Method and device for predicting service life of sealing structure of power equipment based on multi-factor aging experiment

Abstract

The invention relates to a power equipment sealing structure life prediction method and device based on a multi-factor aging experiment. The method comprises the following steps: performing a performance test on an unaged power equipment sealing structure sample to obtain a performance index initial value; performing various single-factor aging experiments on the unaged samples, and performing performance testing according to a set frequency to obtain performance index update values; substituting the performance index update values of various single-factor aging experiments into the degradation index model to obtain a corresponding degradation index value sequence, and obtaining a distribution duration in combination with a preset total degradation index value; according to the distribution duration corresponding to each single-factor aging condition in the multi-factor aging condition combination, carrying out a multi-factor aging experiment on the unaged power equipment sealing structure sample, and carrying out a performance test according to a set frequency to obtain a compression permanent deformation rate curve graph so as to obtain activation energy; and calculating the residual life of the sealing structure of the power equipment in combination with the working temperature. By adopting the method, the residual life prediction accuracy can be improved.

Description

Technical Field

[0001] This application relates to the field of sealing performance condition testing technology for power equipment, and in particular to a method, device, computer equipment, computer-readable storage medium, and computer program product for predicting the life of sealing structures of power equipment based on multi-factor aging experiments. Background Technology

[0002] Electrical equipment is a core component of power system operation. The normal operation of electrical equipment is crucial for the normal operation of the power system. When the internal humidity of electrical equipment is too high, the equipment is prone to malfunction, which in turn affects the normal operation of the power system. To avoid excessive internal humidity, electrical equipment needs to be sealed. Failure of electrical equipment seals poses a significant safety hazard to power grid operation; therefore, research on electrical equipment seal failure is extremely important.

[0003] Currently, the main method for evaluating the sealing failure (i.e., remaining life) of power equipment is artificial accelerated aging test. However, artificial accelerated aging tests are mostly limited to single factors. Single-factor aging tests are difficult to fully simulate actual working conditions, resulting in low accuracy in evaluating sealing failure (i.e., remaining life). Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting the remaining life of power equipment sealing structures based on multi-factor aging experiments, which can improve the accuracy of remaining life prediction, in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for predicting the lifespan of sealing structures in power equipment based on multi-factor aging experiments, including:

[0006] Performance tests were conducted on unaged electrical equipment sealing structure samples to obtain initial values ​​of performance indicators. The types of performance tests included helium leak rate testing, electrical performance testing, physical performance testing of sealing materials, and microstructure testing of sealing materials.

[0007] Single-factor aging experiments were conducted on the unaged power equipment sealing structure samples under various single-factor aging conditions. The performance of the power equipment sealing structure samples was tested at a set frequency during the single-factor aging experiments to obtain the updated performance index values ​​corresponding to various single-factor aging experiments.

[0008] Substituting the updated performance index values ​​of various single-factor aging experiments into the degradation index model yields a degradation index value sequence for each single-factor aging experiment. A degradation index value is randomly selected from each single-factor aging experiment's degradation index value sequence to form a degradation index value group. A target degradation index value group is selected from all degradation index value groups, where the total degradation index value of the target degradation index value group is equal to a preset total degradation index value. The duration corresponding to each degradation index value in the target degradation index value group is the allocated duration of the corresponding single-factor aging condition. The degradation index model is constructed based on the first difference between the updated performance index value and the initial performance index value, and the second difference between the failure limit value of the performance index and the initial performance index value.

[0009] Based on all types of single-factor aging conditions, a combination of multi-factor aging conditions is determined; based on the allocated duration of each single-factor aging condition in the combination of multi-factor aging conditions, a multi-factor aging experiment is conducted on the unaged power equipment sealing structure sample, and performance tests are performed at a set frequency to obtain a compression set curve.

[0010] Based on the compression set curve, the failure duration of the power equipment sealing structure sample is determined, and the activation energy is calculated based on the failure duration and the corresponding operating temperature.

[0011] The remaining lifespan of the power equipment sealing structure is calculated based on the activation energy and the operating temperature of the power equipment sealing structure during real-time operation of the power grid.

[0012] In one embodiment, the electrical performance tests include multiple tests such as volume resistivity, dielectric loss tangent, and partial discharge initiation voltage; the physical performance tests of the sealing material include multiple tests such as appearance inspection, mass loss rate, tensile strength, tear strength, elongation at break, compression set, hardness, and water absorption; the microstructure tests of the sealing material include multiple tests such as Fourier transform infrared spectroscopy, nuclear magnetic resonance, micromorphological observation, and energy dispersive spectroscopy.

[0013] In one embodiment, the types of single-factor aging experiments include compression ratio control experiments, periodic compression disturbance experiments, thermal cycling experiments, humidity cycling experiments, medium interface liquid erosion experiments, salt spray corrosion experiments, and voltage disturbance experiments.

[0014] In one embodiment, each type of performance test corresponds to at least one performance index. The method further includes: before constructing the degradation index model, calculating the correlation coefficient between each performance index and the experimental runtime in each single-factor aging experiment; based on the comparison result between the correlation coefficient and a set threshold, performing dimensionality reduction on the performance index in each single-factor aging experiment to obtain principal component performance indices, wherein the number of types of principal component performance indices is less than the number of types of performance indices before dimensionality reduction; and using the updated value of the principal component performance index of each single-factor aging experiment as the required updated value of the performance index for various single-factor aging experiments.

[0015] In one embodiment, determining the failure duration of the power equipment sealing structure sample based on the compression set curve includes: obtaining a sealing failure deformation rate threshold, and determining the duration at which the compression set first reaches the sealing failure deformation rate threshold based on the compression set curve, wherein the duration is the failure duration of the power equipment sealing structure sample.

[0016] In one embodiment, the step of calculating the activation energy based on the failure duration and the operating temperature corresponding to the failure duration includes: calculating the reciprocal of the failure duration to obtain a degradation rate constant; calculating the quotient between the degradation rate constant and the frequency factor; and calculating the activation energy based on the quotient and the operating temperature corresponding to the failure duration.

[0017] Secondly, this application also provides a power equipment sealing structure life prediction device based on multi-factor aging tests, comprising:

[0018] The initial performance testing module is used to perform performance tests on unaged electrical equipment sealing structure samples to obtain initial values ​​of performance indicators. The types of performance tests include helium leak rate testing, electrical performance testing, physical performance testing of sealing materials, and microstructure testing of sealing materials.

[0019] The single-factor aging test module is used to conduct single-factor aging tests on the unaged power equipment sealing structure sample under various single-factor aging conditions, and to perform performance tests on the power equipment sealing structure sample at a set frequency during the single-factor aging test to obtain the updated performance index values ​​corresponding to various single-factor aging tests.

[0020] The allocation duration determination module is used to substitute the updated performance index values ​​of various single-factor aging experiments into the degradation index model to obtain a degradation index value sequence for the corresponding single-factor aging experiment. A degradation index value is randomly selected from each single-factor aging experiment's degradation index value sequence to form a degradation index value group. A target degradation index value group is selected from all degradation index value groups, where the total degradation index value of the target degradation index value group is equal to a preset total degradation index value. The duration corresponding to each degradation index value in the target degradation index value group is the allocation duration for the corresponding single-factor aging condition. The degradation index model is constructed based on the first difference between the updated performance index value and the initial performance index value, and the second difference between the failure limit value of the performance index and the initial performance index value.

[0021] The multi-factor aging test module is used to determine the combination of multi-factor aging conditions based on all types of single-factor aging conditions; according to the allocated duration of each single-factor aging condition in the multi-factor aging condition combination, the module performs multi-factor aging tests on the unaged power equipment sealing structure sample and conducts performance tests at a set frequency to obtain a compression set curve.

[0022] The data analysis module is used to determine the failure duration of the power equipment sealing structure sample based on the compression set curve, and to calculate the activation energy based on the failure duration and the operating temperature corresponding to the failure duration.

[0023] The life prediction module is used to calculate the remaining life of the power equipment sealing structure based on the activation energy and the operating temperature of the power equipment sealing structure during real-time operation of the power grid.

[0024] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method provided in the first aspect.

[0025] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect.

[0026] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method provided in the first aspect.

[0027] The aforementioned method, device, computer equipment, computer-readable storage medium, and computer program product for predicting the lifespan of power equipment sealing structures based on multi-factor aging experiments include performance testing types such as helium leak rate testing, electrical performance testing, physical performance testing of sealing materials, and microstructure testing of sealing materials. Various single-factor aging experiments are conducted on unaged power equipment sealing structure samples, and performance tests are performed on the power equipment sealing structure samples at a set frequency during the single-factor aging experiments. In this context, comprehensive testing of helium leak rate, electrical performance, physical properties of sealing materials, and microstructure of sealing materials can more fully reflect the performance changes of samples during single-factor aging. This is beneficial for improving the accuracy of subsequent degradation index values. Furthermore, by using a degradation index model and a preset total degradation index value, the allocation duration of each single-factor aging condition can be determined. Based on all types of single-factor aging conditions, a multi-factor aging condition combination can be determined. Based on the allocation duration of each single-factor aging condition in the multi-factor aging condition combination, a multi-factor aging experiment is conducted on unaged power equipment sealing structure samples. In this case, by quantifying the duration of each single factor in the multi-factor aging experiment, the duration of continuous application of each single factor can be more rationally controlled, thus making the aging condition of the samples closer to the aging condition under actual working conditions. Additionally, the failure duration of the power equipment sealing structure samples is determined based on the compression set curve, and the activation energy is calculated based on the failure duration and the corresponding operating temperature. In this case, since the aging of the sample is closer to the aging under actual working conditions, the compression set curve is closer to the compression set change trend under actual working conditions, thereby improving the accuracy of failure duration and activation energy, and thus improving the accuracy of remaining life prediction. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating a method for predicting the lifespan of a power equipment sealing structure based on a multi-factor aging experiment, as shown in one embodiment.

[0030] Figure 2 This is a flowchart illustrating the process of determining the multi-factor aging experiment in one embodiment;

[0031] Figure 3 This is a structural block diagram of a power equipment sealing structure life prediction device based on multi-factor aging experiments in one embodiment.

[0032] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0034] Seal failure in power equipment poses a significant safety hazard to power grid operation, making research on this issue extremely important. Current methods for evaluating the sealing performance of power equipment primarily rely on accelerated aging tests, which are mostly limited to single-factor experiments. A scientific multi-factor accelerated aging test method is lacking, resulting in inaccurate predictions of the lifespan of power equipment sealing structures.

[0035] Based on this, the present invention provides a method for predicting the service life of sealing structures in power equipment based on multi-factor aging experiments. This method aims to evaluate the durability and performance changes of sealing structures under comprehensive environmental conditions. By simulating various aging factors in actual use, combining factors such as temperature, humidity, light exposure, chemical media, and mechanical stress, a comprehensive accelerated aging experiment is conducted to ensure that the results are closer to actual operating conditions. This improves the accuracy and reliability of predicting the service life of sealing structures. The method of this invention has practical value for the power equipment industry and differs significantly from related methods in terms of technological advancement.

[0036] In one exemplary embodiment, such as Figure 1 As shown, a method for predicting the lifespan of a power equipment sealing structure based on a multi-factor aging experiment is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0037] Step 102: Perform performance tests on the unaged electrical equipment sealing structure sample to obtain initial values ​​of performance indicators. The types of performance tests include helium leakage rate test, electrical performance test, physical performance test of sealing material, and microstructure test of sealing material.

[0038] Among them, the unaged power equipment sealing structure sample refers to the original power equipment sealing structure sample that has not undergone performance changes due to environmental factors. The power equipment sealing structure refers to the sealing structure in power equipment that uses elastic sealing rings (such as O-rings, X-rings, and rectangular rings) in conjunction with structural grooves to form a static seal. This sealing structure is usually a single-groove or multi-groove design, including stepped grooves, double grooves, and back-to-back sealing structures. The materials of the sealing rings are mainly elastomers such as EPDM rubber, nitrile rubber, and fluororubber, while the rigid components that the sealing rings mate with are mostly made of metal materials.

[0039] For example, multiple unaged power equipment sealing structure samples are obtained, and various types of performance tests are performed on each unaged power equipment sealing structure sample, such as helium leakage rate test, electrical performance test, physical performance test of sealing material, and microstructure test of sealing material, to obtain initial values ​​of performance indicators.

[0040] Step 104: Use various single-factor aging conditions to conduct single-factor aging experiments on the unaged power equipment sealing structure samples, and conduct performance tests on the power equipment sealing structure samples at a set frequency during the single-factor aging experiments to obtain the updated performance index values ​​corresponding to various single-factor aging experiments.

[0041] In this context, single-factor aging conditions refer to using only a single environmental factor as the aging factor. The set frequency refers to the frequency at which performance indicators are collected. The set frequency can vary for different aging experiments.

[0042] For example, multiple single-factor aging conditions are determined, and each single-factor aging condition corresponds to an unaged power equipment sealing structure sample. For each single-factor aging condition, a single-factor aging experiment is conducted on the corresponding unaged power equipment sealing structure sample using the single-factor aging condition. During the single-factor aging experiment, the performance of the power equipment sealing structure sample is tested at a set frequency to obtain the updated performance index value corresponding to the single-factor aging experiment, thereby obtaining the updated performance index values ​​corresponding to all single-factor aging experiments.

[0043] Step 106: Substitute the updated performance index values ​​of various single-factor aging experiments into the degradation index model to obtain the degradation index value sequence of the corresponding single-factor aging experiments. Randomly select a degradation index value from the degradation index value sequence of each single-factor aging experiment to form a degradation index value group. Select a target degradation index value group from all degradation index value groups. The total degradation index value of the target degradation index value group is equal to the preset total degradation index value. The duration corresponding to each degradation index value in the target degradation index value group is the allocation duration of the corresponding single-factor aging condition. The degradation index model is constructed based on the first difference between the updated performance index value and the initial performance index value, and the second difference between the failure limit value of the performance index and the initial performance index value.

[0044] Among them, the degradation index model is a model that quantifies the contribution of each single-factor aging experiment to the aging of the sample.

[0045] For example, a degradation index model is constructed based on the first difference between the updated performance index value and the initial performance index value, and the second difference between the failure limit value of the performance index and the initial performance index value. The updated performance index value of each single-factor aging experiment is substituted into the degradation index model to obtain the degradation index value sequence for the corresponding single-factor aging experiment, and thus the degradation index value sequence for all single-factor aging experiments. For all single-factor aging experiment degradation index value sequences, a degradation index value is randomly selected from each single-factor aging experiment degradation index value sequence to form a degradation index value group. The degradation index values ​​in each degradation index value group are added together to obtain the corresponding total degradation index value. The degradation index value group whose total degradation index value equals the preset total degradation index value is selected as the target degradation index value group. The duration corresponding to each degradation index value in the target degradation index value group is the allocated duration of the corresponding single-factor aging condition.

[0046] Step 108: Determine the combination of multi-factor aging conditions based on all types of single-factor aging conditions; conduct multi-factor aging experiments on unaged power equipment sealing structure samples according to the allocated duration of each single-factor aging condition in the multi-factor aging condition combination, and perform performance tests at a set frequency to obtain the compression permanent deformation rate curve.

[0047] Among them, the compression permanent deformation rate is the percentage of the amount of deformation that a material cannot recover after compression relative to the original amount of compression.

[0048] For example, all types of single-factor aging conditions are combined to obtain a multi-factor aging condition combination; a multi-factor aging experiment is conducted on the unaged power equipment sealing structure sample according to the multi-factor aging condition combination, wherein each single-factor aging condition in the multi-factor aging condition combination is controlled according to the corresponding allocated duration during the experiment, and performance testing is performed at a set frequency during the multi-factor aging experiment, and a compression permanent deformation rate curve is obtained based on the compression permanent deformation rate in the performance index.

[0049] Step 110: Determine the failure duration of the power equipment sealing structure sample based on the compression set curve, and calculate the activation energy based on the failure duration and the corresponding operating temperature.

[0050] The failure duration is the time from the start of the experiment to the point of functional failure.

[0051] For example, the failure time of the sealing structure sample of the power equipment is obtained from the compression set curve, and the activation energy is calculated using the failure time and the corresponding operating temperature.

[0052] Step 112: Calculate the remaining lifespan of the power equipment sealing structure based on the activation energy and the operating temperature of the power equipment sealing structure during real-time operation of the power grid.

[0053] For example, in actual operating conditions, the operating temperature of the power equipment sealing structure is obtained during real-time operation of the power grid, and the remaining life of the power equipment sealing structure is calculated based on the calculated activation energy and the operating temperature of the power equipment sealing structure.

[0054] In the aforementioned method for predicting the lifespan of power equipment sealing structures based on multi-factor aging experiments, the performance tests include helium leak rate testing, electrical performance testing, physical performance testing of sealing materials, and microstructure testing of sealing materials. Various single-factor aging experiments are conducted on unaged power equipment sealing structure samples, and performance tests are performed on the samples at set frequencies during these experiments. In this approach, combining helium leak rate testing, electrical performance testing, physical performance testing of sealing materials, and microstructure testing of sealing materials provides a more comprehensive reflection of the performance changes of the samples during single-factor aging. This improves the accuracy of subsequent degradation index values. Furthermore, the degradation index model and a preset total degradation index value are used to determine the allocation duration of each single-factor aging condition. Based on all types of single-factor aging conditions, a multi-factor aging condition combination is determined. Based on the allocation duration of each single-factor aging condition in the multi-factor aging condition combination, multi-factor aging experiments are conducted on the unaged power equipment sealing structure samples. In this case, by quantifying the duration of each single factor in the multi-factor aging experiment, the duration of continuous application of each single factor can be more rationally controlled, thus making the aging condition of the samples closer to the aging condition under actual operating conditions. Furthermore, based on the compression set curve, the failure duration of the power equipment sealing structure sample was determined, and the activation energy was calculated based on the failure duration and the corresponding operating temperature. In this case, because the aging condition of the sample is closer to that under actual operating conditions, the compression set curve is closer to the actual compression set change trend, thereby improving the accuracy of failure duration and activation energy, and consequently improving the accuracy of remaining life prediction.

[0055] In an exemplary embodiment, electrical performance testing includes multiple tests such as volume resistivity, dielectric loss tangent, and partial discharge initiation voltage; physical performance testing of the sealing material includes multiple tests such as appearance inspection, mass loss rate, tensile strength, tear strength, elongation at break, compression set, hardness, and water absorption; and microstructure testing of the sealing material includes multiple tests such as Fourier transform infrared spectroscopy, nuclear magnetic resonance, micromorphological observation, and energy dispersive spectroscopy.

[0056] Volume resistivity is the impedance of a unit volume of material to direct current. The tangent of the dielectric loss angle is the ratio of active power loss to reactive power in a dielectric. Partial discharge initiation voltage is the lowest voltage value that allows an insulating material or structure to first exhibit observable partial discharge. Visual inspection includes the macroscopic morphology inspection of power equipment. Tensile strength refers to the maximum ability of a material to resist fracture under axial tensile load. Tear strength is the ability of a material to resist crack propagation. Elongation at break is the percentage increase in length of the fractured portion relative to the original length when the material fractures under tensile stress. Compression set is the permanent deformation of a material that cannot be recovered after compression and removal of the external force.

[0057] In this embodiment, by comprehensively detecting volume resistivity, dielectric loss tangent, partial discharge initiation voltage, appearance inspection, mass loss rate, tensile strength, tear strength, elongation at break, compression set, hardness, water absorption, Fourier transform infrared spectroscopy, nuclear magnetic resonance, microscopic morphology observation and energy dispersive spectroscopy, a more comprehensive evaluation of the sample's performance can be achieved, thereby enriching the data of performance indicators and improving the accuracy of subsequent degradation index values.

[0058] In one exemplary embodiment, the types of single-factor aging tests include compression ratio control tests, periodic compression disturbance tests, thermal cycling tests, humidity cycling tests, medium interface liquid erosion tests, salt spray corrosion tests, and voltage disturbance tests.

[0059] Among these, the compression ratio control experiment is used to simulate the stress state differences of the sealing structure caused by different compression ratios under actual working conditions. The periodic compression disturbance experiment is used to simulate the fatigue effects of stress fluctuations caused by equipment start-up and shutdown, mechanical vibration, etc., on the sealing structure. The medium interface liquid erosion experiment is used to simulate the working conditions where the sealing structure is simultaneously exposed to transformer oil and water vapor during service. The salt spray corrosion experiment is used to simulate the destructive effects of corrosive media on the sealing structure interface and materials during service in coastal or polluted areas. The voltage disturbance experiment is used to simulate the polarization and breakdown risks caused by electric field disturbances in the sealing structure of medium and high voltage power equipment.

[0060] In this embodiment, the compression ratio control experiment, periodic compression disturbance experiment, thermal cycling experiment, humidity cycling experiment, medium interface liquid erosion experiment, salt spray corrosion experiment, and voltage disturbance experiment are used to accurately analyze the aging of the sample under different single-factor aging conditions, and provide accurate data support for subsequent analysis of the allocation time corresponding to different single-factor aging conditions.

[0061] In an exemplary embodiment, each type of performance test corresponds to at least one performance index. The method further includes: before constructing the degradation index model, calculating the correlation coefficient between each performance index and the experimental runtime in each single-factor aging experiment; based on the comparison result between the correlation coefficient and a set threshold, performing dimensionality reduction on the performance index in each single-factor aging experiment to obtain principal component performance indices, wherein the number of types of principal component performance indices is less than the number of types of performance indices before dimensionality reduction; and using the updated value of the principal component performance index of each single-factor aging experiment as the required updated value of the performance index for various single-factor aging experiments.

[0062] The correlation coefficient is a parameter that quantifies the linear relationship between two variables. Principal component performance metrics refer to the performance metrics retained after dimensionality reduction.

[0063] In this embodiment, the performance indicators in each single-factor aging experiment are dimensionality-reduced by comparing the correlation coefficient with a set threshold to obtain the principal component performance indicators. This simplifies the amount of data while maximizing the retention of highly correlated performance indicators, thus improving the efficiency of subsequent calculations while ensuring the accuracy of subsequent calculations.

[0064] In an exemplary embodiment, determining the failure duration of a power equipment sealing structure sample based on a compression set curve includes: obtaining a sealing failure deformation rate threshold, and determining the duration at which the compression set first reaches the sealing failure deformation rate threshold based on the compression set curve, wherein the duration is the failure duration of the power equipment sealing structure sample.

[0065] The sealing failure deformation rate threshold refers to the critical deformation rate threshold at which the material reaches its functional failure point.

[0066] In this embodiment, since the compression permanent deformation rate directly reflects the degree of loss of the material's elastic recovery ability after long-term compression, the time when the compression permanent deformation rate first reaches the sealing failure deformation rate threshold is determined by the compression permanent deformation rate curve, and the failure time is obtained. Thus, the failure time is accurately obtained.

[0067] In an exemplary embodiment, the activation energy is calculated based on the failure duration and the operating temperature corresponding to the failure duration, including: calculating the reciprocal of the failure duration to obtain the degradation rate constant; calculating the quotient between the degradation rate constant and the frequency factor, and calculating the activation energy based on the quotient and the operating temperature corresponding to the failure duration.

[0068] The frequency factor represents the ideal collision rate.

[0069] In this embodiment, the degradation rate constant is obtained through the failure duration, and then the activation energy is calculated by combining the frequency factor and the operating temperature corresponding to the failure duration. Since the accuracy of the failure duration is improved, the accuracy of the activation energy is improved.

[0070] For example, the method for predicting the life of a sealing structure of power equipment based on multi-factor aging experiments specifically includes conducting multi-factor aging experiments and life prediction.

[0071] Among them, such as Figure 2 As shown, the determination process for multi-factor aging experiments includes:

[0072] 1) Initial performance testing: Initial performance testing is conducted on the unaged sealing structure samples of each power equipment (hereinafter referred to as sealing structure samples or samples), and baseline data (i.e., initial values ​​of performance indicators) are recorded. Types of performance testing include helium leak rate testing, electrical performance testing, physical performance testing of sealing materials, and microstructure testing of sealing materials.

[0073] The helium leak rate test utilizes the tracer gas method with a mass spectrometer. Specifically, a carefully cleaned and dried, sealed sample is placed in a chamber containing a pressurized helium mixture and pressurized to allow helium to permeate the interior of the sample chamber. After removal from the pressurized container, the sample is exposed to standard atmospheric conditions to remove adsorbed nitrogen from the sample surface, thus avoiding unacceptable interference signals during the final measurement. The sample is then transferred to a chamber connected to the leak detection system, and the pressure inside the chamber is reduced to the range where the mass spectrometer can operate normally. Considering the difference in external atmospheric pressure, the measured helium leak rate R can be determined by comparing it with a calibrated standard leak. The measured helium leak rate is then converted into an equivalent standard leak rate (i.e., the helium leak rate under standard atmospheric pressure) for comparison of samples of the same volume tested under different conditions.

[0074] Electrical performance testing includes various tests such as volume resistivity, dielectric loss tangent (tanδ), partial discharge initiation voltage, polarization current, and dielectric constant (ε′). Physical performance testing for sealing materials includes various tests such as appearance inspection, mass loss rate, tensile strength, tear strength, elongation at break, compression set, hardness, and water absorption. Microstructure testing for sealing materials includes various tests such as Fourier transform infrared spectroscopy, nuclear magnetic resonance, microstructure observation, and energy dispersive spectroscopy. Each test category corresponds to at least one performance index; for example, the volume resistivity test corresponds to volume resistivity, and the compression set test corresponds to compression set rate. Performance indices corresponding to appearance inspection include volume expansion rate, surface roughness, and surface corrosion degree.

[0075] 2) Single-factor accelerated aging test: For unaged sealed structure samples, the samples were placed under set aging conditions to conduct the following single-factor aging tests: compressibility control test, periodic compression disturbance test, thermal cycling test, humidity cycling test, medium interface liquid erosion test, salt spray / chemical corrosion test, and voltage disturbance test. After each round of aging tests, samples were periodically sampled for intermediate performance testing, i.e., performance index update values ​​were collected according to the set frequency, and the changes in each performance index were recorded. Detailed records were kept of the test data from each sampling to ensure the completeness and accuracy of the data.

[0076] 21) Compression Ratio Control Experiment: This experiment simulates the stress state differences in the sealing structure caused by different compression ratios under actual installation conditions. The sample is installed in a dedicated compression fixture, and three initial compression ratios are set (e.g., 20%, 25%, 30%). The sample is then aged at room temperature for 30 days. Each cycle lasts 6 days, and performance indicators are tested, including sealing performance (e.g., leakage rate), compression set, and changes in interface morphology (obtained through microscopic morphology observation). The impact of compression ratio on long-term sealing performance is analyzed.

[0077] 22) Periodic Compression Disturbance Experiment: This experiment simulates the fatigue effect of stress fluctuations caused by equipment start-up and shutdown, mechanical vibration, etc., on the sealing structure. A mechanical loading device is used to apply a ±10% periodic compression disturbance to the sealing structure sample at a frequency of 1Hz, with 10 cycles per day. 5 The total aging cycle was 7 days. Samples were taken daily, and performance indicators tested included sealing performance, elastic recovery ability (such as compression set), and microcrack development (obtained through microscopic morphology observation), to analyze the performance degradation characteristics caused by fatigue.

[0078] 23) Thermal Cycling Experiment: Simulating the stress changes caused by thermal expansion and contraction during equipment operation. Samples are placed in a temperature-controlled test chamber and subjected to cyclic thermal cycling from -20℃ to +120℃, with each cycle consisting of a 2-hour heating and 2-hour cooling period. The experiment lasts for 10 days, with a total of 60 thermal cycles. Samples are taken every 2 days, and performance indicators tested include compressive strength, volumetric expansion rate (obtained through visual inspection), and interfacial debonding (obtained through visual inspection).

[0079] 24) Humidity Cycling Experiment: Simulates the impact of outdoor morning and evening humidity fluctuations and the moisture absorption-drying process on sealing materials. Samples are placed in a constant temperature test environment (fixed temperature 40℃), and humidity is periodically varied: low humidity (relative humidity RH 60%) for 2 hours → high humidity (relative humidity RH 93%) for 2 hours, continuously cycling through these humidity levels for a total of 15 days. Samples are taken every 3 days, and performance indicators tested include changes in mass (e.g., mass loss rate), moisture absorption rate, surface roughness (obtained through visual inspection), and electrical performance indicators (e.g., volume resistivity, dielectric loss tangent tanδ).

[0080] 25) Salt spray / chemical corrosion test: Simulates the destructive effects of corrosive media on the sealing structure interface and materials during service in coastal or polluted areas. Continuous salt spraying with a 5% NaCl solution is used at an ambient temperature of 35℃ for 96 hours. After the experiment, the samples are cleaned, dried, and restored before undergoing sealing performance testing, metal / rubber interface peel strength testing, and surface corrosion degree analysis (obtained through visual inspection).

[0081] 26) Liquid Erosion Test at the Medium Interface: This test simulates the operating conditions of a sealed structure simultaneously exposed to transformer oil and water vapor during service. One side of the sample is immersed in transformer oil, while the other side is immersed in distilled water, creating a liquid-liquid interface environment. The experimental temperature is set at 60℃, and the total duration is 20 days. Samples are taken every 4 days to determine the degree of interface damage (obtained through visual inspection), oil / water absorption rate, and changes in mechanical properties (obtained through physical property testing). The test analyzes the trend of seal deterioration caused by liquid penetration.

[0082] 27) Voltage Disturbance Test: For sealed structures used in medium and high voltage power equipment, this test simulates the polarization and breakdown risks caused by electric field disturbances. The sample is fixed on a test platform, and an alternating voltage (10kV, 0.1Hz) is applied to both sides for a continuous aging period of 10 days. Dielectric properties such as volume resistivity, partial discharge initiation voltage (PDIV), and tanδ are measured every two days to assess the degradation of insulation performance under the influence of the electric field.

[0083] 3) Determine the allocation duration:

[0084] 31) Establish the failure distribution law of physical property parameters (i.e., performance indicators):

[0085] This embodiment proposes a three-stage coupled path identification model of "aging factor - physical response - change in physical properties". This model clarifies the mapping relationship between different aging factors (also known as aging factors or single-factor aging conditions) and performance indicators, as shown in Table 1. It should be noted that the contents of Table 1 are only a partial illustration. Through staged testing and comparison with physical-electrical properties, the degradation path and key indicators of the sealed structure samples under each single factor can be intuitively analyzed.

[0086] Table 1. Mapping Relationship between Different Aging Factors and Performance Indicators

[0087]

[0088] 32) Screening and dimensionality reduction of degradation features (i.e., performance indicators):

[0089] To identify the most representative and sensitive degradation indicators among various aging factors, the Pearson correlation coefficient r was used to characterize the correlation between degradation characteristics and experimental run time, where the correlation coefficient r satisfies:

[0090] ;

[0091] Where n is the number of sampling points, x i The performance metrics collected for the i-th sampling point For performance index x i The mean, t i Let be the experimental duration (i.e., the experimental running time) for the i-th sampling point. The mean of the experiment duration is given. r is the correlation coefficient. The correlation between each performance index and the experiment duration is analyzed, and the correlation coefficient between each performance index and the experiment duration is calculated. Using the correlation coefficient r and the number of sampling points n, the significance p-value is calculated according to the t-distribution hypothesis testing method. When the significance p-value is less than 0.05, it indicates that there is a significant correlation between the two variables at a 95% confidence level; when the significance p-value is less than 0.01, it indicates that the correlation between the two variables is more significant at a 99% confidence level. Multiple performance indices with collinearity and redundancy (such as tanδ, ε′, PDIV, volume resistivity, compressive permanent deformation rate, etc.) are subjected to principal component dimensionality reduction. If |r| ≥ 0.90, the performance indices are collinear; if the r value is close to ±1, the performance indices are redundant. After dimensionality reduction, 1-2 performance indices remain. These 1-2 performance indices are used as principal component performance indices (i.e., sensitive degradation indices) and substituted into the degradation index model to improve the simplicity of the evaluation and the efficiency of the modeling.

[0092] 33) Constructing a degradation index model:

[0093] To address the issues of varying rates of change and different units of measurement for different performance indicators, this embodiment proposes a method for constructing a unified "deterioration index D value" across multiple indicators. The core formula is as follows:

[0094] ;

[0095] Where D(t) is the degradation index value under experimental duration t, characterizing the degree of degradation of the sample and reflecting the aging process of the sealing structure, with a value range of [0,1]. N is the number of types of performance indicators of the main components. The indicator weight is assigned based on relevance weights (e.g., equal to the correlation coefficient of the i-th principal component performance indicator divided by the sum of correlation coefficients, where the sum of correlation coefficients is the sum of the correlation coefficients of all principal component performance indicators); X i (t) represents the measured value of the performance index of the i-th principal component after aging for experimental duration t (i.e., the updated value of the principal component performance index collected under experimental duration t); X i0 Let X be the initial value of the performance index of the i-th principal component; imax Let D(t) be the failure limit value of the performance index of the i-th principal component; when D(t) > 0.9, it means that the sample is close to the critical failure state. This model can normalize each performance index into a "unified aging evaluation quantity", which makes the aging process under the influence of multiple factors visible, quantifiable and predictable, and has universality and practicality.

[0096] Referring to the degradation index model constructed above, after completing each single-factor aging experiment, the corresponding degradation index value is calculated based on the degree of influence of each aging factor on each performance index at different time periods (i.e., different experimental durations). Therefore, under the action of the j-th type of aging factor, the degradation index value D at experimental duration t is... j (t) satisfies:

[0097] ;

[0098] Among them, the degradation index value D j (t) is used to characterize the degree of normalization degradation. X ij (t) represents the measured value of the i-th principal component performance index under the j-th aging factor after an experimental duration t. Substituting the measured values ​​of the principal component performance indexes, which are continuously updated according to a set frequency, into this formula, we obtain the degradation index value sequence D under the j-th aging factor. j .

[0099] In the design of a multi-factor coupled experiment (i.e., a multi-factor aging test), the target degree of degradation (i.e., the preset total degradation index value) is set as D. total The allocation time of various aging factors can then be determined according to the following formula:

[0100] ;

[0101] Where M represents the number of single-factor aging conditions. This refers to the duration of the experiment. The degradation index value generated by the j-th aging factor alone represents the degradation contribution of the j-th aging factor. For the degradation index value sequence D j A degradation index value is given, and the duration corresponding to this degradation index value is the duration of the experiment. D total The desired uniform aging level (e.g., D) for multi-factor coupling experiments total A value of 0.9 indicates that the sample has reached the critical failure boundary. (D) j,max This represents the upper limit of the degradation contribution caused solely by the j-th aging factor. Among these, the contribution of all aging factors... This constitutes the target degradation index value group. Experiment runtime. This is the required allocation time. It should be noted that the sampling frequency for collecting the measured values ​​of performance indicators is different for each single-factor aging experiment, so the allocation time for each single-factor aging experiment may not be the same.

[0102] To simplify the solution process, in the initial stage, the curves fitted by the degradation index values ​​of each aging factor can be approximated as linear growth, i.e. ;

[0103] Then, by working backwards, the required allocation time for each aging factor is:

[0104] ;

[0105] in, The degradation rate per unit time for the j-th type of aging factor obtained through experimental fitting is given by [the relevant data point]. Let be the weight of the j-th aging factor in the overall degradation. This weight can be allocated based on its Pearson correlation coefficient with the index change (after calculating the average correlation for each aging factor, the average correlation of each aging factor is summed to obtain the weight of the corresponding aging factor, where the average correlation sum is the sum of the average correlations of all aging factors). Using this method, the "equivalent action time" of each factor in the coupled aging experiment can be quantified, thereby reasonably controlling the duration of each aging environment, reducing unnecessary redundancy, and ensuring that the contribution of each factor to the overall degradation degree is within the experimental target control range.

[0106] 4) Multi-factor artificial accelerated aging experiment:

[0107] For unaged sealed structure samples, a multi-factor aging experiment was conducted under set aging conditions. The allocation time for each aging factor was determined according to the degradation index model to ensure the aging effect remained within an acceptable time range. In this embodiment, the multi-factor aging test was a coupled aging test involving temperature, humidity, compression disturbance, liquid medium, salt spray, and electric field. A compression ratio was set to simulate the actual assembly state, maintaining the sealed structure under long-term pressure. Equal volumes of transformer oil and distilled water were injected into the upper and lower sides of the sealing structure in the compression fixture to simulate periodic compression disturbance. After the simulation, the sealed structure was placed in a salt spray aging test chamber for an electric field-salt spray-temperature-humidity aging test. During the multi-factor aging test, each aging factor was controlled according to the allocated time obtained above, and performance tests were conducted at a set frequency. The compression set rate was selected from the collected performance indicators to obtain a compression set rate curve.

[0108] In this embodiment, lifetime prediction specifically includes: using kinetic curve fitting to the compressive permanent deformation rate of a multi-factor coupled experiment, thereby solving for the Arrhenius activation energy E. a The solution formula is shown below:

[0109] ;

[0110] Among them, E a t represents the activation energy of the degradation reaction in the sealed sample; T is the aging temperature; and k is the degradation rate constant. a The failure duration is given. A is the frequency factor (i.e., the pre-exponential factor). R is the gas constant.

[0111] By analyzing the compression permanent deformation rate curve, the failure point (i.e., the point where the compression permanent deformation rate first reaches the sealing failure deformation rate threshold) is located on the curve. The failure time corresponding to this failure point is then obtained and substituted into the failure duration t. a We derive k, then substitute it with the experimental temperature at the failure point time to solve for the activation energy E. a Calculate the activation energy E. a Then, under actual working conditions, the actual operating temperature of the sealing structure is substituted into the above formula to calculate the failure time, which is the remaining life of the sealing structure.

[0112] The method in this embodiment takes into account that the sealing structure is affected by a variety of factors in actual use, such as temperature, humidity, salt spray, and chemical media. Aging tests based on a single factor are insufficient to fully simulate actual working conditions. Therefore, based on the results of single-factor aging experiments, the main deterioration factors (i.e., aging factors) are identified, and the allocation duration of each single factor in the multi-factor aging test is determined to conduct a multi-factor coupled aging test on the sealing structure of power equipment. This ensures that the aging effect is within an acceptable time range, resulting in more accurate results. The multi-factor comprehensive accelerated aging experiment utilized in this embodiment is more closely aligned with actual working conditions, and the calculated activation energy is more accurate, thereby enabling a more precise prediction of the service life and reliability of the sealing structure.

[0113] Furthermore, this embodiment considers that during long-term operation of power equipment, the fit relationship of the sealing structure is closely related to its aging resistance, which is a key factor in ensuring the sealing performance and operational reliability of the equipment. The design of the sealing structure needs to comprehensively consider the material properties, geometric dimensions, tolerance fit methods, assembly stress distribution, and stability under various aging environments of the sealing components. Currently, commonly used sealing materials in power equipment mainly include nitrile rubber (NBR), fluororubber (FKM), ethylene propylene diene monomer (EPDM), and silicone rubber (VMQ). These materials typically form static or dynamic seals with metal structural components and are widely used in equipment such as power transformers, circuit breakers, cable terminals, and combined electrical appliances. The fit method of the sealing structure (such as compression ratio, groove design, contact area, etc.) directly affects its sealing performance and service life. However, in actual service environments, the sealing structure is simultaneously subjected to the coupled effects of multiple factors such as heat, humidity, salt spray, ultraviolet radiation, chemical corrosion, and mechanical stress. Under these complex stress conditions, the sealing material and the mating interface age, harden, experience stress relaxation, or undergo interface desorption, leading to gradual degradation or even failure of the sealing performance. Currently, systematic research on the relationship between sealing structure fit and environmental aging is still relatively weak in the industry. Traditional artificial aging tests are mostly based on testing single material samples, ignoring the influence of sealing structure fit and interface effects, resulting in a large gap between the obtained life prediction results and the actual results. There is a lack of aging adaptability research for different sealing structure fit methods. At the same time, different testing units have problems such as inconsistent selection of environmental factors, unclear coupling modes, and subjective parameter settings in the design of multi-factor aging tests.

[0114] In summary, to better improve the safety and stability of the power system under the same actual operating conditions, the multi-factor aging test in this embodiment is used to optimize the sealing fit of the power equipment, so as to achieve the best sealing performance. The specific optimization method is as follows:

[0115] Considering the sealing parameters involved, such as sealing ring material, sealing ring compression, filler ratio, roughness, chamfer, and contact material, different sealing structure samples of power equipment are obtained based on different combinations of these parameters. Using the multi-factor aging test in this embodiment, each power equipment sealing structure sample is aged to obtain the corresponding compression set curve, thereby identifying the corresponding functional failure point. The sample with the longest duration corresponding to the functional failure point has the longest lifespan and the optimal combination of fitting parameters. This optimal combination of fitting parameters is then used to optimize the sealing structure. In this case, an accelerated aging test method is considered for the sealing structure fit and its performance degradation process under multi-factor coupling conditions. The correlation between fitting parameters and aging behavior is clarified, the design of the sealing structure is optimized, material selection and product design are guided, the operational safety and stability of the equipment are improved, and maintenance costs are reduced.

[0116] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0117] Based on the same inventive concept, this application also provides a device for predicting the lifespan of power equipment sealing structures based on multi-factor aging tests, used to implement the aforementioned method for predicting the lifespan of power equipment sealing structures based on multi-factor aging tests. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for predicting the lifespan of power equipment sealing structures based on multi-factor aging tests provided below can be found in the limitations of the method for predicting the lifespan of power equipment sealing structures based on multi-factor aging tests described above, and will not be repeated here.

[0118] In one exemplary embodiment, such as Figure 3 As shown, a power equipment sealing structure life prediction device based on multi-factor aging test is provided, including: an initial performance test module 302, a single-factor aging test module 304, an allocation duration determination module 306, a multi-factor aging test module 308, a data analysis module 310, and a life prediction module 312, wherein:

[0119] The initial performance test module 302 is used to perform performance tests on unaged electrical equipment sealing structure samples to obtain initial values ​​of performance indicators. The types of performance tests include helium leak rate test, electrical performance test, physical performance test of sealing material, and microstructure test of sealing material.

[0120] The single-factor aging test module 304 is used to conduct single-factor aging tests on unaged power equipment sealing structure samples under various single-factor aging conditions, and to perform performance tests on the power equipment sealing structure samples at a set frequency during the single-factor aging test to obtain updated performance index values ​​corresponding to various single-factor aging tests.

[0121] The allocation duration determination module 306 is used to substitute the updated performance index values ​​of various single-factor aging experiments into the degradation index model to obtain the degradation index value sequence of the corresponding single-factor aging experiment. From the degradation index value sequence of each single-factor aging experiment, a degradation index value is randomly selected to form a degradation index value group. From all degradation index value groups, a target degradation index value group is selected, where the total degradation index value of the target degradation index value group is equal to the preset total degradation index value. The duration corresponding to each degradation index value in the target degradation index value group is the allocation duration of the corresponding single-factor aging condition. The degradation index model is constructed based on the first difference between the updated performance index value and the initial performance index value, and the second difference between the failure limit value of the performance index and the initial performance index value.

[0122] The multi-factor aging test module 308 is used to determine the combination of multi-factor aging conditions based on all types of single-factor aging conditions; and to conduct multi-factor aging tests on unaged power equipment sealing structure samples according to the allocated duration of each single-factor aging condition in the multi-factor aging condition combination, and to perform performance tests at a set frequency to obtain a compression permanent deformation rate curve.

[0123] The data analysis module 310 is used to determine the failure duration of the power equipment sealing structure sample based on the compression set curve, and to calculate the activation energy based on the failure duration and the corresponding operating temperature.

[0124] The life prediction module 312 is used to calculate the remaining life of the power equipment sealing structure based on the activation energy and the operating temperature of the power equipment sealing structure during real-time operation of the power grid.

[0125] In an exemplary embodiment, the initial performance testing module 302 includes various tests for electrical performance, such as volume resistivity, dielectric loss tangent, and partial discharge initiation voltage; various tests for physical performance of the sealing material, such as appearance inspection, mass loss rate, tensile strength, tear strength, elongation at break, compression set, hardness, and water absorption; and various tests for microstructure of the sealing material, such as Fourier transform infrared spectroscopy, nuclear magnetic resonance, micromorphological observation, and energy dispersive spectroscopy.

[0126] In an exemplary embodiment, the single-factor aging test module 304 includes various types of single-factor aging tests, such as compression ratio control test, periodic compression disturbance test, thermal cycling test, humidity cycling test, medium interface liquid erosion test, salt spray corrosion test, and voltage disturbance test.

[0127] In an exemplary embodiment, each type of performance test corresponds to at least one performance index. The device further includes a dimensionality reduction module, which is used to calculate the correlation coefficient between each performance index and the experimental runtime in each single-factor aging experiment before constructing the degradation index model. Based on the comparison result between the correlation coefficient and a set threshold, the performance index in each single-factor aging experiment is dimensionality reduced to obtain principal component performance indices. The number of types of principal component performance indices is less than the number of types of performance indices before dimensionality reduction. The updated value of the principal component performance index of each single-factor aging experiment is used as the required updated value of the performance index of various single-factor aging experiments.

[0128] In an exemplary embodiment, the data analysis module 310 determines the failure duration of the power equipment sealing structure sample based on the compression set curve, including: obtaining the sealing failure deformation rate threshold, and determining the duration at which the compression set first reaches the sealing failure deformation rate threshold based on the compression set curve, wherein the duration is the failure duration of the power equipment sealing structure sample.

[0129] In an exemplary embodiment, the lifetime prediction module 312 calculates the activation energy based on the failure duration and the operating temperature corresponding to the failure duration, including: calculating the reciprocal of the failure duration to obtain the degradation rate constant; calculating the quotient between the degradation rate constant and the frequency factor, and calculating the activation energy based on the quotient and the operating temperature corresponding to the failure duration.

[0130] The modules in the aforementioned power equipment sealing structure life prediction device based on multi-factor aging experiments can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0131] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for predicting the lifespan of a power equipment sealing structure based on multi-factor aging experiments. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0132] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0133] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0134] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0135] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0136] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0137] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0138] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for predicting the lifespan of sealing structures in power equipment based on multi-factor aging experiments, characterized in that, The method includes: Performance tests were conducted on unaged electrical equipment sealing structure samples to obtain initial values ​​of performance indicators. The types of performance tests included helium leak rate testing, electrical performance testing, physical performance testing of sealing materials, and microstructure testing of sealing materials. Single-factor aging experiments were conducted on the unaged power equipment sealing structure samples under various single-factor aging conditions. The performance of the power equipment sealing structure samples was tested at a set frequency during the single-factor aging experiments to obtain the updated performance index values ​​corresponding to various single-factor aging experiments. Substituting the updated performance index values ​​of various single-factor aging experiments into the degradation index model yields a degradation index value sequence for each single-factor aging experiment. A degradation index value is randomly selected from each single-factor aging experiment's degradation index value sequence to form a degradation index value group. A target degradation index value group is selected from all degradation index value groups, where the total degradation index value of the target degradation index value group is equal to a preset total degradation index value. The duration corresponding to each degradation index value in the target degradation index value group is the allocated duration of the corresponding single-factor aging condition. The degradation index model is constructed based on the first difference between the updated performance index value and the initial performance index value, and the second difference between the failure limit value of the performance index and the initial performance index value. Based on all types of single-factor aging conditions, a combination of multi-factor aging conditions is determined; based on the allocated duration of each single-factor aging condition in the combination of multi-factor aging conditions, a multi-factor aging experiment is conducted on the unaged power equipment sealing structure sample, and performance tests are performed at a set frequency to obtain a compression set curve. Based on the compression set curve, the failure duration of the power equipment sealing structure sample is determined, and the activation energy is calculated based on the failure duration and the corresponding operating temperature. The remaining lifespan of the power equipment sealing structure is calculated based on the activation energy and the operating temperature of the power equipment sealing structure during real-time operation of the power grid.

2. The method according to claim 1, characterized in that, The electrical performance tests include multiple tests such as volume resistivity, dielectric loss tangent, and partial discharge initiation voltage; the physical performance tests of the sealing material include multiple tests such as appearance inspection, mass loss rate, tensile strength, tear strength, elongation at break, compression set, hardness, and water absorption; the microstructure tests of the sealing material include multiple tests such as Fourier transform infrared spectroscopy, nuclear magnetic resonance, micromorphological observation, and energy dispersive spectroscopy.

3. The method according to claim 1, characterized in that, The types of single-factor aging experiments include compression ratio control experiments, periodic compression disturbance experiments, thermal cycling experiments, humidity cycling experiments, medium interface liquid erosion experiments, salt spray corrosion experiments, and voltage disturbance experiments.

4. The method according to claim 1, characterized in that, Each type of performance test corresponds to at least one performance metric, and the method further includes: Before constructing the degradation index model, the correlation coefficient between each performance index and the experimental runtime was calculated in each single-factor aging experiment. Based on the comparison results between the correlation coefficient and the set threshold, the performance index in each single-factor aging experiment is dimensionality reduced to obtain the principal component performance index. The number of types of the principal component performance index is less than the number of types of the performance index before dimensionality reduction. The updated value of the principal component performance index of each single-factor aging experiment is used as the required updated value of the performance index of various single-factor aging experiments.

5. The method according to claim 1, characterized in that, The step of determining the failure duration of the power equipment sealing structure sample based on the compression set curve includes: Obtain the sealing failure deformation rate threshold, and determine the time when the compression permanent deformation rate first reaches the sealing failure deformation rate threshold based on the compression permanent deformation rate curve. The time is the failure time of the power equipment sealing structure sample.

6. The method according to claim 1, characterized in that, The calculation of the activation energy based on the failure duration and the corresponding operating temperature includes: The degradation rate constant is obtained by calculating the reciprocal of the failure duration. The quotient between the degradation rate constant and the frequency factor is calculated, and the activation energy is calculated based on the quotient and the operating temperature corresponding to the failure duration.

7. A device for predicting the lifespan of a sealing structure of power equipment based on multi-factor aging experiments, characterized in that, The device includes: The initial performance testing module is used to perform performance tests on unaged electrical equipment sealing structure samples to obtain initial values ​​of performance indicators. The types of performance tests include helium leak rate testing, electrical performance testing, physical performance testing of sealing materials, and microstructure testing of sealing materials. The single-factor aging test module is used to conduct single-factor aging tests on the unaged power equipment sealing structure sample under various single-factor aging conditions, and to perform performance tests on the power equipment sealing structure sample at a set frequency during the single-factor aging test to obtain the updated performance index values ​​corresponding to various single-factor aging tests. The allocation duration determination module is used to substitute the updated performance index values ​​of various single-factor aging experiments into the degradation index model to obtain a degradation index value sequence for the corresponding single-factor aging experiment. A degradation index value is randomly selected from each single-factor aging experiment's degradation index value sequence to form a degradation index value group. A target degradation index value group is selected from all degradation index value groups, where the total degradation index value of the target degradation index value group is equal to a preset total degradation index value. The duration corresponding to each degradation index value in the target degradation index value group is the allocation duration for the corresponding single-factor aging condition. The degradation index model is constructed based on the first difference between the updated performance index value and the initial performance index value, and the second difference between the failure limit value of the performance index and the initial performance index value. The multi-factor aging test module is used to determine the combination of multi-factor aging conditions based on all types of single-factor aging conditions; according to the allocated duration of each single-factor aging condition in the multi-factor aging condition combination, the module performs multi-factor aging tests on the unaged power equipment sealing structure sample and conducts performance tests at a set frequency to obtain a compression set curve. The data analysis module is used to determine the failure duration of the power equipment sealing structure sample based on the compression set curve, and to calculate the activation energy based on the failure duration and the operating temperature corresponding to the failure duration. The life prediction module is used to calculate the remaining life of the power equipment sealing structure based on the activation energy and the operating temperature of the power equipment sealing structure during real-time operation of the power grid.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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