CN112163391A - Method and system for estimating service life of thin film capacitor under influence of humidity - Google Patents
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Abstract
The invention discloses a method and a system for estimating the service life of a thin film capacitor under the influence of humidity, and belongs to the field of power electronics. The method comprises the following steps: collecting data of the capacitance values of a plurality of film capacitors changing along with time under various relative humidities, and carrying out normalization processing on the data to obtain characteristic data; defining a failure characterization factor, establishing a model of the failure characterization factor changing along with time according to the characteristic data, and linearizing the model; defining a relative humidity virtual variable and introducing a linearized model to obtain a failure model which is universal for various relative humidities; performing multiple linear regression to train a failure model; evaluating the trained failure model until the model meets the preset requirement; and estimating the service life of the capacitor according to the established failure model. The invention innovatively sets the relative humidity as a virtual variable, introduces the relative humidity into a failure model of the film capacitor in a qualitative variable mode, and quickly and intuitively analyzes the influence of different humidity conditions on the capacitor.
Description
Technical Field
The invention belongs to the field of power electronics, and particularly relates to a method and a system for estimating the service life of a thin film capacitor under the influence of humidity.
Background
Capacitors are indispensable devices in the fields of power electronics and motor drive, and are widely used in components, equipment, or systems such as converters, power transmission, motor drive, and the like. Electrolytic capacitors, which have high energy density and low cost, are commonly used as dc bus capacitors to buffer energy, limit voltage ripple, and balance power. However, the electrolytic capacitor has problems such as a large size and a short life, and is susceptible to ripple current, high temperature, overvoltage, and overcurrent, and therefore needs to be replaced periodically. In the occasion with higher requirement on service life, in order to solve the problems, the film capacitor can be used for replacing the electrolytic capacitor, thereby not only remarkably improving the reliability of the system, but also greatly reducing the volume of the capacitor in the power system. Therefore, the health of the film capacitor greatly affects the operation of the whole system, and it is necessary to research the state monitoring and life estimation.
State monitoring and lifetime estimation of capacitors is mainly based on the study of the trend of the lifetime characterization factor. The commonly used life characterization factors comprise a capacitance value C and an equivalent series resistance ESR, and when the capacitance value is reduced by 2% -5%, the thin film capacitor is considered to reach the life limit. The existing methods for calculating the service life index can be divided into three types: sensor-based methods, circuit model-based methods, data and algorithm-based methods. Sensor and circuit model based approaches require additional hardware circuitry, direct or indirect use of the sensor, adding complexity and cost. In contrast, software-based condition monitoring methods are promising for practical applications. However, the current research results based on data and algorithms are few, mainly because it is difficult to obtain a large amount of data.
The research results on the influence of temperature on the life of the film capacitor are more remarkable, such as establishing a model of temperature and life characterization factors, estimating the hot spot temperature according to the temperature model, introducing the temperature factor into a capacitor design model and the like. In addition to temperature, humidity is also a key factor that accelerates the aging of thin film capacitors, especially at higher humidity, but the research efforts in humidity are not as good as temperature.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method and a system for estimating the service life of a thin film capacitor under the influence of humidity, and aims to solve the problems that the existing software-based state monitoring method is small in data volume and cannot quantify.
To achieve the above object, an aspect of the present invention provides a method for estimating a lifetime of a thin film capacitor under the influence of humidity, comprising the steps of:
s1, collecting data of the capacitance values of a plurality of film capacitors changing along with time under various relative humidities, and carrying out normalization processing on the data to obtain characteristic data;
s2, defining a failure characterization factor, establishing a model of the failure characterization factor changing along with time according to the characteristic data, and linearizing the model;
s3, defining a relative humidity virtual variable and introducing a linearized model to obtain a failure model which is universal for various relative humidities;
s4, performing multiple linear regression to train a failure model;
s5, evaluating the trained failure model until the model meets the preset requirement;
and S6, estimating the service life of the capacitor according to the established failure model.
Further, S1 specifically includes:
s11, keeping the temperature constant, and collecting data of the capacitance values of the film capacitors changing along with time under various relative humidities;
s12, normalizing the capacitance value attenuation data of each capacitor under each relative humidity by taking the maximum value and the minimum value of the capacitance value attenuation data as standards;
s13, an average value of the normalized data of the plurality of capacitances at each relative humidity is obtained as the characteristic data at the relative humidity.
Further, S2 specifically includes:
s21, defining the failure characterization factor as Δ C (t)% (C)0-C(t))/C0In which C is0Is the initial value of the capacity value, C (t) is the capacity value at the time t;
s22, respectively using the characteristic data under each relative humidity to describe a scatter diagram of the failure characterization factor changing along with time;
s23, determining the distribution obeyed by the decay trend of the failure characterization factor along with time according to the change trend of the scatter diagram to obtain a function model of the failure characterization factor:
ΔC(t)％＝ea(t-b)
wherein a and b are respectively shape and position parameters, and t is time;
s24, linearizing the function model:
yt＝kxt+z
wherein ln (Δ c (t)%) = yt，t＝xt，a＝k，-ab＝z-ab＝z，xtFor time variables, z and k are deterministic constants for multiple sets of relative humidity.
Further, S3 specifically includes:
constructing N relative humidities as N-1 virtual variables by using time as a general explanatory variable, introducing a random noise variable and using RH1And RH2Expressing the humidity virtual variable, introducing the humidity virtual variable into the linearized model in an addition and multiplication mode to obtain a failure characterization factor yt：
yt＝z+kxt+z1RH1+z2RH2+k1RH1xt+k2RH2xt+ut
Wherein u istAnd xtRespectively random noise and time variable, z and k are multiple groups of relative humidity deterministic constants, z1,z2,k1,k2Are random coefficients.
Further, S4 specifically includes:
s41, setting an optimization target of model training with the least square sum of errors of the estimation data and the actual data;
s42, using the characteristic data of each group of relative humidity as a training data set, and setting m for the ith groupiAnd the sets correspond to the degradation failure data at different monitoring times, and the estimation of the model parameters is obtained by the least error square sum in the following formula:
wherein, yi,jThe actual value of the jth failure characterization factor for the ith set of relative humidities,
is yi,jAn estimated value of (d);
s43 using RHi,j,kThe ith relative humidity virtual variable representing the kth degradation failure data of the jth group in vector form
And
representing a training sample set, the estimated model parameters being
The following results were obtained:
wherein the content of the first and second substances,
representing the model parameter to be estimated, XTIs a transposed matrix of X.
Further, S5 specifically includes:
s51, calculating residual square sum RSS and regression square sum ESS of multiple linear regression;
s52, further calculating a judgment coefficient R according to the obtained RSS and ESS2And adjusted decision coefficient
S53, according to the judgment coefficient R2And adjusted decision coefficient
And evaluating the goodness of fit of the model to the data, using F to test the linearity of the evaluation model, and using t test to perform significance test to evaluate the influence of each explanatory variable in the model on the explanatory variable until the goodness of fit, linearity and significance test meet preset requirements.
Further, S6 specifically includes:
s61, when the capacity value is reduced to a preset value, the film capacitor is considered to reach the service life limit, and the value of the service life characterization factor is calculated;
and S62, determining the time corresponding to the value of the life characterization factor when the life reaches the limit as the estimated life according to the established degradation model of the life characterization factor changing along with the time.
Another aspect of the invention provides a system for estimating the lifetime of a thin film capacitor under the influence of humidity, comprising:
the characteristic data acquisition module is used for acquiring data of the capacitance values of the plurality of film capacitors changing along with time under various relative humidities and carrying out normalization processing on the data to obtain characteristic data;
the linearization module is used for defining the failure characterization factor, establishing a model of the failure characterization factor changing along with time according to the characteristic data, and linearizing the model;
the failure model acquisition module is used for defining a relative humidity virtual variable and introducing a linearized model to obtain a failure model which is universal for various relative humidities;
the model training module is used for carrying out multiple linear regression to train a failure model;
the model evaluation module is used for evaluating the trained failure model until the model meets the preset requirement;
and the service life estimation module is used for estimating the service life of the capacitor according to the established failure model.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
(1) the invention provides a method for estimating the service life of a film capacitor under the influence of humidity, which creatively sets relative humidity as a virtual variable, introduces the relative humidity into a failure model of the film capacitor in a qualitative variable mode, inspects the influence of each interpretation variable on the interpreted variable through F test and t test, determines whether the variable needs to be kept in the model or not, adjusts the model, and quickly and intuitively analyzes the influence of different humidity conditions on the capacitor;
(2) the modeling method provided by the invention is suitable for carrying out single-factor analysis on a certain influence factor under the condition of being more similar to the category variable, is simple and easy to implement and has strong universality;
(3) the invention is based on data and software algorithm, avoids extra hardware circuit and sensor, does not need signal injection, reduces complexity and cost, and has high industrial utilization value.
Drawings
FIG. 1 is a flow chart of a virtual variable test method for analyzing the effect of humidity on a thin film capacitor provided by the present invention;
FIG. 2 is a flow chart of the present invention for evaluating the correlation of a model with model variables;
FIGS. 3(a) - (c) are graphs showing the fitting effect of the multiple linear regression model provided by the embodiment of the present invention; wherein FIG. 3(a) is a fitting effect of a failure model corresponding to 55% relative humidity; FIG. 3(b) is a graph of the fit effect of a failure model for 70% relative humidity; FIG. 3(c) is a graph of the fit effect of a failure model for 85% relative humidity;
FIGS. 4(a) - (c) are graphs showing the fitting effect of the adjusted multiple linear regression model provided by the embodiment of the present invention; FIG. 4(a) is a fitting effect of a failure model corresponding to 55% relative humidity; FIG. 4(b) is a graph of the fit effect of a failure model for 70% relative humidity; FIG. 4(c) is a graph of the fit effect of the failure model for 85% relative humidity.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the present invention provides a method of estimating the lifetime of a film capacitor under the influence of humidity, comprising the steps of:
s1, establishing an acceleration test system, collecting data of volume values changing along with time under 55%, 70% and 85% relative humidity conditions, and carrying out normalization and averaging processing on the data;
s2, selecting the capacitance value C of the film capacitor to represent the aging of the capacitor, defining the expression of the failure representation factor, establishing a model of the failure representation factor changing along with time, and linearizing the failure model;
s3, defining a relative humidity virtual variable, introducing a linearized failure model, and deducing a failure model which is universal for three humidity conditions;
s4, performing multiple linear regression by adopting a least square method to train a model;
s5, evaluating the proposed model and the correlation between each explanatory variable and the explained variable, and adjusting the model appropriately;
and S6, deriving specific failure models respectively suitable for three humidities according to the universal capacitor failure model containing the relative humidity factor, and estimating the service life of the film capacitor according to the established models for different humidity conditions.
To clearly describe the virtual variable test method for analyzing the effect of humidity on the film capacitor according to the present invention, the following embodiments are described in detail:
s1, building a test system, which comprises a climate box, an LCR analyzer, an IR analyzer, a ripple current tester, a leakage current analyzer and the like. The climate box is used for providing required temperature and humidity for the experiment, and the LCR analyzer is used for measuring the capacitance. In this embodiment, the fixed temperature is 85 ℃, and data of 10 capacitors whose capacitance values change with time under three humidity conditions of 55%, 70%, and 85% are collected. There were 20 groups at 55% relative humidity, 10 groups at 70% relative humidity and 19 groups at 85% relative humidity.
And (3) carrying out normalization processing on the capacitance value attenuation data of each capacitor under three kinds of humidity by taking the maximum value and the minimum value of the capacitance value attenuation data as standards, respectively calculating the average value of the normalized capacitance values of 10 capacitors, and using the average value as characteristic data of the group for modeling.
S2, selecting a capacitance value from the capacitance value, the equivalent series resistance and the threshold voltage to represent the aging failure of the capacitor, and setting the percentage of the capacitance value at the time t relative to the initial capacitance value as a failure representation factor as an interpreted variable of the model.
Roughly drawing a scatter diagram of the failure characterization factor changing with time, depicting the capacity value changing trend into an exponential distribution according to the changing trend of the scatter diagram and a related background, and observing that the higher the humidity is, the faster the capacitor aging speed is. And an inflection point for accelerating aging is formed under three humidities. An exponential model is chosen as simple as possible to describe the aging process, as shown in the following equation:
ΔC(t)％＝ea(t-b)
a. b, obtaining the shape and position parameters respectively by taking logarithm linearization:
ln(ΔC(t)％)＝at-ab
further, ln (Δ c (t)%) y is givent，t＝xtA-k, -ab-z, which can be abbreviated as linear functions
yt＝kxt+z
S3, there are three qualitative factors in this embodiment, theThe established failure model contains intercept terms, avoids trapping in a virtual variable trap according to the setting rule of the virtual variables, generates complete multiple collinearity, constructs 2 virtual variables for three humidity conditions, and uses RH1And RH2And (4) showing. 55% relative humidity was used as the base type, and 70% and 85% relative humidity were used as the comparative types. Thus, the three humidity conditions correspond to two virtual variables [ 00 ]]，[1 0]And [ 01 ]]。
And introducing a qualitative time factor into the model as a general explanatory variable, wherein random noise needs to be considered in the model, and the random noise follows normal distribution with the mean value of 0 and the covariance. Simultaneously, two relative humidity virtual variables are brought into the model in an addition mode and a multiplication mode to obtain an aging model with both changed slope and intercept, which is expressed as
yt＝z+kxt+z1RH1+z2RH2+k1RH1xt+k2RH2xt+ut
Wherein u istAnd xtRandom noise and time variance, respectively. z and k are three sets of relative humidity determining constants, z1,z2,k1,k2Heterogeneity of comparison type relative to base type is covered for random coefficients.
S4, according to the Markov theorem, selecting the least square linear unbiased estimation training model with the minimum variance, wherein the training target of the model is that the error square sum of the estimation data and the actual data is minimum, namely
At the same time, using vectors
Representing input of training samples by vectors
Indicating a desired inputAnd (6) discharging. In this example, the 55% relative humidity set corresponds to m 120, 70% relative humidity set corresponds to m2Set 10, 85% relative humidity corresponds to m319. The results of the parameter estimation are shown in table 1.
TABLE 1
Further, the step S5 includes the following steps, which are specifically shown in fig. 2.
S51, residual square sum RSS and regression square sum ESS of multiple linear regression by the least square method are calculated, and the calculation results are shown in table 2.
TABLE 2
| Degree of freedom | ||
| Regression sum of squares (ESS) | 176.776338 | 5 |
| Sum of squares residual error (RSS) | 6.57333847 | 42 |
As can be seen from Table 2, the ESS values reflect a total deviation of up to 176.776338 between the estimated and average values of the interpreted variables, i.e., the failure characterization factors, and the RSS reflects a total deviation of 6.57333847 between the estimated and actual values of the interpreted variables. The value of ESS is large enough and RSS is small, meaning that the regression model has a good goodness of fit to the data.
S52, further calculating a judgment coefficient R according to the obtained RSS and ESS2，R2Is defined as:
to eliminate the influence of the number of explanatory variables on the goodness of fit, R is used2The numerator and denominator of (2) are divided by the respective degrees of freedom to obtain adjusted determination coefficients
Represents:
where n is the data sample size, RSS has n-k-1 degrees of freedom and ESS + RSS has n-1 degrees of freedom in a model with k +1 independent variables. R2And
the calculation results are shown in table 3.
TABLE 3
S53, evaluating the goodness of fit of the model to the data, simulatingThe goodness is inversely proportional to RSS and directly proportional to ESS, R2And
the larger the better the goodness of fit.
As can be seen from Table 3, the coefficient R was determined2And adjusted decision coefficient
Are all close to 1, indicating that the model has strong linear correlation. The root mean square error has a value of 0.39561, again indicating that the model fits well to the aging data. In addition, the data are almost distributed on two sides of the regression line, which is intuitively shown in the attached figures 3(a) - (c), and the effectiveness of the proposed model is verified.
And S54, performing significance test on the model. The statistical F test is used to evaluate whether the model linearity is significant, i.e. whether the linear relationship of all the explanatory variables to the explained variables is significant.
Assuming that the influence of all the explanatory variables on the failure characterization factor is 0, the original assumption is made
H0:z＝k＝z1＝z2＝k1＝k2＝0
Psi ═ ESS/k)/(RSS/(n-k-1) can be constructed, subject to the F distribution, with n and k having the meaning as previously described. The results of the F test are shown in table 2, indicating that the probability of accepting the original hypothesis is 0. At a confidence level of over 99.99%, the linear dependence of the model can be considered obvious.
And S55, performing significance test by using a t test method in statistics, evaluating whether the influence of each interpretation variable on the failure characterization factor of the interpreted variable in the model is significant to reflect the correlation between the two variables, and determining whether the model should be kept.
Assuming that the influence of each explanatory variable on the failure characterization factor is 0, the original assumption is made
H0:βj＝0(βj＝z,k,z1,z2,k1,k2)
Can be constructed
Obey a t distribution in which
To explain the variable betajE is the deviation of the estimated value of the characterizing factor from the actual value,
has a variance of CiiThe product of the variance of the noise, n and k, has the meaning as described above. The results of the t-test for each individual explanatory variable are shown in table 1. The error probability, i.e. the p value, of all the parameters for rejecting the original hypothesis is less than 0.5, which indicates that each explanatory variable has an influence on the explained variable to a certain extent. In particular, the coefficients are k, z2，k1The variable of z has a significant effect on the explained variable at a 1% significance level, while the coefficient is k2The variables of (c) had an effect at the 10% significance level. Coefficient z1Seems to have little influence on the variable term of (2), and z1And k2The 95% confidence interval of (c) contains 0, and therefore, whether the term remains requires further investigation, the original hypothesis cannot be directly rejected.
And S56, comparing and analyzing the significance test result, adjusting the model, and returning to S54 for evaluation again.
For detailed understanding of the coefficient is z1The influence of the variable term on the interpreted variable, the variable term is removed, and then regression and evaluation are performed again. The regression results are shown in FIGS. 4(a) - (c). At 55% and 70% relative humidity, z is excluded1And a regression curve containing z1Compared with the regression curve of (1), the slope and intercept are slightly changed. Does not contain z1Of (2) is regressive
And RMSE 0.9599 and 0.3954, respectively. And comprises z1Compared with the model of (a) to (b),
keep unchanged, the RMSE slightly decreases. Therefore, has a coefficient z1The variable term of (2) has no great influence on the explanatory variable and can be ignored.
Further, the step S6 includes the following steps.
S61, the aging model at 55%, 70%, 85% relative humidity was derived from the aging model established in S3. Taking 55% as an example, RH is adjusted1And RH2The corresponding value of (A) is substituted into the aging model in S3, with E (Y)t|Xt,RH1＝0,RH2＝0)＝z+kXt
Similarly, the aging models for the 70% and 85% conditions are
E(Yt|Xt,RH1＝1,RH2＝0)＝z+z1+(k+k1)Xt
E(Yt|Xt,RH1＝0,RH2＝1)＝z+z2+(k+k2)Xt
Wherein the meaning of the relevant variables is as described above.
And S62, calculating the value of the life characterization factor when the tolerance value is reduced to 5%. Giving a set of data with capacity value changing along with time as initial capacity value CinThe final value of the measured capacity value changing with time is CeThe life-span characterization factor l can be written as
And S63, substituting the life characterization factor l calculated in S62 into the aging model under the relative humidity of 55%, 70% and 85% in S61 respectively, and estimating the life of the film capacitor under different humidity conditions.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method of estimating the lifetime of a film capacitor under the influence of humidity, comprising the steps of:
s1, collecting data of the capacitance values of a plurality of film capacitors changing along with time under various relative humidities, and carrying out normalization processing on the data to obtain characteristic data;
s2, defining a failure characterization factor, establishing a model of the failure characterization factor changing along with time according to the characteristic data, and linearizing the model;
s3, defining a relative humidity virtual variable and introducing a linearized model to obtain a failure model which is universal for various relative humidities;
s4, performing multiple linear regression to train the failure model;
s5, evaluating the trained failure model until the model meets the preset requirement;
and S6, estimating the service life of the capacitor according to the established failure model.
2. The method according to claim 1, wherein the S1 specifically includes:
s11, keeping the temperature constant, and collecting data of the capacitance values of the film capacitors changing along with time under various relative humidities;
s12, normalizing the capacitance value attenuation data of each capacitor under each relative humidity by taking the maximum value and the minimum value of the capacitance value attenuation data as standards;
s13, an average value of the normalized data of the plurality of capacitances at each relative humidity is obtained as the characteristic data at the relative humidity.
3. The method according to claim 2, wherein the S2 specifically includes:
s21, defining the failure characterization factor as Δ C (t)% (C)0-C(t))/C0In which C is0Is the initial value of the capacity value, C (t) is the capacity value at the time t;
s22, respectively using the characteristic data under each relative humidity to describe a scatter diagram of the failure characterization factor changing along with time;
s23, determining the distribution obeyed by the decay trend of the failure characterization factor along with time according to the change trend of the scatter diagram to obtain a function model of the failure characterization factor:
ΔC(t)％＝ea(t-b)
wherein a and b are respectively shape and position parameters, and t is time;
s24, linearizing the function model:
yt＝kxt+z
wherein ln (Δ c (t)%) = yt，t＝xt，a＝k，-ab＝z，xtFor time variables, z and k are deterministic constants for multiple sets of relative humidity.
4. The method according to claim 3, wherein the S3 specifically includes:
constructing N relative humidities as N-1 virtual variables by using time as a general explanatory variable, introducing a random noise variable and using RH1And RH2Expressing the humidity virtual variable, introducing the humidity virtual variable into the linearized model in an addition and multiplication mode to obtain a failure characterization factor yt：
yt＝z+kxt+z1RH1+z2RH2+k1RH1xt+k2RH2xt+ut
Wherein u istAnd xtRespectively random noise and time variable, z and k are multiple groups of relative humidity deterministic constants, z1,z2,k1,k2Are random coefficients.
5. The method according to claim 4, wherein the S4 specifically includes:
s41, setting an optimization target of model training with the least square sum of errors of the estimation data and the actual data;
s42, using the characteristic data of each group of relative humidity as a training data set, and setting m for the ith groupiDegradation failure of groups corresponding to different monitoring timesData, then the estimate of the model parameters is obtained by the sum of the squared minimum errors in the following equation:
wherein, yi,jThe actual value of the jth failure characterization factor for the ith set of relative humidities,
is yi,jAn estimated value of (d);
s43 using RHi,j,kThe ith relative humidity virtual variable representing the kth degradation failure data of the jth group in vector form
And
representing a training sample set, the estimated model parameters being
The following results were obtained:
wherein the content of the first and second substances,
representing the model parameter to be estimated, XTIs a transposed matrix of X.
6. The method according to claim 5, wherein the S5 specifically includes:
s51, calculating residual square sum RSS and regression square sum ESS of multiple linear regression;
s52, further calculation according to the obtained RSS and ESSDetermination coefficient R2And adjusted decision coefficient
S53, according to the judgment coefficient R2And adjusted decision coefficient
And evaluating the goodness of fit of the model to the data, using F to test the linearity of the evaluation model, and using t test to perform significance test to evaluate the influence of each explanatory variable in the model on the explanatory variable until the goodness of fit, the linearity and the significance meet preset requirements.
7. The method according to claim 6, wherein the S6 specifically includes:
s61, when the capacity value is reduced to a preset value, the film capacitor is considered to reach the service life limit, and the value of the service life characterization factor is calculated;
and S62, determining the time corresponding to the value of the life characterization factor when the life reaches the limit as the estimated life according to the established degradation model of the life characterization factor changing along with the time.
8. A system for estimating the life of a film capacitor under the influence of humidity, comprising:
the characteristic data acquisition module is used for acquiring data of the capacitance values of the plurality of film capacitors changing along with time under various relative humidities and carrying out normalization processing on the data to obtain characteristic data;
the linearization module is used for defining a failure characterization factor, establishing a model of the failure characterization factor changing along with time according to the characteristic data, and linearizing the model;
the failure model acquisition module is used for defining a relative humidity virtual variable and introducing a linearized model to obtain a failure model which is universal for various relative humidities;
a model training module for performing a multiple linear regression to train the failure model;
the model evaluation module is used for evaluating the trained failure model until the model meets the requirements;
and the service life estimation module is used for estimating the service life of the capacitor according to the established failure model.
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