CN112163391B - 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 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 change of the capacitance values of a plurality of film capacitors 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 change of the failure characterization factor along with time according to the characteristic data, and linearizing the model; defining a virtual variable of the relative humidity, and introducing a linearized model to obtain a failure model which is commonly used for various relative humidities; performing multiple linear regression to train the failure model; evaluating the trained failure model until the model reaches a preset requirement; and estimating the service life of the capacitor according to the established failure model. The method creatively sets the relative humidity as a virtual variable, and introduces the virtual variable into a failure model of the film capacitor in a qualitative variable mode to rapidly and intuitively analyze 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 film capacitor under the influence of humidity.
Background
Capacitors are an indispensable device in the fields of power electronics and motor driving, and are widely used in components, devices or systems such as converters, power transmission, motor driving, and the like. Electrolytic capacitors have high energy density and low cost and are commonly used as dc bus capacitors to buffer energy, limit voltage ripple, and balance power. However, electrolytic capacitors have problems such as large volume and short life, are susceptible to ripple current, high temperature, overvoltage, and overcurrent, and require periodic replacement. In the occasion with higher service life requirement, in order to solve the problems, the electrolytic capacitor can be replaced by a film capacitor, so that the reliability of the system is obviously improved, and the volume of the capacitor in the power system is greatly reduced. Therefore, the health status of the film capacitor affects the operation of the whole system to a great extent, and the research of status monitoring and life estimation is necessary.
State monitoring and lifetime estimation of capacitors is mainly based on research on the trend of lifetime characterization factors. The commonly used life characterization factors have a capacitance value C and an equivalent series resistance ESR, and when the capacitance value is reduced by 2% -5%, the film capacitor is considered to reach the life limit. The existing calculation methods of the life index can be divided into three types: sensor-based methods, circuit model-based methods, data and algorithm-based methods. Methods based on sensors and circuit models require additional hardware circuitry, use sensors directly or indirectly, and add complexity and cost. In contrast, the software-based state monitoring method is promising in practical applications. However, the research results based on the data and the algorithm are less at present, and the main reason is that a large amount of data is difficult to acquire.
Research results on the influence of temperature on the life of the film capacitor are remarkable, such as establishing a model of temperature and life characterization factors, estimating hot spot temperature according to the temperature model, introducing the temperature factors into a capacitor design model and the like. Besides temperature, humidity is also a key factor in accelerating the aging of the thin film capacitor, especially at higher humidity, but the research results 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 film capacitor under the influence of humidity, and aims to solve the problems that the data size is small and the quantification cannot be realized in the existing state monitoring method based on software.
To achieve the above object, in one aspect, 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 change of the capacitance values of a plurality of film capacitors 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 commonly used for various relative humidities;
s4, performing multiple linear regression to train a failure model;
s5, evaluating the trained failure model until the model reaches a preset requirement;
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 change of the capacitance values of the film capacitors along with time under various relative humidities;
S12, carrying out normalization processing on capacitance attenuation data of each capacitor under each relative humidity by taking the maximum value and the minimum value of the capacitance attenuation data as standards;
and S13, respectively obtaining the average value of the normalized data of the plurality of capacitors under each relative humidity, and taking the average value as the characteristic data under the relative humidity.
Further, S2 specifically includes:
s21, defining a failure characterization factor as delta C (t)% = (C 0-C(t))/C0, wherein C 0 is an initial value of a capacitance value, and C (t) is the capacitance value at the moment t;
S22, respectively describing a scatter diagram of the change of the failure characterization factor with time by using the characteristic data under each relative humidity;
S23, determining the distribution obeyed by the attenuation 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 shape and position parameters, respectively, and t is time;
S24, linearizing the function model:
yt＝kxt+z
Where ln (Δc (t)%) =y t,t＝xt,a＝k,-ab＝z-ab＝z,xt is a time variable and z and k are a deterministic constant of a plurality of sets of relative humidity.
Further, S3 specifically includes:
Constructing N relative humidities into N-1 virtual variables by taking time as a general interpretation variable, introducing a random noise variable, expressing the humidity virtual variables by RH 1 and RH 2, and introducing the humidity virtual variables into a linearized model in an additive and multiplicative mode to obtain a failure characterization factor y t:
yt＝z+kxt+z1RH1+z2RH2+k1RH1xt+k2RH2xt+ut
Where u t and x t are random noise and time variable, respectively, z and k are deterministic constants for multiple sets of relative humidity, and z 1,z2,k1,k2 is a random coefficient.
Further, S4 specifically includes:
S41, setting an optimization target for model training by minimum sum of squares of errors of estimated data and actual data;
S42, taking characteristic data of relative humidity of each group as a training data set, and setting that for the ith group, there is degradation failure data of m i groups corresponding to different monitoring times, estimating model parameters by the least error square sum in the following formula:
Wherein y i,j is the actual value of the jth failure characterization factor for the ith set of relative humidities, An estimate of y i,j;
S43, representing the ith relative humidity virtual variable of the kth degradation failure data of the jth group by RH i,j,k in vector form AndRepresenting a training sample set, the estimated model parameters are/>The method can obtain the following steps:
wherein, And representing the model parameters to be estimated, wherein X T is the transposed matrix of X.
Further, S5 specifically includes:
s51, calculating a residual square sum RSS and a regression square sum ESS of multiple linear regression;
S52, further calculating a judgment coefficient R 2 and an adjusted judgment coefficient according to the obtained RSS and ESS
S53, according to the judgment coefficient R 2 and the adjusted judgment coefficientAnd (3) evaluating the fitting goodness of the model to the data, checking the linearity of the evaluation model by using F, and performing a significance test by using t-test to evaluate the influence of each interpretation variable in the model on the interpretation variable until the fitting goodness, the linearity and the significance test meet preset requirements.
Further, S6 specifically includes:
S61, when the capacitance value is reduced to a preset value, considering that the thin film capacitor reaches the life limit, and calculating the value of a life characterization factor;
S62, according to the degradation model of the time change of the established life characteristic factor, determining the time corresponding to the value of the life characteristic factor when the life reaches the limit as the estimated life.
Another aspect of the present invention provides a system for estimating a lifetime of a thin film capacitor under the influence of humidity, comprising:
The characteristic data acquisition module is used for acquiring data of the change of the capacitance values of the film capacitors 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 the relative humidity virtual variables and introducing the linearized models to obtain failure models which are commonly used for various relative humidities;
the model training module is used for performing multiple linear regression to train the failure model;
The model evaluation module is used for evaluating the trained failure model until the model reaches a preset requirement;
and the service life estimating module is used for estimating the service life of the capacitor according to the established failure model.
Compared with the prior art, the technical scheme of 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 is characterized in that relative humidity is innovatively set as a virtual variable, the virtual variable is introduced into a failure model of the film capacitor in a qualitative variable mode, the influence of each interpretation variable on the interpreted variable is inspected through F test and t test, whether the variable needs to be reserved in the model is determined, so that the model is adjusted, and the influence of different humidity conditions on the capacitor is rapidly and intuitively analyzed;
(2) The modeling method provided by the invention is suitable for carrying out single-factor analysis on a certain influencing factor under the condition that the modeling method is more similar to a class variable, is simple and easy to implement and has strong universality, when the data sample size is small, the quantitative analysis is impossible, the influencing factor is a class variable and the state of the thin film capacitor is difficult to comprehensively monitor and estimate the service life of the thin film capacitor;
(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 method for testing virtual variables for analyzing the effect of humidity on thin film capacitors provided by the invention;
FIG. 2 is a flow chart of evaluating model and model variable correlation in accordance with the present invention;
FIGS. 3 (a) - (c) show the fitting effect of the multiple linear regression model provided by the embodiments of the present invention; wherein, fig. 3 (a) is the fitting effect of the failure model corresponding to 55% relative humidity; FIG. 3 (b) is a graph showing the effect of fitting a failure model corresponding to 70% relative humidity; FIG. 3 (c) is a graph showing the effect of fitting a failure model corresponding to 85% relative humidity;
FIGS. 4 (a) - (c) show the fitting effect of the adjusted multiple linear regression model provided by the embodiments of the present invention; FIG. 4 (a) is a graph showing the effect of fitting a failure model corresponding to 55% relative humidity; FIG. 4 (b) is a graph showing the effect of fitting a failure model corresponding to 70% relative humidity; fig. 4 (c) is a graph showing the effect of fitting the failure model corresponding to 85% relative humidity.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not interfere with each other.
As shown in fig. 1, the present invention provides a method for estimating the lifetime of a thin film capacitor under the influence of humidity, comprising the steps of:
s1, constructing an acceleration test system, collecting data of the change of the capacity value with time under the conditions of 55%, 70% and 85% relative humidity, and carrying out normalization and averaging on the data;
s2, selecting a capacitance C of the film capacitor to represent the aging of the capacitor, defining an expression of a failure characterization factor, establishing a model of the change of the failure characterization factor along with time, and linearizing the failure model;
S3, defining a relative humidity virtual variable, introducing a linearized failure model, and pushing on the failure model 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 moderately adjusting the model according to the correlation between each interpretation variable and the interpreted variable;
S6, according to a general capacitor failure model containing relative humidity factors, deriving specific failure models respectively applicable to three types of humidity, and according to different humidity conditions, estimating the service life of the film capacitor according to the established models.
In order to clearly describe the virtual variable test method for analyzing the influence of humidity on a thin film capacitor according to the present invention, the following description will be made with reference to examples:
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 to provide the required temperature, humidity for the experiment and the LCR analyzer is used to determine the capacitance. In this embodiment, the fixed temperature is 85 ℃, and data of time variation of capacitance values of 10 capacitors under three humidity conditions of 55%, 70% and 85% are collected. Of these, there were 20 groups of 55% relative humidity, 10 groups of 70% relative humidity, and 19 groups of 85% relative humidity.
And carrying out normalization processing on capacitance attenuation data of each capacitor under three humidity by taking the maximum value and the minimum value of the capacitance attenuation data as standards, and respectively solving the average value of the normalized capacitance values of 10 capacitors to be used 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, setting the percentage of the capacitance value at the time t relative to the initial capacitance value as a failure representation factor, and taking the percentage as an interpreted variable of the model.
And (3) approximately drawing a scatter diagram of the change of the failure characterization factor along with time, and describing the change trend of the capacitance value into an exponential distribution according to the change trend of the scatter diagram and the related background, wherein the higher the humidity is, the faster the capacitor aging speed is observed. And has an inflection point of accelerated aging at all three humidities. An exponential function model was chosen to describe the aging process as simply as possible, as shown in the following equation:
ΔC(t)％＝ea(t-b)
a. b, respectively obtaining the shape and position parameters by taking log linearization:
ln(ΔC(t)％)＝at-ab
further, let ln (Δc (t)%) =y t,t＝xt, a=k, -ab=z, which can be abbreviated as a linear function
yt＝kxt+z
S3, three qualitative factors are included in the embodiment, an intercept item is included in a failure model to be built, and the virtual variable trap is prevented from being trapped according to the setting rule of the virtual variable, so that complete multiple collinearity is generated, and 2 virtual variables are constructed for three humidity conditions and are represented by RH 1 and RH 2. 55% relative humidity was used as the base type and 70% and 85% relative humidity was used as the comparison type. Thus, three humidity conditions correspond to two virtual variable amounts [ 00 ], [10 ] and [ 01 ], respectively.
The qualitative time factor is introduced into the model as a general interpretation variable, and in addition, random noise is required to be considered in the model, and the random noise obeys normal distribution with the mean value of 0 and the same variance. Simultaneously, two relative humidity virtual variables are taken into the model by using an addition mode and a multiplication mode to obtain an aging model with the change of slope and intercept, which is expressed as
yt＝z+kxt+z1RH1+z2RH2+k1RH1xt+k2RH2xt+ut
Where u t and x t are random noise and time variations, respectively. z and k are three sets of deterministic constants for relative humidity, z 1,z2,k1,k2 is a random coefficient, covering the heterogeneity of the comparison type relative to the base type.
S4, selecting a least square linear unbiased estimation training model with minimum variance according to the Markov theorem, wherein the training target of the model is that the square sum of errors of estimated data and actual data is minimum, namely
At the same time, use vectorsRepresenting the input of training samples, using vectors/>Indicating the desired output. In this embodiment, the 55% relative humidity group corresponds to m 1 =20, the 70% relative humidity group corresponds to m 2 =10, and the 85% relative humidity group corresponds to m 3 =19. 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, calculating a residual square sum RSS and a regression square sum ESS of multiple linear regression by using a least square method, wherein the calculation result is shown in a table 2.
TABLE 2
| Degree of freedom | ||
| Regression sum of squares (ESS) | 176.776338 | 5 |
| Sum of squares of residual errors (RSS) | 6.57333847 | 42 |
As can be seen from table 2, the ESS value reflects a total deviation between the estimated value and the average value of the interpreted variable, i.e., the failure characterization factor, as high as 176.776338, and the rss reflects a total deviation between the estimated value and the actual value of the interpreted variable of 6.57333847. The ESS values are large enough and the RSS is small, meaning that the regression model has a good goodness of fit to the data.
S52, further calculating a judgment coefficient R 2,R2 according to the obtained RSS and ESS, wherein the judgment coefficient R 2,R2 is defined as:
To eliminate the influence of the number of explanatory variables on the goodness of fit, the respective degrees of freedom are divided by the numerator and denominator of R 2 to obtain adjusted judgment coefficients, using The representation is:
Where n is the data sample size, in a model with k+1 independent variables, RSS has n-k-1 degrees of freedom, while ESS+RSS has n-1 degrees of freedom. R 2 and The calculation results are shown in Table 3.
TABLE 3 Table 3
S53, evaluating the goodness of fit of the model to the data, wherein the goodness of fit is inversely proportional to RSS, directly proportional to ESS, R 2 andThe larger the fitting goodness is, the better.
As can be seen from Table 3, the determination coefficient R 2 and the adjusted determination coefficientAll are close to 1, which indicates 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, it can be intuitively seen from fig. 3 (a) - (c) that the data is almost distributed on both sides of the regression line, verifying the validity of the proposed model.
S54, performing significance test on the model. The linearity of the model is evaluated by a statistical F-test as to whether the linear relationship between all interpreted variables and the interpreted variables is significant.
Assuming that the influence of all interpretation variables on the failure characterization factor is 0, namely making an original assumption
H0:z＝k＝z1＝z2＝k1＝k2＝0
It is possible to construct ψ= (ESS/k)/(RSS/(n-k-1)) subject to the F distribution, the meanings of n and k being as described above. The results of the F test are shown in table 2, indicating a probability of accepting the original hypothesis of 0. At a confidence level of over 99.99%, the linear dependence of the model can be considered obvious.
S55, performing significance test by using a t-test method in statistics, evaluating whether the influence of each interpretation variable in the model on the interpreted variable failure characterization factor is significant or not so as to reflect the correlation between the two, and determining whether the interpretation variable should be kept in the model or not.
Assuming that the influence of each interpretation variable on the failure characterization factor is 0, namely making an original assumption
H0:βj＝0(βj＝z,k,z1,z2,k1,k2)
Can be constructedObeys the t distribution, wherein/>To explain the estimated value of the variable beta j, e is the deviation of the estimated value of the characterization factor from the actual value,/>The variance of (C ii) is the product of the noise variance and the meaning of n and k is as described above. The t-test results for each individual interpretation variable are shown in Table 1. The probability of error of rejection of the original hypothesis, i.e., the p value, of all parameters is less than 0.5, indicating that each interpretation variable has an effect on the interpreted variable to some extent. Specifically, a variable of coefficient k, z 2,k1, z has a significant effect on the explained variable at a 1% significance level, while a variable of coefficient k 2 has an effect at a 10% significance level. The effect of the variable term with the coefficient z 1 appears to be small and the 95% confidence interval for z 1 and k 2 contains 0, so whether the term remains or requires further investigation and the original assumption cannot be directly rejected.
S56, comparing and analyzing the significance test result, adjusting the model, and returning to S54 for re-evaluation.
To understand in detail the effect of the variable term with coefficient z 1 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). The regression curve without z 1 showed a slight change in slope and intercept at 55% and 70% relative humidity compared to the regression curve with z 1. Regression without z 1 And RMSE 0.9599 and 0.3954, respectively. In comparison to the model containing z 1,/>The RMSE was slightly decreased, remaining unchanged. Thus, the variable term with the coefficient z 1 has no large influence on the interpretation variable and is negligible.
Further, the step S6 includes the following steps.
S61, deducing an aging model under 55%, 70% and 85% relative humidity from the aging model established in the S3. Taking 55% as an example, the corresponding values of RH 1 and RH 2 are brought into the aging model in S3, with E (Y t|Xt,RH1＝0,RH2＝0)＝z+kXt
Similarly, the aging models in 70% and 85% conditions were respectively
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 meanings of the related variables are as described above.
S62, calculating the value of the life characteristic factor when the capacitance value is reduced to 5%. Given a set of data of time-varying capacitance values, the initial capacitance value is recorded as C in, the measured final capacitance value of time-varying capacitance value is recorded as C e, and the life characterization factor l can be written as when the life reaches the limit
And S63, substituting the life characterization factors l calculated in the S62 into aging models under the relative humidity of 55%, 70% and 85% in the S61 respectively, so that the life of the film capacitor under different humidity conditions can be estimated.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A method for estimating the lifetime of a thin film capacitor under the influence of humidity, comprising the steps of:
S1, collecting data of the change of the capacitance values of a plurality of film capacitors 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; the method specifically comprises the following steps:
s21, defining a failure characterization factor as delta C (t)% = (C 0-C(t))/C0, wherein C 0 is an initial value of a capacitance value, and C (t) is the capacitance value at the moment t;
S22, respectively describing a scatter diagram of the change of the failure characterization factor with time by using the characteristic data under each relative humidity;
S23, determining the distribution obeyed by the attenuation 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 shape and position parameters, respectively, and t is time;
s24, linearizing the function model:
yt＝kxt+z
Where ln (Δc (t)%) =y t,t＝xt,a＝k,-ab＝z,xt is a time variable, and z and k are a plurality of sets of deterministic constants of relative humidity;
S3, defining a relative humidity virtual variable, and introducing a linearized model to obtain a failure model which is commonly used for various relative humidities; the method specifically comprises the following steps:
Constructing N relative humidities into N-1 virtual variables by taking time as a general interpretation variable, introducing a random noise variable, expressing the humidity virtual variables by RH 1 and RH 2, and introducing the humidity virtual variables into a linearized model in an additive and multiplicative mode to obtain a failure characterization factor pair y t:
yt＝z+kxt+z1RH1+z2RH2+k1RH1xt+k2RH2xt+ut
Wherein u t and x t are random noise and time variable, z and k are deterministic constants of multiple groups of relative humidity, and z 1,z2,k1,k2 is a random coefficient;
S4, performing multiple linear regression to train the failure model;
s5, evaluating the trained failure model until the model reaches a preset requirement;
S6, estimating the service life of the capacitor according to the established failure model.
2. The method according to claim 1, wherein S1 specifically comprises:
s11, keeping the temperature constant, and collecting data of the change of the capacitance values of the film capacitors along with time under various relative humidities;
S12, carrying out normalization processing on capacitance attenuation data of each capacitor under each relative humidity by taking the maximum value and the minimum value of the capacitance attenuation data as standards;
and S13, respectively obtaining the average value of the normalized data of the plurality of capacitors under each relative humidity, and taking the average value as the characteristic data under the relative humidity.
3. The method of claim 1, wherein S4 specifically comprises:
S41, setting an optimization target for model training by minimum sum of squares of errors of estimated data and actual data;
S42, taking characteristic data of relative humidity of each group as a training data set, and setting that for the ith group, there is degradation failure data of m i groups corresponding to different monitoring times, estimating model parameters by the least error square sum in the following formula:
Wherein y i,j is the actual value of the jth log of failure characterization factors for the ith set of relative humidities, An estimate of y i,j;
S43, representing the ith relative humidity virtual variable of the kth degradation failure data of the jth group by RH i,j,k in vector form AndRepresenting a training sample set, the estimated model parameters are/>The method can obtain the following steps:
wherein, And representing the model parameters to be estimated, wherein X T is the transposed matrix of X.
4. A method according to claim 3, wherein S5 specifically comprises:
s51, calculating a residual square sum RSS and a regression square sum ESS of multiple linear regression;
S52, further calculating a judgment coefficient R 2 and an adjusted judgment coefficient according to the obtained RSS and ESS
S53, according to the judgment coefficient R 2 and the adjusted judgment coefficientAnd (3) evaluating the fitting goodness of the model to the data, checking the linearity of the evaluation model by using F, and performing a significance test by using t-test to evaluate the influence of each interpretation variable in the model on the interpretation variable until the fitting goodness, the linearity and the significance meet preset requirements.
5. The method of claim 4, wherein S6 specifically comprises:
S61, when the capacitance value is reduced to a preset value, considering that the thin film capacitor reaches the life limit, and calculating the value of a life characterization factor;
S62, according to the degradation model of the time change of the established life characteristic factor, determining the time corresponding to the value of the life characteristic factor when the life reaches the limit as the estimated life.
6. A system for estimating film capacitor life under the influence of humidity, comprising:
The characteristic data acquisition module is used for acquiring data of the change of the capacitance values of the film capacitors 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 method specifically comprises the following steps: defining a failure characterization factor as delta C (t)% = (C 0-C(t))/C0), wherein C 0 is an initial value of a capacitance value, C (t) is a capacitance value at a moment t, respectively using characteristic data under each relative humidity to draw a scatter diagram of the change of the failure characterization factor along with time, 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, wherein a and b are shape and position parameters respectively, t is time, linearizing the function model, namely y t＝kxt +z, wherein ln (delta C (t)%) =y t,t＝xt,a＝k,-ab＝z,xt is a time variable, and z and k are deterministic constants of a plurality of groups of relative humidity;
The failure model acquisition module is used for defining the relative humidity virtual variables and introducing the linearized models to obtain failure models which are commonly used for various relative humidities; the method specifically comprises the following steps: constructing N relative humidities into N-1 virtual variables by taking time as a general interpretation variable, introducing a random noise variable, expressing the humidity virtual variables by RH 1 and RH 2, introducing the humidity virtual variables into a linearized model in an additive and multiplicative mode to obtain a failure characterization factor pair yt：yt＝z+kxt+z1RH1+z2RH2+k1RH1xt+k2RH2xt+ut,, wherein u t and x t are random noise and time variables respectively, z and k are deterministic constants of a plurality of groups of relative humidities, and z 1,z2,k1,k2 is a random coefficient;
the model training module is used for performing 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 requirement;
and the service life estimating module is used for estimating the service life of the capacitor according to the established failure model.
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