbine blade defect detection device based on multi-frequency eddy current according to an embodiment of the present disclosure. The device comprises:

[0110] A multi-frequency eddy current signal generating module 310 is used to generate eddy current signals of at least two frequencies and output them to the detection probe;

[0111] A detection probe 320 is used to sense eddy current signal responses at various frequencies on the surface of the turbine blade;

[0112] The signal acquisition module 330 is used to receive the response signal sensed by the detection probe and convert it into a processable digital signal;

[0113] The signal processing module 340 is used to perform frequency mixing, edge effect suppression and defect response information extraction on the collected response signal;

[0114] The defect analysis module 350 is used to construct a defect distribution model and locate the defect according to the extracted defect response information and the geometric parameters of the turbine blade;

[0115] The display module 360 ​​is used to output the inspection results in the form of graphics or text, including the location, size and direction of the defect.

[0116] An embodiment of the present disclosure provides a turbine blade defect detection method based on multi-frequency eddy currents. The method uses sinusoidal wave signals of multiple frequencies for excitation and performs mixing processing on the received signals to suppress the interference of edge effect signals, thereby accurately extracting defect signals, significantly improving the detection accuracy and sensitivity of turbine blade surface and near-surface crack defects, and reducing the missed detection rate.

[0117] The electronic device 400 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 400 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 400 and does not constitute a limitation of the electronic device 400. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0118] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0119] The memory 402 may be an internal storage unit of the electronic device 400, for example, a hard disk or memory of the electronic device 400. The memory 402 may also be an external storage device of the electronic device 400, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 400. Further, the memory 402 may also include both an internal storage unit of the electronic device 400 and an external storage device. The memory 402 is used to store the computer program 404 and other programs and data required by the electronic device. The memory 402 may also be used to temporarily store data that has been output or is to be output.

[0120] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0121] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should all be included in the protection scope of the present disclosure.

Claims

1. A turbine blade defect detection method based on multi-frequency eddy current, characterized in that: The method comprises: Generate eddy current signals of at least two frequencies by a multi-frequency eddy current detection device; Acquiring detection response signals at various frequencies on the surface of the turbine blade to be detected; Mixing the detection response signals and eliminating edge effect signals therein; Extracting defect response information contained in the mixed signal after eliminating the edge effect signal; The defects of turbine blades are located based on the defect response information.

2. The method according to claim 1, characterized in that The at least two frequencies include a first frequency f1 and a second frequency f2. The first frequency f1 is also used to test the response frequency band of defects on the surface of the turbine blade. The second frequency f2 is smaller than the first frequency f1.

3. The method according to claim 2, characterized in that The step of acquiring detection response signals at various frequencies on the surface of the turbine blade to be detected comprises: Acquiring a response frequency band of defects on the surface of the turbine blade; A detection response signal of the turbine blade surface is acquired based on the response frequency band.

4. The method according to claim 3, characterized in that The response frequency band for obtaining defects on the surface of the turbine blade includes: A first defect is scratched at a preset position on the surface of the turbine blade away from the edge direction, and a second defect, a third defect and a fourth defect are scratched on the surface of the turbine blade at preset positions close to different edge directions and parallel to the corresponding edge, wherein the direction of the second defect is parallel to the fourth defect; Respectively acquiring detection response signals of the first defect, the second defect, the third defect and the fourth defect at the first frequency f1; The response frequency band of the defect on the surface of the turbine blade is determined according to the detection response signals of the first defect, the second defect, the third defect and the fourth defect at the first frequency f1.

5. The method according to claim 2, characterized in that: The mixing of the detection response signals and eliminating the edge effect signals therein comprises: Selecting the detection response signal at the second frequency f2, rotating and scaling it so that it has the same amplitude and phase as the detection response signal at the first frequency f1; The processed second frequency f2 signal is subtracted from the first frequency f1 signal by the following formula to cancel the edge effect signal and retain the defect signal: C1=A1(S)+B1(N), C2=A2(S)+B2(N), Among them, C1 and c2 are the detection signals obtained at frequencies f1 and f2 respectively, A1(S) and A2(S) are the responses of the defect at frequencies f1 and f2 respectively; B1(N) and B2(n) are the responses of the interference source at frequencies f1 and f2 respectively; in, is the regulating factor, 6. The method according to claim 1, characterized in that The extracting of defect response information contained in the mixed signal after the edge effect signal is eliminated includes performing frequency domain analysis on the mixed signal to extract amplitude and phase characteristics of the mixed signal.

7. The method according to claim 1, characterized in that The method of locating the defect of the turbine blade according to the defect response information includes: Using the extracted amplitude and phase characteristics of the mixing signal to construct a defect distribution model and establishing a spatial coordinate mapping relationship in combination with geometric parameters of the turbine blade; The defect area is determined based on the local abnormal changes in signal amplitude and phase, and the size and direction of the defect are determined.

8. A turbine blade defect detection device based on multi-frequency eddy current, used to implement the method described in claims 1 to 7, characterized in that: include: A multi-frequency eddy current signal generating module, used for generating eddy current signals of at least two frequencies and outputting them to the detection probe; A detection probe for sensing eddy current signal responses at various frequencies on the surface of the turbine blade; A signal acquisition module, used to receive the response signal sensed by the detection probe and convert it into a processable digital signal; A signal processing module is used to mix the collected response signals, suppress edge effects, and extract defect response information; The defect analysis module is used to construct a defect distribution model and locate the defect based on the extracted defect response information and the geometric parameters of the turbine blades; The display module is used to output the inspection results in the form of graphics or text, including the location, size and direction of the defects.

9. An electronic device, characterized in that: include: one or more processors; A storage unit, used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the turbine blade defect detection method based on multi-frequency eddy current according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting defects in turbine blades based on multi-frequency eddy currents according to any one of claims 1 to 7 can be implemented.

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