Insulation material life prediction method, device, storage medium and computer equipment

Abstract

The present invention discloses a method, device, storage medium and computer equipment for predicting the life of an insulating material, comprising: setting preset environmental conditions required for a test in a thermal aging test space; controlling a pulse generating device to sequentially apply multi-stage pulse voltages to the insulating material to be predicted in the thermal aging test space, so as to sequentially perform multi-stage radiation on the insulating material to be predicted using the pulse voltage, collecting electrical insulation process parameters and thermal insulation process parameters of the insulating material to be predicted during each radiation stage, and collecting electrical insulation index parameters and thermal insulation index parameters of the insulating material to be predicted after each radiation stage; and determining the insulation life of the insulating material to be predicted based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage, as well as the electrical insulation index parameters and thermal insulation index parameters after each radiation stage.

Description

Technical Field

[0001] The present invention relates to the technical field of high voltage testing, and in particular to a method, device, storage medium and computer equipment for predicting the life of an insulating material. Background Art

[0002] The high-voltage electrical penetrations in hot-cells are part of the hot-cell walls. Relying on excellent shielding and sealing capabilities, they form a safety barrier that prevents the leakage of radioactive materials within the hot-cell and protects the health of operating personnel. High-voltage conductor assemblies, the primary functional components of these penetrations, are made of polymer insulation materials that exhibit high breakdown field strength and low dielectric loss. However, over the long term, these insulation materials inevitably age, leading to increased transmission losses, decreased insulation performance, and even failure, potentially causing equipment failures and accidents. Therefore, studying the lifespan of the insulation materials in the conductor assemblies of hot-cell high-voltage electrical penetrations has important engineering applications.

[0003] Currently, insulation material lifespan is typically assessed based on a single aging factor. However, in actual engineering situations, multiple factors influence insulation material aging. Therefore, this method of assessing insulation material lifespan based solely on a single aging factor is inaccurate and unsuitable for actual engineering situations, making it of little reference value. Summary of the Invention

[0004] The present invention provides a method, device, storage medium and computer equipment for predicting the life of an insulating material, which are mainly capable of improving the prediction accuracy of the life of an insulating material and making the predicted life of the insulating material more valuable for reference.

[0005] According to a first aspect of the present invention, a method, apparatus, storage medium, and computer equipment for predicting the life of an insulating material are provided, comprising:

[0006] Applicable to an aging test system, the aging test system includes a pulse generating device, a thermal aging test space, an insulation process parameter measuring device, and an insulation index parameter measuring device, including:

[0007] Setting preset environmental conditions required for the test for a thermal aging test space containing the insulation material to be predicted, wherein the preset environmental conditions include temperature conditions and humidity conditions, wherein the preset environmental conditions are determined based on an actual working thermal chamber environment corresponding to the insulation material to be predicted, and there are multiple insulation materials to be predicted;

[0008] Controlling the pulse generating device to sequentially apply a pulse voltage of multiple stages of preset frequency, preset amplitude, and preset duty cycle to the insulation material to be predicted in the thermal aging test space, so as to sequentially perform multi-stage radiation on the insulation material to be predicted using the pulse voltage, controlling the insulation process parameter measuring device to collect in real time the electrical insulation process parameters and thermal insulation process parameters of the insulation material to be predicted during each radiation stage, and controlling the insulation index parameter measuring device to collect the electrical insulation index parameters and thermal insulation index parameters of the insulation material to be predicted after each radiation stage, wherein the pulse voltage in each radiation stage has the same preset frequency, the same preset amplitude, the same preset duty cycle, the same radiation duration, and the same voltage waveform;

[0009] The insulation life of the insulation material to be predicted is determined based on the electrical insulation process parameters and the thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and the thermal insulation index parameters after each radiation stage.

[0010] According to a second aspect of the present invention, there is provided an insulating material life prediction device, characterized in that it is applied to an aging test system, wherein the aging test system includes a pulse generating device, a thermal aging test space, an insulation process parameter measuring device, and an insulation index parameter measuring device, including:

[0011] a setting unit, configured to set preset environmental conditions required for the test for a thermal aging test space containing the insulation material to be predicted, wherein the preset environmental conditions include temperature conditions and humidity conditions, wherein the preset environmental conditions are determined based on an actual working thermal chamber environment corresponding to the insulation material to be predicted, and the number of insulation materials to be predicted is multiple;

[0012] An aging test unit is configured to control the pulse generating device to sequentially apply a pulse voltage of multiple stages of preset frequency, preset amplitude, and preset duty cycle to the insulating material to be predicted in the thermal aging test space, so as to sequentially perform multi-stage radiation on the insulating material to be predicted using the pulse voltage, control the insulation process parameter measuring device to collect in real time the electrical insulation process parameters and thermal insulation process parameters of the insulating material to be predicted during each radiation stage, and control the insulation index parameter measuring device to collect the electrical insulation index parameters and thermal insulation index parameters of the insulating material to be predicted after each radiation stage, wherein the pulse voltage in each radiation stage has the same preset frequency, the same preset amplitude, the same preset duty cycle, the same radiation duration, and the same voltage waveform;

[0013] The determination unit is used to determine the insulation life of the insulation material to be predicted based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and thermal insulation index parameters after each radiation stage.

[0014] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which implements the above insulation material life prediction method when executed by a processor.

[0015] According to a fourth aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above insulation material life prediction method when executing the program.

[0016] According to the present invention, a method, apparatus, storage medium, and computer device for predicting the life of an insulating material are provided. Compared with the current method of evaluating the life of an insulating material based solely on a single aging condition, the present invention constructs an aging test system and sets the thermal stress environmental conditions, such as temperature and humidity, required for the aging test in a thermal aging test space within the aging test system. Based on this, a multi-stage pulse voltage is applied to the insulating material to be predicted in the thermal aging test space for radiation. The electrical insulation process parameters and thermal insulation process parameters during each radiation process, as well as the electrical insulation index parameters and thermal insulation index parameters after each radiation stage, are collected. Ultimately, the insulation life of the insulating material to be predicted is determined based on the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters corresponding to each radiation stage. Thus, by constructing the aging test system, the insulating material is subjected to aging tests under temperature aging conditions, humidity aging conditions, and voltage aging conditions. That is, during the aging test process, multiple factors affecting the life of the insulating material and the coupling relationships between these factors are comprehensively analyzed, thereby improving the accuracy of the prediction of the insulation material life, thereby ensuring that the predicted insulation material life is more valuable for reference in actual engineering practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 A flowchart of a method, device, storage medium, and computer equipment for predicting the life of an insulating material provided by an embodiment of the present invention is shown;

[0019] Figure 2 A schematic structural diagram of an aging test system provided by an embodiment of the present invention is shown;

[0020] Figure 3 A flowchart of another insulation material life prediction method, device, storage medium, and computer equipment provided by an embodiment of the present invention is shown;

[0021] Figure 4 A schematic structural diagram of an insulating material life prediction device provided by an embodiment of the present invention is shown;

[0022] Figure 5 A schematic structural diagram of another device for predicting the life of an insulating material provided by an embodiment of the present invention is shown;

[0023] Figure 6 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0024] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0025] At present, the life of insulation materials is evaluated based on only a single factor aging condition. Other factors that affect the life of insulation materials are not considered, resulting in inaccurate prediction of the life of insulation materials.

[0026] In order to solve the above problems, the embodiments of the present invention provide a method, device, storage medium and computer equipment for predicting the life of an insulating material, such as Figure 1 As shown, the method includes:

[0027] 101. Construct an aging test system, which includes a pulse generator, a thermal aging test space, an insulation process parameter measuring device, and an insulation index parameter measuring device.

[0028] In order to improve the prediction accuracy of the insulation material life and make the predicted life more practical in engineering, it is necessary to simulate the aging test of the insulation material under the actual engineering environment and estimate the life of the insulation material based on the simulation results. Based on this, it is necessary to first build an aging test system, such as Figure 2 As shown in the figure, the aging test system includes: a pulse generator (high-frequency and high-voltage pulse generator), a thermal aging test space (aging test chamber), an insulation process parameter measuring device (ultra-high frequency partial discharge sensor, thermocouple temperature sensor, leakage current sensor, color sensor, gravity sensor, visual sensor, etc.), and an insulation index parameter measuring device (insulation resistance tester, dielectric loss factor meter, temperature meter, tensile testing machine, density meter, hardness tester, etc.). For simplicity, Figure 2Only part of the measuring device is shown in the figure, and the aging test box contains a high-voltage conductor assembly with the insulation material to be predicted. Among them, the pulse generating device can output high-frequency and high-voltage pulses in a single stage. Specifically, the insulation material to be predicted is placed in a thermal aging test space, and then the thermal aging test space is set with environmental conditions that are the same as the actual engineering environment to simulate thermal aging test conditions such as temperature and humidity. Then, the pulse generating device is used to radiate pulse voltage to the thermal aging test space to simulate high-frequency and high-voltage electrical aging test conditions. Then, under the coupling effect of the thermal aging test conditions and the electrical aging test conditions, the insulation material to be predicted is subjected to an aging test to predict the life of the insulation material. The aging test conditions are set by comprehensively analyzing the various influencing factors (including temperature, humidity, high frequency and high voltage) that affect the life of the insulation material. In the process of predicting the life of the insulation material, not only the individual influences of the various influencing factors are considered, but also the coupling influences between the various influencing factors are considered, thereby improving the prediction accuracy of the life of the insulation material.

[0029] 102. Set the preset environmental conditions required for the test for the thermal aging test space containing the insulation material to be predicted, wherein the preset environmental conditions include temperature conditions and humidity conditions. The preset environmental conditions are determined based on the actual working hot chamber environment corresponding to the insulation material to be predicted, and the number of insulation materials to be predicted is multiple.

[0030] The high-voltage electrical penetrations in the hot chamber are part of the hot chamber wall. Relying on excellent shielding and sealing capabilities, they form a safety barrier to prevent the leakage of radioactive materials within the hot chamber and protect the health of operating personnel. The high-voltage conductor assembly, the primary functional component of the high-voltage electrical penetrations in the hot chamber, contains polymer insulation materials with high breakdown field strength and low dielectric loss. Therefore, the insulation material to be predicted in the embodiments of the present invention can be a component of the high-voltage conductor assembly. Specifically, to simulate the actual operating hot chamber environmental conditions, it is first necessary to set a preset temperature and humidity for the thermal aging test space containing the insulation material to be predicted, which are identical to the actual operating hot chamber environmental conditions. A pulse generator is then used to simulate a high-frequency voltage identical to the actual operating hot chamber environmental conditions. In summary, aging tests can be performed on the insulation material to be predicted under various aging test conditions, such as preset temperature, preset humidity, and preset voltage. The aging test results are then used to determine the lifespan of the insulation material to be predicted. This fully considers the coupling of multiple factors and the uncertainty factors during long-term operation, resulting in more accurate lifespan prediction results.

[0031] 103. Control the pulse generating device to sequentially apply multi-stage pulse voltages of preset frequency, preset amplitude, and preset duty cycle to the insulation material to be predicted in the thermal aging test space, so as to perform multi-stage radiation on the insulation material to be predicted using the pulse voltage, control the insulation process parameter measuring device to collect in real time the electrical insulation process parameters and thermal insulation process parameters of the insulation material to be predicted during each radiation stage, and control the insulation index parameter measuring device to collect the electrical insulation index parameters and thermal insulation index parameters of the insulation material to be predicted after each radiation stage, wherein the pulse voltage in each radiation stage has the same preset frequency, preset amplitude, preset duty cycle, radiation duration, and voltage waveform.

[0032] Among them, the electrical insulation process parameters include: leakage current amplitude, leakage current frequency, partial discharge amplitude, partial discharge phase, partial discharge number, temperature value, etc. of the insulation material to be predicted during the aging test; the thermal insulation process parameters include: color, quality, appearance, and roughness of the insulation material to be predicted during the aging test; the electrical insulation index parameters include: insulation resistance value (insulation resistance value includes 15s insulation resistance R15s and 60s insulation resistance) and dielectric loss factor of the insulation material to be predicted after each aging test stage; the thermal insulation index parameters include: temperature index, relative temperature index, tensile strength, elongation at break, density, hardness, etc. of the insulation material; the preset frequency, preset amplitude, and preset duty cycle are determined based on the actual working conditions of the project in which the insulation material is applied.

[0033] Specifically, the aging test of a hot chamber high-voltage conductor assembly is divided into multiple test phases of equal duration and identical operating procedures (wherein, the identical operating procedures include: the radiation time, voltage waveform, voltage frequency, voltage amplitude, and voltage duty cycle of each voltage radiation phase are identical). A thermal aging test space and a high-frequency, high-voltage pulse generator (pulse generator) are used to provide the insulation material to be predicted in the hot chamber high-voltage conductor assembly with electrical and thermal stress conditions under actual engineering operating conditions. During the power-on process, electrical insulation process parameters such as leakage current, partial discharge, and temperature signals of the insulation material to be predicted, as well as thermal insulation process parameters such as color, mass, and roughness, are collected, monitored, and displayed in real time during the aging test. After power is turned off in each test phase, electrical insulation parameters such as insulation resistance and dielectric loss factor, as well as thermal insulation parameters such as temperature index, relative temperature index, tensile strength, elongation at break, density, and hardness, are measured during the aging test. By comparing and analyzing the various electrical insulation process parameters, thermal insulation process parameters, electrical insulation parameters, and thermal insulation parameters, the aging condition of the insulation material in the hot chamber high-voltage conductor assembly is evaluated and the insulation life is predicted. In the process of predicting the lifespan of insulation materials, aging tests conducted under actual engineering working conditions can improve the accuracy of insulation material lifespan predictions and ensure that the predicted lifespans are more valuable for practical engineering reference. Furthermore, the test method provides a specific enclosed space and high frequency and high voltage for the test environment, enabling flexible and convenient real-time adjustment and maintenance of the required temperature and humidity conditions and applied voltage waveform. This aging test method enables the automatic collection, monitoring, centralized display, and data recording and storage of parameters such as leakage current, partial discharge, and temperature, as well as remote control of the entire test apparatus, thus ensuring the smooth and stable conduct of aging tests.

[0034] 104. Determine the insulation life of the insulation material to be predicted based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and thermal insulation index parameters after each radiation stage.

[0035] Specifically, the electrical and thermal insulation process parameters, electrical and thermal insulation index parameters, and thermal insulation index parameters collected during each radiation phase are used to analyze the aging trends of the insulation material being predicted. The insulation material's lifespan is then predicted based on the trend analysis results. By simulating thermal aging tests on the insulation material under actual engineering conditions, the predicted insulation material lifespan is more accurate and more valuable for practical engineering applications.

[0036] According to the present invention, a method for predicting the life of an insulating material is provided. Compared with the current method of evaluating the life of an insulating material based solely on a single aging condition, the present invention constructs an aging test system and sets the thermal stress environmental conditions, such as temperature and humidity, required for the aging test in a thermal aging test space within the aging test system. Based on this, a multi-stage pulse voltage is applied to the insulating material to be predicted in the thermal aging test space for radiation. The electrical insulation process parameters and thermal insulation process parameters during each radiation process, as well as the electrical insulation index parameters and thermal insulation index parameters after each radiation stage, are collected. Ultimately, the insulation life of the insulating material to be predicted is determined based on the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters corresponding to each radiation stage. Thus, by constructing the aging test system, the insulating material is subjected to aging tests under temperature aging conditions, humidity aging conditions, and voltage aging conditions. That is, during the aging test process, multiple factors affecting the life of the insulating material and the coupling relationships between these factors are comprehensively analyzed, thereby improving the accuracy of the prediction of the insulation material life, thereby ensuring that the predicted insulation material life is more valuable for reference in actual engineering practice.

[0037] Furthermore, in order to better illustrate the above process of classifying data, as a refinement and extension of the above embodiment, the embodiment of the present invention provides another insulation material life prediction method, device, storage medium and computer equipment, such as Figure 3 As shown, the method includes:

[0038] 201. Construct an aging test system, which includes a pulse generating device, a thermal aging test space, an insulation process parameter measuring device, and an insulation index parameter measuring device.

[0039] Specifically, if Figure 2As shown, the aging test system includes a host computer, a pulse generating device (high-frequency and high-voltage pulse generating power supply), a thermal aging test space (aging test chamber), an insulation process parameter measuring device, and an insulation index parameter measuring device; wherein, the host computer is electrically connected to the pulse generating device, and the pulse generating device is electrically connected to the thermal aging test space, the host computer is used to control the pulse generating device to transmit pulse voltage to the thermal aging test space, the host computer is electrically connected to the insulation process parameter measuring device and the insulation index parameter measuring device respectively, the host computer controls the insulation process parameter measuring device to collect electrical insulation process parameters and thermal insulation process parameters, the host computer controls the insulation index parameter measuring device to collect electrical insulation index parameters and thermal insulation index parameters, and uploads the collected electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters to the host computer, the host computer is used to perform trend analysis on the aging state of the insulation material to be predicted based on the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters, so as to predict the life of the insulation material based on the trend analysis results.

[0040] 202. Set the preset environmental conditions required for the test for the thermal aging test space containing the insulating material to be predicted, wherein the preset environmental conditions include temperature conditions and humidity conditions. The preset environmental conditions are determined based on the actual working hot chamber environment corresponding to the insulating material to be predicted, and the number of insulating materials to be predicted is multiple.

[0041] Specifically, based on the actual engineering environment, a closed space (thermal aging test space) is provided for the insulation material to be predicted before the start of a single radiation stage, and the temperature and humidity of the thermal aging test space are adjusted to the conditions required for the test, so as to realize the aging test simulation of the insulation material to be predicted under the thermal aging test conditions.

[0042] 203. Control the pulse generating device to apply multi-stage pulse voltages of preset frequency, preset amplitude, and preset duty cycle to the insulating material to be predicted in the thermal aging test space in sequence, so as to perform multi-stage radiation on the insulating material to be predicted by using the pulse voltage, control the insulation process parameter measuring device to collect the electrical insulation process parameters and thermal insulation process parameters of the insulating material to be predicted in real time during each radiation stage, and control the insulation index parameter measuring device to collect the electrical insulation index parameters and thermal insulation index parameters of the insulating material to be predicted after each radiation stage, wherein the pulse voltage in each radiation stage has the same preset frequency, the same preset amplitude, the same preset duty cycle, the same radiation duration, and the same voltage waveform.

[0043] In the embodiment of the present invention, a pulse generating device is used to output high-frequency and high-voltage pulses in a single stage to the insulating material to be predicted in the thermal aging test space to simulate the actual engineering conditions, adjust the waveform of the high-frequency and high-voltage pulses, and apply a voltage with an amplitude of v, a frequency of f, and a duty cycle of k to the high-voltage conductor component under test. A multi-stage voltage with an amplitude of v, a frequency of f, and a duty cycle of k is applied to simulate the electrical aging process in the actual environmental conditions of the engineering. During the power-on process of the aging test, the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters and thermal insulation index parameters of the insulation material to be tested are collected and monitored in real time. Then, based on the collected electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters and thermal insulation index parameters, the life of the insulation material to be predicted is predicted. The specific method for life prediction includes: judging whether the insulation material to be predicted meets the preset conditions for life prediction based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage; if the insulation material to be predicted meets the preset conditions, obtaining control electrical insulation index parameters and control thermal insulation index parameters of a control insulation material in a normal state that has not been irradiated with a pulse voltage and has not been thermally irradiated, wherein the material properties of the control insulation material are the same as those of the insulation material to be predicted before the aging test, and the material properties include material type, shape and size, hardness, density, elastic modulus, and mechanical strength; based on the control electrical insulation index parameters and the electrical insulation index parameters, as well as the control thermal insulation index parameters and the thermal insulation index parameters, determining the aging status data of the insulation material to be predicted.Among them, the method for judging whether the insulating material to be predicted meets the preset conditions for life prediction includes: judging whether the insulating material to be predicted has insulation defects based on the leakage current amplitude, leakage current frequency, partial discharge amplitude, partial discharge phase, number of partial discharges, and temperature value. Among them, the specific judgment method includes: judging whether the leakage current amplitude is greater than a preset current amplitude threshold, judging whether the leakage current frequency is greater than a preset frequency threshold, judging whether the partial discharge amplitude is greater than a preset partial discharge amplitude threshold, judging whether the partial discharge phase is greater than a preset phase threshold, judging whether the number of partial discharges is greater than a preset number threshold, and judging whether the temperature value is greater than a preset temperature threshold. If the leakage current amplitude is greater than the preset current amplitude threshold, the leakage current frequency is greater than the preset frequency threshold, the partial discharge amplitude is greater than the preset partial discharge amplitude threshold, the partial discharge phase is greater than the preset phase threshold, the number of partial discharges is greater than the preset number threshold, and the temperature value is greater than the preset temperature threshold, then it is judged that the insulating material to be predicted has insulation defects. trap; based on the color, quality, shape, and roughness, determine whether the insulating material to be predicted has appearance defects, wherein the specific judgment method includes: determining whether the color difference between the color of the insulating material to be predicted and the standard color is greater than a preset color difference threshold, determining whether the quality difference between the quality and the standard quality is greater than a preset quality threshold, determining whether the appearance difference between the appearance and the standard appearance is greater than a preset appearance threshold, and determining whether the roughness difference between the roughness and the marked roughness is greater than a preset roughness threshold; if the color difference is greater than the preset color difference threshold, the quality difference is greater than the preset quality threshold, the appearance difference is greater than the preset appearance threshold, and the roughness difference is greater than the preset roughness threshold, then it is determined that the insulating material to be predicted has appearance defects; if the insulating material to be predicted has at least one of insulation defects and appearance defects, then it is determined that the insulating material to be predicted does not meet the preset conditions for life prediction; if the insulating material to be predicted does not have insulation defects and appearance defects, then it is determined that the insulating material to be predicted meets the preset conditions for life prediction.

[0044] Specifically, when the aging state of the insulating material is relatively light, the aging test can be continued to predict the life of the insulating material. At this time, the life prediction is meaningful, that is, the insulating material to be predicted meets the preset conditions for life prediction. If the aging state of the insulating material is relatively serious, it is meaningless to predict the life of the insulating material at this time, which wastes both computing resources and time. That is, the insulating material to be predicted does not meet the preset conditions for life prediction. Based on this, during the current radiation stage, it is necessary to determine whether the insulating material to be predicted meets the preset conditions for life prediction. If it meets the preset conditions, continue to the next radiation stage. Otherwise, stop performing the aging test on the insulating material. Among them, the specific method for judging whether the insulation material to be predicted meets the preset conditions for life prediction is: if the leakage current amplitude of the insulation material to be predicted is greater than the preset current amplitude threshold, the leakage current frequency is greater than the preset frequency threshold, the partial discharge amplitude is greater than the preset partial discharge amplitude threshold, the partial discharge phase is greater than the preset phase threshold, the number of partial discharges is greater than the preset number threshold, and the temperature value is greater than the preset temperature threshold, then it is determined that the insulation material to be predicted has insulation defects; if the color difference of the insulation material to be predicted is greater than the preset color difference threshold, the quality difference is greater than the preset quality threshold, the shape difference is greater than the preset shape threshold, and the roughness difference is greater than the preset roughness threshold, It is determined that the insulating material to be predicted has appearance defects. When the insulating material to be predicted has insulation defects or appearance defects, it is determined that the insulating material to be predicted does not meet the preset conditions for life prediction, that is, the insulating material to be predicted has reached its life, and it is meaningless to continue the life prediction. At this time, the aging test of the insulating material is stopped, so as to save test resources. When the insulating material to be predicted has no insulation defects and no appearance defects, it is determined that the insulating material to be predicted meets the preset conditions for life prediction, that is, the insulating material to be predicted has not reached its life, and the insulating material is continued to undergo the next stage of radiation and other aging test processes.

[0045] Specifically, the total number of insulating materials to be predicted is M, and m insulating materials are taken as test pieces (m<M), and the remaining number of insulating materials (Mm) is taken as control pieces (control insulating materials). The aging test is evenly divided into multiple test stages of equal duration. The test piece is fixed inside the aging test chamber and connected to the loop. At the same time, leakage current sensors, partial discharge acquisition sensors, and thermocouple temperature sensors are arranged around the test piece. The aging test chamber is closed during the test. The various parameters of the voltage required for the aging test are set by a high-frequency, high-voltage pulse power supply, and the temperature and humidity in the aging test chamber are set in the host computer. When the preset temperature ta and humidity ha are reached, a high-frequency voltage pulse is output to the test piece to start the first stage of the test. During the test, the leakage current, partial discharge, temperature signal, color, quality, appearance, roughness and other information of the test piece are collected and monitored in real time. After the test phase is completed, the power supply automatically shuts off. The circuit is disconnected and the test piece is removed. Electrical insulation parameters such as insulation resistance and dielectric loss factor are measured for each test piece, as well as thermal insulation parameters such as temperature index, relative temperature index, tensile strength, elongation at break, density, and hardness, using measuring instruments such as an insulation resistance tester and a dielectric loss meter. These parameters are recorded as test group data. After completing a test phase, the test process for that phase is repeated, and the test piece undergoes the next aging test phase. After the last test phase is completed and the electrical and thermal insulation parameters are measured, a number (Mm) of control pieces are measured for these parameters, each recorded as control group data. Based on the control and experimental group data, a trend analysis is performed on the aging process of the insulation material to be predicted. Ultimately, the insulation material life is predicted based on the aging trend analysis results. In an embodiment of the present invention, the high-frequency, high-voltage pulse generator can be a pulse generator with an output voltage range of -15kV to +15kV, an output frequency range of 0 to 15kHz, and an adjustable duty cycle. In a single-stage aging test, the required voltage is set to a high-frequency voltage signal with an amplitude of v, a frequency of f, a duty cycle of k, and a duration of (T / N). The voltage waveform can be powered to the high-voltage conductor of the hot chamber if the relative error between it and the preset value is within 3%. The total duration of the electrical aging test is T and is evenly divided into N test stages of equal duration. The test duration of each stage is (T / N) and should not be less than 5h. The test steps of each stage are the same. After completing a test stage and measuring the insulation resistance and dielectric loss factor, continue to perform the next aging test of (T / N) on the test piece. The measurement time between two single stages should not exceed 0.5h. After the last test stage of (T / N) is completed, the aging test time reaches the total duration T.During a single-stage aging test, the leakage current amplitude, partial discharge amplitude, phase, number of discharges, and temperature signals collected in real time by each sensor are transmitted to the host computer monitoring screen, and each data is stored in the host computer. The insulation resistance tester is specifically an electronic tester with an adjustable voltage range of 250V to 5kV. After each single stage of the aging test, the 15s insulation resistance R15s and 60s insulation resistance R60s of the hot chamber high-voltage conductor assembly are measured three times in sequence. The relative error between each measurement data should not exceed 5%. The dielectric loss measuring instrument is specifically an automatic anti-interference dielectric loss measuring instrument with a built-in variable frequency power supply, dielectric loss bridge, and standard capacitor, and a maximum test voltage of 10kV. After each stage of the aging test, the dielectric loss factor tanδ (%) of the hot chamber high-voltage conductor assembly is measured three times in sequence. The relative error between each measurement data should not exceed 5%. The above description is only exemplary, and the embodiments of the present invention are not limited to the above description. The present invention utilizes multiple test samples and conducts aging tests on them. This provides more data points, thereby enhancing the reliability and statistical significance of the test results. By comparing the aging performance of different samples under the same conditions, the lifespan of the insulation material being predicted can be more accurately assessed.

[0046] Furthermore, after collecting the electrical insulation process parameters and thermal insulation process parameters during each radiation stage, as well as the electrical insulation index parameters and thermal insulation index parameters after each radiation stage, in order to ensure the accuracy of the collected parameters and thus ensure the accuracy of the insulation material life prediction, it is necessary to eliminate the abnormal parameters in each parameter. Based on this, the method includes: respectively taking the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters corresponding to each radiation stage as target parameters, and counting the data density within a preset neighborhood corresponding to any parameter in each of the target parameters; if the data density is greater than a preset density threshold, determining the arbitrary parameter as a clustering center; based on the clustering center, clustering each of the target parameters to obtain a clustering result; determining the target parameter that does not belong to any cluster in the clustering result as an abnormal parameter; eliminating the abnormal parameters in each of the target parameters to obtain the cleaned electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters.

[0047] Specifically, the electrical insulation process parameters corresponding to each radiation stage are determined as target parameters. First, several definitions are determined: core points: points with a radius r containing more than the number of target parameters (MinPts); boundary points (boundary parameters): points with a radius r containing fewer than the number of target parameters (MinPts) but falling within the neighborhood of the core points; and noise points (abnormal parameters): points that are neither core points nor boundary points. The specific clustering method is as follows: for each target parameter, a set of data within its preset neighborhood is calculated. When the number of data within the set exceeds a preset density threshold, the target parameter is determined as the core point (cluster center). The remaining points are then checked to see if they are within the neighborhood of the core point. The remaining points within the neighborhood of the core point are determined as boundary points, and the remaining points not within the neighborhood of the core point are determined as noise points. The parameters corresponding to the noise points are then determined as abnormal parameters, and finally, the abnormal parameters are eliminated from each target parameter. In this way, abnormal parameters can be eliminated from the thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters, thereby obtaining the cleaned electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters. The embodiment of the present invention can avoid the interference of abnormal parameters on the life prediction of insulation materials by eliminating abnormal parameters from the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters. It can also avoid the waste of computing resources caused by excessive analysis of abnormal parameters during the life prediction process, thereby improving the prediction accuracy of the insulation material life and saving computing resources.

[0048] 204. Based on the electrical insulation process parameters during each radiation stage and their corresponding parameter collection time, the electrical insulation process time series data is constructed. Based on the thermal insulation process parameters during each radiation stage and their corresponding parameter collection time, the thermal insulation process time series data is constructed. Based on the electrical insulation index parameters after each radiation stage and their corresponding parameter collection time, the electrical insulation index time series data is determined. Based on the thermal insulation index parameters after each radiation stage and their corresponding parameter collection time, the thermal insulation index time series data is determined.

[0049] Specifically, the parameter collection time corresponding to the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters during each radiation stage is determined. Based on the parameter collection time, the electrical insulation process time series data, thermal insulation process time series data, electrical insulation index time series data, and thermal insulation index time series data are then determined. By constructing the electrical insulation process time series data, the thermal insulation process time series data, the electrical insulation index time series data, and the thermal insulation index time series data, and arranging the various data in a time series, the temporal trends of the various parameters can be revealed, facilitating analysis of insulation material aging trends.

[0050] 205. Determine the electrical insulation process characteristic vector corresponding to the electrical insulation process time series data, the thermal insulation process characteristic vector corresponding to the thermal insulation process time series data, the electrical insulation index characteristic vector corresponding to the electrical insulation index time series data, and the thermal insulation index characteristic vector corresponding to the thermal insulation index time series data.

[0051] Specifically, word embedding and other methods are used to determine the electrical insulation process feature vector corresponding to the electrical insulation process time series data, the thermal insulation process feature vector corresponding to the thermal insulation process time series data, the electrical insulation index feature vector corresponding to the electrical insulation index time series data, and the thermal insulation index feature vector corresponding to the thermal insulation index time series data.

[0052] 206. Perform cross processing on the electrical insulation process characteristic vector, the thermal insulation process characteristic vector, the electrical insulation index characteristic vector, and the thermal insulation index characteristic vector to obtain a cross aging characteristic vector.

[0053] In practical applications, the electrical insulation process time series data, thermal insulation process time series data, electrical insulation index time series data, and thermal insulation index time series data belong to data of different dimensions, and need to be processed into data of the same latitude, that is, they need to be processed into electrical insulation process feature vectors, thermal insulation process feature vectors, and electrical insulation index feature vectors. Then, in order to extract more implicit features, it is necessary to cross-process the above-mentioned vectors. Based on this, step 206 specifically includes: multiplying each element in the electrical insulation process feature vector with the corresponding position element in the thermal insulation process feature vector to obtain an initial insulation process cross vector, and determining the process convolution transformation function and process vector weight corresponding to the initial insulation process cross vector; based on the process vector weight, using the process convolution transformation function to convolve the initial insulation process cross vector Product transformation is performed to obtain an insulation process cross vector; each element in the electrical insulation index characteristic vector is multiplied by the corresponding position element in the thermal insulation index characteristic vector to obtain an initial insulation index cross vector, and the index convolution transformation function and index vector weight corresponding to the initial insulation index cross vector are determined. Based on the index vector weight, the initial insulation process cross vector is convoluted using the index convolution transformation function to obtain an insulation index cross vector; element-level cross processing is performed on the insulation process cross vector and the insulation index cross vector to obtain an element cross vector; low-order cross processing is performed on the insulation process cross vector and the insulation index cross vector to obtain a low-order cross vector; the element cross vector and the low-order cross vector are transformed using a preset transformation function to obtain a cross-aging characteristic vector.

[0054] Among them, the transformation function and vector weight are set according to actual needs. Specifically, for example, if the electrical insulation process characteristic vector is (a1, a2, a3), the thermal insulation process characteristic vector is (b1, b2, b3), the electrical insulation index characteristic vector is (c1, c2, c3), and the thermal insulation index characteristic vector is (d1, d2, d3), first, the electrical insulation process characteristic vector and the thermal insulation process characteristic vector are cross-featured at the feature level, that is, all elements between the vectors are Hadamard products, and then convolution transformation is performed under certain weights. The insulation process cross vector obtained is f(w1*(a1*b1, a2*b2, a3*b3)). Similarly, the electrical insulation index characteristic vector and the thermal insulation index characteristic vector are cross-featured at the feature level, that is, all elements between the vectors are Hadamard products, and then convolution transformation is performed under certain weights. The insulation index cross vector obtained is f(w2*(c1*d1, c2*d2, c3*d3)). Then, the insulation process cross vector and the insulation index cross vector are cross-featured. Element-level cross processing, that is, after performing Hadamard product on each element between vectors, different weight values ​​are assigned to the results of each product, and then linear transformation is performed. The obtained element cross vector is f(w3(w1*a1*b1*w2*c1*d1),w4(w1*a2*b2*w2*c2*d2),w5(w1*a3*b3*w2*c3*d3)), and low-order cross processing is performed on the insulation process cross vector and the insulation index cross vector, that is, the insulation process cross vector and the insulation index cross vector are horizontally spliced ​​to obtain a low-order cross vector of f(w1*(a1*b1,a2*b2,a3*b3),w2*(c1*d1,c2*d2,c3*d3)). Finally, the element cross vector and the low-order cross vector are combined and transformed using a preset transformation function to obtain a cross-aging feature vector. The preset transformation function can be set according to actual conditions, and this embodiment does not limit this. Therefore, by cross-processing the electrical insulation process feature vector, thermal insulation process feature vector, electrical insulation index feature vector, and thermal insulation index feature vector, different features can be automatically or explicitly combined to generate new feature combinations. These combined features may contain complex nonlinear relationships between the original features, so that the model can capture more detailed and rich information in the data, that is, it can make full use of the relationship between various data, extract more implicit features, and take into account high-order and low-order processing at the same time, so that data utilization is more sufficient, and the subsequent life prediction results are more accurate, meeting the needs of actual application scenarios.

[0055] 207. Input the cross-aging feature vector into a preset life prediction model to perform life prediction, and obtain the insulation life of the insulation material to be predicted.

[0056] For the embodiment of the present invention, in order to improve the prediction accuracy of the preset life prediction model, it is first necessary to train and construct the preset life prediction model. Based on this, the method includes: constructing multiple preset initial life prediction models; obtaining a sample data set corresponding to the sample insulation material, wherein the sample data set includes sample electrical insulation process parameters and sample thermal insulation process parameters of the sample insulation material in each working time period under the actual hot chamber high-voltage working condition, as well as sample electrical insulation index parameters and sample thermal insulation index parameters after the end of each working time period, and the actual life of the sample insulation material; based on the number of models of the preset initial life prediction model, the sample data set is divided into multiple groups of training data and multiple groups of test data; using each group of training data to predict the corresponding preset initial life The model is trained to obtain a trained preset initial life prediction model, wherein, during the training process, the sample electrical insulation process parameters, sample thermal insulation process parameters of each working time period in the training data, the sample electrical insulation index parameters, and the sample thermal insulation index parameters after the end of each working time period are used as input data, and the actual life is used as output data; each group of test data is used to test the corresponding trained preset initial life prediction model to obtain the test results, and based on the test results, the prediction accuracy corresponding to each of the trained preset initial life prediction models is determined; a target preset initial life prediction model with the maximum prediction accuracy is determined among each of the trained preset initial life prediction models, and the target preset initial life prediction model is determined as the preset life prediction model.

[0057] Among them, the sample electrical insulation process parameters in the sample data set include: leakage current amplitude, leakage current frequency, partial discharge amplitude, partial discharge phase, partial discharge number, temperature value, etc. of the sample insulation material in the actual hot chamber working environment; the sample thermal insulation process parameters include: color, quality, shape, and roughness of the sample insulation material in the actual hot chamber working environment; the sample electrical insulation index parameters include: insulation resistance value and dielectric loss factor of the sample insulation material in the actual hot chamber working environment; the sample thermal insulation index parameters include: temperature index, relative temperature index, tensile strength, elongation at break, density, hardness, etc. of the sample insulation material in the actual hot chamber working environment.

[0058] Specifically, multiple preset initial lifespan prediction models can be constructed based on a very deep factorization machine model, which includes a linear module and a deep neural network module. The model structures of the multiple preset initial lifespan prediction models can be the same or different. When training and constructing a life prediction model, first obtain a sample data set, and divide the sample data set into multiple groups of training data and multiple groups of test data according to the number of preset initial life prediction models. For example, the preset initial life prediction models include: model 1, model 2, and model 3; the divided training data include: training data 1, training data 2, and training data 3; the divided test data include: test data 1, test data 2, and test data 3; use training data 1 to train model 1, use training data 2 to train model 2, and use training data 3 to train model 3, then use test data 1 to test the trained model 1, use test data 2 to test the trained model 2, and use test data 3 to test the trained model 3. Finally, based on the test results, select the model with the highest test prediction accuracy as the preset life prediction model, and then input the cross-aging feature vector into the preset life prediction model for life prediction, and obtain the insulation life of the insulating material to be predicted, thereby improving the prediction accuracy of the insulation material life.

[0059] According to another insulation material life prediction method provided by the present invention, compared with the current method of evaluating the life of insulation materials based solely on a single aging condition, the present invention constructs an aging test system and sets the thermal stress environmental conditions, such as temperature and humidity, required for the aging test in a thermal aging test space within the aging test system. On this basis, a multi-stage pulse voltage is applied to the insulation material to be predicted in the thermal aging test space for radiation, and electrical insulation process parameters and thermal insulation process parameters during each radiation process, as well as electrical insulation index parameters and thermal insulation index parameters after each radiation stage, are collected. Ultimately, the insulation life of the insulation material to be predicted is determined based on the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters corresponding to each radiation stage. Thus, by constructing the aging test system, the insulation material is subjected to aging tests under temperature aging conditions, humidity aging conditions, and voltage aging conditions. That is, during the aging test process, multiple factors affecting the insulation material life and the coupling relationships between these factors are comprehensively analyzed, thereby improving the accuracy of insulation material life prediction and ensuring that the predicted insulation material life is more valuable for reference in actual engineering applications.

[0060] Further, as Figure 1 In a specific implementation, an embodiment of the present invention provides an insulation material life prediction device, which is applied to an aging test system. The aging test system includes a pulse generating device, a thermal aging test space, an insulation process parameter measuring device, and an insulation index parameter measuring device. Figure 4 As shown, the device includes: a setting unit 31, an aging test unit 32, and a determination unit 33.

[0061] The setting unit 31 can be used to set the preset environmental conditions required for the test for the thermal aging test space containing the insulating material to be predicted, wherein the preset environmental conditions include temperature conditions and humidity conditions, wherein the preset environmental conditions are determined based on the actual working hot chamber environment corresponding to the insulating material to be predicted, and the number of insulating materials to be predicted is multiple.

[0062] The aging test unit 32 can be used to control the pulse generating device to apply multi-stage pulse voltages of preset frequency, preset amplitude, and preset duty cycle to the insulating material to be predicted in the thermal aging test space in sequence, so as to use the pulse voltage to perform multi-stage radiation on the insulating material to be predicted in sequence, control the insulation process parameter measuring device to collect the electrical insulation process parameters and thermal insulation process parameters of the insulating material to be predicted in real time during each radiation stage, and control the insulation index parameter measuring device to collect the electrical insulation index parameters and thermal insulation index parameters of the insulating material to be predicted after each radiation stage, wherein the pulse voltage in each radiation stage has the same preset frequency, the same preset amplitude, the same preset duty cycle, the same radiation duration, and the same voltage waveform.

[0063] The determination unit 33 can be used to determine the insulation life of the insulation material to be predicted based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and thermal insulation index parameters after each radiation stage.

[0064] In a specific application scenario, in order to determine the insulation life of the insulation material to be predicted, such as Figure 5 As shown, the determining unit 33 includes a judging module 331 , an acquiring module 332 , and a determining module 333 .

[0065] The judgment module 331 can be used to judge whether the insulation material to be predicted meets the preset conditions for life prediction based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage.

[0066] The acquisition module 332 can be used to obtain control electrical insulation index parameters and control thermal insulation index parameters of a control insulation material in a normal state that has not been irradiated with pulse voltage and heat, if the insulation material to be predicted meets the preset conditions. The material properties of the control insulation material are the same as the material properties of the insulation material to be predicted before the aging test, and the material properties include material type, shape and size, hardness, density, elastic modulus, and mechanical strength.

[0067] The determination module 333 may be configured to determine the aging status data of the insulation material to be predicted based on the reference electrical insulation index parameter and the electrical insulation index parameter, and the reference thermal insulation index parameter and the thermal insulation index parameter.

[0068] In a specific application scenario, the electrical insulation process parameters include: leakage current amplitude, leakage current frequency, partial discharge amplitude, partial discharge phase, number of partial discharges, and temperature value; the thermal insulation process parameters include: color, quality, shape, and roughness; in order to determine whether the insulation material to be predicted meets the preset conditions for life prediction, the judgment module 331 can be specifically used to determine whether the insulation material to be predicted has insulation defects based on the leakage current amplitude, leakage current frequency, partial discharge amplitude, partial discharge phase, number of partial discharges, and temperature value, wherein the specific judgment method includes: determining whether the leakage current amplitude is greater than a preset current amplitude threshold, determining whether the leakage current frequency is greater than a preset frequency threshold, determining whether the partial discharge amplitude is greater than a preset partial discharge amplitude threshold, determining whether the partial discharge phase is greater than a preset phase threshold, determining whether the number of partial discharges is greater than a preset number threshold, and determining whether the temperature value is greater than a preset temperature threshold. If the leakage current amplitude is greater than the preset current amplitude threshold, the leakage current frequency is greater than the preset frequency threshold, the partial discharge amplitude is greater than the preset partial discharge amplitude threshold, and the partial discharge phase is greater than the preset phase threshold, the partial discharge number is greater than the preset number threshold. value, the number of partial discharges is greater than a preset number threshold, and the temperature value is greater than a preset temperature threshold, then it is determined that the insulating material to be predicted has an insulation defect; based on the color, quality, appearance, and roughness, it is determined whether the insulating material to be predicted has an appearance defect, wherein the specific judgment method includes: determining whether the color difference between the color of the insulating material to be predicted and the standard color is greater than a preset color difference threshold, determining whether the quality difference between the quality and the standard quality is greater than a preset quality threshold, determining whether the appearance difference between the appearance and the standard appearance is greater than a preset appearance threshold, and determining whether the roughness difference between the roughness and the marked roughness is greater than a preset roughness threshold; if the color difference is greater than the preset color difference threshold, the quality difference is greater than the preset quality threshold, the appearance difference is greater than the preset appearance threshold, and the roughness difference is greater than the preset roughness threshold, then it is determined that the insulating material to be predicted has an appearance defect; if the insulating material to be predicted has at least one of an insulation defect and an appearance defect, then it is determined that the insulating material to be predicted does not meet the preset conditions for life prediction; if the insulating material to be predicted has no insulation defect or appearance defect, then it is determined that the insulating material to be predicted meets the preset conditions for life prediction.

[0069] In a specific application scenario, in order to determine the insulation life of the insulation material to be predicted, the determination unit 33 further includes a construction module 334 , a cross-processing module 335 , and a prediction module 336 .

[0070] The construction module 334 can be used to construct electrical insulation process time series data based on the electrical insulation process parameters and their corresponding parameter collection time during each radiation stage, construct thermal insulation process time series data based on the thermal insulation process parameters and their corresponding parameter collection time during each radiation stage, determine electrical insulation index time series data based on the electrical insulation index parameters and their corresponding parameter collection time after the end of each radiation stage, and determine thermal insulation index time series data based on the thermal insulation index parameters and their corresponding parameter collection time after the end of each radiation stage.

[0071] The determination module 333 can also be used to determine the electrical insulation process characteristic vector corresponding to the electrical insulation process time series data, the thermal insulation process characteristic vector corresponding to the thermal insulation process time series data, the electrical insulation index characteristic vector corresponding to the electrical insulation index time series data, and the thermal insulation index characteristic vector corresponding to the thermal insulation index time series data.

[0072] The cross-processing module 335 may be configured to perform cross-processing on the electrical insulation process characteristic vector, the thermal insulation process characteristic vector, the electrical insulation index characteristic vector, and the thermal insulation index characteristic vector to obtain a cross-aging characteristic vector.

[0073] The prediction module 336 may be configured to input the cross-aging feature vector into a preset life prediction model to perform life prediction, thereby obtaining the insulation life of the insulation material to be predicted.

[0074] In a specific application scenario, in order to perform cross-processing on the electrical insulation process characteristic vector, the thermal insulation process characteristic vector, the electrical insulation index characteristic vector, and the thermal insulation index characteristic vector, the cross-processing module 335 can be specifically used to multiply each element in the electrical insulation process characteristic vector with the corresponding position element in the thermal insulation process characteristic vector to obtain an initial insulation process cross vector, and determine the process convolution transformation function and process vector weight corresponding to the initial insulation process cross vector. Based on the process vector weight, the initial insulation process cross vector is convoluted using the process convolution transformation function to obtain an insulation process cross vector; each element in the electrical insulation index characteristic vector is multiplied with the corresponding position element in the thermal insulation process characteristic vector to obtain an insulation process cross vector. The elements at corresponding positions in the indicator characteristic vector are multiplied to obtain an initial insulation indicator cross vector, and the indicator convolution transformation function and indicator vector weight corresponding to the initial insulation indicator cross vector are determined. Based on the indicator vector weight, the initial insulation process cross vector is convoluted using the indicator convolution transformation function to obtain an insulation indicator cross vector; element-level cross processing is performed on the insulation process cross vector and the insulation indicator cross vector to obtain an element cross vector; low-order cross processing is performed on the insulation process cross vector and the insulation indicator cross vector to obtain a low-order cross vector; the element cross vector and the low-order cross vector are transformed using a preset transformation function to obtain a cross-aging characteristic vector.

[0075] In a specific application scenario, in order to preset an initial life prediction model, the construction module 334 can also be used to construct multiple preset initial life prediction models; obtain a sample data set corresponding to the sample insulation material, wherein the sample data set includes sample electrical insulation process parameters and sample thermal insulation process parameters of the sample insulation material in each working time period under the actual hot chamber high-voltage working condition, as well as sample electrical insulation index parameters and sample thermal insulation index parameters after the end of each working time period, and the actual life of the sample insulation material; based on the number of models of the preset initial life prediction model, divide the sample data set into multiple groups of training data and multiple groups of test data; use each group of training data to train the corresponding preset initial life prediction model, and obtain A trained preset initial life prediction model, wherein, during the training process, the sample electrical insulation process parameters, sample thermal insulation process parameters of each working time period in the training data, the sample electrical insulation index parameters, and the sample thermal insulation index parameters after the end of each working time period are used as input data, and the actual life is used as output data; each group of test data is used to test the corresponding trained preset initial life prediction model to obtain the test results, and based on the test results, the prediction accuracy corresponding to each of the trained preset initial life prediction models is determined; a target preset initial life prediction model with the maximum prediction accuracy is determined among each of the trained preset initial life prediction models, and the target preset initial life prediction model is determined as the preset life prediction model.

[0076] In a specific application scenario, in order to determine abnormal parameters, the device further includes: a parameter cleaning unit 34.

[0077] The parameter cleaning unit 34 can be specifically used to respectively use the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters corresponding to each radiation stage as target parameters, and to count the data density within a preset neighborhood corresponding to any parameter of each target parameter; if the data density is greater than a preset density threshold, the arbitrary parameter is determined as a clustering center; based on the clustering center, each target parameter is clustered to obtain a clustering result; the target parameter that does not belong to any cluster in the clustering result is determined as an abnormal parameter; the abnormal parameters are eliminated from each target parameter to obtain the cleaned electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters.

[0078] It should be noted that for other corresponding descriptions of the functional modules involved in the device for predicting the life of an insulating material provided by the embodiment of the present invention, reference can be made to Figure 1 The corresponding description of the method shown will not be repeated here.

[0079] Based on the above Figure 1The method shown, accordingly, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps: applied to an aging test system, the aging test system including a pulse generating device, a thermal aging test space, an insulation process parameter measuring device, and an insulation index parameter measuring device, including:

[0080] Preset environmental conditions required for the test are set for a thermal aging test space containing the insulation material to be predicted, wherein the preset environmental conditions include temperature conditions and humidity conditions, and the preset environmental conditions are determined based on the actual working thermal chamber environment corresponding to the insulation material to be predicted, and there are multiple insulation materials to be predicted; the pulse generating device is controlled to sequentially apply pulse voltages of multiple stages of preset frequencies, preset amplitudes, and preset duty cycles to the insulation material to be predicted in the thermal aging test space, so as to sequentially irradiate the insulation material to be predicted in multiple stages using the pulse voltages; the insulation process parameter measuring device is controlled to collect in real time the electrical insulation process parameters and thermal insulation process parameters of the insulation material to be predicted during each radiation stage; and the insulation index parameter measuring device is controlled to collect the electrical insulation index parameters and thermal insulation index parameters of the insulation material to be predicted after each radiation stage, wherein the pulse voltage in each radiation stage has the same preset frequency, preset amplitude, preset duty cycle, radiation duration, and voltage waveform; and the insulation life of the insulation material to be predicted is determined based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and thermal insulation index parameters after each radiation stage.

[0081] Based on the above Figure 1 The method shown and Figure 4 The embodiment of the device shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 6 As shown, the computer device includes: a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor, wherein the memory 42 and the processor 41 are both arranged on a bus 43. When the processor 41 executes the program, the following steps are implemented: applied to an aging test system, the aging test system including a pulse generating device, a thermal aging test space, an insulation process parameter measuring device, and an insulation index parameter measuring device, including:

[0082] Preset environmental conditions required for the test are set for a thermal aging test space containing the insulation material to be predicted, wherein the preset environmental conditions include temperature conditions and humidity conditions, and the preset environmental conditions are determined based on the actual working thermal chamber environment corresponding to the insulation material to be predicted, and there are multiple insulation materials to be predicted; the pulse generating device is controlled to sequentially apply pulse voltages of multiple stages of preset frequencies, preset amplitudes, and preset duty cycles to the insulation material to be predicted in the thermal aging test space, so as to sequentially irradiate the insulation material to be predicted in multiple stages using the pulse voltages; the insulation process parameter measuring device is controlled to collect in real time the electrical insulation process parameters and thermal insulation process parameters of the insulation material to be predicted during each radiation stage; and the insulation index parameter measuring device is controlled to collect the electrical insulation index parameters and thermal insulation index parameters of the insulation material to be predicted after each radiation stage, wherein the pulse voltage in each radiation stage has the same preset frequency, preset amplitude, preset duty cycle, radiation duration, and voltage waveform; and the insulation life of the insulation material to be predicted is determined based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and thermal insulation index parameters after each radiation stage.

[0083] Through the technical solution of the present invention, the present invention constructs an aging test system and sets the thermal stress environmental conditions such as temperature and humidity required for the aging test for the thermal aging test space in the aging test system. On this basis, multi-stage pulse voltage is applied to the insulation material to be predicted in the thermal aging test space for radiation, and the electrical insulation process parameters and thermal insulation process parameters in each radiation process, as well as the electrical insulation index parameters and thermal insulation index parameters after each radiation stage are collected. Finally, based on the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters corresponding to each radiation stage, the insulation life of the insulation material to be predicted is determined. Therefore, by constructing the aging test system, the aging test of the insulation material under temperature aging conditions, humidity aging conditions, and voltage aging conditions is realized through the aging test system. That is, during the aging test process, the various influencing factors affecting the life of the insulation material and the coupling relationship between the various factors are comprehensively analyzed, which can improve the prediction accuracy of the life of the insulation material, thereby ensuring that the predicted life of the insulation material is more valuable for reference in engineering practice.

[0084] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, centralized on a single computing device, or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0085] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for predicting the life of an insulating material, characterized in that: Applicable to an aging test system, the aging test system includes a pulse generating device, a thermal aging test space, an insulation process parameter measuring device, and an insulation index parameter measuring device, including: Setting preset environmental conditions required for the test for the thermal aging test space containing the insulation material to be predicted, wherein the preset environmental conditions include temperature conditions and humidity conditions, and the preset environmental conditions are determined based on the actual working thermal chamber environment corresponding to the insulation material to be predicted, and there are multiple insulation materials to be predicted; Controlling the pulse generating device to sequentially apply a pulse voltage of multiple stages of preset frequency, preset amplitude, and preset duty cycle to the insulation material to be predicted in the thermal aging test space, so as to sequentially perform multi-stage radiation on the insulation material to be predicted using the pulse voltage, controlling the insulation process parameter measuring device to collect in real time the electrical insulation process parameters and thermal insulation process parameters of the insulation material to be predicted during each radiation stage, and controlling the insulation index parameter measuring device to collect the electrical insulation index parameters and thermal insulation index parameters of the insulation material to be predicted after each radiation stage, wherein the pulse voltage in each radiation stage has the same preset frequency, the same preset amplitude, the same preset duty cycle, the same radiation duration, and the same voltage waveform; Determining the insulation life of the insulation material to be predicted based on the electrical insulation process parameters and the thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and the thermal insulation index parameters after each radiation stage; The step of determining the insulation life of the insulation material to be predicted based on the electrical insulation process parameters and the thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and the thermal insulation index parameters after each radiation stage, includes: Based on the electrical insulation process parameters during each radiation stage and their corresponding parameter collection time, the electrical insulation process time series data is constructed; based on the thermal insulation process parameters during each radiation stage and their corresponding parameter collection time, the thermal insulation process time series data is constructed; based on the electrical insulation index parameters after the end of each radiation stage and their corresponding parameter collection time, the electrical insulation index time series data is determined; based on the thermal insulation index parameters after the end of each radiation stage and their corresponding parameter collection time, the thermal insulation index time series data is determined; the electrical insulation process characteristic vector corresponding to the electrical insulation process time series data, the thermal insulation process characteristic vector corresponding to the thermal insulation process time series data, the electrical insulation index characteristic vector corresponding to the electrical insulation index time series data, and the thermal insulation index characteristic vector corresponding to the thermal insulation index time series data are determined; the electrical insulation process characteristic vector, the thermal insulation process characteristic vector, the electrical insulation index characteristic vector, and the thermal insulation index characteristic vector are cross-processed to obtain a cross-aging characteristic vector; the cross-aging characteristic vector is input into a preset life prediction model for life prediction to obtain the insulation life of the insulation material to be predicted.

2. The method according to claim 1, characterized in that Determining the insulation life of the insulation material to be predicted based on the electrical insulation process parameters and the thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and the thermal insulation index parameters after each radiation stage, includes: Based on the electrical insulation process parameters and the thermal insulation process parameters during each radiation stage, determining whether the insulation material to be predicted meets the preset conditions for life prediction; If the insulation material to be predicted meets the preset conditions, then obtaining control electrical insulation index parameters and control thermal insulation index parameters of a control insulation material in a normal state that has not been subjected to pulse voltage radiation and heat radiation, wherein the material properties of the control insulation material are the same as the material properties of the insulation material to be predicted before the aging test, and the material properties include material type, shape and size, hardness, density, elastic modulus, and mechanical strength; Based on the reference electrical insulation index parameter and the electrical insulation index parameter, as well as the reference thermal insulation index parameter and the thermal insulation index parameter, aging status data of the insulation material to be predicted is determined.

3. The method according to claim 2, characterized in that The electrical insulation process parameters include: leakage current amplitude, leakage current frequency, partial discharge amplitude, partial discharge phase, partial discharge times, and temperature value; the thermal insulation process parameters include: color, quality, shape, and roughness; The determining whether the insulation material to be predicted meets the preset conditions for life prediction based on the electrical insulation process parameters and the thermal insulation process parameters during each radiation stage includes: Based on the leakage current amplitude, leakage current frequency, partial discharge amplitude, partial discharge phase, number of partial discharges, and temperature value, determining whether the insulation material to be predicted has an insulation defect, wherein a specific determination method includes: determining whether the leakage current amplitude is greater than a preset current amplitude threshold, determining whether the leakage current frequency is greater than a preset frequency threshold, determining whether the partial discharge amplitude is greater than a preset partial discharge amplitude threshold, determining whether the partial discharge phase is greater than a preset phase threshold, determining whether the number of partial discharges is greater than a preset number threshold, and determining whether the temperature value is greater than a preset temperature threshold; if the leakage current amplitude is greater than the preset current amplitude threshold, the leakage current frequency is greater than the preset frequency threshold, the partial discharge amplitude is greater than the preset partial discharge amplitude threshold, the partial discharge phase is greater than the preset phase threshold, the number of partial discharges is greater than the preset number threshold, and the temperature value is greater than the preset temperature threshold, then determining that the insulation material to be predicted has an insulation defect; Based on the color, quality, appearance, and roughness, determining whether the insulating material to be predicted has an appearance defect, wherein a specific determination method includes: determining whether a color difference between the color of the insulating material to be predicted and a standard color is greater than a preset color difference threshold, determining whether a quality difference between the quality and the standard quality is greater than a preset quality threshold, determining whether an appearance difference between the appearance and the standard appearance is greater than a preset appearance threshold, and determining whether a roughness difference between the roughness and the marked roughness is greater than a preset roughness threshold; if the color difference is greater than the preset color difference threshold, the quality difference is greater than the preset quality threshold, the appearance difference is greater than the preset appearance threshold, and the roughness difference is greater than the preset roughness threshold, then determining that the insulating material to be predicted has an appearance defect; If the insulation material to be predicted has at least one of an insulation defect and an appearance defect, it is determined that the insulation material to be predicted does not meet the preset conditions for life prediction; If the insulation material to be predicted does not have insulation defects and appearance defects, it is determined that the insulation material to be predicted meets the preset conditions for life prediction.

4. The method according to claim 1, wherein The cross-processing of the electrical insulation process characteristic vector, the thermal insulation process characteristic vector, the electrical insulation index characteristic vector, and the thermal insulation index characteristic vector to obtain the cross-aging characteristic vector includes: Multiplying each element in the electrical insulation process characteristic vector with the corresponding position element in the thermal insulation process characteristic vector to obtain an initial insulation process cross vector, determining a process convolution transformation function and a process vector weight corresponding to the initial insulation process cross vector, and performing a convolution transformation on the initial insulation process cross vector using the process convolution transformation function based on the process vector weight to obtain an insulation process cross vector; Multiplying each element in the electrical insulation index characteristic vector with the corresponding position element in the thermal insulation index characteristic vector to obtain an initial insulation index cross vector, and determining an index convolution transformation function and an index vector weight corresponding to the initial insulation index cross vector; based on the index vector weight, performing a convolution transformation on the initial insulation process cross vector using the index convolution transformation function to obtain an insulation index cross vector; Performing element-level cross processing on the insulation process cross vector and the insulation index cross vector to obtain an element cross vector; Performing low-order cross processing on the insulation process cross vector and the insulation index cross vector to obtain a low-order cross vector; The element cross vector and the low-order cross vector are transformed using a preset transformation function to obtain a cross-aging feature vector.

5. The method according to claim 1, wherein Before inputting the cross-aging feature vector into a preset life prediction model to perform life prediction and obtain the insulation life of the insulation material to be predicted, the method further includes: Construct multiple preset initial life prediction models; Obtaining a sample data set corresponding to a sample insulation material, wherein the sample data set includes sample electrical insulation process parameters and sample thermal insulation process parameters of the sample insulation material in each working time period under an actual hot chamber high-voltage working condition, as well as sample electrical insulation index parameters and sample thermal insulation index parameters after each working time period, and the actual life of the sample insulation material; Based on the number of models of the preset initial life prediction model, dividing the sample data set into multiple groups of training data and multiple groups of test data; Using each set of training data to train the corresponding preset initial life prediction model, a trained preset initial life prediction model is obtained, wherein during the training process, the sample electrical insulation process parameters, the sample thermal insulation process parameters, the sample electrical insulation index parameters, and the sample thermal insulation index parameters after each working time period in the training data are used as input data, and the actual life is used as output data; Using each set of test data to test the corresponding trained preset initial life prediction model to obtain test results, and based on the test results, determining the prediction accuracy corresponding to each of the trained preset initial life prediction models; A target preset initial life prediction model with the maximum prediction accuracy is determined among the trained preset initial life prediction models, and the target preset initial life prediction model is determined as the preset life prediction model.

6. The method according to claim 1, wherein Before determining the insulation life of the insulation material to be predicted based on the electrical insulation process parameters and the thermal insulation process parameters during each radiation stage, and the electrical insulation index parameters and the thermal insulation index parameters after each radiation stage, the method further includes: The electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters corresponding to each radiation stage are respectively used as target parameters, and the data density within a preset neighborhood corresponding to any parameter of the target parameters is counted; If the data density is greater than a preset density threshold, determining the arbitrary parameter as a cluster center; Based on the cluster centers, clustering the target parameters to obtain clustering results; Determining the target parameter that does not belong to any cluster in the clustering result as an abnormal parameter; The abnormal parameters are eliminated from the target parameters to obtain the electrical insulation process parameters, thermal insulation process parameters, electrical insulation index parameters, and thermal insulation index parameters after cleaning.

7. An insulating material life prediction device, characterized in that: Applicable to an aging test system, the aging test system includes a pulse generating device, a thermal aging test space, an insulation process parameter measuring device, and an insulation index parameter measuring device, including: a setting unit, configured to set preset environmental conditions required for the test for a thermal aging test space containing the insulation material to be predicted, wherein the preset environmental conditions include temperature conditions and humidity conditions, wherein the preset environmental conditions are determined based on an actual working thermal chamber environment corresponding to the insulation material to be predicted, and the number of insulation materials to be predicted is multiple; An aging test unit is configured to control the pulse generating device to sequentially apply a pulse voltage of multiple stages of preset frequency, preset amplitude, and preset duty cycle to the insulating material to be predicted in the thermal aging test space, so as to sequentially perform multi-stage radiation on the insulating material to be predicted using the pulse voltage, control the insulation process parameter measuring device to collect in real time the electrical insulation process parameters and thermal insulation process parameters of the insulating material to be predicted during each radiation stage, and control the insulation index parameter measuring device to collect the electrical insulation index parameters and thermal insulation index parameters of the insulating material to be predicted after each radiation stage, wherein the pulse voltage in each radiation stage has the same preset frequency, the same preset amplitude, the same preset duty cycle, the same radiation duration, and the same voltage waveform; A determination unit is used to determine the insulation life of the insulating material to be predicted based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage, as well as the electrical insulation index parameters and thermal insulation index parameters after the end of each radiation stage, wherein the insulation life of the insulating material to be predicted is determined based on the electrical insulation process parameters and thermal insulation process parameters during each radiation stage, as well as the electrical insulation index parameters and thermal insulation index parameters after the end of each radiation stage, including: constructing electrical insulation process time series data based on the electrical insulation process parameters during each radiation stage and their corresponding parameter acquisition time, constructing thermal insulation process time series data based on the thermal insulation process parameters during each radiation stage and their corresponding parameter acquisition time, and constructing thermal insulation process time series data based on the electrical insulation index parameters and their corresponding parameter acquisition time after the end of each radiation stage. The electric insulation index time series data is determined based on the thermal insulation index parameters after each radiation stage and their corresponding parameter collection time; the thermal insulation index time series data is determined; the electric insulation process characteristic vector corresponding to the electric insulation process time series data, the thermal insulation process characteristic vector corresponding to the thermal insulation process time series data, the electric insulation index characteristic vector corresponding to the electric insulation index time series data, and the thermal insulation index characteristic vector corresponding to the thermal insulation index time series data are determined; the electric insulation process characteristic vector, the thermal insulation process characteristic vector, the electric insulation index characteristic vector, and the thermal insulation index characteristic vector are cross-processed to obtain a cross-aging characteristic vector; the cross-aging characteristic vector is input into a preset life prediction model for life prediction to obtain the insulation life of the insulation material to be predicted.

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

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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