Insulating part defect identification method based on synthetic aperture ultrasonic imaging technology

Abstract

The invention provides an insulating part defect identification method based on a synthetic aperture ultrasonic imaging technology, which belongs to the technical field of ultrasonic defect identification, and comprises the following steps: step 01, setting parameters of an ultrasonic transducer to enable the ultrasonic transducer to emit ultrasonic waves, and attaching the ultrasonic transducer to the surface of a detected insulating part; 02, connecting the ultrasonic sensor with an acquisition card, and acquiring real-time echo data by using acquisition software; step 03, importing the data into data processing software for data processing, and performing defect imaging by applying an imaging algorithm; and step 04, performing defect analysis and positioning on the formed ultrasonic image. According to the method, the accuracy, reliability and practicability of defect detection of the insulating part are remarkably improved, and more effective technical support is provided for quality control in the industrial field. Due to the automatic and intelligent characteristics of the system, the detection efficiency is greatly improved, the uncertainty caused by human factors is reduced, and the system has remarkable practical value and popularization significance.

Description

Technical Field

[0001] The present invention belongs to the technical field of ultrasonic defect recognition, and in particular, relates to a method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology. Background Art

[0002] The insulation components inside electrical equipment play a vital role in power transmission and distribution. The insulation components inside electrical equipment, such as bushings, insulators, disconnectors, etc., have multiple functions such as isolating live parts, preventing electrical breakdown, protecting equipment and personal safety, and improving equipment reliability and stability.

[0003] When the high-voltage switchgear is operating normally, it will generate relatively stable heat. However, when the actual thermal power of the high-voltage switchgear is abnormal or the insulation structure is heated, the internal temperature of the high-voltage switchgear will be abnormal, causing serious local overheating of the equipment. Abnormal temperature difference will appear in the internal and external environment of the high-voltage switchgear, resulting in a sharp increase in thermal stress. If the high-voltage switch cannot dissipate the internal heat to the surroundings in time, a large amount of heat will continue to accumulate inside the equipment, causing the high-voltage switchgear to fail, and even burn the equipment in severe cases. In recent years, the accident rate and failure rate of insulating components in switchgear such as bushings, insulators, and disconnectors have been on the rise. Therefore, non-destructive testing should be carried out, and timely inspection, maintenance and testing should be carried out, and preventive measures should be taken in advance to ensure the safe operation of the equipment.

[0004] The research on ultrasonic flaw detection equipment at home and abroad has a relatively mature theory and research foundation, such as the use of eddy current flaw detection technology to perform non-destructive detection of internal defects of ferromagnetic casings, and the analysis and research of casing defect detection methods based on the theoretical model of pulse eddy current detection. Ultrasonic imaging of electrical equipment is not widely used. However, traditional ultrasonic testing equipment at home and abroad still has shortcomings: ultrasonic waves are attenuated during propagation, and the measurement process is complicated. The casings are of different sizes and numbers. Sometimes the type of probe needs to be changed for testing casings of different shapes. The detection accuracy is low and there are large errors. Summary of the invention

[0005] In view of this, the present invention provides an insulating component defect identification method based on synthetic aperture ultrasonic imaging technology, which can solve the technical problems of low detection accuracy and large errors of existing detection equipment.

[0006] The present invention is achieved in that:

[0007] The present invention provides a method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology, comprising the following steps:

[0008] Step 01, set the ultrasonic transducer parameters to make it emit ultrasonic waves, and place the ultrasonic sensor close to the surface of the insulating component to be tested;

[0009] Step 02: Connect the ultrasonic sensor to the acquisition card and use the acquisition software to collect real-time echo data;

[0010] Step 03: Import the data into the data processing software for data processing and apply the imaging algorithm for defect imaging;

[0011] Step 04: Analyze and locate defects on the resulting ultrasonic image.

[0012] On the basis of the above technical solution, the defect identification method of insulating components based on synthetic aperture ultrasonic imaging technology of the present invention can also be improved as follows:

[0013] Wherein, the step 01 specifically includes:

[0014] Step 11, using a sine function as an excitation source, applied to the upper surface of the piezoelectric material to generate an inverse piezoelectric effect;

[0015] Step 12, converting electrical energy into ultrasonic waves to propagate in the elastic material;

[0016] Step 13, emitting ultrasonic longitudinal waves through an ultrasonic transducer;

[0017] Step 14: The ultrasonic wave is refracted into a transverse wave through the inclined wedge and propagates inside the material.

[0018] Furthermore, the step 02 specifically includes:

[0019] Step 21, placing an ultrasonic sensor on the surface of the insulating component;

[0020] Step 22, collecting electromagnetic wave echoes transmitted from the defect and the bottom of the workpiece;

[0021] Step 23, connect the ultrasonic sensor and the acquisition card with a coaxial cable;

[0022] Step 24: collect real-time echo data to the host computer through the acquisition software.

[0023] Furthermore, the step 03 specifically includes:

[0024] Step 31, performing Fourier transform processing on the collected ultrasonic signal to reduce the noise signal;

[0025] Step 32, dynamically synthesizing an equivalent virtual large-aperture transducer using a small-aperture transducer array;

[0026] Step 33, moving at a constant speed on the linear motion trajectory and transmitting and receiving echo signals sequentially at the determined positions;

[0027] Step 34: perform coherent superposition processing on echo signals at different positions according to spatial positions and phase relationships.

[0028] Furthermore, the step 04 specifically includes:

[0029] Step 41: image preprocessing and signal enhancement;

[0030] Step 42: Boundary feature extraction and analysis;

[0031] Step 43: depth positioning and measurement;

[0032] Step 44: defect morphology feature analysis;

[0033] Step 45: quantitatively measure the defect size.

[0034] Optionally, it also includes: step 46, comprehensive evaluation and reliability analysis; step 47, result display and report generation.

[0035] Furthermore, the step 41 specifically includes:

[0036] Step 411, performing selective denoising to retain defect feature information;

[0037] Step 412: performing contrast enhancement based on local area statistical characteristics;

[0038] Step 413: Establish an adaptive threshold system for image segmentation;

[0039] Step 414: Evaluate the signal-to-noise ratio after processing.

[0040] Furthermore, the step 42 specifically includes:

[0041] Step 421, using a multi-scale edge detection method to identify defect boundaries;

[0042] Step 422, smoothing the boundary contour;

[0043] Step 423, calculating the local curvature and directional characteristics of the boundary;

[0044] Step 424: Create a boundary feature descriptor.

[0045] Furthermore, the step 43 specifically includes:

[0046] Step 431, determining the defect surface position based on the first wave signal time;

[0047] Step 432, using multiple echo information to improve depth measurement accuracy;

[0048] Step 433, using the depth correction equation group to perform depth correction;

[0049] Step 434: Evaluate the uncertainty of the depth measurement.

[0050] Furthermore, the step 44 specifically includes:

[0051] Step 441, calculating the basic geometric parameters of the area and perimeter of the defect;

[0052] Step 442, extracting the shape factor and directional characteristics of the defect;

[0053] Step 443, establishing quantitative description indicators of morphological characteristics;

[0054] Step 444: Analyze the symmetry and regularity of the defects.

[0055] Furthermore, the step 45 specifically includes:

[0056] Step 451, determining a negative 6 dB signal amplitude threshold;

[0057] Step 452: Count the area of ​​the region that meets the threshold condition;

[0058] Step 453, calculating the equivalent circle diameter as a representation of the defect size;

[0059] Step 454 , evaluate the systematic error and random error of the dimensional measurement.

[0060] Compared with the prior art, the method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology provided by the present invention has the following beneficial effects:

[0061] This defect identification method for insulating components based on synthetic aperture ultrasonic imaging technology has achieved significant improvements in detection accuracy. By using a small-aperture transducer array to dynamically synthesize an equivalent virtual large-aperture transducer, the spatial resolution of the system is significantly improved. In the signal processing link, the method introduces Fourier transform processing to reduce noise, and coherently superimposes echo signals at different positions, effectively improving the signal-to-noise ratio. At the same time, the adaptive threshold segmentation and multi-scale edge detection methods are used to improve the accuracy of defect boundary identification, and the accuracy of depth measurement is improved through multiple echo information and depth correction equations.

[0062] In terms of measurement error control, this method has established a complete error evaluation system, including the analysis of systematic errors and random errors. Through the deep measurement uncertainty evaluation, the reliability of the measurement results is quantified. The method uses the negative 6 dB signal amplitude threshold method for dimensional measurement, which provides a standardized measurement basis and ensures the standardization of the measurement process. By establishing quantitative description indicators of morphological characteristics, the errors that may be caused by human judgment are significantly reduced, making the measurement results more objective and accurate.

[0063] Improving detection reliability is another important technical effect of this method. This method realizes the automation of the entire process from signal acquisition, processing to result analysis, establishes a complete image preprocessing and enhancement mechanism, and ensures data quality from the source. At the same time, by adopting a multi-parameter comprehensive evaluation method, the credibility of the detection results is improved, and through a standardized report generation system, the traceability of the results is ensured, providing a strong guarantee for the accuracy of the detection results.

[0064] In terms of defect characterization capability, this method realizes multi-dimensional feature extraction of defects, including comprehensive analysis of geometric parameters, shape factors and directional characteristics. Through symmetry and regularity analysis, the method can provide more comprehensive and in-depth defect morphology information. At the same time, by establishing a complete boundary feature descriptor system, the accuracy of defect identification is significantly improved. The method also innovatively adopts equivalent circle diameter calculation, provides a standardized defect size characterization method, and makes the description of defect characteristics more scientific and standardized.

[0065] In summary, the present invention solves the technical problems of low detection accuracy and large errors in existing detection equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is an overall flow chart of the insulating component defect identification method based on synthetic aperture ultrasonic imaging of the present invention;

[0067] Figure 2 It is a test block diagram of an insulation defect acquisition and detection system of an insulation component defect recognition method based on synthetic aperture ultrasonic imaging of the present invention;

[0068] Figure 3 It is a schematic diagram of ultrasonic propagation of the insulating component defect recognition method based on synthetic aperture ultrasonic imaging of the present invention;

[0069] Figure 4 It is a schematic diagram of a detection data set in a method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging of the present invention;

[0070] Figure 5 It is a schematic diagram of SAFT processing of detection data in the insulating component defect recognition method based on synthetic aperture ultrasonic imaging of the present invention;

[0071] Figure 6 This is the schematic diagram of synthetic aperture focusing for single probe scanning;

[0072] Figure 7 is a diagram of the relationship between the scattering points and the transmitting and receiving elements;

[0073] Figure 8 It is a defect identification analysis flow chart. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0075] like Figure 1 As shown, it is a flow chart of a method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology provided by the present invention. The specific implementation mode of the present invention is described in detail below.

[0076] The present invention considers realizing an ultrasonic detection system with online detection, simultaneous defect and defect imaging, high accuracy and high reliability. Based on the above purpose, the embodiment of the present invention discloses a method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology. Figure 1 As shown, the method of the embodiment of the present invention includes the following steps:

[0077] Step S101: setting the parameters of the ultrasonic transducer to make it emit ultrasonic waves, and placing the ultrasonic transducer close to the surface of the insulating component to be tested.

[0078] The sine function acts as an excitation source. When applied to the surface of the piezoelectric material, it produces an inverse piezoelectric effect, converting electrical energy into ultrasonic waves that propagate in the elastic material. When encountering defects or interfaces inside the material, part of the energy will be reflected back. The probe receives the reflected ultrasonic signal and converts it into an electrical signal.

[0079] Since ultrasonic waves propagate at different speeds in different materials, it is necessary to measure the ultrasonic wave velocity for defect identification and positioning. The relationship between ultrasonic wave velocity u, propagation time t, and propagation distance l is as follows:

[0080]

[0081] The ultrasonic transducer is used to excite ultrasonic waves and observe the echo to identify and locate defects. Angle beam transducers are designed to generate refracted shear waves in the test sample. The waves propagate to the sample surface at a certain angle, usually 40° to 70°, see Figure 2 .

[0082] The angle or refraction depends on the material properties of the transducer, the test sample and the angle of incidence of the ultrasonic signal. p1 The speed of the wedge is c, producing refracted longitudinal and transverse waves, p2 and c s2 The speed of propagation in the sample is as follows Figure 3 As shown. The refraction angle is defined according to Snell's law:

[0083]

[0084] The first critical angle and the second critical angle are the critical angles that produce β = 90° and γ = 90°, respectively. p1 = 2080m / s plastic wedge pair and c p2 =6200m / s and c s2 =3120m / s aluminum specimen, the first critical angle is about 19.5°, and the second critical angle is about 44.7°.

[0085] Based on the piezoelectric effect, an excitation pulse is applied to the upper and lower surfaces of the piezoelectric material to excite the ultrasonic wave. The incident angle is changed by the inclined wedge, so that the ultrasonic wave refracts and propagates inside the material. The model is as follows Figure 3 As shown:

[0086] Based on the piezoelectric effect, an ultrasonic self-transmitting and self-receiving probe is established to emit ultrasonic longitudinal waves, which are refracted after reaching the material surface through the inclined wedge block and propagate inside the material as refracted transverse waves.

[0087] Step S102: Connect the sensor to the acquisition card and use LabVIEW to collect real-time echo data.

[0088] An ultrasonic sensor is placed on the surface of the defective insulation part to collect the electromagnetic wave echo from the defect and the bottom of the workpiece, the sensor is connected to the acquisition card with a coaxial cable, and the real-time echo data is collected to the host computer through LabVIEW software;

[0089] Step S103: Import the data into MATLAB for data processing and apply imaging algorithms to perform defect imaging.

[0090] Since ultrasonic signals are attenuated during propagation and the noise introduced during ultrasonic testing will interfere with the determination of the test results, especially when the defect is very small or the thickness of the test piece is large, the echo voltage signal is very weak, only at the millivolt level or even smaller, and is easily submerged by noise. In order to improve the accuracy and reliability of the test, it is very necessary to denoise the collected ultrasonic signals.

[0091] Ultrasonic signals have the characteristics of narrowband, sparseness and super-Gaussianity. These characteristics make ultrasonic signals have certain particularities in the transmission and processing process, and it is necessary to select appropriate signal processing methods to obtain the best processing effect. It is a good choice to process ultrasonic signals with Fourier transform, which can convert ultrasonic signals from time domain to frequency domain, so as to conduct more in-depth analysis and processing and filter out noise. The sampling frequency of the acquisition card must be greater than twice the highest frequency in the ultrasonic signal to avoid aliasing. The collected ultrasonic signal is imported into MATLAB for Fourier transform to reduce the interference of noise signals.

[0092] By using a small-aperture transducer array to dynamically synthesize an equivalent virtual large-aperture transducer, high imaging resolution can be achieved with a small-aperture probe and a lower operating frequency without increasing hardware costs or changing existing detection conditions. This technology not only has the advantages of high signal-to-noise ratio, azimuth resolution independent of detection depth, and "near-field" applicability, but can also be combined with B-scan and C-scan imaging, making it particularly suitable for improving the signal-to-noise ratio and resolution of online imaging for automated detection.

[0093] Synthetic aperture imaging uses a small aperture array to move at a constant speed on a linear motion trajectory, and transmits, receives and stores echo signals in sequence at a certain position. According to the spatial position and phase relationship, the echo signals at different positions are coherently superimposed to synthesize a virtual large aperture array, thereby obtaining high resolution along the direction of motion.

[0094] Ultrasonic propagation diverges with distance, so synthetic aperture technology needs to improve lateral resolution and focus methods. Different from traditional acoustic lens focusing and concave transducer focusing, synthetic aperture focusing uses electronic focusing methods, that is, focusing is achieved by changing the phase (or changing time) of each transducer channel. Assuming the focusing radius is R, the beam width at the focus, that is, the lateral resolution, can be expressed as:

[0095]

[0096] Where D is the transmit aperture of the transducer (the result of the above formula is an ideal result, without considering the beam directivity of the transducer). Therefore, under the condition that the focusing distance and transducer frequency are fixed, the method to improve the lateral resolution is to increase the transmit aperture of the transducer.

[0097] z represents the vertical distance from the point to be synthesized to the surface of the test piece. Assuming that the step of the probe during linear scanning is Δx, the number of probe positions n participating in signal synthesis is:

[0098]

[0099] Where λ represents the wavelength, D represents the wafer diameter of the probe, and the length of the position that contributes to signal processing in the synthetic aperture process is defined as the effective synthetic aperture length, denoted by L m .

[0100] For the points to be synthesized at different depths, the number of probe positions participating in the signal synthesis is also different. The farther the point to be synthesized is from the surface, the more probe positions participate in the signal synthesis, which is beneficial to improve the imaging resolution. The probe emits an ultrasonic pulse signal at each scanning position, which is reflected by the internal defect or the bottom surface of the workpiece to form an echo signal that is received by the probe again. Finally, the pulse echo signal carrying the internal information of the workpiece is converted into a digital signal through A / D conversion and stored in the computer.

[0101] Before SAFT processing, the signal interval of defect point A is M. If the defect is quantified based on size M, it will undoubtedly introduce a large measurement error. k (t) represents the echo signal received by the effective probe at each detection position, and the signal strength of point A after SAFT processing is S SAFT In (x,z), the delayed superposition formula of the SAFT algorithm is:

[0102]

[0103] Where i represents the probe position opposite to point A, τ ki represents the signal delay time of the kth probe position relative to the i-th position. ki for:

[0104]

[0105] In the formula, C represents the propagation speed of the sound wave in the object being detected. For the point A to be synthesized, its distance to each probe is different, and the signal delay time τ ki When we use synthetic aperture focusing technique (SAFT) to process Figure 4 When the data set in is collected, different delay times are applied to the digital signals corresponding to each point in the region of interest inside the test piece according to the different scanning positions of the probe, and the signals are added and averaged to obtain a beam focusing image, such as Figure 5 The SAFT delay superposition process can improve the imaging contrast. The signal interval M can be transformed into point M′ after SAFT delay superposition processing, thereby reducing the measurement error and improving the imaging resolution.

[0106] In the process of reconstructing the signal using synthetic aperture focusing technology (SAFT), a precise delay adjustment strategy is implemented so that the reflected signals from the defects can overlap at a specific position in the reconstructed waveform. This overlap phenomenon significantly enhances the amplitude of these defect echoes through the superposition effect, making the defects more prominent in the image. However, for random interference signals such as electronic noise, they do not appear stably in the same position every time the probe collects data, and these noise signals also have the characteristics of positive and negative fluctuations. This means that when these noisy signals are subjected to superposition averaging (DAS) processing, due to the randomness and volatility of the noise signals, the enhancement effect between them will be greatly weakened compared to the stable defect signals.

[0107] like Figure 6As shown in the figure, the probe captures three A-wave signals containing defect echoes at three different scanning positions, R′1, R′2, and R′3. After the three RF signals are summed and averaged, the amplitude of the defect echo in the reconstructed signal is significantly enhanced. This enhancement effect not only widens the amplitude gap between the defect echo and the noise signal, but also significantly improves the signal-to-noise ratio of the detection, so that the defect signal can be more effectively highlighted from the complex noise environment and clearly distinguished from the noise signal. This processing mechanism of SAFT provides us with a powerful tool to accurately identify and extract weak defect signals in the case of large noise interference.

[0108] In the time domain, the detailed steps of synthesizing and reconstructing the echo signal received from the target imaging point R are summarized as follows:

[0109]

[0110] Among them, I saft (R) is the synthetic signal at the target imaging point R, n is the number of sub-signals that contribute to signal reconstruction, S(R′,τ DAS ) is the defect echo signal received by the probe at different scanning positions R′, τ DAS is the delay difference of the sound wave propagating between R and R′, which can be expressed as:

[0111]

[0112] In formula (8), c is the group velocity of ultrasonic longitudinal waves in the material.

[0113] The above is the general theory of time-domain synthetic aperture imaging, but in practical applications, attention should also be paid to the accuracy of signal superposition.

[0114] In actual application, after the experimental equipment receives the ultrasonic echo signal, it generally needs to be converted into digital form before it can be stored. Therefore, the recorded ultrasonic data are all discrete digital signals in the time domain, that is,

[0115] t=[0,1,2,3…(m-1)] / f s +t0 (9)

[0116] Where m is the number of sampling points, f s is the sampling frequency, and t0 is the time when sampling starts. The τ calculated by formula (8) is DAS It is often not an integer multiple of the sampling period, but falls between two adjacent sampling moments, that is, τ DAS ∈[t k ,t k+1 ], then numerical interpolation is required to ensure the accuracy of delay superposition. A simple and commonly used processing method is to convert S(R′,τDAS ) is approximately equal to τ DAS The closest sampling point amplitude is:

[0117] S(R′,τ DAS )≈S(R′,near(t k ,t k+1 )) (10)

[0118] In formula (10), the operator near(·) determines the closest DAS The sampling point moment of the value. However, this approximation method is only effective at the sampling frequency f s Much larger than the probe center frequency f c It can be used only when s >>f c When the sampling frequency is low, no obvious imaging error will be introduced. For the case of low sampling frequency, other interpolation methods need to be used for calculation. This paper mainly uses the linear interpolation method, and the specific process is as follows:

[0119]

[0120] The linear interpolation processing method reduces the error of delay calculation in the actual signal synthesis process, which is beneficial to ensure imaging accuracy.

[0121] Assume that the 3D object consists of randomly positioned point scatterers embedded in a uniform background, supporting a constant sound velocity c, and the transducer is located in the z = 0 plane. The elements used as transmitter and receiver are located at (x t ,y t ,0) and (x r ,y r ,0) Figure 7 , when p(t) is used as the excitation signal, the received signal scattered from the point located at (x, y, z) is expressed as:

[0122]

[0123] Since the imaging system is linear, the received object scattered signal can be regarded as the linear superposition of all echo signals scattered from each point, as shown below:

[0124] r object (x t ,y t ,x t ,y t ,t)=∫∫∫r one (x t ,y t ,x t ,y t,x,y,z,t)dxdydz (13) and formula (12), (13) are connected to become:

[0125]

[0126] Applying the time domain Fourier transform object (x t ,y t ,x r ,y r ,t) becomes

[0127]

[0128] Where A(k) is the time Fourier transform of p(t), k is the wave number, k = ω / c = 2πf / c.

[0129] Describe the received signal in the spectral domain when only one element is used for transmission. Assume that the geometry of the array transducer located in the z = 0 plane is given by W g (x t ,y t ) description; the transmitted signal is weighted to become W t (x t ,y t ,k). When an array transducer is used, under the linear assumption of the imaging system, all elements of the array transducer are regarded as independent transmitters, and the received signal is the linear superposition of all echo signals from each transmitter, as shown below:

[0130] R(x r ,y r ,k)=∫∫W g (x t ,y t )W t (x t ,y t ,k)R object (x t ,y t ,x r ,y r ,k)dx t dy t (16)

[0132] 3D imaging method using a moving single-element sensor:

[0133] Here, frequency domain SA with a single element transducer moving in a rectangular area has a selected weighting function. This method uses a single element transducer to transmit a signal and the echo is received by the same transducer. Then, the transducer is moved to the next position for transmission and reception and the same operation is repeated when the scan is completed. This imaging method is described in detail below:

[0134] When the single element sensor moves on the z=0 plane, W g (x t ,y t )=δ(x t -x r ,y t -y r ) and W t (x t ,y t )=1 and formula (16) indicate that the signal received by the transducer is

[0135]

[0136] Received signal R(x r ,y r ,k) The spatial Fourier transform on the x and y axes is obtained

[0137]

[0138] According to the fixed phase method, the amplitude function which does not play an important role in imaging is omitted in the analysis, and formula (18) becomes

[0139]

[0140] Formula (19) can be simply expressed as follows

[0141] Y(k x ,k y ,k)=A(k)·F(k′ x ,k′ y ,k′ z ) (20)

[0142] Where F(k′ x ,k′ y ,k′ z ) is the spatial Fourier transform of f(x,y,z), and

[0143]

[0144] Then, the three-dimensional image of the target is constructed using the inverse Fourier transform of the three-dimensional space as follows

[0145] f(x,y,z)=F -1 (F(k′x ,k′ y ,k′ z )) (twenty two)

[0146] Among them, F -1 (·) denotes the spatial inverse Fourier transform.

[0147] Step S104: performing defect analysis and positioning on the ultrasonic image.

[0148] Since ultrasonic waves will be reflected when two objects have different acoustic impedances during propagation, the first reflected wave will appear when encountering a defect. Therefore, the first reflection with the strongest intensity in the image is the defect position, which can realize the depth positioning of the defect.

[0149] In the field of defect analysis, we focus on two core aspects: positioning and quantitative analysis. In order to determine the intensity of artifacts in the imaging area, we usually perform background noise analysis. When locating defects, we mainly search the amplitude of the grid points in the imaging area, find the point with the largest amplitude and calibrate the coordinates. Combined with the set coordinates (X\Y) of the imaging area, we can quickly determine the specific position of the defect in the overall coordinate system of the test block.

[0150] In terms of quantitative analysis, the half-wave attenuation measurement method is mainly used. Taking the defect location coordinates as the reference point, we radiate detection to the surrounding area to determine which grid points have signal strengths that meet the -6db threshold. After counting the grid points that meet the conditions, we compare them with the ratio of the theoretical defect size to the actual defect size, so that we can have a relatively clear judgment on the size of the defect after imaging, providing a comprehensive analysis method for the defect size and location. The analysis process is shown in Figure 8 .

[0151] Another implementation of step S104 is provided below, which specifically includes the following:

[0152] Step 41: Image preprocessing and signal enhancement, specifically:

[0153] The acquired ultrasonic images are preprocessed, including:

[0154] Perform selective denoising to retain defect feature information while reducing background noise;

[0155] Contrast enhancement based on local area statistical characteristics;

[0156] Establish an adaptive threshold system for image segmentation;

[0157] Evaluate the signal-to-noise ratio after processing to ensure that the signal quality meets the requirements of subsequent analysis;

[0158] Step 42: Boundary feature extraction and analysis, specifically:

[0159] Boundary extraction and analysis of defective areas:

[0160] Use multi-scale edge detection method to identify defect boundaries;

[0161] Smooth the boundary contour and remove false boundary points;

[0162] Compute local curvature and directional characteristics of the boundary;

[0163] Establish boundary feature descriptors for subsequent defect classification;

[0164] Step 43: Depth positioning and measurement, specifically:

[0165] Accurately locate and measure defect depth:

[0166] Determine the surface position of the defect based on the first wave signal time;

[0167] Using multiple echo information to improve depth measurement accuracy;

[0168] Considering the influence of sound velocity changes, depth correction is performed using depth equations; the uncertainty of depth measurement is evaluated;

[0169] Step 44: Defect morphological characteristics analysis, specifically:

[0170] Analyze the geometric characteristics of defects:

[0171] Calculate the basic geometric parameters of defects such as area and perimeter;

[0172] Extract the shape factor and directional characteristics of defects;

[0173] Establish quantitative descriptive indicators of morphological characteristics;

[0174] Analyze the symmetry and regularity of defects;

[0175] Step 45: Quantitative measurement of defect size, specifically:

[0176] Quantitative analysis of defect size using the half-wave attenuation method:

[0177] Determine the -6dB signal amplitude threshold;

[0178] Count the area that meets the threshold condition;

[0179] Calculate the equivalent circle diameter as a representation of the defect size;

[0180] Evaluate systematic and random errors in dimensional measurements;

[0181] Step 46: Comprehensive evaluation and reliability analysis, specifically:

[0182] Comprehensive evaluation of the test results:

[0183] Establish a multi-dimensional evaluation indicator system;

[0184] Analyze the reliability and confidence of test results;

[0185] Evaluate sources of uncertainty in the measurement process;

[0186] Give the final quantitative description results of defect characteristics;

[0187] Step 47: Results display and report generation, specifically:

[0188] Organize the analysis results and generate a test report:

[0189] Graphically display the location and morphological characteristics of defects;

[0190] List the measurement results of key quantitative parameters;

[0191] Provide reliability assessment of test results;

[0192] Form a standardized test and analysis report.

[0193] The depth correction equation group includes:

[0194] The basic depth calculation equation is used to calculate the defect depth through the propagation speed of sound waves and the echo time. In addition to the basic calculation, a system compensation term is added to correct the inherent error of the system. This equation provides a preliminary depth estimate.

[0195] The sound speed correction equation solves the problem of the influence of temperature on the sound speed. Since the sound speed of the material will change with temperature, the sound speed needs to be corrected according to the current temperature. It takes the sound speed at standard temperature as the benchmark, combines the temperature change and material properties, and calculates the true sound speed at the actual temperature.

[0196] The depth measurement uncertainty equation takes into account three main sources of error: sound velocity measurement error, time measurement error, and incident angle measurement error; through the error propagation principle, the uncertainty range of the final depth measurement value is calculated.

[0197] The multi-point depth comprehensive equation is used to process the data of multiple measurement points. Instead of simply taking the average value, different weights are assigned to each measurement point according to its reliability. Measurement points with high reliability receive greater weights, thereby improving the accuracy of the final result.

[0198] 1. The depth basis calculation equation is specifically expressed as follows:

[0199]

[0200] Where, d b is the base depth estimate in millimeters; v s is the speed of sound wave propagation at standard temperature, in meters per second; t r is the echo time, in microseconds; θ is the incident angle of the sound wave, in degrees; k s is the system compensation coefficient, ranging from 0.01 to 0.1; ΔT is the temperature deviation, in degrees Celsius; α i is the i-th error correction weight; δ i is the i-th systematic error term; n is the number of error terms considered.

[0201] 2. The sound velocity correction equation is specifically expressed as follows:

[0202]

[0203] In the formula, v c is the corrected speed of sound, in meters per second; v s is the standard temperature T s The speed of sound under the condition of β is the first-order temperature coefficient, and the typical value is 2.5×10 -4 ; γ is the second-order temperature coefficient, with a typical value of 1.5×10 -6 ; T is the current temperature; T s is the standard temperature, usually 20℃; j is the jth material property correction coefficient; η j is the jth material characteristic parameter; m is the number of material characteristic parameters.

[0204] 3. The depth measurement uncertainty equation is specifically expressed as follows:

[0205]

[0206] In the formula, u d is the total uncertainty of depth measurement; x k denote the speed of sound, time and incident angle respectively; is the standard uncertainty of each parameter; r kl is the correlation coefficient between parameters; is the partial derivative of depth with respect to each parameter.

[0207] 4. The multi-point depth comprehensive equation is specifically expressed as follows:

[0208]

[0209] Where D is the final depth value; d p is the depth value of the pth measurement point; w p is the corresponding weight; u pis the measurement uncertainty of this point; is the average depth of all measurement points; σ is the standard deviation of depth; P is the total number of measurement points.

[0210] The depth basic calculation equation adopts the geometric relationship of sound wave propagation, takes into account temperature compensation and system errors, introduces multiple error correction terms, and improves the accuracy of initial depth estimation. The effect of oblique incidence is considered through the cosθ term, and the linear relationship of the temperature compensation term is based on the first-order approximation of the sound velocity with temperature.

[0211] The sound velocity correction equation is based on the physical characteristics of sound wave propagation in materials. It uses a second-order polynomial to describe the temperature effect, which more accurately reflects the nonlinear relationship between temperature and sound velocity. The material property correction term takes into account various influencing factors of the material itself.

[0212] The uncertainty equation of depth measurement is based on the error transfer theory, which not only considers the independent uncertainty contribution of each parameter, but also introduces the correlation between parameters, making the uncertainty assessment more comprehensive and accurate. The partial derivative term reflects the sensitivity of each parameter to the final depth measurement.

[0213] The multi-point depth comprehensive equation adopts the idea of ​​weighted average, and the weight function combines the uncertainty of the measurement point and the discreteness of the data. The introduction of the exponential term realizes the automatic suppression of outliers and improves the reliability of the final result.

[0214] Specifically, the principle of the present invention is:

[0215] First, an ultrasonic detection method is provided that can realize online detection, simultaneous defect detection and defect imaging, high precision and high reliability, and uses a synthetic aperture ultrasonic imaging algorithm to realize the recognition, judgment, positioning and host computer image display of different insulating components. The system includes a test casing, an ultrasonic sensor, a signal acquisition module and a host computer system.

[0216] Secondly, in terms of defect collection, the ultrasonic sensor is used to emit ultrasonic waves by setting the ultrasonic transducer parameters, and the sensor is connected to the acquisition card, and LabVIEW is used to collect real-time echo data to collect fault signals. After the ultrasonic signal is emitted, the ultrasonic wave will be reflected when it encounters an interface with different acoustic impedance inside the ferromagnetic casing material. The propagation of ultrasonic waves in the medium requires a certain amount of vibration energy. The entire energy of the object is conserved. If one energy increases, the other energy must be weakened. As the propagation distance increases, due to the increase of other energies, the ultrasonic energy will decay, and its voltage will change suddenly.

[0217] Third, in terms of defect imaging, the ultrasonic imaging algorithm is studied. The ultrasonic synthetic aperture algorithm is selected for defect imaging. Synthetic aperture imaging uses a small aperture array to move at a uniform speed on a straight motion trajectory, and transmits, receives and stores echo signals in a determined position. According to the spatial position and phase relationship, the echo signals at different positions are coherently superimposed to synthesize a virtual large aperture array to obtain high resolution along the direction of motion. In terms of defect positioning, since ultrasonic waves will be reflected when the acoustic impedance of two objects is different during propagation, it is the first reflected wave when encountering a defect. Therefore, the first reflection with the strongest intensity in the image is the defect position, which can achieve the depth positioning of the defect.

[0218] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for identifying defects in insulating components based on synthetic aperture ultrasonic imaging technology, characterized in that: The following steps are involved: Step 01, set the ultrasonic transducer parameters to make it emit ultrasonic waves, and place the ultrasonic sensor close to the surface of the insulating component to be tested; Step 02: Connect the ultrasonic sensor to the acquisition card and use the acquisition software to collect real-time echo data; Step 03: Import the data into the data processing software for data processing and apply the imaging algorithm for defect imaging; Step 04: Analyze and locate defects on the resulting ultrasonic image.

2. The method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology according to claim 1, characterized in that: The step 01 specifically includes: Step 11, using a sine function as an excitation source, applied to the upper surface of the piezoelectric material to generate an inverse piezoelectric effect; Step 12, converting electrical energy into ultrasonic waves to propagate in the elastic material; Step 13, emitting ultrasonic longitudinal waves through an ultrasonic transducer; Step 14: The ultrasonic wave is refracted into a transverse wave through the inclined wedge and propagates inside the material.

3. The method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology according to claim 2, characterized in that: The step 02 specifically includes: Step 21, placing an ultrasonic sensor on the surface of the insulating component; Step 22, collecting electromagnetic wave echoes transmitted from the defect and the bottom of the workpiece; Step 23, connect the ultrasonic sensor and the acquisition card with a coaxial cable; Step 24: collect real-time echo data to the host computer through the acquisition software.

4. The method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology according to claim 3, characterized in that: The step 03 specifically includes: Step 31, performing Fourier transform processing on the collected ultrasonic signal to reduce the noise signal; Step 32, dynamically synthesizing an equivalent virtual large-aperture transducer using a small-aperture transducer array; Step 33, moving at a constant speed on the linear motion trajectory and transmitting and receiving echo signals sequentially at the determined positions; Step 34: perform coherent superposition processing on echo signals at different positions according to spatial positions and phase relationships.

5. The method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology according to claim 4, characterized in that: The step 04 specifically includes: Step 41: image preprocessing and signal enhancement; Step 42: Boundary feature extraction and analysis; Step 43: depth positioning and measurement; Step 44: defect morphology feature analysis; Step 45: quantitatively measure the defect size.

6. The method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology according to claim 5, characterized in that: The step 41 specifically includes: Step 411, performing selective denoising to retain defect feature information; Step 412: performing contrast enhancement based on local area statistical characteristics; Step 413: Establish an adaptive threshold system for image segmentation; Step 414: Evaluate the signal-to-noise ratio after processing.

7. The method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology according to claim 6, characterized in that: The step 42 specifically includes: Step 421, using a multi-scale edge detection method to identify defect boundaries; Step 422, smoothing the boundary contour; Step 423, calculating the local curvature and directional characteristics of the boundary; Step 424: Create a boundary feature descriptor.

8. The method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology according to claim 7, characterized in that: The step 43 specifically includes: Step 431, determining the defect surface position based on the first wave signal time; Step 432, using multiple echo information to improve depth measurement accuracy; Step 433, using the depth correction equation group to perform depth correction; Step 434: Evaluate the uncertainty of the depth measurement.

9. The method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology according to claim 8, characterized in that: The step 44 specifically includes: Step 441, calculating the basic geometric parameters of the area and perimeter of the defect; Step 442, extracting the shape factor and directional characteristics of the defect; Step 443, establishing quantitative description indicators of morphological characteristics; Step 444: Analyze the symmetry and regularity of the defects.

10. The method for identifying defects of insulating components based on synthetic aperture ultrasonic imaging technology according to claim 9, characterized in that: The step 45 specifically includes: Step 451, determining a negative 6 dB signal amplitude threshold; Step 452: Count the area of ​​the region that meets the threshold condition; Step 453, calculating the equivalent circle diameter as a representation of the defect size; Step 454 , evaluate the systematic error and random error of the dimensional measurement.

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