A method and apparatus for defect detection based on NV color centers

Abstract

The application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical field.The present application discloses a kind of defect detection method and equipment based on NV color center, it is related to nondestructive testing technical

Description

TECHNICAL FIELD

[0001] The present application relates to the field of nondestructive testing, in particular to a defect detection method and device based on NV color centers. BACKGROUND

[0002] Most of the existing instruments for detecting ferromagnetic plates can only qualitatively detect cracks, that is, they can only detect whether there are cracks; most of the quantitative detection instruments use visual detection, and use industrial cameras to cooperate with computer software algorithms to quantitatively detect cracks. The accuracy of detection not only depends on the precision of the camera, but also depends on the algorithm, and in the imaging process, it is easy to be affected by vibration, dust, temperature and other factors in the environment, resulting in poor image quality and affecting the detection accuracy.

[0003] The precision measurement technology based on diamond NV color centers can realize nondestructive testing through magnetic measurement, and has high spatial resolution and sensitivity. Existing defect detection is divided into qualitative and quantitative detection. Qualitative detection is whether there is a defect, and quantitative detection is to detect the physical size and type of the defect. In actual detection tasks, the physical size of the defect often determines the use performance of the measured object and whether it needs to be replaced. The current defect detection based on NV color centers can only realize qualitative detection, and how to realize quantitative detection becomes a problem to be solved. SUMMARY

[0004] In view of the problems existing in the prior art, the present application is proposed.

[0005] Therefore, the problem to be solved by the present application is how to use a diamond NV color center with high precision instead of an expensive and low-precision device such as an industrial camera to realize quantitative detection of defects.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a defect detection method based on NV color centers, which comprises: pre-processing detection data obtained by magnetic flux leakage detection based on diamond NV color centers to filter out abnormal signals; wherein the magnetic flux leakage detection is to scan a magnetized measured object by using a diamond NV color center, and simultaneously irradiate the diamond NV color center with laser and microwave, detect the fluorescence generated by the diamond NV color center, and obtain detection data, the detection data at least including fluorescence electrical signals and corresponding scanning positions;

[0008] Filtering and denoising the pre-processed data;

[0009] Determining a defect signal and calculating the depth and width of the defect according to the processed data.

[0010] As a preferred embodiment of the defect detection method based on NV color centers of the present invention, the preprocessing includes:

[0011] For all fluorescence signals, calculate their root mean square (RMS) values.

[0012] Determine whether each fluorescent electrical signal is lower than the RMS, and whether the absolute value of the difference between the two exceeds a preset threshold;

[0013] If the current fluorescent electrical signal is lower than the RMS, and the absolute value of the difference between the two exceeds the preset threshold, then the signal is identified as an abnormal signal, and all signals lower than the RMS are set to the RMS.

[0014] As a preferred embodiment of the defect detection method based on NV color centers of the present invention, the filtering and noise reduction process includes:

[0015] A high-pass filtering algorithm is used to perform low-frequency noise reduction on the preprocessed fluorescent electrical signal by setting the cutoff frequency and the filtering order;

[0016] A wavelet denoising algorithm is used to smooth the abrupt signal of the low-frequency denoised fluorescence signal by setting the wavelet denoising basis function, threshold, and threshold processing function.

[0017] As a preferred embodiment of the defect detection method based on NV color centers of the present invention, wherein: the calculation of the depth and width of the defect includes:

[0018] Calculate the root mean square (RMS) value of the fluorescence signal after filtering and denoising.

[0019] Determine whether the signal-to-noise ratio of each signal point is higher than a specified value. If it is higher than the specified value, it is determined that there is a surface defect at the location corresponding to the signal.

[0020] All locations on both sides of the given position that have continuous fluorescent electrical signals higher than the RMS value are classified as the same defect region.

[0021] Count the number of fluorescent electrical signal spots and the maximum value of the fluorescent electrical signal for this defect;

[0022] The defect width is calculated using the formula W = N × Δx, where W is the defect width, N is the number of fluorescent signal points, and Δx is the sampling interval; and

[0023] The defect depth is calculated using the formula D = (Vmax / RMS) × D0, where D is the defect depth, Vmax is the maximum value of the fluorescent electrical signal, and D0 is the reference value, which is calibrated from the detection data of known defects.

[0024] As a preferred embodiment of the defect detection method based on NV color centers of the present invention, the parameters used in the algorithm are obtained in the filtering and denoising process by the following method:

[0025] Magnetic flux leakage detection based on diamond NV centers was performed on known defects to obtain detection data;

[0026] The detection data is filtered and denoised using any algorithm. By adjusting the parameters, each adjusted data is obtained. For each adjusted data, the evaluation value is calculated using the formula 10lg[(fluorescence signal at known defect - average value of all fluorescence signals) / (fluorescence signal at known defect after denoising - average value of all fluorescence signals)]. The parameters corresponding to the required evaluation value are selected as the parameters used by the corresponding algorithm.

[0027] As a preferred embodiment of the defect detection method based on NV color centers of the present invention, wherein: in the magnetic flux leakage detection, the microwaves radiated toward the diamond NV color centers are of a fixed frequency.

[0028] Secondly, embodiments of the present invention provide a defect detection device based on NV color centers, including a memory and a processor. The memory stores a computer program, wherein the processor executes the computer program to implement the steps of the defect detection method based on NV color centers as described in the first aspect of the present invention.

[0029] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the defect detection method based on NV color centers as described in the first aspect of the present invention.

[0030] Fourthly, embodiments of the present invention provide a defect detection system based on NV color centers, which includes a magnetization module, a detection and acquisition module, and a defect detection device based on NV color centers as described in the second aspect;

[0031] The magnetization module is used to magnetize the test piece;

[0032] The detection and acquisition module is used to detect the magnetic flux leakage of the test piece based on the diamond NV center, and to acquire the detected fluorescent electrical signal and its corresponding scanning position to obtain detection data, and transmit the detection data to the defect detection equipment based on the NV center.

[0033] As a preferred embodiment of the defect detection system based on NV color centers of the present invention, the detection and acquisition module includes:

[0034] Diamond NV color center probe, displacement control mechanism, laser, dual-color filter, filter, photodiode, acquisition module, microwave module; among which:

[0035] The laser emitted by the laser is reflected by a dichroic filter and then shines on the diamond NV color center probe.

[0036] The microwave module is used to generate microwaves and radiate them to the diamond NV color center probe.

[0037] The feedback fluorescence generated by the diamond NV color center probe is transmitted through the dual-color film and then filtered by the filter. After that, it is received by the photodiode and converted into a fluorescent electrical signal.

[0038] The displacement control mechanism is used to control the diamond NV color center probe and magnetization module to scan the workpiece under test and to position the scanning location.

[0039] The acquisition module is used to acquire fluorescence electrical signals and scanning positions to obtain detection data.

[0040] The beneficial effects of this invention are as follows: The defect detection method and equipment based on NV color centers provided by this invention ensure the reliability and accuracy of the detection data by establishing a data preprocessing and filtering noise reduction process, improving signal quality, reducing interference, and improving the accuracy and reliability of the final defect detection results; by detecting NV color centers based on the magnetized leakage magnetic field and performing hierarchical processing on the detection data, and adopting corresponding data processing algorithms, the spatial resolution and sensitivity of the detection are improved, while also reducing detection costs and environmental dependence, ensuring the technical and application feasibility of defect detection; by establishing an evaluation system based on defect depth and width parameters, and regularly optimizing and updating the detection process, detection efficiency is improved, and the false judgment rate is reduced, ensuring the advanced nature and practicality of the detection method. This invention achieves better results in terms of detection accuracy, data processing, and detection reliability. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of a defect detection method based on NV color centers;

[0043] Figure 2 The ODMR spectrum of the defect detection method based on NV color centers is shown.

[0044] Figure 3 This is a structural diagram of a defect detection system based on NV color centers;

[0045] Figure 4This is an exemplary structural diagram of a defect detection system based on NV color centers;

[0046] Figure 5 A schematic diagram of the magnetization module in a defect detection system based on NV color centers;

[0047] Figure 6 This is a raw data graph of the defect detection method based on NV color centers;

[0048] Figure 7 This is a data image after data preprocessing for a defect detection method based on NV color centers;

[0049] Figure 8 This is a high-pass filtered image of the defect detection method based on NV color centers.

[0050] Figure 9 This is a data image after wavelet denoising processing for a defect detection method based on NV color centers. Detailed Implementation

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0054] Example 1

[0055] Reference Figure 1 This is the first embodiment of the present invention, which provides a defect detection method based on NV color centers, including:

[0056] The detection data obtained by magnetic flux leakage detection based on diamond NV centers are preprocessed to filter out abnormal signals. The magnetic flux leakage detection involves scanning the magnetized test piece with diamond NV centers while simultaneously irradiating the diamond NV centers with laser and microwave radiation to detect the fluorescence generated by the diamond NV centers, thereby obtaining detection data. The detection data includes at least the fluorescence electrical signal and its corresponding scanning position.

[0057] The preprocessed data is then filtered and denoised.

[0058] The defect signal is determined based on the processed data, and the depth and width of the defect are calculated.

[0059] Data preprocessing: Raw data such as Figure 6 As shown, the root mean square (RMS) value of the fluorescence electrical signal in the entire data segment is taken. Then, each fluorescence electrical signal in the data is judged. If the current signal is lower than the RMS value and the absolute value of the difference between the two exceeds 500mV (here, the fluorescence electrical signal is a voltage value), the current signal is considered an abnormal signal. All signals lower than the RMS value are then set to the RMS value, resulting in the following... Figure 7 As shown;

[0060] This step is used to identify and remove abnormal signals, replacing them with RMS. This is because abnormal signals may exist during testing. For example, when the test piece is a ferromagnetic plate, leakage magnetic signals generated by the gaps between the plates, as well as the introduction of other noises in the environment, can also cause abnormal signals. For crack signals, the signal is higher than RMS. The 500mV in this embodiment is an empirical value, which can be determined based on the detected abnormal signals. It may vary for different testing equipment, but the value is uniform for the same testing equipment. If no abnormal signals are found, filtering and noise reduction processing is performed.

[0061] Filtering and denoising: By setting different cutoff frequencies and filter orders, a high-pass filtering algorithm is used to flatten the data baseline, resulting in... Figure 8 As shown, flattening is done to filter out low-frequency noise; then, wavelet denoising basis functions, thresholds, and threshold processing functions are set to smooth abrupt signal changes, resulting in the following: Figure 9 As shown in the figure. In this embodiment, the wavelet denoising basis function used is the Dobesi wavelet, the threshold is 500, and the threshold processing function is a soft function. Other types of parameters can also be selected, mainly adjusted according to the denoising effect.

[0062] High-pass filtering and wavelet denoising are common data denoising and filtering algorithms for one-dimensional signals. Cutoff frequency and filter order are parameters in high-pass filtering algorithms. Low-frequency noise can be filtered out by adjusting these two parameters; a Butterworth filter can be used to implement high-pass filtering. The wavelet denoising process for one-dimensional signals consists of wavelet decomposition of the signal, thresholding of high-frequency signals, and wavelet reconstruction of the signal. Abrupt signal changes can be smoothed by modifying the wavelet basis function, thresholding function, and threshold value.

[0063] The parameters for the two algorithms can be determined experimentally. Specifically, this involves performing magnetic flux leakage detection on known defects, processing the detection data using the algorithms, and adjusting the parameters to select those that enhance the defect signal. A judgment formula can be used: 10lg[(fluorescence signal at the known defect - average of all fluorescence signals) / (fluorescence signal at the known defect after denoising - average of all fluorescence signals)]. This formula yields an evaluation value; a smaller value is better. A positive value represents negative growth and weakening, while a negative value represents positive growth and signal enhancement. This value should be <3dB. Appropriate parameters are then selected based on the evaluation value.

[0064] When performing magnetic flux leakage detection on components under test, such as ferromagnetic plates, low-frequency components may be present due to factors such as instrument drift and environmental changes. Additionally, some noise signals may exist in the low-frequency region of the data, which could mask the true changes in the signal. Applying a high-pass filter can remove these low-frequency components, reducing signal noise interference and making the signal clearer. Adjustments can be made by changing the cutoff frequency and filter order of the high-pass filter to flatten the data baseline and remove low-frequency noise.

[0065] Wavelet denoising can significantly improve the signal-to-noise ratio of a signal, increasing the proportion of useful signal components relative to noise, thereby improving signal clarity and readability. It achieves noise removal by setting low-amplitude high-frequency components to zero. This method smooths abrupt changes in signals, preventing drastic jumps when defective signals have not reached extreme values.

[0066] Surface defect signal determination: Calculate the overall RMS of the filtered and denoised fluorescent electrical signals, and determine the defect signal for each signal individually.

[0067]

[0068] Because the output signal of the magnetic flux leakage detection in this embodiment is a voltage signal, the measurement data is as follows: Figures 6-9 As shown, the fluorescent electrical signals are all voltage values.

[0069] A surface defect is determined to exist when the signal-to-noise ratio (SNR) is higher than a specified value. This specified value is an empirical value, taken as 3 in this embodiment, but generally between 3 and 5. All positions corresponding to consecutive electrical signals higher than the RMS on both sides of the defect are considered part of the defect (by checking each signal individually to see if it is higher than RMS, until it is less than or equal to RMS). The number of fluorescent electrical signal points and the maximum value of the fluorescent electrical signal in this defect segment are counted. The width and depth of this defect segment can be calculated using the following formula:

[0070]

[0071] The baseline value is obtained through calibration.

[0072] Preferably, the preprocessing includes:

[0073] For all fluorescence signals, calculate their root mean square (RMS) values.

[0074] Determine whether each fluorescent electrical signal is below RMS and whether the absolute value of the difference between the two exceeds a preset threshold;

[0075] If the current fluorescence voltage is lower than RMS and the absolute value of the difference between the two exceeds a preset threshold, the signal is identified as an abnormal signal, and all signals lower than RMS are set to RMS.

[0076] Preferably, the filtering and noise reduction process includes:

[0077] A high-pass filtering algorithm is used to perform low-frequency noise reduction on the preprocessed fluorescent electrical signal by setting the cutoff frequency and the filtering order;

[0078] A wavelet denoising algorithm is used to smooth the abrupt signal of the low-frequency denoised fluorescent electrical signal by setting the wavelet denoising basis function, threshold, and threshold processing function.

[0079] Preferably, calculating the depth and width of the defect includes:

[0080] Calculate the root mean square (RMS) value of the fluorescence signal after filtering and denoising.

[0081] Determine whether the signal-to-noise ratio of each signal point is higher than a specified value. If it is higher than the specified value, it is determined that there is a surface defect at the location corresponding to the signal.

[0082] All locations with consecutive fluorescent electrical signals higher than RMS on both sides of the given position are classified as the same defect region.

[0083] Count the number of fluorescent electrical signal spots and the maximum value of the fluorescent electrical signal for this defect;

[0084] The defect width is calculated using the formula W = N × Δx, where W is the defect width, N is the number of fluorescent signal points, and Δx is the sampling interval; and

[0085] The defect depth is calculated using the formula D = (Vmax / RMS) × D0, where D is the defect depth, Vmax is the maximum value of the fluorescent electrical signal, and D0 is the reference value, which is calibrated from the detection data of known defects.

[0086] Preferably, in the filtering and denoising process, the parameters used in the algorithm are obtained through the following methods:

[0087] Detection data was obtained by using magnetic flux leakage detection based on diamond NV centers for known defects;

[0088] The detection data is filtered and denoised using any algorithm. By adjusting the parameters, each adjusted data is obtained. For each adjusted data, the evaluation value is calculated using the formula 10lg[(fluorescence signal at known defect - average value of all fluorescence signals) / (fluorescence signal at known defect after denoising - average value of all fluorescence signals)]. The parameters corresponding to the required evaluation value are selected as the parameters used by the corresponding algorithm.

[0089] Preferably, the evaluation value is less than 3dB.

[0090] In this embodiment, the magnetic flux leakage detection employs a fixed-frequency method, meaning the microwaves radiated onto the diamond NV center are at a fixed frequency. This fixed frequency is obtained by performing microwave frequency sweeping ODMR measurements on the diamond NV center. Specifically, this involves irradiating the diamond NV center with a laser, radiating microwaves, scanning the microwave frequency, detecting the feedback fluorescence, and plotting the fluorescence against the corresponding microwave frequency. Figure 2 The ODMR spectrum shown uses the resonance frequency on the ODMR spectrum or the microwave frequency corresponding to the maximum slope of the spectral line on one side of the resonance peak as the fixed frequency for performing magnetic flux leakage detection.

[0091] Example 2

[0092] This embodiment provides a defect detection device based on NV color centers, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the defect detection method based on NV color centers as proposed in Embodiment 1.

[0093] The defect detection equipment based on NV color centers can be a computer device, which can be a terminal. This computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC, or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0094] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the defect detection method based on NV color centers as proposed in the above embodiments.

[0095] In summary, it has beneficial effects.

[0096] Example 3

[0097] This embodiment provides a defect detection system based on NV color centers, such as... Figure 3 As shown, it includes: a magnetization module, a detection and acquisition module, and the NV color center-based defect detection device in Example 2. Figure 4 (marked as 110);

[0098] The magnetization module is used to magnetize the test piece;

[0099] The detection and acquisition module is used to detect the magnetic flux leakage of the test piece based on the diamond NV center, and to acquire the detected fluorescent electrical signal and its corresponding scanning position to obtain detection data. The detection data is then transmitted to the defect detection equipment based on the NV center.

[0100] The magnetization module includes a magnetizer 109, which can be shaped as follows: Figure 5 As shown, it includes an armature 109a and magnets 109b located at both ends thereon. It is placed together with a diamond NV color center probe 101 in a fixed cover 201, and the movement and scanning of both on the surface of the workpiece are controlled by a displacement control mechanism 102.

[0101] Preferred, such as Figure 4 As shown, the detection and acquisition module includes: a diamond NV color center probe 101, a displacement control mechanism 102, a laser 103, a dual-color filter 104, a filter 105, a photodiode 106, an acquisition module 107, and a microwave module 108.

[0102] in:

[0103] The laser emitted by the laser 103 is reflected by the dichroic filter 104 and then shines on the diamond NV color center probe 101. The laser is generally selected with a wavelength of 532nm to excite the NV color center to generate 637nm feedback fluorescence.

[0104] Microwave module 108 is used to generate microwaves and radiate them to the diamond NV color center probe 101. Microwave module 108, as... Figure 4 As shown, the exemplary embodiment includes a microwave source 108a, a microwave amplifier 108b, a microwave circulator 108c, and a microwave antenna 108d connected in sequence. The microwave signal generated by the microwave source 108a is amplified by the microwave amplifier 108b and then transmitted to the microwave antenna 108d through the microwave circulator 108c. The microwave antenna 108d then radiates the signal to the diamond NV color center probe 101.

[0105] The feedback fluorescence generated by the diamond NV color center 101 probe is transmitted through the dual-color filter 104 and then filtered by the filter 105. After that, it is received by the photodiode 106 and converted into a fluorescent electrical signal.

[0106] The displacement control mechanism 102 is used to control the diamond NV color center probe 101 and the magnetization module to perform scanning on the test piece and to locate the scanning position. The positioning method can be sensor positioning or the scanning distance can be calculated based on the scanning speed and time.

[0107] The acquisition module 107 is used to acquire fluorescence electrical signals and scanning positions to obtain detection data. The acquisition module may include an analog integrated circuit.

[0108] The mobile diamond NV color center probe 101 and magnetization module scan on a ferromagnetic plate, which is then magnetized to detect magnetic leakage at defects. The detection principle is as follows: a 532nm laser 103 emits a laser beam, which is reflected by a dichroic filter 104 and then shines onto the diamond NV color center probe 101. A microwave source 108a generates a microwave signal, which is amplified by a microwave amplifier 108b. A microwave circulator 108c prevents the microwave signal from being transmitted in reverse. The microwave signal is then transmitted to a microwave antenna 108d, which radiates it to the diamond NV color center. Under the influence of the laser, microwave, and magnetic field, a 637nm feedback fluorescence is generated. This fluorescence returns to the dichroic filter 104 via the original path, is transmitted through a filter 105, and is then received by a photodiode 106, converted into a fluorescent electrical signal. The acquisition module 107 acquires the fluorescent electrical signal and the scanning position, and transmits the detection data to the NV color center-based defect detection device 110. The defect detection device 110 can be implemented by a host computer.

[0109] To verify the beneficial effects of this invention, scientific demonstration was conducted through economic benefit calculations and simulation experiments.

[0110] In this embodiment, a batch of standardized Q235 steel plates was selected as test samples, with sample dimensions of 100mm × 100mm × 5mm. Surface cracks of varying depths and widths were artificially created as standard defects. To ensure the reliability of the test data, five standard cracks of different specifications were machined on each steel plate, with crack depths ranging from 0.1mm to 2mm and widths ranging from 0.05mm to 0.5mm. The standard cracks were fabricated using a precision machining center, and the crack dimensions were calibrated using a coordinate measuring machine as the detection benchmark.

[0111] The testing process employed a self-developed defect detection system based on NV color centers. First, the steel plate was pre-magnetized using a permanent magnet with a magnetization intensity set to 1.2T. The distance between the NV color center probe and the sample surface was maintained at 100μm, and the scanning step size was set to 50μm. 100 data points were collected at each measurement point, and the average value was taken to improve the signal-to-noise ratio. To verify the advantages of the data processing method of this invention, the same batch of samples was tested using both the traditional direct sampling method and the data processing method of this invention.

[0112] In the data preprocessing stage, root mean square analysis was performed on the raw signal, and a threshold of 500mV was set for anomaly signal identification. A Butterworth high-pass filter with a cutoff frequency of 10Hz was used for baseline correction, and the filter order was set to 4. In the wavelet denoising stage, a 4th-order Dobesie wavelet was used for 3-level decomposition, and a soft thresholding function was applied. The threshold was optimized experimentally and determined to be 500. In the defect detection and judgment stage, a signal-to-noise ratio (SNR) threshold of 3.5 was set to distinguish valid defect signals.

[0113] The test data are as follows:

[0114] Table 1. Data before processing

[0115]

[0116]

[0117] Table 2 Data after processing

[0118]

[0119] It should be noted that the following conclusions can be drawn from the analysis of the experimental data:

[0120] The detection accuracy of the method of this invention generally reaches over 97%, with a maximum of 99.4%, while the accuracy of traditional methods fluctuates between 86.8% and 92.6%. This indicates that the data processing method of this invention can significantly improve the accuracy of defect detection, with an average improvement of approximately 10 percentage points.

[0121] Traditional methods result in 24-48 missed detections, with a false negative rate of 4.8%-9.6%. In contrast, the method of this invention results in only 2-8 missed detections, reducing the false negative rate to 0.4%-1.6%. This improvement is significant for practical engineering applications, as missed detections can lead to potential safety hazards.

[0122] Traditional methods have 10-25 false positives, with a false positive rate of 2%-5%; the method of this invention reduces the number of false positives to 1-9, and the false positive rate to 0.2%-1.8%. This reduction in the false positive rate means less unnecessary rework and inspection, thus improving detection efficiency.

[0123] In terms of data fluctuation range, the standard deviation of the accuracy of the traditional method is approximately 0.026, while the standard deviation of the method of this invention is only 0.008, indicating that the method of this invention has better stability and repeatability. This stability is extremely important for practical applications in industrial settings.

[0124] This invention achieves a dual reduction in both false negative and false positive rates while maintaining a high detection accuracy rate—a balance that previous technologies have struggled to achieve. The fact that it maintains such high accuracy even in large-scale testing with sample sizes reaching 500 fully demonstrates the practical value of this method.

[0125] These data fully demonstrate that the data processing method of this invention has significant advantages in improving detection accuracy, reducing false negative and false positive rates, and enhancing detection stability. Through innovative data preprocessing and filtering / denoising algorithms, this method effectively overcomes the problem of signal interference in complex environments, providing more reliable technical support for non-destructive testing in industrial settings.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A defect detection method based on NV color centers, characterized in that: include, The detection data obtained by magnetic flux leakage detection based on diamond NV centers are preprocessed to filter out abnormal signals. The magnetic flux leakage detection is performed by scanning the magnetized test piece with diamond NV centers while simultaneously irradiating the diamond NV centers with laser and radiating microwaves to detect the fluorescence generated by the diamond NV centers, thereby obtaining detection data. The detection data includes at least the fluorescence electrical signal and its corresponding scanning position. The preprocessed data is then filtered and denoised. Defect signals are identified based on the processed data, and the depth and width of the defects are calculated. Wherein: the preprocessing includes: For all fluorescence signals, calculate their root mean square (RMS) values. Determine whether each fluorescent electrical signal is lower than the RMS, and whether the absolute value of the difference between the two exceeds a preset threshold; If the current fluorescent electrical signal is lower than the RMS and the absolute value of the difference between the two exceeds the preset threshold, then the signal is identified as an abnormal signal, and all signals lower than the RMS are set to the RMS. The depth and width of the computational defect include: Calculate the root mean square (RMS) value of the fluorescence signal after filtering and denoising. Determine whether the signal-to-noise ratio of each signal point is higher than a specified value. If it is higher than the specified value, it is determined that there is a surface defect at the location corresponding to the signal. All locations on both sides of the given position that have continuous fluorescent electrical signals higher than the RMS value are classified as the same defect region. Count the number of fluorescent electrical signal spots and the maximum value of the fluorescent electrical signal for this defect; The defect width is calculated using the formula W = N × Δx, where W is the defect width, N is the number of fluorescent signal points, and Δx is the sampling interval; and The defect depth is calculated using the formula D=(Vmax / RMS)×D0, where D is the defect depth, Vmax is the maximum value of the fluorescent electrical signal, and D0 is the reference value, which is calibrated from the detection data of known defects.

2. The defect detection method based on NV color centers as described in claim 1, characterized in that: The filtering and noise reduction process includes: A high-pass filtering algorithm is used to perform low-frequency noise reduction on the preprocessed fluorescent electrical signal by setting the cutoff frequency and the filtering order; A wavelet denoising algorithm is used to smooth the abrupt signal of the low-frequency denoised fluorescence signal by setting the wavelet denoising basis function, threshold, and threshold processing function.

3. The defect detection method based on NV color centers as described in claim 2, characterized in that: In the filtering and denoising process, the parameters used in the algorithm are obtained through the following methods: Magnetic flux leakage detection based on diamond NV centers was performed on known defects to obtain detection data; The detection data is filtered and denoised using any algorithm. By adjusting the parameters, each adjusted data is obtained. For each adjusted data, the evaluation value is calculated using the formula 10lg[(fluorescence signal at known defect - average value of all fluorescence signals) / (fluorescence signal at known defect after denoising - average value of all fluorescence signals)]. The parameters corresponding to the required evaluation value are selected as the parameters used by the corresponding algorithm.

4. The defect detection method based on NV color centers as described in claim 1, characterized in that: In the aforementioned magnetic flux leakage detection, the microwaves radiated toward the NV color center of the diamond are of a fixed frequency.

5. A defect detection device based on NV color centers, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the defect detection method based on NV color centers as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the defect detection method based on NV color centers as described in any one of claims 1 to 4.

7. A defect detection system based on NV color centers, characterized in that, include: Magnetization module, detection and acquisition module, and defect detection device based on NV color centers as described in claim 5; The magnetization module is used to magnetize the test piece; The detection and acquisition module is used to detect the magnetic flux leakage of the test piece based on the diamond NV center, and to acquire the detected fluorescent electrical signal and its corresponding scanning position to obtain detection data, and transmit the detection data to the defect detection equipment based on the NV center.

8. The defect detection system based on NV color centers as described in claim 7, characterized in that: The detection and acquisition module includes: The system comprises a diamond NV color center probe (101), a displacement control mechanism (102), a laser (103), a dual-color filter (104), a filter (105), a photodiode (106), a data acquisition module (107), and a microwave module (108); among which: The laser emitted by the laser (103) is reflected by the dichroic filter and then shines on the diamond NV color center probe. The microwave module (108) is used to generate microwaves and radiate microwaves to the diamond NV color center probe; The feedback fluorescence generated by the diamond NV color center probe (101) is transmitted through the dual-color film (104) and then filtered by the filter (105). After that, it is received by the photodiode (106) and converted into a fluorescent electrical signal. The displacement control mechanism (102) is used to control the diamond NV color center probe (101) and the magnetization module to perform scanning on the test piece and to position the scanning position. The acquisition module (107) is used to acquire fluorescence electrical signals and scanning positions to obtain detection data.

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