Bolt monitoring method and device based on electromagnetic ultrasound and 3D phased array

Abstract

The invention provides a bolt monitoring method and device based on electromagnetic ultrasound and a 3D phased array, and relates to the technical field of bolt monitoring. A two-stage detection process is adopted, firstly, preliminary screening is conducted through electromagnetic ultrasound, an electromagnetic ultrasound device excites ultrasonic waves and receives echo signals, and according to the relation between the intensity of the echo signals and the passing time, a 3D phased array is obtained; constructing a time-intensity curve of each detection section, and rapidly screening candidate sections with abnormal echo signals; the method comprises the following steps: performing comprehensive three-dimensional space scanning on a candidate section through a 3D phased array technology, performing accurate scanning in a three-dimensional space through focused ultrasonic waves to obtain an ultrasonic echo signal of each space point, extracting the ultrasonic echo signal intensity of each space point and calculating a standardized deviation to mark an abnormal point exceeding a set threshold value, and finally marking the abnormal point. And through spatial aggregation analysis, a plurality of abnormal points are aggregated into an abnormal region, so that the defect positioning precision is further improved.

Description

Technical Field

[0001] This invention relates to the field of bolt monitoring technology, specifically to a bolt monitoring method and device based on electromagnetic ultrasound and 3D phased array. Background Technology

[0002] The connecting bolts (such as tower bolts and blade root bolts) of key parts of wind turbines, such as towers and blades, are the "lifeline" supporting the safe operation of the entire unit. Therefore, in the production and maintenance of wind turbines, bolts are key fastening components that ensure the stable connection of various parts of the unit. Especially in key parts such as wind turbine towers, blades, and rotors, the quality of bolts directly affects the safety and stability of the unit. The bolts of wind turbines must withstand long-term extreme conditions such as wind force, temperature difference changes, and vibration. Therefore, the quality of bolts must be strictly tested at the factory to ensure that they can maintain good performance during long-term use.

[0003] Currently, the factory inspection of wind turbine bolts mainly relies on mechanical performance testing, physical inspection, and some traditional non-destructive testing methods, such as ultrasound and X-rays. However, these testing methods are often limited to the detection of surface defects. For example, traditional ultrasonic testing methods can detect defects on the bolt surface and some internal defects, but they have certain limitations. Because the propagation depth of ultrasonic beams is limited, it is difficult to accurately identify micro-cracks or damage deep inside the bolt. It cannot provide comprehensive and accurate detection of micro-cracks, corrosion, stress concentration, and other problems that may exist inside the bolt. Although X-ray testing can detect internal defects, X-ray testing technology generates two-dimensional images. Although more comprehensive images can be obtained by taking pictures from multiple angles, it is still impossible to accurately locate and display the distribution of defects in three-dimensional space. Defects inside the bolt may be hidden in areas that are not easy to detect from a single angle. The lack of three-dimensional spatial information makes it impossible for X-ray technology to fully and accurately reveal all potential damage inside the bolt, especially multi-point damage or local stress concentration.

[0004] With the continuous development of wind power technology, the requirements for the accuracy and efficiency of bolt inspection are also increasing. How to use more advanced technical means to accurately and quickly conduct comprehensive inspection of bolt quality has become an urgent problem to be solved in the wind power industry.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a bolt monitoring method and device based on electromagnetic ultrasound and 3D phased array, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The bolt monitoring method based on electromagnetic ultrasound and 3D phased array includes the following steps: Step 1: Divide the bolt to be monitored into multiple continuous detection sections evenly along the axial direction. Under the set monitoring conditions, in each detection section, use an electromagnetic ultrasonic device to excite ultrasonic body waves perpendicular to the bolt axis and simultaneously receive echo signals. Based on the relationship between the time elapsed since the self-excitation moment and the intensity of the corresponding echo signal, construct the time-intensity curve for each detection section. Step 2: Based on the specifications of the bolt to be monitored and the length of the test section, set a control section on the qualified bolt. Under the set monitoring conditions, obtain the time-intensity curve of the control section. Compare and analyze the time-intensity curve of each test section with the time-intensity curve of the control section, screen out the test sections with abnormal signals, and mark them as damaged candidate sections. Step 3: Use a 3D phased array to perform full matrix capture scanning on the damaged candidate segment, form a focused ultrasound beam inside the damaged candidate segment and perform three-dimensional spatial scanning to obtain the ultrasound echo signal of each spatial point inside the damaged candidate segment. Based on the ultrasound echo signal and the corresponding three-dimensional spatial coordinates, construct a three-dimensional ultrasound feature dataset. Step 4: In the three-dimensional ultrasound feature dataset of the damaged candidate segment, extract the ultrasound echo signal intensity of each spatial point to obtain the amplitude value, calculate the standardized deviation of the damaged candidate segment, compare and analyze the standardized deviation with the preset standardized deviation threshold, and screen out spatial points that exceed the preset standardized deviation threshold and mark them as candidate anomalies. Step 5: Based on the candidate anomalies, and using the three-dimensional spatial neighborhood as the discrimination unit, count the number of candidate anomalies contained in each discrimination unit. When the number of candidate anomalies exceeds the set threshold, the three-dimensional neighborhood is determined to constitute an anomaly region.

[0008] Furthermore, the steps of comparing and analyzing the time-intensity curves of each detection segment with the time-intensity curves of the control segment include: The time-intensity curve of the control section is designated as the first curve, and the time-intensity curve of the section to be detected is designated as the second curve. The first and second curves are analyzed to obtain their corresponding signal energy and amplitude attenuation coefficients. Fourier transforms are performed on the first and second curves respectively to extract their center frequencies. The signal energy, amplitude attenuation coefficient, and center frequency corresponding to each curve are used as the identification features of the curve. The identification features of the first curve and the second curve are compared to calculate the degree of deviation. Based on the degree of deviation, it is determined whether the second curve is abnormal relative to the first curve.

[0009] Furthermore, the method for obtaining the signal energy and amplitude attenuation coefficient is as follows: For the entire time-intensity curve of the detection section, the echo signal intensity value at each sampling time point is squared to obtain the squared value of the echo signal; The signal energy is obtained by summing the squared values ​​of the echo signals. On the time-intensity curve of the detection section, two consecutive bottom echoes are identified. The ratio of the peak amplitude of the second bottom echo to the peak amplitude of the first bottom echo is calculated to obtain the bottom echo ratio. Take the natural logarithm of the bottom surface echo ratio to obtain the logarithmic value of the ratio; The estimated attenuation coefficient of the peak amplitude of the bottom echo is obtained by using the negative reciprocal of the distance the sound wave travels in the material as a weight.

[0010] Furthermore, the method for obtaining the center frequency is as follows: Select the echo signal of the entire detection section and perform a fast Fourier transform on it to obtain the one-sided amplitude spectrum of the signal. Each frequency component is multiplied by its corresponding one-sided amplitude spectrum value to obtain the frequency component amplitude value, and the frequency component amplitude value is integrated to obtain the weighted frequency sum. The total signal amplitude is obtained by integrating the one-sided amplitude spectrum values ​​at all frequency points. The center frequency is obtained by dividing the weighted sum of frequencies by the total signal amplitude.

[0011] Furthermore, the method for identifying and comparing features is as follows: Based on the time-intensity curves of the detection section and the control section, characteristic parameters of signal energy, center frequency, and amplitude attenuation coefficient are obtained. The Manhattan distance between the monitored section and the control section in terms of three characteristics—signal energy, center frequency, and amplitude attenuation coefficient—is calculated to obtain the absolute values ​​of signal energy, center frequency, and amplitude attenuation coefficient. Divide the absolute value of signal energy, the absolute value of center frequency, and the absolute value of amplitude attenuation coefficient by the reference value of the corresponding feature to obtain the energy difference, center frequency difference, and attenuation coefficient difference. A weight coefficient is assigned to each degree of difference, and the comprehensive anomaly score is obtained by weighting and summing each degree of difference with its corresponding weight coefficient. The comprehensive anomaly score of each detection segment is compared with the decision threshold using a binary classifier algorithm. If the comprehensive anomaly score of a detection segment is greater than the decision threshold, the segment is determined to be a signal anomaly segment.

[0012] Furthermore, the specific steps for determining the decision threshold are as follows: The detection sections of several healthy bolts are marked as healthy sections. Based on the time-intensity curves of each healthy section under the set monitoring conditions, the characteristic parameters of signal energy, center frequency and amplitude attenuation coefficient are obtained. The energy difference, center frequency difference and attenuation coefficient difference of each healthy section compared with the control section are calculated. Using the same weighting coefficients as the bolts to be monitored, a comprehensive anomaly score for each healthy segment is calculated based on energy difference, center frequency difference, and attenuation coefficient difference. The comprehensive anomaly score of each healthy segment is summed to obtain the total comprehensive anomaly score. Based on the total comprehensive anomaly score and the number of healthy segments, the average segment anomaly score of all healthy segments is obtained. The sensitivity score is obtained by multiplying the average anomaly of the section with the sensitivity coefficient. The decision threshold is obtained by summing the sensitivity score and the mean of the segment anomalies.

[0013] Furthermore, the specific steps for calculating the standardized deviation of the damaged candidate segment are as follows: Based on the three-dimensional ultrasound feature dataset obtained by the control section under the same detection conditions, and statistical analysis of the amplitude values ​​of all spatial points in the three-dimensional ultrasound feature dataset of the control section, the mean and standard deviation of the control section are calculated. For each spatial point in the damaged candidate segment, the difference between its amplitude value and that of the control segment is calculated, and then standardized using the standard deviation of the healthy data to obtain the standardized bias.

[0014] The present invention also provides a bolt monitoring device based on electromagnetic ultrasound and 3D phased array, the monitoring device being used to perform the above-described monitoring method, comprising: The curve construction module is used to uniformly divide the bolt to be monitored into multiple continuous detection segments along the axial direction. Under the set monitoring conditions, in each detection segment, an ultrasonic body wave perpendicular to the bolt axis is excited by an electromagnetic ultrasonic device and the echo signal is received at the same time. Based on the relationship between the time elapsed since the self-excitation moment and the intensity of the corresponding echo signal, the time-intensity curve of each detection segment is constructed. The damaged marking module is used to set a control section on qualified bolts according to the specifications of the bolts to be monitored and the length of the detection section. Under the set monitoring conditions, the time-intensity curve of the control section is obtained. The time-intensity curve of each detection section is compared and analyzed with the time-intensity curve of the control section to screen out the detection sections with abnormal signals and mark them as damaged candidate sections. The 3D construction module is used to perform full-matrix capture scanning of the damaged candidate segment using a 3D phased array. It forms a focused ultrasound beam inside the damaged candidate segment and performs a 3D spatial scan to obtain the ultrasound echo signal of each spatial point inside the damaged candidate segment. Based on the ultrasound echo signal and the corresponding 3D spatial coordinates, a 3D ultrasound feature dataset is constructed. The anomaly labeling module is used to extract the ultrasonic echo signal intensity of each spatial point in the three-dimensional ultrasonic feature dataset of the damaged candidate segment to obtain the amplitude value, calculate the standardized deviation of the damaged candidate segment, compare and analyze the standardized deviation with the preset standardized deviation threshold, and screen out spatial points that exceed the preset standardized deviation threshold and mark them as candidate anomalies. The anomaly detection module is used to count the number of candidate anomalies in each three-dimensional spatial neighborhood as the discrimination unit, based on candidate anomalies. When the number of candidate anomalies exceeds a set threshold, the three-dimensional neighborhood is determined to constitute an anomaly region.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention employs a two-stage detection process. First, electromagnetic ultrasound is used for preliminary screening to identify potential damage areas. Then, 3D phased array technology is applied to these candidate sections for in-depth scanning, enabling precise three-dimensional damage localization and analysis. Electromagnetic ultrasound is used for preliminary screening of bolts. The electromagnetic ultrasound device excites ultrasonic waves in the bolt detection section and receives the echo signals. Based on the relationship between the intensity of the echo signal and the elapsed time, a time-intensity curve is constructed for each detection section. Electromagnetic ultrasound can effectively identify surface and shallow to middle layer damage of bolts, especially preliminary damage such as cracks, corrosion, and stress concentration. By comparing with the control section of qualified bolts, the signal intensity changes can be quantitatively analyzed, and candidate sections with abnormal echo signals can be quickly screened out. After screening potential damaged sections, these candidate sections are comprehensively scanned in three-dimensional space using 3D phased array technology. By using focused ultrasound to perform precise scanning in three-dimensional space, the ultrasonic echo signal of each spatial point is obtained. This invention can effectively identify potential damage such as deep microcracks, corrosion, and local stress concentration inside the bolt, overcoming the limitations of traditional two-dimensional imaging. Especially when the damage distribution is complex or hidden, 3D phased array technology can provide clear three-dimensional images, avoiding the problem of accurately locating deep and multi-point damage in traditional two-dimensional images. By extracting the ultrasonic echo signal intensity of each spatial point and calculating the standardized deviation, abnormal points exceeding the set threshold can be marked. Through spatial clustering analysis, multiple abnormal points are clustered into abnormal regions, further improving the accuracy of defect location. Attached Figure Description

[0016] Figure 1This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a time-signal intensity comparison chart of the control segment and the candidate damaged segment; Figure 3 A comparison chart of amplitude values ​​between the control section and the candidate points of anomalies; Figure 4 This is a schematic diagram of the overall device of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example: Please see Figure 1-3 The present invention provides a technical solution: The bolt monitoring method based on electromagnetic ultrasound and 3D phased array includes the following steps: Step 1: Divide the bolt to be monitored into multiple continuous detection sections along the axial direction. Under the set monitoring conditions, in each detection section, use an electromagnetic ultrasonic device to excite ultrasonic waves perpendicular to the bolt axis and simultaneously receive echo signals. Based on the relationship between the time elapsed since the self-excitation moment and the intensity of the corresponding echo signal, construct the time-intensity curve for each detection section.

[0020] In this embodiment, the bolt to be monitored is uniformly divided into continuous detection sections along the axial direction. The core is to determine the number and length of the sections based on the bolt's axial length and the required detection accuracy, ensuring full axial coverage, no overlap of sections, and clear boundaries. The specific steps are as follows: Measure the actual range of bolt axial direction that needs to be inspected, and exclude non-stressed areas that do not need to be inspected (such as the thread end section and the chamfer transition area). The spatial resolution of the electromagnetic ultrasonic equipment is determined (usually specified in the equipment manual, such as the axial length corresponding to the minimum identifiable defect). The value should be 1 to 2 times the spatial resolution of the device to avoid the defect signal being canceled out by an average due to the segment being too long. According to the formula calculate( (To round up), ensuring the total coverage length is greater than or equal to the effective detection length; Taking one axial end of the bolt (such as the head end face) as the starting point (coordinate 0), according to the starting point... Mark the boundaries of each segment, and the axial range of each segment is... ( arrive Each data point is assigned a unique number to facilitate subsequent association of detection data with spatial location (e.g., ...). , Section 1 is Section 2 is ...Section 50 is ).

[0021] Step 2: Based on the specifications of the bolt to be monitored and the length of the test section, set a control section on the qualified bolt. Under the set monitoring conditions, obtain the time-intensity curve of the control section. Compare and analyze the time-intensity curve of each test section with the time-intensity curve of the control section, screen out the test sections with abnormal signals, and mark them as damaged candidate sections.

[0022] In this embodiment, the step of comparing and analyzing the time-intensity curve of each detection segment with the time-intensity curve of the control segment includes: The time-intensity curve of the control section is designated as the first curve, and the time-intensity curve of the section to be detected is designated as the second curve. The first and second curves are analyzed to obtain their corresponding signal energy and amplitude attenuation coefficients. Fourier transforms are performed on the first and second curves respectively to extract their center frequencies. The signal energy, amplitude attenuation coefficient, and center frequency corresponding to each curve are used as the identification features of the curve. The identification features of the first curve and the second curve are compared to calculate the degree of deviation. Based on the degree of deviation, it is determined whether the second curve is abnormal relative to the first curve.

[0023] In this embodiment, 50 sets of time-signal strength data were collected, and some of the data are shown in Table 1: Table 1: Time-Signal Strength Comparison Table between Control Section and Candidate Damaged Section

[0024] According to Table 1 and Figure 2 It can be seen that during the rising phase and peak stage of the signal (time points 1-10), the signal intensity of the damaged candidate segment is much lower than that of the control segment. At the peak time point 8, the intensity of the control segment is 56.3mV, while that of the damaged candidate segment is only 33.9mV, with an attenuation of up to 40%, indicating that the ultrasonic wave has suffered serious energy loss in the early propagation path. During the phase when the normal signal should have decayed rapidly (time points 11-17), the signal of the damaged candidate segment did not decay accordingly, but remained at an abnormally high level (about 30mV), forming a clear plateau region. At time point 13, the control signal had decayed to 43.5mV, while the damaged candidate segment remained at 30.5mV. At time point 16, the control signal had decayed to 31.4mV, while the damaged candidate segment was still as high as 29.3mV. At this time, the amplitudes of the two curves were almost the same, which completely violated the decay law of healthy ultrasound signals. This situation is very likely caused by multiple reflections due to defects or the superposition of reflected waves from different sound paths. There may be a closed crack, delamination, or cavity inside the bolt. When ultrasound encounters these defects, part of the energy is reflected, and the other part may propagate along a longer path (e.g., reflecting back and forth on both sides of the defect), eventually superimposing with the main signal, resulting in an abnormal bulge or plateau at the end of the signal. Starting from time point 18, the amplitudes of the two curves become very close, and the decay trends are basically the same, indicating that in the deeper regions corresponding to this time, the acoustic properties of the material are no different from those in the healthy region.

[0025] In this embodiment, the time-intensity curve is a graphical representation. Its horizontal axis represents the time elapsed since the ultrasonic excitation moment. In bolt detection, the zero point of time corresponds to the bolt surface where the probe is located. The first large echo peak may correspond to the first bottom surface echo of the bolt, and subsequent peaks may correspond to the echoes of the ultrasonic waves reflected multiple times between the upper and lower bottom surfaces. Its vertical axis represents the intensity (amplitude) of the echo signal received at the corresponding moment. It depicts the complete history of the echo energy change over time during the process of the ultrasonic waves propagating inside the bolt and returning to the probe. A high amplitude usually means that there is a strong abrupt change in acoustic impedance at that location, such as the bottom surface of the bolt (good specular reflection) or macroscopic defects such as cracks, inclusions, and holes. Low amplitude / background noise: represents the background noise level of the material or a weak backscattered signal caused by microstructure (such as grain boundaries).

[0026] In this embodiment, the method for obtaining the signal energy and amplitude attenuation coefficient is as follows: For the entire time-intensity curve of the detection section, the echo signal intensity value at each sampling time point is squared to obtain the squared value of the echo signal; The signal energy is obtained by summing the squared values ​​of the echo signals. The calculation formula is: ; In the formula, Represents the discrete echo signal strength. Represents a time series index; This indicates the total number of sampling points within this section; On the time-intensity curve of the detection section, two consecutive bottom echoes are identified. The ratio of the peak amplitude of the second bottom echo to the peak amplitude of the first bottom echo is calculated to obtain the bottom echo ratio. Take the natural logarithm of the bottom surface echo ratio to obtain the logarithmic value of the ratio; Using the negative reciprocal of the total distance the sound wave travels in the material as a weight, the estimated attenuation coefficient of the peak amplitude of the bottom echo is obtained. The mathematical expression for estimating and calculating the attenuation coefficient is then: ; In the formula, This represents the estimated attenuation coefficient. In this embodiment, the unit of the attenuation coefficient is [unit missing]. ; Indicates the first bottom surface echo Peak amplitude value; Indicates the second bottom surface echo Peak amplitude value; This represents the one-way distance a sound wave travels through a material. For axial testing of bolts, this is typically the length of the testing section (or the bolt diameter, depending on the wave type). Signal energy is used to quantify the total intensity of a specific signal or echo within a specific time / space window. It is an indicator that reflects the overall strength of the signal and is sensitive to continuous distributed damage (such as uniform corrosion or micropores). The amplitude attenuation coefficient is used to quantify the rate attenuation of the amplitude of ultrasonic waves as they propagate in a material with distance (or time). Damage (such as microcracks or plastic deformation) will exacerbate the scattering and absorption of ultrasonic waves, thereby increasing the attenuation coefficient.

[0027] In this embodiment, the method for obtaining the center frequency is as follows: Select the echo signal of the entire detection section and perform a fast Fourier transform on it to obtain the one-sided amplitude spectrum of the signal. Each frequency component is multiplied by its corresponding one-sided amplitude spectrum value to obtain the frequency component amplitude value, and the frequency component amplitude value is integrated to obtain the weighted frequency sum. The total signal amplitude is obtained by integrating the one-sided amplitude spectrum values ​​at all frequency points. The center frequency is obtained by dividing the weighted sum of frequencies by the total signal amplitude. The mathematical expression for calculating the center frequency is: ; In the formula, The center frequency is the weighted average frequency of the ultrasonic echo signal energy in the frequency domain, and its unit is usually Hertz (Hz). It is not the frequency with the largest amplitude in the spectrum (peak frequency), but the "center of mass" of the entire spectrum. Frequency represents the individual frequency components in the spectrum, and its unit is Hertz (Hz). The integration process involves iterating through and summing all frequency components. Indicates the signal at frequency The one-sided amplitude spectrum is the complex spectrum obtained after the signal undergoes a fast Fourier transform. Indicates taking The magnitude (amplitude) is used to obtain the amplitude spectrum, which describes the signal at different frequencies. The vibration intensity on the upper side, unilaterally, means that this amplitude spectrum only includes the portion from 0 Hz to the positive Nyquist frequency, and the amplitude of the positive frequency components (excluding DC and the Nyquist point) has been corrected by multiplying by 2 to compensate for the energy of the discarded negative frequency components. Therefore, here... It accurately reflects the true amplitude of each frequency component.

[0028] In this embodiment, the method for identifying and comparing features is as follows: Based on the time-intensity curves of the detection section and the control section, characteristic parameters of signal energy, center frequency, and amplitude attenuation coefficient are obtained. The Manhattan distance between the monitored section and the control section in terms of three characteristics—signal energy, center frequency, and amplitude attenuation coefficient—is calculated to obtain the absolute values ​​of signal energy, center frequency, and amplitude attenuation coefficient. Divide the absolute value of signal energy, the absolute value of center frequency, and the absolute value of amplitude attenuation coefficient by the reference value of the corresponding feature to obtain the energy difference, center frequency difference, and attenuation coefficient difference. Assign a weight coefficient to each degree of difference, and sum the weighted values ​​of each degree of difference with its corresponding weight coefficient to obtain the comprehensive anomaly score. The mathematical expression for calculating the comprehensive anomaly score is as follows: ; In the formula, Indicates the first The comprehensive abnormality score of each detection segment represents the overall "unhealthy" or "abnormal" degree of that segment. It is composed of a weighted fusion of three difference scores. The higher the value, the higher the possibility of damage in that segment. Indicates the bolt number to be monitored The measured value of signal energy in each segment, Indicates the number of the detection section; The reference value indicating the signal energy of the qualified bolt comparison section; Indicates the bolt number to be monitored Measured values ​​of the center frequency of each segment; The reference value representing the center frequency of the section where qualified bolts are compared; Indicates the bolt number to be monitored Measured values ​​of amplitude attenuation coefficient for each section; The reference value represents the amplitude attenuation coefficient of the section where qualified bolts are compared. The weighting coefficient representing the signal energy difference score has a range of values. ; The weighting coefficient representing the center frequency difference score has a range of values. ; The weighting coefficient representing the difference score of amplitude attenuation coefficient is given, and its value range is as follows: ,and ; The comprehensive anomaly score of each detection segment is compared with the decision threshold using a binary classifier algorithm. If the comprehensive anomaly score of a detection segment is greater than the decision threshold, the segment is determined to be a signal anomaly segment.

[0029] In this embodiment, the bolts in the wind turbine are subject to factory quality inspection; therefore, a higher setting is required. and , and It is extremely sensitive to changes in microstructure (such as microcracks and grain coarsening). This positioning can issue early warnings at the damage initiation stage and achieve predictive maintenance. The weighting coefficients of the absolute value of signal energy, the absolute value of center frequency, and the amplitude attenuation coefficient are determined through historical data.

[0030] In this embodiment, the specific steps for determining the decision threshold are as follows: The detection sections of several healthy bolts are marked as healthy sections. Based on the time-intensity curves of each healthy section under the set monitoring conditions, the characteristic parameters of signal energy, center frequency and amplitude attenuation coefficient are obtained. The energy difference, center frequency difference and attenuation coefficient difference of each healthy section compared with the control section are calculated. Using the same weighting coefficients as the bolts to be monitored, a comprehensive anomaly score for each healthy segment is calculated based on energy difference, center frequency difference, and attenuation coefficient difference. The comprehensive anomaly score of each healthy segment is summed to obtain the total comprehensive anomaly score. Based on the total comprehensive anomaly score and the number of healthy segments, the average segment anomaly score of all healthy segments is obtained. The sensitivity score is obtained by multiplying the average anomaly of the section with the sensitivity coefficient. The decision threshold is obtained by summing the sensitivity score and the mean of segment anomalies. The mathematical expression for calculating the decision threshold is as follows: ; In the formula, Indicates the decision threshold; This represents the average comprehensive anomaly score for all sections of all healthy bolts; This represents the standard deviation of the overall anomaly score for all sections of all healthy bolts; Represents the sensitivity coefficient. A larger value will result in a very high threshold, meaning the system will only alert for those extreme deviations from the health baseline. This will lead to a lower false alarm rate, but the false alarm rate may also increase. Smaller values ​​and lower thresholds make the system more sensitive to deviations and more likely to trigger alarms, which can lead to a lower false alarm rate (allowing for faster problem detection) but may also increase the false alarm rate.

[0031] The threshold is not an arbitrarily chosen value, but is determined through statistical analysis based on the distribution of comprehensive anomaly scores from a large number of qualified bolts (healthy samples) and unqualified bolts (damaged samples). Typically, the threshold is set at a level that allows for the correct identification of damaged samples with a high probability, while keeping the false positive rate (i.e., false positives) of healthy samples within an acceptable range.

[0032] Step 3: Use a 3D phased array to perform full matrix capture scanning on the damaged candidate segment, form a focused ultrasound beam inside the damaged candidate segment and perform three-dimensional spatial scanning to obtain the ultrasound echo signal of each spatial point inside the damaged candidate segment. Based on the ultrasound echo signal and the corresponding three-dimensional spatial coordinates, construct a three-dimensional ultrasound feature dataset.

[0033] In this embodiment, the specific steps for constructing the three-dimensional ultrasound feature dataset are as follows: A 3D phased array probe (typically a two-dimensional matrix transducer array) is coupled to the surface of the bolt candidate section. In transmit mode, each element in the array is sequentially... As a transmitter, it emits an ultrasonic pulse, and during each emission, all elements in the array (including the transmitting element itself) simultaneously act as receivers, recording the complete time-domain echo signal. Based on the geometry of the bolt and the location of the candidate segment, a three-dimensional imaging region is defined in the computer. This region is discretized into a regular three-dimensional grid, where each grid point is a potential scattering point (which may be a defect or normal tissue). The scattering intensity is obtained by repeatedly performing time-delay superposition calculations on each grid point. At this time, each three-dimensional coordinate corresponds to an ultrasonic characteristic value (i.e., scattering intensity).

[0034] In this embodiment, the time delay superposition is to calculate, for any point in the imaging area, the time required for the sound beam to travel from each transmitting element to the point and then back to each receiving element if the sound beam is to be focused at that point. Based on this time, the corresponding signal amplitude is extracted from the full matrix data, and the contributions of all paths are superimposed. The total intensity after superposition represents the probability that the point is a scatterer.

[0035] Step 4: In the three-dimensional ultrasound feature dataset of the damaged candidate segment, extract the ultrasound echo signal intensity of each spatial point to obtain the amplitude value, calculate the standardized deviation of the damaged candidate segment, compare and analyze the standardized deviation with the preset standardized deviation threshold, and screen out spatial points that exceed the preset standardized deviation threshold and mark them as candidate anomalies.

[0036] In this embodiment, the specific steps for calculating the standardized deviation of the damaged candidate segment are as follows: Based on the three-dimensional ultrasound feature dataset obtained by the control section under the same detection conditions, and statistical analysis of the amplitude values ​​of all spatial points in the three-dimensional ultrasound feature dataset of the control section, the mean and standard deviation of the control section are calculated. For each spatial point in the damaged candidate segment, calculate the difference between its amplitude value and that of the control segment, and standardize it using the standard deviation of healthy data to obtain the standardized deviation. The mathematical expression for the standardized deviation is as follows: ; In the formula, Indicates that it is located at The standardized bias of spatial points, where, This represents the position index along the X-axis. Indicates the position index in the Y-axis direction. Indicates the position index along the Z-axis; Indicates the location of the damaged candidate segment in space. The amplitude of the ultrasonic echo at the location; This represents the mean amplitude of the control segment; This represents the standard deviation of the amplitude values ​​in the control segment.

[0037] In this embodiment, 50 sets of three-dimensional ultrasound feature data were collected, and some of the data are shown in Table 2: Table 2: Comparison of peak values ​​between control sections and candidate abnormal points

[0038] According to Table 2 and Figure 3It can be seen that the amplitude values ​​of healthy bolts at various spatial points are stable in the range of 55.2-60.2mV, with minimal fluctuations (maximum difference of only 5mV), reflecting the absence of microscopic defects within healthy bolts and uniform energy loss during ultrasonic propagation. The amplitudes of the bolts under test show significant regional differentiation; some spatial points have amplitudes close to those of healthy bolts (e.g., numbers 24-49, amplitude 50.1-59.6mV), with fluctuations ≤3mV, indicating no significant abnormalities in ultrasonic propagation characteristics. The core region (numbers 9-23) experiences a sharp drop in amplitude, ranging from 29.3-40.2mV, a 32%-52% decrease compared to healthy bolts, indicating severe ultrasonic energy loss. Based on the standardized deviation formula and the statistical characteristics of the healthy bolt amplitude (mean 57.8mV), further analysis is needed. V (with a standard deviation of 1.2mV), calculate the standardized deviation of the key region and set a threshold of 1.8. It can be seen that: point 9, with spatial coordinates (1,3,3), has a standardized deviation of 6.42, which is much greater than the set threshold, making it a candidate outlier; points 10-18, with spatial coordinates (2,1,1)-(2,3,3), have standardized deviations of 14.75-23.75, which are much greater than the set threshold, making them candidate outliers; points 19-23, with spatial coordinates (3,1,1)-(3,2,3), have standardized deviations of 11.08-19.67, which are much greater than the set threshold, making them candidate outliers; while points 24-49 all have standardized deviations less than the threshold, so the remaining points are not candidate outliers.

[0039] In this embodiment, the three-dimensional ultrasonic feature dataset is a multi-level, high-dimensional dataset containing detailed ultrasonic characteristic information of the candidate damaged sections of bolts. "Three-dimensional" refers to the spatial dimension of the data; each data point in the dataset is associated with a specific three-dimensional spatial coordinate within the bolt. "Ultrasonic features" refers to various ultrasonic parameters or attributes measured or calculated at each spatial point. Any element in the three-dimensional ultrasonic feature dataset records the time-dependent properties of the data at a given spatial location. The amplitude of the received ultrasonic echo signal.

[0040] In this embodiment, after calculating the standardized deviation of all spatial points, a point-by-point comparison is performed with a preset standardized deviation threshold. If... If the value is greater than the preset standardized deviation threshold, it indicates that the point is a candidate outlier. If the value is less than or equal to the preset standardized deviation threshold, it indicates that the point is a normal point.

[0041] In this embodiment, the preset standardization deviation threshold is a positive number, which is preset according to the distribution of the control segment data. A higher preset standardization deviation threshold has a lower false negative rate (it is less likely to miss real anomalies), but the false positive rate may also increase (it may judge some noise as anomalies); a lower preset standardization deviation threshold is more sensitive, with a lower false positive rate but an increased risk of false negatives.

[0042] Step 5: Based on the candidate anomalies, and using the three-dimensional spatial neighborhood as the discrimination unit, count the number of candidate anomalies contained in each discrimination unit. When the number of candidate anomalies exceeds the set threshold, the three-dimensional neighborhood is determined to constitute an anomaly region.

[0043] In this embodiment, the three-dimensional spatial neighborhood is a three-dimensional cubic region inside the bolt, centered on a certain spatial point and including all its surrounding adjacent spatial points. Its core function is to eliminate isolated noise points through cluster analysis and accurately locate the true defect area. As shown in Table 2, candidate anomalies are concentrated at coordinates... (1-3mm axial) (Circumference 1-3mm) If a region (radial 1-3mm) forms a continuous three-dimensional spatial neighborhood (3×3×3), then the region is an anomalous region.

[0044] Please see Figure 4 The present invention also provides a bolt monitoring device based on electromagnetic ultrasound and 3D phased array, the monitoring device being used to perform the above-described monitoring method, including: The curve construction module is used to uniformly divide the bolt to be monitored into multiple continuous detection segments along the axial direction. Under the set monitoring conditions, in each detection segment, an ultrasonic body wave perpendicular to the bolt axis is excited by an electromagnetic ultrasonic device and the echo signal is received at the same time. Based on the relationship between the time elapsed since the self-excitation moment and the intensity of the corresponding echo signal, the time-intensity curve of each detection segment is constructed. The damaged marking module is used to set a control section on qualified bolts according to the specifications of the bolts to be monitored and the length of the detection section. Under the set monitoring conditions, the time-intensity curve of the control section is obtained. The time-intensity curve of each detection section is compared and analyzed with the time-intensity curve of the control section to screen out the detection sections with abnormal signals and mark them as damaged candidate sections. The 3D construction module is used to perform full-matrix capture scanning of the damaged candidate segment using a 3D phased array. It forms a focused ultrasound beam inside the damaged candidate segment and performs a 3D spatial scan to obtain the ultrasound echo signal of each spatial point inside the damaged candidate segment. Based on the ultrasound echo signal and the corresponding 3D spatial coordinates, a 3D ultrasound feature dataset is constructed. The anomaly labeling module is used to extract the ultrasonic echo signal intensity of each spatial point in the three-dimensional ultrasonic feature dataset of the damaged candidate segment to obtain the amplitude value, calculate the standardized deviation of the damaged candidate segment, compare and analyze the standardized deviation with the preset standardized deviation threshold, and screen out spatial points that exceed the preset standardized deviation threshold and mark them as candidate anomalies. The anomaly detection module is used to count the number of candidate anomalies in each three-dimensional spatial neighborhood as the discrimination unit, based on candidate anomalies. When the number of candidate anomalies exceeds a set threshold, the three-dimensional neighborhood is determined to constitute an anomaly region.

[0045] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0046] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0047] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0048] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A bolt monitoring method based on electromagnetic ultrasound and 3D phased array, characterized in that, The specific steps include: Step 1: Divide the bolt to be monitored into multiple continuous detection sections evenly along the axial direction. Under the set monitoring conditions, in each detection section, use an electromagnetic ultrasonic device to excite ultrasonic body waves perpendicular to the bolt axis and simultaneously receive echo signals. Based on the relationship between the time elapsed since the self-excitation moment and the intensity of the corresponding echo signal, construct the time-intensity curve for each detection section. Step 2: Based on the specifications of the bolt to be monitored and the length of the test section, set a control section on the qualified bolt. Under the set monitoring conditions, obtain the time-intensity curve of the control section. Compare and analyze the time-intensity curve of each test section with the time-intensity curve of the control section, screen out the test sections with abnormal signals, and mark them as damaged candidate sections. Step 3: Use a 3D phased array to perform full matrix capture scanning on the damaged candidate segment, form a focused ultrasound beam inside the damaged candidate segment and perform three-dimensional spatial scanning to obtain the ultrasound echo signal of each spatial point inside the damaged candidate segment. Based on the ultrasound echo signal and the corresponding three-dimensional spatial coordinates, construct a three-dimensional ultrasound feature dataset of the damaged candidate segment. Step 4: In the three-dimensional ultrasound feature dataset of the damaged candidate segment, extract the ultrasound echo signal intensity of each spatial point to obtain the amplitude value, calculate the standardized deviation of the damaged candidate segment, compare and analyze the standardized deviation with the preset standardized deviation threshold, and screen out spatial points that exceed the preset standardized deviation threshold and mark them as candidate anomalies. Step 5: Based on the candidate anomalies, and using the three-dimensional spatial neighborhood as the discrimination unit, count the number of candidate anomalies contained in each discrimination unit. When the number of candidate anomalies exceeds the set threshold, the three-dimensional neighborhood is determined to constitute an anomaly region.

2. The bolt monitoring method based on electromagnetic ultrasound and 3D phased array according to claim 1, characterized in that: The steps for comparing and analyzing the time-intensity curve of each detection segment with the time-intensity curve of the control segment include: The time-intensity curve of the control section is designated as the first curve, and the time-intensity curve of the section to be detected is designated as the second curve. The first and second curves are analyzed to obtain their corresponding signal energy and amplitude attenuation coefficients. Fourier transforms are performed on the first and second curves respectively to extract their center frequencies. The signal energy, amplitude attenuation coefficient, and center frequency corresponding to each curve are used as the identification features of the curve. The identification features of the first curve and the second curve are compared to calculate the degree of deviation. Based on the degree of deviation, it is determined whether the second curve is abnormal relative to the first curve.

3. The bolt monitoring method based on electromagnetic ultrasound and 3D phased array according to claim 2, characterized in that: The method for obtaining signal energy and amplitude attenuation coefficient is as follows: For the entire time-intensity curve of the detection section, the echo signal intensity value at each sampling time point is squared to obtain the squared value of the echo signal; The signal energy is obtained by summing the squared values ​​of the echo signals. On the time-intensity curve of the detection section, two consecutive bottom echoes are identified. The ratio of the peak amplitude of the second bottom echo to the peak amplitude of the first bottom echo is calculated to obtain the bottom echo ratio. Take the natural logarithm of the bottom surface echo ratio to obtain the logarithmic value of the ratio; The estimated attenuation coefficient of the peak amplitude of the bottom echo is obtained by using the negative reciprocal of the distance the sound wave travels in the material as a weight.

4. The bolt monitoring method based on electromagnetic ultrasound and 3D phased array according to claim 2, characterized in that: The method for obtaining the center frequency is as follows: Select the echo signal of the entire detection section and perform a fast Fourier transform on it to obtain the one-sided amplitude spectrum of the signal. Each frequency component is multiplied by its corresponding one-sided amplitude spectrum value to obtain the frequency component amplitude value, and the frequency component amplitude value is integrated to obtain the weighted frequency sum. The total signal amplitude is obtained by integrating the one-sided amplitude spectrum values ​​at all frequency points. The center frequency is obtained by dividing the weighted sum of frequencies by the total signal amplitude.

5. The bolt monitoring method based on electromagnetic ultrasound and 3D phased array according to claim 2, characterized in that: The method for identifying and comparing features is as follows: Based on the time-intensity curves of the detection section and the control section, characteristic parameters of signal energy, center frequency, and amplitude attenuation coefficient are obtained. The Manhattan distance between the monitored section and the control section in terms of three characteristics—signal energy, center frequency, and amplitude attenuation coefficient—is calculated to obtain the absolute values ​​of signal energy, center frequency, and amplitude attenuation coefficient. Divide the absolute value of signal energy, the absolute value of center frequency, and the absolute value of amplitude attenuation coefficient by the reference value of the corresponding feature to obtain the energy difference, center frequency difference, and attenuation coefficient difference. A weight coefficient is assigned to each degree of difference, and the comprehensive anomaly score is obtained by weighting and summing each degree of difference with its corresponding weight coefficient. The comprehensive anomaly score of each detection segment is compared with the decision threshold using a binary classifier algorithm. If the comprehensive anomaly score of a detection segment is greater than the decision threshold, the segment is determined to be a signal anomaly segment.

6. The bolt monitoring method based on electromagnetic ultrasound and 3D phased array according to claim 5, characterized in that: The specific steps for determining the decision threshold are as follows: The detection sections of several healthy bolts are marked as healthy sections. Based on the time-intensity curves of each healthy section under the set monitoring conditions, the characteristic parameters of signal energy, center frequency and amplitude attenuation coefficient are obtained. The energy difference, center frequency difference and attenuation coefficient difference of each healthy section compared with the control section are calculated. Using the same weighting coefficients as the bolts to be monitored, a comprehensive anomaly score for each healthy segment is calculated based on energy difference, center frequency difference, and attenuation coefficient difference. The comprehensive anomaly score of each healthy segment is summed to obtain the total comprehensive anomaly score. Based on the total comprehensive anomaly score and the number of healthy segments, the average segment anomaly score of all healthy segments is obtained. The sensitivity score is obtained by multiplying the average anomaly of the section with the sensitivity coefficient. The decision threshold is obtained by summing the sensitivity score and the mean of the segment anomalies.

7. The bolt monitoring method based on electromagnetic ultrasound and 3D phased array according to claim 1, characterized in that: The specific steps for calculating the standardized deviation of the damaged candidate segment are as follows: Based on the three-dimensional ultrasound feature dataset obtained by the control section under the same detection conditions, and statistical analysis of the amplitude values ​​of all spatial points in the three-dimensional ultrasound feature dataset of the control section, the mean and standard deviation of the control section are calculated. For each spatial point in the damaged candidate segment, the difference between its amplitude value and that of the control segment is calculated, and then standardized using the standard deviation of the healthy data to obtain the standardized bias.

8. A bolt monitoring device based on electromagnetic ultrasound and 3D phased array, characterized in that: The monitoring device is used to perform the monitoring method according to any one of claims 1-7, including: The curve construction module is used to uniformly divide the bolt to be monitored into multiple continuous detection segments along the axial direction. Under the set monitoring conditions, in each detection segment, an ultrasonic body wave perpendicular to the bolt axis is excited by an electromagnetic ultrasonic device and the echo signal is received at the same time. Based on the relationship between the time elapsed since the self-excitation moment and the intensity of the corresponding echo signal, the time-intensity curve of each detection segment is constructed. The damaged marking module is used to set a control section on qualified bolts according to the specifications of the bolts to be monitored and the length of the detection section. Under the set monitoring conditions, the time-intensity curve of the control section is obtained. The time-intensity curve of each detection section is compared and analyzed with the time-intensity curve of the control section to screen out the detection sections with abnormal signals and mark them as damaged candidate sections. The 3D construction module is used to perform full-matrix capture scanning of the damaged candidate segment using a 3D phased array. It forms a focused ultrasound beam inside the damaged candidate segment and performs a 3D spatial scan to obtain the ultrasound echo signal of each spatial point inside the damaged candidate segment. Based on the ultrasound echo signal and the corresponding 3D spatial coordinates, a 3D ultrasound feature dataset is constructed. The anomaly labeling module is used to extract the ultrasonic echo signal intensity of each spatial point in the three-dimensional ultrasonic feature dataset of the damaged candidate segment to obtain the amplitude value, calculate the standardized deviation of the damaged candidate segment, compare and analyze the standardized deviation with the preset standardized deviation threshold, and screen out spatial points that exceed the preset standardized deviation threshold and mark them as candidate anomalies. The anomaly detection module is used to count the number of candidate anomalies in each three-dimensional spatial neighborhood as the discrimination unit, based on candidate anomalies. When the number of candidate anomalies exceeds a set threshold, the three-dimensional neighborhood is determined to constitute an anomaly region.

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