Method and device for predicting service life of silicone rubber, electronic equipment and program product

Abstract

The invention belongs to the technical field of power systems, particularly relates to the field of service life prediction of silicone rubber sheaths of composite insulators, and provides a silicone rubber service life prediction method and device, electronic equipment and a program product. According to the method for predicting the service life of the silicone rubber, initial characteristic data and aging characteristic data of a plurality of silicone rubber are obtained, the aging characteristic data are obtained by simulating a preset temperature condition and a corona discharge condition and carrying out accelerated aging test on the plurality of silicone rubber, and the initial characteristic data comprise an initial elongation rate, an initial elongation rate and a corona discharge rate. The aging characteristic data comprises elongation at break; if the elongation at break of the silicone rubber is smaller than the initial elongation of the preset proportion, determining that the silicone rubber is aged and invalid, and obtaining the aging failure time of the silicone rubber; and predicting the service life of the silicone rubber based on the aging failure time and the aging characteristic data of the silicone rubber. The problem of low accuracy of a related silicone rubber life prediction scheme is effectively solved.

Description

Technical Field

[0001] The present application belongs to the technical field of power systems, and specifically relates to the field of service life prediction of silicone rubber sheaths of composite insulators, and more specifically, to a method, device, electronic equipment and program product for predicting the service life of silicone rubber sheaths. Background Art

[0002] Composite insulators are vital in the power system and play a key role. They are one of the important equipment to ensure the stable operation of the power system.

[0003] The performance of the silicone rubber sheath of composite insulators has a great impact on their overall service life and reliability. When the power system is in extremely harsh environmental conditions, composite insulators will face many challenges, such as high temperature, sub-zero temperature, ultraviolet radiation, corona discharge, etc.

[0004] Therefore, predictive research on the life of silicone rubber sheaths of composite insulators has theoretical and practical significance, which helps to optimize the operation and management of power systems and improve the safety and reliability of power grids. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method, device, electronic device and program product for predicting the life of silicone rubber, aiming to solve the problem of low accuracy of related silicone rubber life prediction schemes.

[0006] To achieve the above object, according to a first aspect of the present application, a method for predicting the life of silicone rubber is provided, the method comprising:

[0007] Acquire initial characteristic data and aging characteristic data of a plurality of silicone rubbers, wherein the aging characteristic data is obtained by simulating predetermined temperature conditions and corona discharge conditions and performing accelerated aging tests on the plurality of silicone rubbers, the initial characteristic data includes: initial elongation, and the aging characteristic data includes: elongation at break;

[0008] If the elongation at break of the silicone rubber is less than a predetermined proportion of the initial elongation, it is determined that the silicone rubber has aged and failed, and the aging failure time of the silicone rubber is obtained;

[0009] The service life of the silicone rubber is predicted based on the aging failure time of the silicone rubber and the aging characteristic data.

[0010] Optionally, in a possible implementation manner of the first aspect, acquiring aging characteristic data of multiple silicone rubbers includes:

[0011] By simulating predetermined temperature conditions and corona discharge conditions, a multi-needle corona aging platform is continuously used to simulate and calculate the field strength near the silicone rubber surface of multiple silicone rubbers within a predetermined period of time to obtain the aging characteristic data.

[0012] Optionally, in a possible implementation manner of the first aspect, the predicting the service life of the silicone rubber based on the aging failure time of the silicone rubber and the aging characteristic data includes:

[0013] Determining failure analysis probability distribution data of the silicone rubber based on the aging failure time of the silicone rubber and the aging characteristic data;

[0014] If the failure analysis probability distribution data of the silicone rubber satisfies a predetermined failure analysis probability distribution function, a life prediction model based on a temperature acceleration factor model and a corona acceleration factor model is obtained, wherein the failure analysis probability distribution function is used to describe the aging failure time or service life of the silicone rubber under different conditions, the temperature acceleration factor model is used to describe the relationship between the aging failure of the silicone rubber and the temperature, and the corona acceleration factor model is used to explain the influence of the corona discharge condition on the aging failure of the silicone rubber;

[0015] The service life of the silicone rubber is predicted by the life prediction model based on the aging failure time of the silicone rubber and the aging characteristic data.

[0016] Optionally, in a possible implementation manner of the first aspect, obtaining a life prediction model based on a temperature acceleration factor model and a corona acceleration factor model includes:

[0017] Obtaining the material activation energy of the silicone rubber, the absolute temperature value of the environment of the silicone rubber in normal use, and the absolute temperature value of the environment of the silicone rubber under constant temperature stress during testing, wherein the constant temperature stress is applied to the silicone rubber under the predetermined temperature condition;

[0018] Determine the temperature acceleration factor model of the silicone rubber under predetermined temperature conditions according to the material activation energy of the silicone rubber, the absolute temperature value of the environment of the silicone rubber in normal use, and the absolute temperature value of the environment of the silicone rubber under constant temperature stress during testing;

[0019] Obtaining the number of free radicals generated by aging of the silicone rubber and the electric field strength determined based on the corona discharge conditions, wherein the number of free radicals is used to characterize the corona aging strength;

[0020] Determine the corona acceleration factor model according to the number of free radicals, the electric field strength, the aging failure time of the silicone rubber and related constants, wherein the related constants are parameters related to the test voltage and the silicone rubber material;

[0021] The life prediction model is generated based on the temperature acceleration factor model and the corona acceleration factor model.

[0022] Optionally, in a possible implementation manner of the first aspect, the temperature acceleration factor model AF(T) is:

[0023]

[0024] In the above formula, Ea is the material activation energy of the silicone rubber, k is the Boltzmann constant, T u is the absolute temperature of the environment in normal use / K, T s is the absolute temperature value of the environment under constant temperature stress during the test / K; wherein, Ea is obtained by substituting the number of aging cycles with the same field intensity near the silicone rubber surface into the temperature acceleration factor model AF(T) and performing linear regression calculation.

[0025] Optionally, in a possible implementation manner of the first aspect, the corona acceleration factor model AF(E) is:

[0026] AF(E)=(E / E u ) 2b ;

[0027] In the above formula, E u is the field intensity near the silicone rubber surface under the first corona aging stress, E is the field intensity near the silicone rubber surface under the second corona aging stress, b is a model parameter, the first corona aging stress is less than the second corona aging stress, wherein b is calculated by substituting the infrared spectrum peak height values ​​corresponding to different aging failure times into the corona acceleration factor model AF(E) and taking the average value.

[0028] Optionally, in a possible implementation manner of the first aspect, the life prediction model AF(T, E) is:

[0029] AF(T,E)=AF(T)×AF(E)×f(T,E);

[0030] In the above formula, f(T, E) is the coupling term of the temperature acceleration factor model AF(T) and the corona acceleration factor model AF(E).

[0031] Optionally, in a possible implementation manner of the first aspect, predicting the service life of the silicone rubber based on the aging failure time of the silicone rubber and the aging characteristic data by using the service life prediction model includes:

[0032] The life prediction model is used to obtain the aging rate of the silicone rubber under the constant temperature stress applied under the predetermined temperature condition and the corona aging stress applied under the corona discharge condition, and the acceleration factor is obtained based on the acceleration degree of the aging rate of the silicone rubber under normal stress conditions.

[0033] Derived the failure life data of the silicone rubber under normal stress level through the acceleration factor, the aging failure time of the silicone rubber and the aging characteristic data;

[0034] The service life of the silicone rubber is predicted based on the failure life data and the failure analysis probability distribution function.

[0035] According to a second aspect of the present application, a life prediction device for silicone rubber is provided, the device comprising:

[0036] An acquisition unit, used for acquiring initial characteristic data and aging characteristic data of a plurality of silicone rubbers, wherein the aging characteristic data is obtained by simulating predetermined temperature conditions and corona discharge conditions and performing accelerated aging tests on the plurality of silicone rubbers, the initial characteristic data includes: initial elongation, and the aging characteristic data includes: elongation at break;

[0037] a determination unit, configured to determine that the silicone rubber is aged and failed if the elongation at break of the silicone rubber is less than a predetermined proportion of the initial elongation, and to obtain the aging and failure time of the silicone rubber;

[0038] The prediction unit is used to predict the service life of the silicone rubber based on the aging failure time of the silicone rubber and the aging characteristic data.

[0039] The second aspect and any implementation of the second aspect correspond to the first aspect and any implementation of the first aspect, respectively. The technical effects corresponding to the second aspect and any implementation of the second aspect can refer to the technical effects corresponding to the first aspect and any implementation of the first aspect, which will not be repeated here.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electronic device implements any of the methods described in one of the embodiments.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements any of the methods described above.

[0042] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes any one of the methods described in the first aspect.

[0043] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0044] The embodiments of the present application provide a method, device, electronic device and program product for predicting the life of silicone rubber. Compared with the prior art, the method for predicting the life of silicone rubber provided by the present application has the following beneficial effects:

[0045] By obtaining the initial characteristic data and aging characteristic data of multiple silicone rubbers, wherein the aging characteristic data is obtained by simulating predetermined temperature conditions and corona discharge conditions and performing accelerated aging tests on multiple silicone rubbers, the initial characteristic data includes: initial elongation, and the aging characteristic data includes: elongation at break; if the elongation at break of the silicone rubber is less than a predetermined proportion of the initial elongation, the silicone rubber is determined to be aged and failed, and the aging failure time of the silicone rubber is obtained; based on the aging failure time of the silicone rubber and the aging characteristic data, the service life of the silicone rubber is predicted. The problem of low accuracy of related silicone rubber life prediction schemes is effectively solved.

[0046] It can be seen that when obtaining the aging characteristic data of silicone rubber, this method not only simulates the predetermined temperature conditions, but also further simulates the corona discharge conditions to perform accelerated aging tests on the silicone rubber, thereby being able to more accurately simulate the various temperature environments that the silicone rubber may face in actual use; and aging the silicone rubber in a corona discharge environment, fully considering the impact of corona aging on the life of the silicone rubber, improving the accuracy of the silicone rubber life prediction, and effectively solving the problem of low accuracy of related silicone rubber life prediction schemes. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 It is a schematic diagram of a process of predicting the life of silicone rubber provided in an embodiment of the present application;

[0049] Figure 2 This is a schematic diagram of corona discharge for predicting the life of a silicone rubber provided in an embodiment of the present application;

[0050] Figure 3 This is an optional schematic diagram of the change of elongation at break with the number of aging cycles provided in an embodiment of the present application;

[0051] Figure 4 is another optional schematic diagram of the change of elongation at break with the number of aging cycles provided in an embodiment of the present application;

[0052] Figure 5 This is another optional schematic diagram of the change of elongation at break with the number of aging cycles provided in an embodiment of the present application;

[0053] Figure 6 It is a schematic flow chart of an optional method for predicting the life of silicone rubber provided in an embodiment of the present application;

[0054] Figure 7 It is a schematic flow chart of an optional method for predicting the life of silicone rubber provided in an embodiment of the present application;

[0055] Figure 8 It is a schematic flow chart of an optional method for predicting the life of silicone rubber provided in an embodiment of the present application;

[0056] Fig. 9 is a schematic diagram of a silicone rubber life reliability curve provided in an embodiment of the present application;

[0057] Fig.10 It is a schematic structural diagram of a life prediction device for silicone rubber provided in an embodiment of the present application;

[0058] Fig.11 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0060] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0061] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0062] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0063] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0064] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0065] First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0066] The Arrhenius model is mainly used to describe the relationship between chemical reaction rate and temperature and is widely used in material aging, reliability analysis and other fields.

[0067] The Weibull probability distribution function is a continuous probability distribution function, which is often used in fields such as reliability engineering to describe the distribution of product life or failure time.

[0068] COMSOL simulation analysis is a multi-physics simulation analysis performed using COMSOL Multiphysics software.

[0069] Aging chamber is a device used to simulate various environmental conditions to accelerate the aging process of products.

[0070] The above is a brief introduction to the nouns involved in the embodiments of the present application, which will not be repeated below.

[0071] In the related art, the compression permanent deformation rate of silicone rubber (e.g., rubber ring) samples is determined through aging experiments, and the rubber aging formula and the acceleration model based on Weibull distribution are used to predict the life of the rubber ring and analyze the reliability of the rubber ring. The above-mentioned rubber ring life prediction method has the following defects or deficiencies, resulting in low accuracy of the analysis results: 1) The aging environment temperature below zero or above 100°C is not considered, and the temperature setting is not comprehensive enough; 2) Various environmental factors such as corona aging and humidity are not comprehensively considered, and the aging stress setting is too single; 3) The parameters required for the calculation model are complex and diverse, and it is difficult to obtain them from the power equipment and power grid system on site.

[0072] In order to solve the above problems, this application example provides an example of a method for predicting the life of silicone rubber. Please refer to Figure 1 As shown, Figure 1 A schematic flow chart of a method for predicting the life of silicone rubber provided in the present application is shown. As an example but not a limitation, the method can be applied to or run in an electronic device.

[0073] S101, obtaining initial characteristic data and aging characteristic data of a plurality of silicone rubbers, wherein the aging characteristic data is obtained by simulating predetermined temperature conditions and corona discharge conditions and performing accelerated aging tests on the plurality of silicone rubbers, the initial characteristic data includes: initial elongation, and the aging characteristic data includes: elongation at break.

[0074] S102, if the elongation at break of the silicone rubber is less than a predetermined proportion of the initial elongation, it is determined that the silicone rubber has aged and failed, and the aging and failure time of the silicone rubber is obtained.

[0075] S103, predicting the service life of the silicone rubber based on the aging failure time and aging characteristic data of the silicone rubber.

[0076] Optionally, the silicone rubber may be a silicone rubber sheath of a composite insulator, which is an important component for protecting an insulator core rod. The silicone rubber sheath of a composite insulator is usually wrapped around the outside of the insulator core rod to form a tight protective layer. It is generally made of silicone rubber material, has good flexibility and elasticity, and can adapt to insulator core rods of different shapes.

[0077] Silicone rubber sheath has aging resistance and can resist the influence of ultraviolet rays, ozone, temperature changes and other factors in long-term outdoor use, which is conducive to maintaining the physical and electrical properties of composite insulators. In addition, silicone rubber sheath has good electrical insulation performance, which can effectively isolate the insulator core rod from the surrounding environment, prevent current leakage and breakdown, and ensure the safe operation of the power system.

[0078] The life prediction method of silicone rubber provided in the present application aims to obtain relevant data and analyze it by conducting an accelerated aging test on silicone rubber (for example, the silicone rubber sheath of a composite insulator) under simulated specific conditions to predict the service life of the silicone rubber. When it is determined that the silicone rubber has aged based on the service life, the aged insulator is promptly maintained and replaced to avoid power system failures. The prediction method is easy to implement, can effectively save operation and maintenance costs, and improve the economy and operation efficiency of the power system.

[0079] First, before the accelerated aging test, multiple state data of silicone rubber before the aging test, namely, initial characteristic data, are recorded. The initial characteristic data at least include: initial elongation, which reflects the mechanical properties of silicone rubber such as flexibility and ductility in the initial state. In addition, the initial characteristic data may also include the initial infrared spectrum peak height value.

[0080] By simulating predetermined temperature conditions and corona discharge conditions, multiple silicone rubbers are subjected to accelerated aging tests. As an example and not a limitation, the predetermined temperature conditions include: sub-zero temperatures (for example, but not limited to -20°C, -40°C, etc.) and extreme ambient temperatures above 100°C (for example, but not limited to 120°C, 140°C, etc.) to accelerate the aging process of silicone rubber. The corona discharge conditions are achieved by building a multi-needle corona aging platform to simulate the corona discharge conditions that may be encountered in actual use.

[0081] After a period of accelerated aging test (e.g., 25 days, 30 days, 35 days, etc.), aging characteristic data are obtained, which at least include elongation at break. In addition, it can also include infrared spectrum peak height after aging test, etc. These data can reflect the chemical structure changes of silicone rubber during the aging process.

[0082] When the elongation at break of the silicone rubber is less than a predetermined proportion (e.g., 50%) of the initial elongation, the silicone rubber can be judged to have aged and failed. It should be noted that the selection criteria for the predetermined proportion are determined based on the mechanical properties of the silicone rubber falling to a certain extent, because the elongation at break is an important indicator for measuring the flexibility and ductility of silicone rubber. When the elongation at break falls to a certain extent, it means that the performance of the silicone rubber has been seriously damaged and may no longer meet the actual use requirements. Furthermore, if it is determined that the silicone rubber has aged and failed, the aging and failure time of the silicone rubber is recorded, that is, the time from the start of the accelerated aging test to the judgment of aging and failure.

[0083] By using the aging failure time of silicone rubber and the infrared spectrum peak height data in the aging characteristic data, combined with the predetermined temperature conditions and corona discharge conditions, accelerated aging experiments were carried out on multiple silicone rubbers (S1-S10 experimental groups) by comprehensively considering multiple factors (the specific experimental conditions are shown in Table 1 below). The synergistic effect and coupling of multiple aging variables such as temperature, corona discharge and humidity were comprehensively considered to predict the life of silicone rubber, and the service life of multiple silicone rubbers was obtained.

[0084] Table 1

[0085]

[0086]

[0087] In the example of this application, when obtaining the aging characteristic data of silicone rubber, the accelerated aging test is specifically simulated under predetermined temperature conditions, which include sub-zero temperatures and extreme ambient temperatures above 100°C. By setting a wide temperature range, the effects of different temperature conditions on the aging of silicone rubber are fully considered, making up for the deficiency of not considering these extreme temperatures in the past, making the temperature setting more comprehensive, so as to more accurately simulate the various temperature environments that silicone rubber may face in actual use, and improving the accuracy of life prediction.

[0088] When obtaining aging characteristic data, not only temperature conditions are considered, but also corona discharge conditions are further simulated to conduct accelerated aging tests on silicone rubber. By building a multi-needle corona aging platform, silicone rubber is aged in a corona discharge environment, fully considering the impact of corona aging on the life of silicone rubber.

[0089] It is still necessary to explain that the influence of humidity on the life of silicone rubber is also indirectly considered in the example of this application: although the humidity factor is not directly mentioned in the method, in the process of obtaining aging characteristic data, an aging box used to simulate various environmental conditions to accelerate the aging process of the product is used for temperature and humidity control, which means that the influence of humidity on the aging of silicone rubber is considered to a certain extent. Although the specific effect of humidity on life prediction is not clearly given, the humidity is also controlled in the aging experiment through the aging box parameters of the constant temperature and humidity aging box shown in Table 2 below, which can reduce the uncertainty of the humidity variable on the experimental results and indirectly improve the accuracy of the silicone rubber life prediction.

[0090] In summary, the life prediction method of silicone rubber overcomes the problem of overly single aging stress setting in related technologies, which leads to low accuracy of related silicone rubber life prediction schemes, by simultaneously considering multiple environmental factors such as temperature, corona discharge, and indirectly considering humidity, making the silicone rubber life prediction more accurate.

[0091] Table 2

[0092]

[0093] In a possible implementation, obtaining aging characteristic data of multiple silicone rubbers includes:

[0094] By simulating predetermined temperature conditions and corona discharge conditions, a multi-needle corona aging platform is continuously used within a predetermined period of time to simulate and calculate the field strength near the silicone rubber surface of multiple silicone rubbers to obtain aging characteristic data.

[0095] In the example of this application, when acquiring aging characteristic data of multiple silicone rubbers, a specific method is used to simulate extreme conditions that may be encountered in actual use, so as to accelerate the aging process of the silicone rubber and acquire data that can reflect its aging state.

[0096] Optionally, in order to determine the field strength near the silicone rubber surface, a multi-needle corona aging platform is continuously used to simulate and calculate the field strength near the silicone rubber surface of multiple silicone rubbers within a predetermined period of time, for example, but not limited to, multi-physics field simulation analysis using COMSOL Multiphysics software, as shown in FIG. Figure 2 The schematic diagram of corona discharge for predicting the life of silicone rubber shown in the figure shows that when the applied voltage U is 11.5 kV and 7.7 kV respectively, the field strength E near the silicone rubber surface of multiple silicone rubbers is 15 kV / cm and 10 kV / cm. Figure 2 (a) shows that when U = 7.7 kV, then E = 10 kV / cm. Figure 2 (b) in the figure shows that when U = 11.5 kV, E = 15 kV / cm.

[0097] Specifically, since silicone rubber may work in different temperature environments in actual use scenarios, and extreme temperatures will have a significant impact on its performance. Therefore, in order to accelerate the aging reaction of silicone rubber, predetermined temperature conditions are simulated in the example of this application: sub-zero temperatures and extreme ambient temperatures above 100°C. Since corona discharge is a common phenomenon in environments such as actual power systems, it will have an aging effect on silicone rubber materials. Therefore, the example of this application specifically simulates the situation of corona discharge by using a multi-needle corona aging platform. In order to determine the effect of corona discharge on the field strength near the surface of silicone rubber, so as to better understand the mechanism of action of corona discharge on silicone rubber aging. Within a predetermined period of time (for example, 30 days), the multi-needle corona aging platform is continuously used to simulate and calculate the field strength near the silicone rubber surface of multiple silicone rubbers.

[0098] Specifically, the change in field strength can reflect the intensity of corona discharge, and different field strengths will cause different degrees of silicone rubber aging. Through simulation calculation of field strength, aging characteristic data related to corona discharge can be obtained. The aging characteristic data includes but is not limited to: elongation at break (relative to the initial elongation at break, specifically the elongation at break after aging), infrared spectrum peak height after aging, etc. The elongation at break reflects the change in mechanical properties of silicone rubber during the aging process, and the infrared spectrum peak height data after aging can reflect the change in chemical structure of silicone rubber during the aging process. The change in elongation at break (%) of multiple silicone rubber samples under multi-factor accelerated aging experiments with the number of aging cycles N is shown in the figure. Figures 3 to 5 As shown, S1-S8 are different sample groups. Taking sample group S1 as an example, S1-1, S1-2 and S1-3 are three parallel group samples of sample group S1. That is, in order to improve data stability and ensure the accuracy of experimental results, each sample group has three parallel samples. For example, S2-1, S2-2 and S2-3 are three parallel group samples of sample group S2, S3-1, S3-2 and S3-3 are three parallel group samples of sample group S3, and so on. They will not be repeated here.

[0099] It should be noted that the number of aging cycles usually refers to the number of times a material undergoes a complete transition from the initial state to a specific aging state and then returns to the initial state (or undergoes a specific aging cycle) under certain aging conditions. The aging failure time refers to the total length of time the material is continuously exposed to the aging conditions. In some cases, the number of aging cycles and the aging failure time can be converted to each other: for example, if the length of each aging cycle is known, the approximate aging failure time can be obtained by multiplying the number of aging cycles by the time of each cycle.

[0100] By simulating predetermined temperature conditions and corona discharge conditions and performing continuous field strength simulation calculations, we can obtain aging characteristic data that can fully reflect the aging state of silicone rubber under extreme conditions, providing an important basis for subsequent silicone rubber life prediction.

[0101] In one possible implementation, Figure 6 is a flow chart of an optional method for predicting the life of silicone rubber provided in an embodiment of the present application, with reference to Figure 6 As shown, based on the aging failure time and aging characteristic data of silicone rubber, the service life of silicone rubber is predicted, including:

[0102] S201, determining failure analysis probability distribution data of the silicone rubber based on the aging failure time and aging characteristic data of the silicone rubber.

[0103] S202, if the failure analysis probability distribution data of the silicone rubber satisfies a predetermined failure analysis probability distribution function, a life prediction model based on a temperature acceleration factor model and a corona acceleration factor model is obtained.

[0104] S203, predicting the service life of the silicone rubber based on the aging failure time and aging characteristic data of the silicone rubber through a life prediction model.

[0105] Optionally, in the example of the present application, the failure analysis probability distribution function is used to describe the aging failure time or service life of silicone rubber under different conditions.

[0106] Optionally, in the example of this application, a temperature acceleration factor model is used to describe the relationship between the aging failure of silicone rubber and temperature. The temperature acceleration factor model can specifically adopt the Arrhenius model, which takes into account parameters such as material activation energy, Boltzmann constant, normal use environment absolute temperature and test stress environment absolute temperature, and quantifies the acceleration effect of temperature on silicone rubber aging.

[0107] Optionally, in the example of this application, the corona acceleration factor model is used to explain the effect of corona discharge conditions on the aging failure of silicone rubber. Based on the trap theory, the model takes into account constants related to voltage and materials, model parameters, electric field strength, aging failure time, and free radical quantity, and establishes a corona aging model to describe the mechanism of action of corona discharge on silicone rubber aging.

[0108] Using the aging failure time and aging characteristic data of silicone rubber (such as elongation at break data, infrared spectrum peak height data, etc.), the aging of silicone rubber can be analyzed and the failure analysis probability distribution data of silicone rubber can be determined (for example, Weibull probability distribution diagram). This data reflects the probability distribution of aging failure of silicone rubber at different time points.

[0109] In the example of this application, the failure analysis probability distribution function (eg, Weibull probability distribution function) is used to describe the aging failure time or service life of silicone rubber under different conditions, which can help understand the life distribution characteristics of silicone rubber under various environmental conditions.

[0110] If the failure analysis probability distribution data of silicone rubber meets the predetermined failure analysis probability distribution function, it indicates that the service life prediction model can be used to predict the service life of silicone rubber. The service life prediction model consists of a temperature acceleration factor model and a corona acceleration factor model. Through the service life prediction model, the effects of temperature and corona discharge on the aging of silicone rubber are considered separately, that is, the synergy and coupling of multiple aging variables are comprehensively considered, and the service life of silicone rubber can be predicted by combining the aging failure time and aging characteristic data of silicone rubber.

[0111] In one possible implementation, Figure 7 is a flow chart of an optional method for predicting the life of silicone rubber provided in an embodiment of the present application, with reference to Figure 7 As shown, the life prediction model based on the temperature acceleration factor model and the corona acceleration factor model is obtained, including:

[0112] S301, obtaining material activation energy of the silicone rubber, the absolute temperature value of the environment in which the silicone rubber is in normal use, and the absolute temperature value of the environment under constant temperature stress of the silicone rubber during testing, wherein a constant temperature stress is applied to the silicone rubber under predetermined temperature conditions.

[0113] S302, determining a temperature acceleration factor model of the silicone rubber under predetermined temperature conditions according to the material activation energy of the silicone rubber, the absolute temperature value of the environment in which the silicone rubber is in normal use, and the absolute temperature value of the environment under constant temperature stress of the silicone rubber during the test.

[0114] S303, obtaining the number of free radicals generated by aging of the silicone rubber and the electric field strength determined based on the corona discharge condition, wherein the number of free radicals is used to characterize the corona aging strength.

[0115] S304, determining a corona acceleration factor model according to the number of free radicals, the electric field strength, the aging failure time of the silicone rubber and related constants, wherein the related constants are parameters related to the test voltage and the silicone rubber material.

[0116] S305, generating a life prediction model based on the temperature acceleration factor model and the corona acceleration factor model.

[0117] In the example of this application, the material activation energy of the silicone rubber obtained reflects the energy required for the aging reaction of the silicone rubber. The absolute temperature value of the environment in which the silicone rubber is used in normal use is obtained, that is, the temperature conditions of the silicone rubber in actual use. The absolute temperature value of the environment under constant temperature stress of the silicone rubber during the test is obtained, that is, the temperature when the accelerated aging test is performed under simulated predetermined temperature conditions. Constant temperature stress is applied to the silicone rubber under predetermined temperature conditions, that is, a fixed temperature is maintained in the experiment to accelerate the aging process of the silicone rubber.

[0118] Obtaining the number of free radicals generated by aging of silicone rubber can be used to characterize the corona aging intensity. Under corona discharge conditions, silicone rubber undergoes a series of chemical reactions to generate free radicals, and the number of these free radicals can reflect the degree of corona aging effect on silicone rubber.

[0119] Obtaining the electric field strength determined based on corona discharge conditions is an important feature of corona discharge. Different electric field strengths will have different effects on the aging of silicone rubber.

[0120] In the optional implementation provided in this example, the corona acceleration factor model is based on the trap theory to explain the effect of corona discharge conditions on the aging failure of silicone rubber. The number of free radicals Q = CE b t 1 / 2 ; C is the related constant, b is the model parameter, E is the electric field strength, and t is the aging failure time (t is also called service life, life day (d)).

[0121] For the same material, assuming that the number of free radicals Q generated by aging accumulates to the same value, the polymer will break down, and the silicone rubber material can be judged to have failed. When the electric field strength is less than 103kV / cm, the method of space charge injection is the same, so the constant C of the corona aging model is the same. Therefore, the aging failure time t of silicone rubber is proportional to E2b, and the corona acceleration factor model is constructed based on different corona discharge intensities.

[0122] In addition, based on the obtained material activation energy of silicone rubber, the absolute temperature value of the environment in normal use, and the absolute temperature value of the environment under constant temperature stress during the test, the temperature acceleration factor model of silicone rubber under predetermined temperature conditions is determined to quantify the accelerated effect of temperature on the aging of silicone rubber through the temperature acceleration factor model. Based on the obtained number of free radicals, electric field strength, aging failure time of silicone rubber, and related constants, the corona acceleration factor model is determined to explain the influence of corona discharge conditions on the aging failure of silicone rubber.

[0123] Based on the determined temperature acceleration factor model and corona acceleration factor model, a life prediction model is generated, which comprehensively considers the effects of temperature and corona discharge on silicone rubber aging, and can more accurately predict the service life of silicone rubber. In practical applications, the service life of silicone rubber can be calculated by substituting the aging failure time and aging characteristic data of silicone rubber into the life prediction model.

[0124] In one possible implementation, the temperature acceleration factor model AF(T) is:

[0125]

[0126] In the above formula, Ea is the material activation energy of silicone rubber, which reflects the amount of energy required for the aging reaction of silicone rubber. The higher the activation energy, the greater the change in the aging rate of silicone rubber under the same temperature change. Ea is calculated by substituting the aging cycle number with the same field strength near the silicone rubber surface into the temperature acceleration factor model and performing linear regression. Specifically:

[0127] First, multiple silicone rubber samples were subjected to accelerated aging tests at different temperatures to measure the number of aging cycles and other data at different temperatures. Then, these data were substituted into the temperature acceleration factor formula. s , T u ) can be determined by experimental conditions or known physical constants, so the material activation energy Ea can be solved by linear regression. Linear regression is a mathematical method that fits the data to find the best parameter value so that the model can best describe the change pattern of the data. By adjusting other parameter values, the temperature acceleration factor model can best fit the experimental data, thereby determining the most appropriate Ea value.

[0128] Specifically, k is the Boltzmann constant, a physical constant used to link temperature and energy, and plays a role in regulating the influence of temperature on the aging rate in the temperature acceleration factor model. u The absolute temperature value of the environment in normal use = (unit: Kelvin / K), which indicates the temperature conditions of silicone rubber in actual use. s It is the absolute temperature value of the environment under constant temperature stress during the test / K, that is, the constant temperature condition set during the accelerated aging test.

[0129] In one possible implementation, the corona acceleration factor model AF(E) is:

[0130] AF(E)=(E / E u ) 2b ;

[0131] In the above formula, Eu is the electric field strength near the silicone rubber surface under the first corona aging stress, E is the electric field strength near the silicone rubber surface under the second corona aging stress, and b is a model parameter, which reflects the relationship between the characteristics of corona aging and the electric field strength.

[0132] The first corona aging stress is less than the second corona aging stress, where b is calculated by substituting the infrared spectrum peak height corresponding to different aging failure times into the corona acceleration factor model AF(E) and taking the average value. Specifically:

[0133] First, experiments were conducted under different corona aging stress conditions to measure the infrared spectrum peak height of silicone rubber corresponding to different aging failure times. The infrared spectrum peak height can reflect the chemical structure changes of silicone rubber during the aging process.

[0134] Then, the infrared spectrum peak height values ​​corresponding to these different aging failure times are substituted into the corona acceleration factor model. u , E) can be obtained through experimental measurement, so the value of b can be solved by calculation. The specific method is to substitute the infrared spectrum peak height values ​​corresponding to multiple different aging failure times into the model, calculate multiple model parameter values, and then calculate the average value of the model parameter values ​​to obtain the final model parameter b, which can more accurately reflect the characteristics of corona aging and improve the reliability of the corona acceleration factor model.

[0135] Specifically, when solving the parameters of the corona aging acceleration factor model, the results of the electric field simulation show that the electric field strength on the surface of silicone rubber is 15kV / cm and 10kV / cm, respectively, both less than 106kV / cm. The free radical content K is selected after the sample (silicone rubber) is aged. In the infrared spectrum analysis, different chemical bonds will produce absorption peaks at specific wavenumber positions. When silicone rubber ages, its chemical structure will change, such as the breaking of chemical bonds, the formation of new bonds, or the change of groups. These structural changes will cause the absorption peak height of the corresponding chemical bonds in the infrared spectrum to change. For the absorption peaks corresponding to the silicon (Si) atom connected to the methyl (-CH3) group Si-(CH3)2, silicon oxygen silicon Si-O-Si, carbon-hydrogen (CH) bond CH, and silicon atom connected to the methyl Si-CH3, the absorption peak height will decrease as the silicone rubber ages. The sum of the reduction values ​​of each peak height can be used as a comprehensive indicator to measure the degree of chemical structure change during the aging process of silicone rubber. Substituting this comprehensive index (free radical content K) together with other parameters such as electric field strength into the corona acceleration factor model can more accurately describe the effect of corona aging on the chemical structure of silicone rubber, thereby improving the reliability of the model's description of corona aging characteristics.

[0136] Under the same temperature cycle conditions, the corona acceleration factor can be obtained:

[0137] In the above formula, K1 is the sum of the peak values ​​of the infrared spectrum of silicone rubber when the electric field strength is 15kV / cm; K2 is the sum of the peak values ​​of the infrared spectrum of silicone rubber when the electric field strength is 10kV / cm. Substitute the infrared spectrum peak height data corresponding to different aging failure times into the above formula and calculate the average value to calculate the model parameter b.

[0138] In addition, in another possible implementation, the life prediction model AF(T, E) is:

[0139] AF(T,E)=AF(T)×AF(E)×f(T,E);

[0140] In the above formula, f(T,E) is the coupling term of the temperature acceleration factor model AF(T) and the corona acceleration factor model AF(E), which takes into account the influence of the interaction between temperature and corona discharge on the aging of silicone rubber.

[0141] in,

[0142] In the above formula, E and T are the field strength simulation data and average temperature measurement data near the surface of the composite insulator during the accelerated aging process, and Eu and Tu are the field strength simulation data and average temperature measurement data near the surface of the composite insulator after field aging, respectively.

[0143] Since temperature and corona discharge often exist simultaneously in practice and affect each other in the aging process of silicone rubber, the coupling term f(T, E) is introduced in the example of this application, and the aging and life prediction of silicone rubber under multiple factors can be more accurately described based on the dielectric strength degradation model of the insulator. Through the analysis of the results of the multi-factor accelerated aging experiment of silicone rubber materials, it can be seen that the synergistic effect of multiple aging variables such as temperature, corona discharge and humidity improves the aging efficiency of silicone rubber materials, and the existence of coupling between multiple factors makes the remaining useful life (Remaining Useful Life) RUL prediction (life prediction model) of silicone rubber more complicated.

[0144] In addition, a life prediction model based on the temperature acceleration factor model and the corona acceleration factor model was specifically constructed. Among them, the parameters in the temperature acceleration factor model, such as the material activation energy, can be obtained by substituting the aging cycle number with the same field intensity near the silicone rubber surface into the temperature acceleration factor model and performing linear regression calculation to obtain the parameters in the corona acceleration factor model, such as the model parameter b. After that, the infrared spectrum peak height values ​​corresponding to different aging failure times can be substituted into the corona acceleration factor model and the average value can be calculated.

[0145] In this way, the complex parameter solution is transformed into a calculation process based on experimental data, making the parameter acquisition more systematic and operational, avoiding the difficulty of directly obtaining parameters from power equipment and power grid systems on site. In summary, this method effectively solves the problem of low accuracy in silicone rubber life prediction by considering extreme temperatures, integrating multiple environmental factors, and optimizing parameter acquisition methods.

[0146] Since the life prediction model in the example of this application comprehensively considers the separate effects of temperature and corona discharge on the aging of silicone rubber as well as the interaction between them, by combining the temperature acceleration factor model and the corona acceleration factor model and adding coupling terms, the aging behavior of silicone rubber under complex environmental conditions can be more comprehensively analyzed, thereby providing a more reliable basis for accurately predicting the service life of silicone rubber.

[0147] In one possible implementation, Figure 8 is a flow chart of an optional method for predicting the life of silicone rubber provided in an embodiment of the present application, with reference to Figure 8 As shown, through the life prediction model, based on the aging failure time and aging characteristic data of silicone rubber, the service life of silicone rubber is predicted, including:

[0148] S401, using a life prediction model, obtaining the aging rate of silicone rubber under constant temperature stress applied under predetermined temperature conditions and corona aging stress applied under corona discharge conditions, and obtaining an acceleration factor based on the acceleration degree of the aging rate of silicone rubber under normal stress conditions.

[0149] S402, deriving the failure life data of the silicone rubber under a normal stress level through the acceleration factor, the aging failure time and the aging characteristic data of the silicone rubber.

[0150] S403, predicting the service life of the silicone rubber according to the failure life data and the failure analysis probability distribution function.

[0151] In the example of this application, the life prediction model comprehensively considers the effects of temperature and corona discharge on the aging of silicone rubber. Specifically, the temperature acceleration factor in the life prediction model reflects the acceleration of the aging of silicone rubber by the constant temperature stress applied under the predetermined temperature conditions; the corona acceleration factor reflects the acceleration of the aging of silicone rubber by the corona aging stress applied under the corona discharge conditions.

[0152] Through model calculation, the acceleration degree of the aging rate of silicone rubber under predetermined temperature and corona discharge conditions relative to the aging rate of silicone rubber under normal stress conditions can be obtained. This acceleration degree is the acceleration factor.

[0153] The failure life data of silicone rubber under normal stress level is derived by using the acceleration factor, aging failure time and aging characteristic data of silicone rubber. The aging failure time is the time point of aging failure of silicone rubber determined under accelerated aging conditions. The aging characteristic data includes elongation at break data, infrared spectrum peak height data, etc. These data reflect the various changes of silicone rubber during the aging process.

[0154] It should be noted that in the example of this application, there is a certain relationship between the acceleration factor and the aging failure time and aging characteristic data, through which the data obtained under accelerated stress conditions can be converted into equivalent data under normal stress levels. Specifically, according to the acceleration factor, the failure time and characteristic data under accelerated aging conditions can be converted into the time and characteristic data under normal stress levels, thereby deriving the failure life data of silicone rubber under normal stress levels.

[0155] For example, the acceleration factors of the accelerated stress levels of the S1-S10 group to the normal stress level can be calculated, which are recorded as AF1,…,AF10 respectively. At the same time, a set of failure life data under normal stress level is obtained, which are recorded as t1,…,t10 respectively. Finally, based on the failure life data, the Weibull life distribution is verified, and it can be obtained that m7kV / cm=9.16,η7kV / cm=3135; m4.5kV / cm=9.25,η4.5kV / cm=10937 under two electric field strengths.

[0156] like Fig. 9 As shown, by substituting the shape parameter m and characteristic life η under two electric field strengths into the failure analysis probability distribution function (also called reliability function) R(t), the life reliability curves of silicone rubber with two electric field strengths under normal temperature cyclic stress conditions can be obtained.

[0157] In an optional implementation, the failure analysis probability distribution function R(t) is:

[0158]

[0159] In the above formula, m is the shape parameter, η is the characteristic life, and t is the aging failure time or service life.

[0160] Since the failure analysis probability distribution function (for example, the Weibull probability distribution function) describes the probability distribution of the aging failure time or service life of silicone rubber under different conditions. Furthermore, the example of this application can predict the service life of silicone rubber based on the derived failure life data and the failure analysis probability distribution function. Specifically, by substituting the failure life data into the failure analysis probability distribution function, the predicted service life value of silicone rubber under different reliabilities can be obtained. For example, according to different reliability requirements, the service life range of silicone rubber under normal stress conditions can be determined, providing more detailed information for reliability assessment and life prediction in practical applications.

[0161] The example of this application proposes a method for predicting the service life of silicone rubber for composite insulators in combination with extreme temperature conditions and corona discharge conditions. First, an artificial accelerated aging test combining extreme ambient temperature (sub-zero temperature or above 100°C) and corona discharge is carried out; then, aging characteristic data of silicone rubber samples before and after aging are obtained, and the aging characteristic data include: elongation at break data and infrared spectrum peak height data, and the aging failure time of each group of samples is determined; at the same time, a life prediction model is constructed based on the Arrhenius model and trap theory; finally, the acceleration factor model parameters are solved based on the aging characteristic data, and the service life of silicone rubber under normal stress is derived, which can provide a reference for the operation and maintenance management of aging composite insulators.

[0162] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0163] Corresponding to the life prediction method of silicone rubber in the above embodiment, Fig.10 is a schematic diagram of the structure of a silicone rubber life prediction device provided in an embodiment of the present application. The device can be implemented as part or all of a computer device by software, hardware, or a combination of both. The computer device can be Fig.11 Electronic equipment shown.

[0164] Reference Fig.10 , the life prediction device of silicone rubber comprises:

[0165] The acquisition unit 601 is used to acquire initial characteristic data and aging characteristic data of multiple silicone rubbers, wherein the aging characteristic data is obtained by simulating predetermined temperature conditions and corona discharge conditions and performing accelerated aging tests on multiple silicone rubbers. The initial characteristic data includes: initial elongation, and the aging characteristic data includes: elongation at break.

[0166] The determination unit 602 is used to determine that the silicone rubber is aged and failed if the elongation at break of the silicone rubber is less than a predetermined proportion of the initial elongation, and obtain the aging and failure time of the silicone rubber.

[0167] The prediction unit 603 is used to predict the service life of the silicone rubber based on the aging failure time and aging characteristic data of the silicone rubber.

[0168] Further, based on any of the above embodiments, as an example of the present application, the acquisition unit includes:

[0169] The first acquisition subunit is used to simulate and calculate the field strength near the silicone rubber surface of multiple silicone rubbers by simulating predetermined temperature conditions and corona discharge conditions within a predetermined period of time using a multi-needle corona aging platform to obtain aging characteristic data.

[0170] Further, based on any of the above embodiments, as an example of the present application, the prediction unit includes:

[0171] A determination subunit is used to determine failure analysis probability distribution data of the silicone rubber based on the aging failure time and aging characteristic data of the silicone rubber;

[0172] A second acquisition subunit is used to obtain a life prediction model based on a temperature acceleration factor model and a corona acceleration factor model if the failure analysis probability distribution data of the silicone rubber meets a predetermined failure analysis probability distribution function, wherein the failure analysis probability distribution function is used to describe the aging failure time or service life of the silicone rubber under different conditions, the temperature acceleration factor model is used to describe the relationship between the aging failure of the silicone rubber and the temperature, and the corona acceleration factor model is used to explain the influence of the corona discharge condition on the aging failure of the silicone rubber;

[0173] The prediction subunit is used to predict the service life of the silicone rubber based on the aging failure time and aging characteristic data of the silicone rubber through a life prediction model.

[0174] Further, based on any of the above embodiments, as an example of the present application, the prediction subunit is specifically used for:

[0175] Obtaining the material activation energy of the silicone rubber, the absolute temperature value of the environment in which the silicone rubber is used normally, and the absolute temperature value of the environment under the constant temperature stress of the silicone rubber during the test, wherein the constant temperature stress is applied to the silicone rubber under the predetermined temperature condition;

[0176] According to the material activation energy of silicone rubber, the absolute temperature value of the environment in which silicone rubber is used normally, and the absolute temperature value of the environment under constant temperature stress of silicone rubber during testing, the temperature acceleration factor model of silicone rubber under predetermined temperature conditions is determined;

[0177] Obtaining the number of free radicals generated by aging of silicone rubber and the electric field strength determined based on corona discharge conditions, wherein the number of free radicals is used to characterize the corona aging strength;

[0178] The corona acceleration factor model is determined based on the number of free radicals, electric field strength, aging failure time of silicone rubber and related constants, wherein the related constants are parameters related to the test voltage and silicone rubber material;

[0179] Based on the temperature acceleration factor model and the corona acceleration factor model, a life prediction model is generated.

[0180] Further, based on any of the above embodiments, as an example of the present application, the temperature acceleration factor model AF(T) is:

[0181]

[0182] In the above formula, Ea is the material activation energy of silicone rubber, k is the Boltzmann constant, T u is the absolute temperature of the environment in normal use / K, T s is the absolute temperature value of the environment under constant temperature stress during the test / K; where Ea is obtained by substituting the number of aging cycles with the same field intensity near the silicone rubber surface into the temperature acceleration factor model AF(T) and performing linear regression calculation.

[0183] Further, based on any of the above embodiments, as an example of the present application, the corona acceleration factor model AF(E) is:

[0184] AF(E)=(E / E u ) 2b ;

[0185] In the above formula, Eu is the field strength near the silicone rubber surface under the first corona aging stress, E is the field strength near the silicone rubber surface under the second corona aging stress, b is a model parameter, the first corona aging stress is less than the second corona aging stress, and b is calculated by substituting the infrared spectrum peak height values ​​corresponding to different aging failure times into the corona acceleration factor model AF(E) and taking the average value.

[0186] Further, based on any of the above embodiments, as an example of the present application, the life prediction model AF(T, E) is:

[0187] AF(T,E)=AF(T)×AF(E)×f(T,E);

[0188] In the above formula, f(T,E) is the coupling term of the temperature acceleration factor model AF(T) and the corona acceleration factor model AF(E).

[0189] Further, based on any of the above embodiments, as an example of the present application, the prediction subunit is specifically used for:

[0190] The aging rate of silicone rubber under constant temperature stress applied under predetermined temperature conditions and corona aging stress applied under corona discharge conditions is obtained through the life prediction model, and the acceleration factor is obtained by comparing the acceleration degree of the aging rate of silicone rubber under normal stress conditions.

[0191] The failure life data of silicone rubber under normal stress level is derived through the acceleration factor, aging failure time and aging characteristic data of silicone rubber;

[0192] Based on the failure life data and failure analysis probability distribution function, the service life of silicone rubber is predicted.

[0193] It should be noted that the silicone rubber life prediction device provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0194] The functional units and modules in the above embodiments may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the above integrated units may be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present application.

[0195] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0196] An embodiment of the present application further provides an electronic device, the electronic device comprising one or more processors and a memory;

[0197] The memory is coupled to one or more processors, and the memory is used to store computer program codes, the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the electronic device to execute the aforementioned silicone rubber life prediction method.

[0198] Fig.11The schematic diagram of the structure of an electronic device provided in the embodiment of the present application is that the electronic device 700 can be a mobile phone, a smart screen, a tablet computer, a wearable electronic device, an in-vehicle electronic device, an augmented reality (AR) device, a virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a projector, or a communication device such as a server, a storage device, a base station, or a smart car, etc. The embodiment of the present application does not impose any restrictions on the specific type of the electronic device.

[0199] The memory 701 can be used to store computer software programs 702 and modules, and the processor 703 executes various functional applications and data processing of the electronic device by running the software programs and modules stored in the memory 701. The memory 701 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the electronic device (such as audio data, a phone book, etc.), etc. In addition, the memory 701 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0200] Among them, the processor 703 may include one or more processors such as a central processing unit, an application processor (AP), a baseband processor, etc. The processor may be the nerve center and command center of the wireless router. The processor 703 may generate an operation control signal according to the instruction operation code and the timing signal to complete the control of fetching and executing instructions. The memory 701 may be used to store computer executable program codes, and the executable program codes include instructions. The processor 703 executes various functional applications and data processing of the network device by running the instructions stored in the memory. The memory 701 may include a program storage area and a data storage area, such as storing data of a sound signal to be played. For example, the memory may be a double rate synchronous dynamic random access memory DDR or a flash memory Flash.

[0201] An embodiment of the present application further provides a computer-readable storage medium, in which computer instructions are stored; when the computer-readable storage medium is run on an electronic device, the electronic device executes the aforementioned method for predicting the life of silicone rubber.

[0202] The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium, or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0203] The embodiment of the present application further provides a computer program product including computer instructions. When the computer program product is run on an electronic device, the electronic device can execute the aforementioned method for predicting the life of silicone rubber.

[0204] The computer storage medium and computer program product provided in the above-mentioned embodiments of the present application are used to execute the method provided above. Therefore, the beneficial effects that can be achieved can refer to the corresponding beneficial effects of the method provided above, and will not be repeated here.

[0205] In the above embodiments, it can also be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (such as: coaxial cable, optical fiber, data subscriber line (Digital Subscriber Line, DSL)) or wireless (such as: infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a digital versatile disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)).

[0206] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0207] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments applied for herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0208] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0209] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0210] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for predicting the life of silicone rubber, characterized in that: include: Acquire initial characteristic data and aging characteristic data of a plurality of silicone rubbers, wherein the aging characteristic data is obtained by simulating predetermined temperature conditions and corona discharge conditions and performing accelerated aging tests on the plurality of silicone rubbers, the initial characteristic data includes: initial elongation, and the aging characteristic data includes: elongation at break; If the elongation at break of the silicone rubber is less than a predetermined proportion of the initial elongation, it is determined that the silicone rubber has aged and failed, and the aging failure time of the silicone rubber is obtained; The service life of the silicone rubber is predicted based on the aging failure time of the silicone rubber and the aging characteristic data.

2. The method according to claim 1, characterized in that The step of obtaining aging characteristic data of a plurality of silicone rubbers comprises: By simulating predetermined temperature conditions and corona discharge conditions, a multi-needle corona aging platform is continuously used to simulate and calculate the field strength near the silicone rubber surface of multiple silicone rubbers within a predetermined period of time to obtain the aging characteristic data.

3. The method according to claim 1, characterized in that The predicting of the service life of the silicone rubber based on the aging failure time of the silicone rubber and the aging characteristic data includes: Determining failure analysis probability distribution data of the silicone rubber based on the aging failure time of the silicone rubber and the aging characteristic data; If the failure analysis probability distribution data of the silicone rubber satisfies a predetermined failure analysis probability distribution function, a life prediction model based on a temperature acceleration factor model and a corona acceleration factor model is obtained, wherein the failure analysis probability distribution function is used to describe the aging failure time or service life of the silicone rubber under different conditions, the temperature acceleration factor model is used to describe the relationship between the aging failure of the silicone rubber and the temperature, and the corona acceleration factor model is used to explain the influence of the corona discharge condition on the aging failure of the silicone rubber; The service life of the silicone rubber is predicted by the life prediction model based on the aging failure time of the silicone rubber and the aging characteristic data.

4. The method according to claim 3, characterized in that Obtain the life prediction model based on the temperature acceleration factor model and the corona acceleration factor model, including: Obtaining the material activation energy of the silicone rubber, the absolute temperature value of the environment of the silicone rubber in normal use, and the absolute temperature value of the environment of the silicone rubber under constant temperature stress during testing, wherein the constant temperature stress is applied to the silicone rubber under the predetermined temperature condition; Determine the temperature acceleration factor model of the silicone rubber under predetermined temperature conditions according to the material activation energy of the silicone rubber, the absolute temperature value of the environment of the silicone rubber in normal use, and the absolute temperature value of the environment of the silicone rubber under constant temperature stress during testing; Obtaining the number of free radicals generated by aging of the silicone rubber and the electric field strength determined based on the corona discharge conditions, wherein the number of free radicals is used to characterize the corona aging strength; Determine the corona acceleration factor model according to the number of free radicals, the electric field strength, the aging failure time of the silicone rubber and related constants, wherein the related constants are parameters related to the test voltage and the silicone rubber material; The life prediction model is generated based on the temperature acceleration factor model and the corona acceleration factor model.

5. The method according to claim 4, characterized in that The temperature acceleration factor model AF(T) is: In the above formula, Ea is the material activation energy of the silicone rubber, k is the Boltzmann constant, T u is the absolute temperature of the environment in normal use / K, T s is the absolute temperature value of the environment under constant temperature stress during the test / K; wherein, Ea is obtained by substituting the number of aging cycles with the same field intensity near the silicone rubber surface into the temperature acceleration factor model AF(T) and performing linear regression calculation.

6. The method according to claim 4, characterized in that The corona acceleration factor model AF(E) is: AF(E)=(E / E u ) 2b ; In the above formula, E u is the field intensity near the silicone rubber surface under the first corona aging stress, E is the field intensity near the silicone rubber surface under the second corona aging stress, b is a model parameter, the first corona aging stress is less than the second corona aging stress, wherein b is calculated by substituting the infrared spectrum peak height values ​​corresponding to different aging failure times into the corona acceleration factor model AF(E) and taking the average value.

7. The method according to claim 4, characterized in that The life prediction model AF(T,E) is: AF(T,E)=AF(T)×AF(E)×f(T,E); In the above formula, f(T, E) is the coupling term of the temperature acceleration factor model AF(T) and the corona acceleration factor model AF(E).

8. The method according to any one of claims 3 to 7, characterized in that The predicting of the service life of the silicone rubber based on the aging failure time of the silicone rubber and the aging characteristic data by the service life prediction model includes: The life prediction model is used to obtain the aging rate of the silicone rubber under the constant temperature stress applied under the predetermined temperature condition and the corona aging stress applied under the corona discharge condition, and the acceleration factor is obtained based on the acceleration degree of the aging rate of the silicone rubber under normal stress conditions. Derived the failure life data of the silicone rubber under normal stress level through the acceleration factor, the aging failure time of the silicone rubber and the aging characteristic data; The service life of the silicone rubber is predicted based on the failure life data and the failure analysis probability distribution function.

9. A life prediction device for silicone rubber, characterized in that: include: An acquisition unit, used for acquiring initial characteristic data and aging characteristic data of a plurality of silicone rubbers, wherein the aging characteristic data is obtained by simulating predetermined temperature conditions and corona discharge conditions and performing accelerated aging tests on the plurality of silicone rubbers, the initial characteristic data includes: initial elongation, and the aging characteristic data includes: elongation at break; a determination unit, configured to determine that the silicone rubber is aged and failed if the elongation at break of the silicone rubber is less than a predetermined proportion of the initial elongation, and to obtain the aging and failure time of the silicone rubber; A prediction unit is used to predict the service life of the silicone rubber based on the aging failure time of the silicone rubber and the aging characteristic data.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the electronic device implements the method according to any one of claims 1 to 8.

11. A computer program product, characterized in that The invention comprises a computer program which, when executed, causes the method according to any one of claims 1 to 8 to be performed.

12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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