An electrofusion sleeve connection monitoring method for a polyurethane directly buried thermal insulation pipe

Abstract

The invention discloses a monitoring method for electrofusion sleeve connection of a polyurethane directly buried thermal insulation pipe, belonging to the technical field of electrofusion sleeve installation monitoring, which specifically includes: embedding a distributed electrode group in the annular contact surfaces on both sides of the electrofusion sleeve to form an electrical coupling network with the conductive enhancement structure in the polyurethane thermal insulation layer; when energizing and heating, applying a broadband alternating current signal with dynamic modulation and covering the dielectric relaxation frequency band of the molten interface to the electrode group; collecting the voltage response signals of the electrode pairs in real time, and calculating a complex impedance spectrum containing real and imaginary impedance components; analyzing the non-linear attenuation characteristics of the real part impedance, and giving an early warning if the rules are not met; analyzing the fluctuation law of the imaginary part impedance phase angle, extracting the dielectric anomaly characteristics, matching with a preset model, and giving an early warning if it exceeds the tolerance range; finally, positioning the spatial distribution of the molten defect based on the impedance response of the electrode group.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring the installation of electrofusion sleeves, and particularly to a method for monitoring the electrofusion sleeve connection of polyurethane directly buried thermal insulation pipes. Background Technique

[0002] In the field of energy transmission, polyurethane directly buried thermal insulation pipes are widely used in many systems such as central heating, cooling, and oil transportation due to their good heat insulation performance, corrosion resistance, and long service life. These systems have extremely high requirements for the sealing and reliability of pipe connections. Because once problems occur in the pipe connection parts, not only will a large amount of energy be wasted, but also safety accidents may be triggered, affecting the normal operation of the entire transmission system. Therefore, ensuring the connection quality of polyurethane directly buried thermal insulation pipes is of crucial importance. And as a commonly used connection method, the monitoring of the connection quality of electrofusion sleeves has become a key link to ensure the stable operation of the pipeline system.

[0003] Currently, there have been some studies and practices on monitoring the connection quality of electrofusion sleeves of polyurethane directly buried thermal insulation pipes. Some methods evaluate the connection quality by detecting the change in conductivity. Electrodes are set at the connection part to measure the current and voltage, and then the conductivity is calculated. Whether the connection is good is judged according to the change in conductivity. There are also some methods using ultrasonic flaw detection, by emitting ultrasonic waves and analyzing the characteristics of the reflected waves to detect whether there are defects in the connection part. In addition, there are also monitoring methods based on the change in resistance, by monitoring the real-time change in the resistance of the connection part to evaluate the connection state.

[0004] However, these existing monitoring methods have many deficiencies. On the one hand, the real-time performance is poor. Most methods detect after the connection is completed, and it is difficult to grasp the connection state in real time during the process of heating and melting the electrofusion sleeve. Once a problem is found, irreversible losses are often caused. On the other hand, the detection accuracy is limited. For some microscopic defects, such as tiny bubbles and impurities, it is difficult to accurately detect them, and these microscopic defects may gradually expand over time, affecting the long-term use performance of the pipeline. And the spatial positioning ability is insufficient, and it is difficult to accurately determine the specific spatial position of the defect in the connection part, which makes the subsequent repair work lack pertinence and low efficiency. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for monitoring the electrofusion sleeve connection of polyurethane directly buried thermal insulation pipes to solve the following technical problems:

[0006] The existing electrofusion sleeve monitoring methods have poor real-time performance and are difficult to locate some microscopic defects.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for monitoring the electrofusion sleeve connection of a polyurethane directly buried insulation pipe, comprising the following steps:

[0009] Embed a distributed electrode group in the annular contact surfaces on both sides of the electrofusion sleeve, and the distributed electrode group forms an electrical coupling network with the conductive enhancement structure inside the polyurethane insulation layer;

[0010] During the process of energizing and heating the electrofusion sleeve, apply a dynamically modulated broadband alternating current signal to the electrode group, and the frequency range of the broadband alternating current signal covers the dielectric relaxation frequency band of the molten interface;

[0011] Real-time collect the voltage response signals of each electrode pair in the distributed electrode group, and calculate the complex impedance spectrum of the electrofusion sleeve based on the voltage response signals. The complex impedance spectrum includes a real part impedance component and an imaginary part impedance component;

[0012] Analyze the non-linear attenuation characteristics of the real part impedance component. If the non-linear attenuation characteristics do not conform to the expected law, generate a molten defect warning signal;

[0013] By analyzing the fluctuation law of the phase angle of the imaginary part impedance component changing with frequency, extract the dielectric anomaly characteristics of the microstructure of the molten interface; match the dielectric anomaly characteristics with a preset molten interface uniformity model. If the matching result exceeds the preset tolerance range, generate a molten defect warning signal;

[0014] Locate the spatial distribution of the molten defect based on the impedance response of the distributed electrode group.

[0015] As a further solution of the present invention: the conductive enhancement structure is a metal wire mesh or a carbon fiber array embedded in the polyurethane insulation layer. The metal wire mesh or the carbon fiber array is uniformly distributed along the circumferential direction of the polyurethane insulation layer, and is electrically connected to the annular electrode mounting surface of the distributed electrode group through welding or conductive adhesive to form a full circumferential electrical connection.

[0016] As a further solution of the present invention: the dynamic modulation method of the broadband alternating current signal includes:

[0017] Mark the time interval of energizing and heating the electrofusion sleeve as [t1, t2]. The broadband alternating current signal includes a low-frequency continuous sweep signal and a high-frequency pulse modulation signal;

[0018] When in the time interval [t1, t0], t0 is a preset value, apply a low-frequency continuous sweep signal, and the frequency range of the low-frequency band corresponds to the response frequency band of the overall conductivity of the polyurethane material in the molten state;

[0019] When in the time interval (t0, t2], a high-frequency pulse modulation signal is superimposed on the low-frequency continuous frequency-sweeping signal. The frequency range of the high-frequency band corresponds to the local dielectric relaxation frequency band caused by microscopic defects at the melting interface, and the microscopic defects include bubbles or impurities.

[0020] During the electrothermal sleeve energization heating process, impedance response data of the electrode group is collected in real time. According to the impedance response data, the interval size of the frequency change in the low-frequency continuous frequency-sweeping signal and the ratio of the pulse duration to the period in the high-frequency pulse modulation signal are adjusted, so that the broadband alternating current signal is adaptively matched with the melting process of the polyurethane material.

[0021] As a further solution of the present invention: The calculation of the complex impedance spectrum specifically includes:

[0022] Perform synchronous detection processing on the voltage response signal, separate and mark the part of the voltage response signal that is in the same frequency and phase as the excitation signal as the real part voltage component, and separate and mark the part that is orthogonal to the phase of the excitation signal as the imaginary part voltage component.

[0023] Calculate the real part impedance through the real part voltage component, obtain the change rate of the real part impedance in the radial space, and construct a dynamic distribution model of the melting layer thickness; calculate the imaginary part impedance based on the imaginary part voltage component, use the spatial position as the coordinate, represent the value of the imaginary part impedance in the form of a cloud map, generate a dielectric property distribution cloud map, and obtain the local extreme points of the imaginary part impedance in the cloud map.

[0024] As a further solution of the present invention: The process of extracting the dielectric anomaly characteristics of the microscopic structure of the melting interface includes:

[0025] Perform multi-scale time-frequency analysis on the imaginary part impedance component, decompose the imaginary part impedance component signal into different frequency components, obtain the fluctuation changes of different frequency components at different time points, and extract the non-stationary fluctuation components of the phase angle in a specific frequency band.

[0026] According to the extracted non-stationary fluctuation components of the phase angle, construct an energy spectrum. The energy spectrum is used to represent the energy distribution of the phase angle fluctuation at different frequencies, calculate the integral value of the energy spectrum within a preset defect characteristic frequency band, and the integral value represents the phase angle fluctuation energy of all frequencies within this frequency band; when the integral value exceeds the reference energy threshold, it is determined that there are unfused defects at the melting interface.

[0027] Obtain the frequency position of the peak in the energy spectrum. When there is a low-frequency energy peak in the energy spectrum, the unfused defect is a macroscopic crack defect; when there is a high-frequency energy peak in the energy spectrum, the unfused defect is a microscopic bubble / impurity defect.

[0028] As a further solution of the present invention: The arrangement method of the distributed electrode group is:

[0029] In the axial direction of the electrofusion sleeve, n groups of annular electrodes are arranged, and the detection regions of adjacent annular electrodes have axial overlap; each group of annular electrodes includes m concentric annular electrodes with different radial directions, and each layer of annular electrodes includes o electrode units, and the electrode units are distributed on the annular electrodes in a circumferentially symmetric manner. n, m, and o are preset values. Each electrode unit is connected to a switching circuit, and through the control of the switching circuit, different combinations of electrode units are formed to form multiple independent detection circuits.

[0030] As a further solution of the present invention: Locating the spatial distribution of melting defects includes axial positioning, radial positioning, and circumferential positioning. The positioning process is as follows:

[0031] Obtain the impedance response gradient change in the axially overlapping detection region, perform axial detection starting from one end of the melting interface. When it is recognized that the impedance response gradient at any position increases beyond the preset value, mark this position as the starting point of the axial defect, continue to identify along the axial direction until the value at any position is lower than the preset value, then mark this position as the ending point of the axial defect. The region between the starting point and the ending point of the defect is the axially positioned region of the defect;

[0032] Obtain the impedance responses of different layers of concentric annular electrodes within the axially positioned region. According to the impedance response differences of the concentric annular electrodes at different radial positions, calculate the concentric annular electrode layer where the defect is located, which is the radially positioned range of the defect;

[0033] Obtain the impedance responses generated by each detection circuit within the radially positioned range of the axially positioned region. By analyzing the impedance response differences of the detection circuits, calculate the uniformity of the melting layer in the circumferential direction. If the impedance difference between any adjacent electrode units exceeds the set ratio, it is determined that there is a melting defect in the corresponding circumferential region.

[0034] As a further solution of the present invention: The construction method of the preset melting interface uniformity model includes:

[0035] Under standard experimental conditions, monitor and collect data on the completely sealed melting interface. Record the change of the real part impedance with time at different heating stages and different temperatures to form a real part impedance decay curve; extract the fluctuation patterns of the imaginary part phase angle at different frequencies to form an imaginary part phase fluctuation template, and generate an impedance reference database;

[0036] The melting interface uniformity model includes a relationship model between the real part impedance gradient and the melting layer thickness and an imaginary part phase fluctuation pattern classification model;

[0037] Input the real part impedance data in the impedance reference database into a convolutional neural network to learn the variation characteristics of the real part impedance at different positions and under different conditions, establish a non-linear mapping relationship between the real part impedance gradient and the molten layer thickness, and obtain a relationship model between the real part impedance gradient and the molten layer thickness;

[0038] Input the imaginary part phase fluctuation template data in the impedance reference database into a support vector machine algorithm. The template data contains the imaginary part phase fluctuation patterns corresponding to different defect types. The support vector machine algorithm performs classification training on the template data, stores the imaginary part phase fluctuation characteristics corresponding to different defect types, generates several phase fingerprint recognition libraries, and obtains an imaginary part phase fluctuation pattern classification model;

[0039] Input the real-time monitoring data into the trained molten interface uniformity model, and output the molten quality score of the real part impedance data and the defect confidence parameter of the imaginary part phase data.

[0040] As a further solution of the present invention: it further includes:

[0041] Based on the axial positioning, radial positioning, circumferential positioning and heating time parameters, establish a three-dimensional defect evolution model, which includes the growth rate of the defect volume with heating time and the change trajectory of the shape position.

[0042] The beneficial effects of the present invention:

[0043] The present invention adopts a distributed electrode group and a conductive enhancement structure to form a full circumferential electrical coupling network, combined with the dynamic modulation technology of a broadband alternating current signal, and can synchronously collect impedance response data during the electrothermal sleeve heating process. By real-time analyzing the non-linear attenuation characteristics of the real part impedance and the fluctuation law of the imaginary part phase angle, it can directly identify interface defects during the melting stage, breaking through the lag limitation of traditional offline detection.

[0044] Through a multi-scale time-frequency analysis algorithm, decompose the imaginary part impedance component into different frequency components, and extract the phase angle fluctuation characteristics of a specific frequency band. This technology can effectively distinguish macroscopic cracks from microscopic bubble / impurity defects, and its detection sensitivity is improved by an order of magnitude compared with ultrasonic flaw detection technology, and it can capture interface non-uniformity problems that are difficult to detect by traditional methods.

[0045] Based on the impedance gradient change analysis of the axially overlapping detection area, combined with the response difference of the radially concentric electrode layer and the circumferential electrode unit combination detection, realize the precise positioning of defects in three-dimensional space. This method can accurately determine the axial distribution range, radial depth and circumferential position of defects by quantifying the impedance response differences at different positions, providing a reliable basis for targeted repair.

[0046] According to the change of the electrical characteristics during the melting process of the polyurethane material, the sweep frequency interval and pulse duty cycle are dynamically adjusted to ensure that the excitation signal always matches the material state. This technology effectively solves the problem of signal distortion in traditional fixed-frequency detection and ensures stable monitoring accuracy during the material phase change process. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention will be further described below in conjunction with the accompanying drawings.

[0048] Figure 1 is a schematic flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The technical solutions 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. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] Please refer to Figure 1 As shown, the present invention is a method for monitoring the electrofusion sleeve connection of a polyurethane directly buried insulation pipe, including the following steps:

[0051] Step 1: Construction of a distributed electrode network:

[0052] A multi-layer distributed electrode group is symmetrically embedded in the annular contact surfaces on both sides of the electrofusion sleeve. The electrode group is made of high-temperature resistant conductive material, and its arrangement density is optimized according to the dielectric characteristics of the polyurethane material. The electrode group forms an integrated structure with the electrofusion sleeve matrix through a precision injection molding process to ensure stable electrical performance during the high-temperature melting process. The distributed electrode group and the conductive enhancement structure (such as a metal wire mesh or a carbon fiber array) inside the polyurethane insulation layer are conductively connected in a full circumferential direction to form a three-dimensional electrical coupling network, which covers the entire melting interface area and provides a low-impedance channel for broadband signal transmission.

[0053] Step 2: Dynamic broadband signal excitation:

[0054] During the electrofusion sleeve power-on heating stage, an intelligent signal generator is used to apply a dynamically modulated broadband alternating current signal to the electrode group. The signal includes a low-frequency continuous sweep signal (10 Hz - 100 kHz) and a high-frequency pulse modulation signal (1 MHz - 10 MHz), and its frequency range covers the entire dielectric relaxation frequency band of the polyurethane material from the solid state to the molten state. The signal modulation strategy adopts a double closed-loop control: the temperature feedback loop adjusts the signal amplitude in real time according to the heating power, and the impedance feedback loop dynamically optimizes the sweep frequency interval and pulse duty cycle to ensure that the excitation signal is synchronized with the material phase change process.

[0055] Step 3. Multi-dimensional impedance data acquisition:

[0056] The voltage response signals of each electrode pair in the distributed electrode group are acquired in real time through a high-precision synchronous acquisition system, and the sampling rate reaches over 100 kS / s. The lock-in amplification technology is used to perform orthogonal decomposition on the voltage signals, separating the real part voltage component with the same frequency and phase as the excitation signal and the imaginary part voltage component with a phase orthogonal to it. Based on Ohm's law, the complex impedance values of the corresponding electrode pairs are calculated, and a three-dimensional impedance database containing more than 2000 measurement points is constructed to provide data support for full-space coverage for subsequent analysis.

[0057] Step 4. Real part impedance non-linear analysis:

[0058] The polynomial fitting algorithm is used to extract the characteristic parameters of the attenuation curve of the real part impedance, including the attenuation rate, inflection point position, curve symmetry, etc. A non-linear attenuation model based on support vector regression is established. This model integrates parameters such as the temperature field distribution and material thermal conductivity and can predict the impedance attenuation trajectory under ideal conditions. When the deviation between the measured curve and the prediction model exceeds the set threshold, the melting defect warning mechanism is triggered.

[0059] Step 5. Extraction of imaginary part phase fluctuation characteristics:

[0060] The continuous wavelet transform is performed on the imaginary part impedance component to decompose the phase fluctuation signals in 12 different frequency bands. The instantaneous phase of each frequency band signal is extracted through the Hilbert transform to construct a time-frequency domain joint feature matrix. A convolutional neural network is used to perform pattern recognition on the feature matrix. When the phase fluctuation energy in a specific frequency band (such as 100 kHz - 500 kHz) is detected to exceed 1.5 times the reference value, it is determined that there are micro-defects.

[0061] Step 6. Three-dimensional spatial positioning of defects:

[0062] Based on the impedance gradient analysis of the axial overlapping electrode group, the cubic spline interpolation algorithm is used to determine the axial distribution range of the defects. Through the impedance response difference of the radial layered electrodes, the Bayesian estimation method is used to invert the radial depth of the defects. Using the impedance matrix of the circumferential electrode array and combining with the Tikhonov regularization algorithm, the thickness distribution cloud map of the melting layer is reconstructed to identify the circumferential non-uniform area. Finally, a defect positioning report containing the XYZ three-dimensional coordinates is generated to provide accurate guidance for subsequent repair.

[0063] In a preferred embodiment of the present invention, the conductive enhancement structure is a metal wire mesh or a carbon fiber array embedded in the polyurethane insulation layer. The metal wire mesh or carbon fiber array is evenly distributed along the circumferential direction of the polyurethane insulation layer and forms a full circumferential electrical connection with the annular electrode mounting surface of the distributed electrode group through welding or conductive adhesive.

[0064] In another preferred embodiment of the present invention, the dynamic modulation method of the broadband alternating current signal includes:

[0065] Set the time interval for energizing and heating the electrothermal melting sleeve as [t1, t2]. This broadband alternating current signal is composed of a low-frequency continuous sweep signal and a high-frequency pulse modulation signal.

[0066] In the time interval [t1, t0] (t0 represents the time point at the end of the initial stage of energizing and heating the electrothermal melting sleeve), apply a low-frequency continuous sweep signal. The frequency range of this low-frequency band, for example, is from 10 Hz to 100 kHz, corresponding to the response frequency band of the overall conductivity of the polyurethane material in the molten state. In the initial stage of heating, the polyurethane material changes from a solid state to a molten state, and its overall conductivity begins to change. The low-frequency continuous sweep signal can effectively detect this change in overall conductivity. For example, as the material gradually melts, the change in the internal molecular structure makes the movement of free electrons more active, resulting in an increase in overall conductivity. The low-frequency continuous sweep signal can capture this change trend and provide basic data for subsequent monitoring and analysis.

[0067] When entering the time interval (t0, t2], superimpose a high-frequency pulse modulation signal on the basis of the low-frequency continuous sweep signal. The frequency range of the high-frequency band, such as from 1 MHz to 10 MHz, corresponds to the local dielectric relaxation frequency band caused by microscopic defects (including bubbles, impurities, etc.) at the molten interface. In the later stage of heating, when the polyurethane material further melts, microscopic defects may occur at the molten interface. These defects affect the local dielectric properties of the material and cause local dielectric relaxation phenomena. The high-frequency pulse modulation signal can sensitively detect this change in local dielectric properties and help discover potential microscopic defects. For example, when there are bubbles at the molten interface, the electric field distribution around the bubbles changes, resulting in a change in the local dielectric constant, and the high-frequency pulse modulation signal can detect this change.

[0068] During the whole process of energizing and heating the electrothermal melting sleeve, real-time collect the impedance response data of the electrode group. Based on these data, dynamically adjust the low-frequency continuous sweep signal and the high-frequency pulse modulation signal. For the low-frequency continuous sweep signal, if the collected impedance response data shows that the conductivity of the material changes slowly, appropriately increase the frequency change interval, such as increasing from 1 kHz to 2 kHz; if the conductivity changes quickly, then decrease the frequency change interval, such as decreasing from 1 kHz to 0.5 kHz. For the high-frequency pulse modulation signal, when it is detected that there may be microscopic defects, increase the ratio of the pulse duration to the period (duty cycle), such as increasing from 30% to 50%; when there are no obvious signs of microscopic defects, appropriately decrease the duty cycle, such as decreasing from 30% to 20%. Through this dynamic adjustment, the broadband alternating current signal can be adaptively matched with the melting process of the polyurethane material, significantly improving the accuracy and effectiveness of monitoring.

[0069] In another preferred embodiment of the present invention, the calculation of the complex impedance spectrum specifically includes:

[0070] First, synchronous demodulation processing is carried out on the voltage response signal collected in real time. This process is realized by means of a high-precision synchronous demodulation circuit or relevant software algorithms. Specifically, through the phase-locked loop technology, the voltage response signal is accurately compared with the excitation signal. On this basis, the part of the voltage response signal that is in the same frequency and phase as the excitation signal can be accurately identified and separated, and then this part of the signal is marked as the real part voltage component; at the same time, using the phase quadrature detection technology, the part of the voltage response signal that is in quadrature with the excitation signal is also separated and marked as the imaginary part voltage component. This accurate separation process lays a solid foundation for subsequent impedance calculation and related characteristic analysis.

[0071] Next, the real part impedance is calculated based on the separated real part voltage component. According to Ohm's law, combined with the known magnitude of the excitation current, through the corresponding calculation program, the real part impedance value can be accurately obtained. To further understand the molten state in depth, it is also necessary to obtain the change rate of the real part impedance in the radial space. This requires multi-point measurement of the real part impedance at different radial positions. For example, measurement points are set at regular intervals (such as 0.5 mm) along the radial direction, and the real part impedance values at each point are collected. Through the analysis of these multi-point data, using the differential algorithm or the method of curve fitting to find the slope, the change rate of the real part impedance in the radial space is calculated. Based on these change rate data, combined with the physical characteristic parameters of the polyurethane material during the melting process and the heat transfer principle, a model that can accurately reflect the dynamic distribution of the molten layer thickness is constructed. This model can visually show the change of the molten layer thickness in the radial direction over time during the electrothermal sleeve energized heating process.

[0072] After the relevant processing of the real part impedance is completed, the calculation of the imaginary part impedance is carried out based on the imaginary part voltage component. Similarly, according to Ohm's law and the excitation current information, the imaginary part impedance is calculated through a specially designed algorithm. In order to more intuitively and comprehensively present the distribution characteristics of the imaginary part impedance in space, taking the spatial position as the coordinate, with the help of professional drawing software or self-written drawing programs, the values of the imaginary part impedance are visually represented in the form of a cloud map, thereby generating a dielectric property distribution cloud map. In this cloud map, different colors or gray values represent different magnitudes of the imaginary part impedance values. Through careful observation and analysis of the cloud map, using image recognition algorithms or manual screening methods, the local extreme points of the imaginary part impedance in the cloud map are obtained. These local extreme points often correspond to the regions where the dielectric properties change significantly at the molten interface, and have important indicative significance for judging the existence of microscopic defects and evaluating the melting quality.

[0073] In a preferred case of this embodiment, the process of extracting the dielectric anomaly characteristics of the molten interface microstructure includes:

[0074] First, multi-scale time-frequency analysis is carried out on the imaginary impedance component. This process uses advanced mathematical tools, such as wavelet transform algorithms. By selecting appropriate wavelet basis functions, the imaginary impedance component signal is decomposed according to different time scales and frequency scales, thereby decomposing it into a series of different frequency components. During the decomposition process, the time sliding window technology is used to accurately obtain the fluctuation changes of these different frequency components at different time points during the heating process of the electric hot melt sleeve. Then, the phase angle analysis algorithm is used to focus on a specific frequency band, such as the frequency range of 100kHz to 500kHz, which is more sensitive to microstructural changes, and carefully extract the non-stationary fluctuation components of the phase angle in this frequency band. These non-stationary fluctuation components often contain key information on the microstructural changes of the molten interface and are the core data for subsequent analysis.

[0075] Next, an energy spectrum is constructed based on the extracted non-stationary fluctuation components of the phase angle. The energy spectrum is a tool used to intuitively represent the energy distribution of phase angle fluctuations at different frequencies. When constructing the energy spectrum, the phase angle fluctuation signal in the time domain is converted to the frequency domain using mathematical methods such as Fourier transform, and the energy value carried by the phase angle fluctuation at different frequencies is calculated according to the corresponding energy calculation formula. By plotting the curves of these energy values ​​changing with frequency, a complete energy spectrum is formed. On this basis, the integral value of the energy spectrum within the preset defect characteristic frequency band is further calculated. The preset defect characteristic frequency band is determined through a large number of experiments and theoretical analysis, and different types of defects correspond to different frequency band ranges. The integral value actually represents the sum of the phase angle fluctuation energy of all frequencies within this specific frequency band. By comparing with the pre-set reference energy threshold, when the integral value exceeds the reference energy threshold, it can be determined that there is an unfused defect at the molten interface. This is because unfused defects can cause abnormal changes in dielectric properties, which can cause significant changes in the phase angle fluctuation energy within a specific frequency band.

[0076] Finally, deeply analyze the frequency position of the peak in the energy spectrum. When there is a low-frequency energy peak in the energy spectrum, such as an energy peak appearing in the low-frequency range from 1 kHz to 10 kHz, based on a large amount of experimental data and experience summary, the unfused defect corresponding at this time is most likely a macroscopic crack defect. This is because the existence of macroscopic cracks will have a large-scale impact on the electric field distribution, causing an obvious peak in the phase angle fluctuation energy in the low-frequency band. When there is a high-frequency energy peak in the energy spectrum, for example, an energy peak appears in the high-frequency range from 1 MHz to 10 MHz, then the unfused defect in this case is usually a microscopic bubble / impurity defect. Microscopic bubbles or impurities will cause slight perturbations in the electric field in a local area, and such slight perturbations are more easily detected in the high-frequency band, thereby resulting in a peak in the phase angle fluctuation energy in the high-frequency band. By accurately judging the peak frequency position of the energy spectrum, different types of unfused defects existing at the fusion interface can be accurately identified, providing a strong basis for subsequent quality assessment and repair measures.

[0077] In another preferred embodiment of the present invention, the arrangement mode of the distributed electrode group is as follows:

[0078] In the axial direction of the electrothermal sleeve, n groups of annular electrodes are reasonably arranged. Here, n is a preset value determined according to various factors such as the length of the electrothermal sleeve and the required detection accuracy. For example, for a longer electrothermal sleeve, the value of n will increase accordingly to ensure that the entire axial range can be fully covered. It should be noted that there is an axial overlap in the detection areas of adjacent annular electrodes. This axial overlap design is of great significance. It avoids the emergence of detection blind spots, enabling continuous and non-missing monitoring of the connection part in the axial direction. For example, if the detection area of a group of annular electrodes covers an axial length of L, then there will be a certain proportion (such as 30%) of overlap in the detection areas of adjacent annular electrodes, that is, the overlap length is 0.3L. In this way, it can be ensured that every part in the entire axial length can be detected by at least two groups of annular electrodes, greatly improving the reliability of detection.

[0079] Each group of annular electrodes further includes m layers of concentric annular electrodes with different radial directions. The value of m is also a preset value determined according to the detection requirements at different radial depths. Each layer of concentric annular electrode has a specific detection task. From the inner wall to the outer wall of the electrothermal sleeve, different layers of concentric annular electrodes can respectively sense the changes in electrical characteristics at different radial depth positions. In each layer of annular electrode, there are o electrode units. The number of o is also a preset value determined according to the detection resolution required in the circumferential direction. These electrode units are evenly distributed on the annular electrode in a circumferentially symmetric manner. For example, if the circumference of a layer of annular electrode is C, when o electrode units are evenly distributed, the arc length between adjacent electrode units is C / o. This uniform distribution method ensures the balance and comprehensiveness of detection in the circumferential direction.

[0080] More importantly, each electrode unit is connected to a switching circuit. This switching circuit is like an intelligent control hub. Through its flexible control function, it can combine different electrode units in various ways. For example, at a certain moment, several adjacent electrode units in the same layer can be combined into a detection circuit to detect the local electrical conditions at that circumferential position; at another moment, electrode units in different layers and different circumferential positions can be combined to form a cross-layer and cross-circumferential detection circuit for analyzing the electrical relationships between different radial and circumferential positions. Through such rich and diverse combination methods, multiple independent detection circuits can be formed. These independent detection circuits can detect the connection part of the electrothermal fusion sleeve from different angles and dimensions, collect a large amount of electrical data, provide sufficient and comprehensive data support for subsequent defect judgment and analysis, and greatly improve the flexibility and accuracy of the monitoring system.

[0081] In a preferred case of this embodiment, the spatial distribution of locating the melting defect includes axial positioning, radial positioning, and circumferential positioning. The positioning process is as follows:

[0082] Axial positioning: Utilize the special design of the axially overlapping detection area to deeply analyze the gradient change of the impedance response in this area. The specific operation starts from a certain section of the melting interface and carefully conducts detection work along the axial direction. During the detection process, continuously monitor the change of the impedance response gradient value. The preset value mentioned here is a key reference value determined comprehensively through a large amount of experimental data and theoretical research on the electrothermal fusion sleeve connection of polyurethane directly buried thermal insulation pipes. Once it is identified during the detection process that the impedance response gradient increases and exceeds the preset value at a certain position, this position is accurately marked as the starting point of the axial defect. Subsequently, the detection work continues to steadily advance along the axial direction until at a certain position, the impedance response gradient value decreases to below the preset value, and this position is immediately marked as the ending point of the axial defect. In this way, the area between the starting point of the axial defect and the ending point of the axial defect is the positioning area of the defect in the axial direction. For example, if the preset value is set to X, during axial detection, when the impedance response gradient at a certain position suddenly increases from a value less than X to X + ΔX (ΔX is the increment exceeding the preset value), this position is the starting point; in subsequent detection, when the impedance response gradient at a certain position decreases from a value greater than X to X - ΔX (ΔX is the decrement below the preset value), this position is the ending point, and the area between these two points is the axial defect positioning area.

[0083] Radial positioning: After completing the axial positioning, focus on the determined axial positioning area. Concentric annular electrodes of different layers are distributed within this area, and impedance response data of these different layers of concentric annular electrodes are collected. Since the distances between the concentric annular electrodes at different radial positions and the molten layer are different, their sensitivities to defects are also different. Based on the impedance response differences of the concentric annular electrodes at different radial positions, a special algorithm is used for calculation. For example, by comparing the characteristics of the impedance response curves of different layers of electrodes and calculating the ratio of impedance differences, etc., the concentric annular electrode layer where the defect is located can be accurately deduced. The radial range corresponding to this concentric annular electrode layer is the radial positioning range of the defect. For example, if there are three layers of concentric annular electrodes with impedance responses Z1, Z2, and Z3 respectively, by analyzing the difference relationship between them, such as the comparison of the magnitudes of |Z1 - Z2| and |Z2 - Z3|, and combining the known distance relationship between different layers of electrodes and the molten layer, the specific electrode layer where the defect is located can be determined, and then the radial positioning range of the defect can be determined.

[0084] Circumferential positioning: After clarifying the axial positioning area and the radial positioning range, collect the impedance response data generated by each detection loop within this range. These detection loops are composed of different combinations of electrode units, and their impedance responses can reflect the characteristics of the molten layer in the circumferential direction. By deeply analyzing the impedance response differences of the detection loops, a quantitative method is used to calculate the uniformity of the molten layer in the circumferential direction. The set ratio here is also an important index determined through a large number of experiments and data analyses. If it is found during the analysis that the impedance difference between any two adjacent electrode units exceeds the set ratio, for example, the set ratio is Y%, when the ratio obtained by dividing the impedance difference between adjacent electrode units by the impedance value of one of the electrode units is greater than Y%, it can be determined that there is a molten defect in the corresponding circumferential area. In this way, the position of the defect can be accurately determined in the circumferential direction, realizing the full-range precise positioning of the spatial distribution of the molten defect.

[0085] In another preferred embodiment of the present invention, the method for constructing the preset molten interface uniformity model includes:

[0086] First, carry out relevant work under standard experimental conditions. The so-called standard experimental conditions are carefully designed and strictly controlled, with various parameters such as environmental temperature, humidity, and pressure maintained in a stable and repeatable state. Under such conditions, comprehensively monitor and collect data on the completely sealed molten interface. During the entire electrothermal sleeve heating process, different heating stages are covered, such as the initial heating period, rapid temperature rise period, constant temperature melting period, etc., and at different temperatures, carefully record the change of the real part impedance over time. Through long-term, multi-stage, and multi-temperature point recordings, a large amount of real part impedance-time data is collected, and then these data are sorted and analyzed to plot the real part impedance decay curve. This curve intuitively reflects the change trend of the real part impedance over time under different heating conditions, providing important basic data for subsequent model construction. At the same time, analyze and extract the fluctuation of the imaginary part phase angle at different frequencies. By using professional signal processing algorithms and tools, summarize and generalize the fluctuation characteristics of the imaginary part phase angle at different frequencies to form an imaginary part phase fluctuation template. Integrate relevant data such as the real part impedance decay curve and the imaginary part phase fluctuation template to generate an impedance reference database. This database contains various electrical property data under the condition of an ideal uniform molten interface, providing rich and reliable reference basis for the training and verification of subsequent models.

[0087] The molten interface uniformity model mainly consists of two key parts, namely the relationship model between the real part impedance gradient and the molten layer thickness and the imaginary part phase fluctuation mode classification model.

[0088] For the construction of the relationship model between the real part impedance gradient and the molten layer thickness, input the real part impedance data in the impedance reference database into a convolutional neural network. A convolutional neural network is a powerful deep learning model with excellent feature extraction and learning capabilities. By allowing the convolutional neural network to learn the real part impedance data, it can automatically identify the change characteristics of the real part impedance at different positions and under different conditions. For example, in different heating stages, different temperature environments, and different material properties, etc., the change laws of the real part impedance may vary. Through learning and analyzing a large amount of data, the convolutional neural network can capture these subtle change characteristics and establish a non-linear mapping relationship between the real part impedance gradient and the molten layer thickness. This non-linear mapping relationship takes into account the influence of various complex factors and can more accurately describe the internal connection between the real part impedance gradient and the molten layer thickness, thereby obtaining the relationship model between the real part impedance gradient and the molten layer thickness.

[0089] For the construction of the imaginary part phase fluctuation pattern classification model, the imaginary part phase fluctuation template data in the impedance reference database is input into the support vector machine algorithm. These template data contain the imaginary part phase fluctuation patterns corresponding to different defect types. For example, different defect types such as macro cracks, micro bubbles, and impurities will cause the imaginary part phase angle to exhibit different fluctuation characteristics. The support vector machine algorithm is an effective classification algorithm that conducts classification training on the template data. During the training process, the support vector machine algorithm analyzes the imaginary part phase fluctuation characteristics corresponding to different defect types, finds out the differences and regularities between them. Then, the imaginary part phase fluctuation characteristics corresponding to these different defect types are stored to generate several phase fingerprint recognition libraries. These phase fingerprint recognition libraries are like a "defect feature dictionary" that can quickly and accurately determine the possible defect types based on the actually monitored imaginary part phase fluctuation conditions, thereby obtaining the imaginary part phase fluctuation pattern classification model.

[0090] Finally, the real-time monitoring data is input into the trained molten interface uniformity model. The model will analyze and evaluate the real-time data based on the knowledge learned and trained previously. For the real part impedance data, a molten quality score will be output, which intuitively reflects the quality of the current molten interface in terms of the real part impedance. For the imaginary part phase data, a defect confidence parameter will be output, which represents the matching degree between the current imaginary part phase fluctuation condition and the known defect types, thereby helping to judge whether there are defects and the likelihood of defects.

[0091] In another preferred embodiment of the present invention, it further includes:

[0092] On the basis of having completed the accurate axial positioning, radial positioning, and circumferential positioning of the defects in the early stage, the important parameter of heating time is fully considered. By collecting the position data of the defects in the axial, radial, and circumferential directions at different heating moments, and using advanced three-dimensional modeling algorithms and data analysis techniques, a three-dimensional evolution model of the defects is established.

[0093] This model has powerful functions and can dynamically present the growth rate of the defect volume with heating time. For example, in the initial stage of heating, by measuring and calculating the size changes of the defects in the three dimensions at different time points and using the volume calculation formula, the growth value of the defect volume in this stage is obtained, and further the growth rate of the volume with time is calculated. As the heating process progresses, these data are continuously updated to obtain a complete curve of the growth rate change of the defect volume with heating time.

[0094] Meanwhile, the model can also accurately depict the change trajectory of the defect shape and position. During the electrothermal sleeve heating process, the shape of the defect may gradually change from its initial irregular state. For example, tiny cracks may extend and expand over time, and bubbles may fuse or deform. By continuously tracking and analyzing the axial, radial, and circumferential positioning data, the model visualizes in three dimensions the changes in the defect shape in various directions and its position movement trajectory in space.

[0095] The above has described in detail one embodiment of the present invention. However, the content described is only the preferred embodiment of the present invention and cannot be considered as defining the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention shall still fall within the scope covered by the patent of the present invention.

Claims

1. A method for monitoring the electrofusion sleeve connection of a polyurethane directly buried thermal insulation pipe, characterized in that, It includes the following steps: Embed a distributed electrode group into the annular contact surfaces on both sides of the electrothermal fusion sleeve, and the distributed electrode group forms an electrical coupling network with the conductive enhancement structure inside the polyurethane insulation layer; During the process of energizing and heating the electrothermal fusion sleeve, apply a dynamically modulated broadband alternating current signal to the electrode group, and the frequency range of the broadband alternating current signal covers the dielectric relaxation frequency band of the melting interface; Real-time collect the voltage response signals of each electrode pair in the distributed electrode group, and calculate the complex impedance spectrum of the electrothermal fusion sleeve based on the voltage response signals. The complex impedance spectrum includes a real part impedance component and an imaginary part impedance component; Analyze the non-linear attenuation characteristics of the real part impedance component. If the non-linear attenuation characteristics do not conform to the expected law, generate a melting defect warning signal; By analyzing the fluctuation law of the phase angle of the imaginary part impedance component changing with frequency, extract the dielectric anomaly characteristics of the microstructure of the melting interface; match the dielectric anomaly characteristics with a preset melting interface uniformity model. If the matching result exceeds the preset tolerance range, generate a melting defect warning signal; Locate the spatial distribution of the melting defect based on the impedance response of the distributed electrode group.

2. The method for monitoring the electrofusion sleeve connection of a polyurethane directly buried thermal insulation pipe according to claim 1, wherein, The conductive enhancement structure is a metal wire mesh or a carbon fiber array embedded in the polyurethane insulation layer. The metal wire mesh or the carbon fiber array is evenly distributed along the circumferential direction of the polyurethane insulation layer, and forms a full circumferential electrical connection with the annular electrode mounting surface of the distributed electrode group through welding or conductive adhesive.

3. The electrofusion sleeve connection monitoring method of a polyurethane directly buried insulation pipe according to claim 1, characterized in that, The dynamic modulation method of the broadband alternating current signal includes: Mark the time interval of energizing and heating the electrothermal fusion sleeve as [t1, t2]. The broadband alternating current signal includes a low-frequency continuous frequency sweep signal and a high-frequency pulse modulation signal; When in the time interval [t1, t0], t0 is a preset value, apply a low-frequency continuous frequency sweep signal, and the frequency range of the low-frequency band corresponds to the response frequency band of the overall conductivity of the polyurethane material in the molten state; When in the time interval (t0, t2], superimpose a high-frequency pulse modulation signal on the low-frequency continuous frequency sweep signal. The frequency range of the high-frequency band corresponds to the local dielectric relaxation frequency band caused by microscopic defects at the melting interface. The microscopic defects include bubbles or impurities; During the process of energizing and heating the electrothermal fusion sleeve, real-time collect the impedance response data of the electrode group, and adjust the interval size of the frequency change in the low-frequency continuous frequency sweep signal and the ratio of the pulse duration to the period in the high-frequency pulse modulation signal according to the impedance response data, so that the broadband alternating current signal is adaptively matched with the melting process of the polyurethane material.

4. A method for monitoring the electrofusion sleeve connection of a polyurethane directly buried thermal insulation pipe according to claim 1, characterized in that, The calculation of the complex impedance spectrum specifically includes: Perform synchronous demodulation processing on the voltage response signal, separate and mark the part of the voltage response signal that is in the same frequency and phase as the excitation signal as the real part voltage component, and separate and mark the part that is orthogonal to the phase of the excitation signal as the imaginary part voltage component; Calculate the real part impedance through the real part voltage component, obtain the change rate of the real part impedance in the radial space, and construct a dynamic distribution model of the molten layer thickness; calculate the imaginary part impedance based on the imaginary part voltage component, use the spatial position as the coordinate, represent the value of the imaginary part impedance in the form of a contour map, generate a dielectric property distribution contour map, and obtain the local extreme points of the imaginary part impedance in the contour map.

5. A method for monitoring the electrofusion sleeve connection of a polyurethane directly buried insulation pipe according to claim 4, characterized in that, The process of extracting the dielectric anomaly characteristics of the microstructure of the molten interface includes: Perform multi-scale time-frequency analysis on the imaginary part impedance component, decompose the imaginary part impedance component signal into different frequency components, obtain the fluctuation changes of different frequency components at different time points, and extract the non-stationary fluctuation components of the phase angle in a specific frequency band; Construct an energy spectrum according to the extracted non-stationary fluctuation components of the phase angle. The energy spectrum is used to represent the energy distribution of the phase angle fluctuation at different frequencies, calculate the integral value of the energy spectrum within a preset defect characteristic frequency band, and the integral value represents the phase angle fluctuation energy of all frequencies within this frequency band; when the integral value exceeds the reference energy threshold, it is determined that there is an unfused defect at the molten interface; Obtain the frequency position of the peak in the energy spectrum. When a low-frequency energy peak appears in the energy spectrum, the unfused defect is a macroscopic crack defect; when a high-frequency energy peak appears in the energy spectrum, the unfused defect is a microscopic bubble / impurity defect.

6. The electrofusion sleeve connection monitoring method of a polyurethane directly buried thermal insulation pipe according to claim 1, characterized in that, The arrangement method of the distributed electrode group is: In the axial direction of the electrothermal melting sleeve, arrange n groups of annular electrodes, and there is axial overlap in the detection areas of adjacent annular electrodes; each group of annular electrodes includes m layers of concentric annular electrodes with different radii, and each layer of annular electrodes includes o electrode units, and the electrode units are distributed on the annular electrodes in a circumferentially symmetric manner. n, m, and o are preset values. Each electrode unit is connected to a switching circuit, and through the control of the switching circuit, different electrode units are combined in different ways to form multiple independent detection circuits.

7. A method for monitoring the electrofusion sleeve connection of a polyurethane directly buried thermal insulation pipe according to claim 6, characterized in that, Locating the spatial distribution of the molten defect includes axial positioning, radial positioning, and circumferential positioning. The positioning process is: Obtain the impedance response gradient change in the axially overlapping detection area, start axial detection from one end of the molten interface. When it is recognized that the impedance response gradient increases by more than the preset value at any position, mark this position as the axial defect starting point, continue to identify along the axial direction until the value at any position is lower than the preset value, then mark this position as the axial defect ending point, and the area between the defect starting point and the defect ending point is the axial positioning area of the defect; Obtain the impedance responses of different layers of concentric annular electrodes within the axially positioned area, and calculate the concentric annular electrode layer where the defect is located according to the impedance response differences of the concentric annular electrodes at different radial positions, which is the radial positioning range of the defect; Obtain the impedance responses generated by each detection circuit within the radial positioning range of the axially positioned area. By analyzing the impedance response differences of the detection circuits, calculate the uniformity of the molten layer in the circumferential direction. If the impedance difference between any adjacent electrode units exceeds the set ratio, it is determined that there is a molten defect in the corresponding circumferential area.

8. A method for monitoring the electrofusion sleeve connection of a polyurethane directly buried thermal insulation pipe according to claim 1, characterized in that, The construction method of the preset molten interface uniformity model includes: Under standard experimental conditions, monitor and collect data on the completely sealed melting interface. Record the variation of the real part of impedance with time at different heating stages and different temperatures to form a real part impedance decay curve; extract the fluctuation patterns of the imaginary part phase angle at different frequencies to form an imaginary part phase fluctuation template, and generate an impedance reference database; The melting interface uniformity model includes a relationship model between the real part impedance gradient and the melting layer thickness and an imaginary part phase fluctuation pattern classification model; Input the real part impedance data in the impedance reference database into a convolutional neural network to learn the variation characteristics of the real part impedance at different positions and different conditions, establish a non-linear mapping relationship between the real part impedance gradient and the melting layer thickness, and obtain a relationship model between the real part impedance gradient and the melting layer thickness; Input the imaginary part phase fluctuation template data in the impedance reference database into a support vector machine algorithm. The template data contains the imaginary part phase fluctuation patterns corresponding to different defect types. The support vector machine algorithm performs classification training on the template data, stores the imaginary part phase fluctuation characteristics corresponding to different defect types, generates several phase fingerprint recognition libraries, and obtains an imaginary part phase fluctuation pattern classification model; Input the real-time monitoring data into the trained melting interface uniformity model, and output the melting quality score of the real part impedance data and the defect confidence parameter of the imaginary part phase data.

9. A method for monitoring the electrofusion sleeve connection of a polyurethane directly buried insulation pipe according to claim 7, characterized in that, It also includes: Based on the axial positioning, radial positioning, circumferential positioning, and heating time parameters, establish a three-dimensional defect evolution model, which includes the growth rate of the defect volume with heating time and the change trajectory of the shape and position.

Images