CN115526080A - Switching power supply reliability prediction method based on multi-physical-field digital prototype model - Google Patents
Switching power supply reliability prediction method based on multi-physical-field digital prototype model Download PDFInfo
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Abstract
The invention discloses a switching power supply reliability prediction method based on a multi-physical-field digital prototype model, which comprises the following steps: s1, establishing a multi-physical-field digital prototype model of the switching power supply suitable for reliability prediction; s2, analyzing and determining key failure modes and failure mechanisms of the switching power supply based on a digital prototype model, and determining electronic components with weak reliability; s3, establishing a switching power supply reliability prediction model fusing performance degradation and functional failure based on the digital prototype model and the electronic components with weak reliability; and S4, solving to obtain a reliability curve under a given predicted working environment based on the switching power supply reliability prediction model. According to the invention, by utilizing the digital prototype model, the influence factors of the functional failure and performance degradation of key components of the switching power supply on the reliability under the conditions of electric stress, thermal stress and vibration stress are considered, the accuracy of the reliability prediction of the switching power supply can be improved, and powerful support is provided for improving the reliability of the switching power supply.
Description
Technical Field
The invention relates to a method for predicting the reliability of a switching power supply, in particular to a method for predicting the reliability of a switching power supply based on a multi-physical-field digital prototype model.
Background
In order to improve the reliability of the switching power supply, it is important to predict the reliability of the switching power supply accurately. Most of the existing switch power supply reliability prediction methods are methods based on mathematical statistics, and the failure rate of electronic components is calculated by using an empirical formula containing factors such as quality grade, use conditions and the like according to failure data in a reliability prediction manual or standard, so that the reliability of the electronic components and the switch power supply is predicted. This method is disjointed from failure mechanisms and does not take into account the influence of degradation processes on reliability, resulting in poor prediction accuracy.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a switching power supply reliability prediction method based on a multi-physical-field digital prototype model. The method can improve the accuracy of the reliability prediction of the switching power supply, and further provides powerful support for improving the reliability of the switching power supply.
The purpose of the invention is realized by the following technical scheme:
a switching power supply reliability prediction method based on a multi-physical-field digital prototype model comprises the following steps:
s1, establishing a multi-physical-field digital prototype model of the switching power supply suitable for reliability prediction;
s2, analyzing and determining key failure modes and failure mechanisms of the switching power supply based on the digital prototype model established in the S1, and determining electronic components with weak reliability;
s3, establishing a switching power supply reliability prediction model fusing performance degradation and function failure based on the digital prototype model established in the step S1 and the electronic component with weak reliability determined in the step S2;
and S4, solving to obtain a reliability curve under a given predicted working environment based on the switching power supply reliability prediction model established in the step S3.
Compared with the prior art, the invention has the following advantages:
according to the invention, by utilizing the digital prototype model, the influence factors of the functional failure and performance degradation of key components of the switching power supply on the reliability under the conditions of electric stress, thermal stress and vibration stress are considered, the accuracy of the reliability prediction of the switching power supply can be improved, and powerful support is provided for improving the reliability of the switching power supply.
Drawings
FIG. 1 is a flow chart of a method for predicting reliability of a switching power supply based on a multi-physical field digital prototype model;
FIG. 2 is a circuit topology diagram of a switching power supply in an embodiment;
FIG. 3 is a model of the switching power supply configuration in an embodiment;
FIG. 4 is a data interaction flow of the iSIGHT platform in the embodiment for realizing the electric-thermal coupling simulation of the switching power supply;
FIG. 5 shows the sensitivity analysis results of the switching power supply in the example;
FIG. 6 shows the analysis result of the switching power supply fault tree in the embodiment;
FIG. 7 is a flow of modeling prediction of reliability of the switching power supply in consideration of performance degradation and functional failure of electronic components in the embodiment;
fig. 8 is a reliability curve of the switching power supply and a functional reliability curve and a performance reliability curve thereof in the embodiment.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but not limited thereto, and any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be covered by the protection scope of the present invention.
The invention provides a switching power supply reliability prediction method based on a multi-physical-field digital prototype model, which comprises the following steps of:
s1, establishing a multi-physical-field digital prototype model of the switching power supply suitable for reliability prediction, which comprises the following specific steps:
s11, establishing a switching power supply circuit model through EDA software, and performing circuit simulation analysis;
s12, establishing a switching power supply structure model through three-dimensional modeling software and finite element analysis software, and performing thermal simulation analysis and vibration simulation analysis;
and S13, realizing data interaction and process control of the switch power supply electrothermal coupling simulation through the simulation coupling platform, further completing electrothermal coupling variable transmission, and realizing indirect coupling of multiple physical fields.
S2, analyzing and determining key failure modes and failure mechanisms of the switching power supply based on the digital prototype model established in the step S1, and determining electronic components with weak reliability, wherein the method specifically comprises the following steps:
s21, combing typical failure modes of the electronic components according to the types of the electronic components forming the switching power supply;
step S22, referring to the failure mode of the performance degradation type of the electronic component in the step S21, determining the electronic component and the sensitive parameter thereof which have great influence on the output performance of the switching power supply by using a sensitivity analysis method;
step S23, referring to the failure mode of the electronic component function failure type in the step S21, determining the electronic component which has the function failure to influence the function of the switching power supply by using a fault tree analysis method;
and step S24, integrating the electronic components analyzed and determined in step S22 and step S23, namely, the electronic components with weak reliability.
S3, establishing a switching power supply reliability prediction model fusing performance degradation and function failure based on the digital prototype model established in the step S1 and the electronic components with weak reliability determined in the step S2, and specifically comprising the following steps:
step S31, according to the switching power supply digital prototype model established in the step S1, a working stress mapping model [ I ] capable of describing the relation between the working condition of the switching power supply and the electric, thermal and vibration stresses of the electronic component is established by combining various data acquisition modes such as simulation and actual measurement 1 ,V 1 ,T 1 ,a 1 ,f 1 ,…,I i ,V i ,T i ,a i ,f i ,…]＝S(V s ,I S ,T S ,A S ,f S ) Wherein, I i 、V i 、T i 、a i 、f i Respectively representing the effective values of current, voltage, surface average temperature, sinusoidal vibration acceleration and sinusoidal vibration frequency, V of the component i S 、I S 、T S 、A S 、f S Respectively representing an input voltage effective value, an output current effective value, an environment average temperature, an equivalent sinusoidal vibration acceleration and an equivalent sinusoidal vibration frequency of the switching power supply, wherein S (-) is a working stress mapping model;
step S32, establishing a time-varying performance degradation model P of the electronic component i according to performance parameter degradation data obtained by an electrical, thermal and vibration accelerated stress test of the electronic component i (t)＝D i (s, t), wherein s = [ I = i ,V i ,T i ,a i ,f i ]，D i (. Is) a model of performance degradation, and D i (s, t) has a distribution property, P i (t) a time-varying performance parameter vector having a distribution characteristic of the performance-degrading electronic component i;
step S33, establishing a functional failure model F of the electronic component i according to functional failure time data (service life data) obtained by an electric, thermal and vibration accelerated stress test of the electronic component i (t)＝K i (s,t)，K i (. Is) a model of functional failure, and K i (s, t) has a distribution characteristic, F i (t) is the failure time of the functional failure type electronic component i;
step S34, determining the output characteristic parameter limit state according to the use requirement of the switching power supply, and constructing the relation capable of describing the electronic component degradation and the output characteristic of the switching power supply by using a digital prototype modelPerformance mapping model P S (t)＝X(P 1 (t),…,P i (t) \ 8230;), wherein P S (t) is a switching power supply output characteristic parameter vector, and X (·) is a performance mapping model;
step S35, a switching power supply reliability block diagram is constructed by utilizing the fault tree analysis result, and a function mapping model F capable of describing the function failure corresponding relation between the electronic components and the switching power supply is established S (t)＝G(F 1 (t),…,F i (t) \8230whereinF S (t) is the failure time of the switch power supply function, and G (·) is a function mapping model;
step S36, inputting the stress to the electronic component performance degradation model D by the working stress mapping model S (-) i (. O) and functional failure model K i (·)，D i (. O) and K i (. Cndot.) respective outputs are delivered to a performance mapping model X (-) and a function mapping model G (-) respectively, according to X (-) and G (-) and an output characteristic parameter threshold vector P th Respectively obtaining R p (t) and R f (t), and describing the reliability R of the switching power supply at any time t through the formula (1) S (t), obtaining a reliability prediction model of the switching power supply:
wherein, α = k/N s ，N s The total specification number of the electronic components in the switching power supply is shown, and k represents the specification number of the electronic components which are not only performance degradation type electronic components but also function failure type electronic components.
And S4, solving to obtain a reliability curve under a given predicted working environment based on the switching power supply reliability prediction model established in the step S3, wherein the specific steps are as follows:
step S41, performing equivalent decomposition on the stress of the task section to obtain the predicted values of temperature, vibration and electric stress { V } used by the switching power supply S ,I S ,T S ,A S ,f S }；
Step S42, based on the switching power supply working stress mapping model established in the step S31, converting the { V into a { V } value S ,I S ,T S ,A S ,f S Substituting the obtained values into a calculation model to obtain temperature, vibration and electric stress response values (I) of all electronic components forming the switching power supply 1 ,V 1 ,P 1 ,T 1 ,a 1 ,f 1 ,…,I i ,V i ,P i ,T i ,a i ,f i ,…]；
Step S43, sequentially calculating the [ I ] of the electronic component I calculated in the step S42 i ,V i ,P i ,T i ,a i ,f i ]Substituting into D of the electronic component i i (. O) and K i In the (-) model, a time-varying performance parameter vector P with distribution characteristics of the electronic component i is obtained i (t) and time to failure F i (t) sampling to obtain N individual performance parameters P of the electronic component i based on Monte Carlo mode i ' (t) and N functional failure times F i ′(t)；
Step S44, the time-varying performance parameter [ P ] of each electronic component batch obtained in the step S43 1 ′(t),…,P i ′(t),…]Substituting the performance mapping model X (-) of the switching power supply into the performance mapping model X (-) of the switching power supply to obtain N time-varying output characteristic parameter vectors P of the corresponding switching power supply S (t), counting P at any time t S (t) satisfies P th Probability R of P (t)＝Pr{P S (t)∈P th Get the reliability curve R of switch power performance degradation p (t)；
Step S45, the function failure time [ F ] of the electronic component batch obtained in the step S43 is processed 1 ′(t),…,F i ′(t),…]Substituting the function mapping model G (-) of the switching power supply to obtain N corresponding function failure times F of the switching power supply S (t), counting F at any time t S (t) probability of non-occurrence R f (t)＝Pr{F S (t) > t }, and then a reliability curve R of the switch power supply functional failure can be obtained f (t)；
Step S46, according to the homology of the performance degradation model and the function failure model, calculating by the formula (1) to obtain a switching power supply reliability curve R fusing performance degradation and function failure S (t)。
Example (b):
the engineering object in this embodiment is a switching power supply, the main function of which is energy conversion, and which is a basic guarantee for normal operation of electrical equipment, and the switching power supply used in this embodiment totally includes 50 specifications and 87 electronic components. By utilizing a digital prototype model of the switching power supply, the functional failure and the performance degradation of internal electronic components of the switching power supply are considered under three stress environments of electricity, heat and vibration, and the process of predicting the reliability of the switching power supply mainly comprises the following aspects:
firstly, a digital prototype model of the switching power supply is established. According to the operating principle of the switching power supply, a circuit topology thereof can be established as shown in fig. 2, wherein Q1-Q6 represent MOSFETs. And then, based on a circuit topological graph, a switch power supply circuit model is established by using Saber software, and circuit simulation analysis is carried out to obtain the power loss of each electronic component in the switch power supply. Meanwhile, the structural composition of the switching power supply is analyzed, and a switching power supply structural model is built by utilizing SolidWorks, and the result is shown in FIG. 3. And then, importing the structural model into ANSYS, and performing vibration simulation and thermal simulation through model simplification, material attribute setting, net separation and boundary condition setting. The thermal simulation needs to convert the power consumption of the electronic components and substitute the converted power consumption into the internal heat generation power in the thermal simulation model based on the iSIGHT platform, so as to obtain the thermal field distribution of the switching power supply, and the specific flow is shown in fig. 4. And then, obtaining the parameters of the electronic component through a function relation of the temperature and the device parameters, and transmitting the parameters to the electrical simulation model. And circularly iterating in the above way until the iteration difference is less than 0.1 ℃, and determining that the electrothermal coupling simulation result is converged at the moment, so that the steady-state response of the switching power supply circuit and the thermal field can be obtained.
And secondly, determining the electronic components with weak reliability of the switching power supply. Based on the digital prototype model, the types of the electronic components and the typical failure modes thereof are carded, and the results are shown in table 1.
TABLE 1
And determining the electronic component with larger influence on the output performance of the switching power supply by referring to the failure mode of the performance degradation type of the electronic component and utilizing a sensitivity analysis method. The relative sensitivity is obtained by the ratio of the relative change rate of the technical index P (mainly comprising voltage, ripple and efficiency) of the circuit to the relative change rate of the component parameter x, and the corresponding mathematical expression is as follows:
if relative sensitivity L i The higher the representative component i parameter is, the more the relative change of the representative component i parameter can affect the voltage, ripple and efficiency of the circuit. For the switching power supply studied in this example, sensitivity analysis was performed to determine that 57 performance-degraded electronic components are present in the switching power supply, and some results are shown in fig. 5.
And determining the electronic components with the function failure affecting the function of the switching power supply by referring to the failure mode of the function failure type of the electronic components and utilizing a fault tree analysis method. The output failure of the switching power supply is taken as the top event of a failure tree, all possible factors and reasons causing the event are found out and connected by a specified logic symbol, and the failure tree analysis is carried out. And analyzing from the top event to the middle event downwards, and deeply analyzing layer by layer until the basic reason of the event, namely the bottom event of the fault tree is found. And then solving the minimum cut set of the fault tree, thereby determining the electronic components which influence the functional failure of the switching power supply. Finally, it is determined that the switching power supply comprises 56 electronic components with failure types, and partial results of the fault tree analysis are shown in fig. 6.
And integrating the results of the sensitivity analysis and the fault tree analysis, wherein the results are merged into electronic components with weak reliability of the switching power supply, and the specification number of the electronic components with performance degradation and functional failure is determined to be 13.
Then, a switching power supply reliability prediction model considering the performance degradation and the functional failure of the components is established, and a specific flow is shown in fig. 7. Firstly, according to a digital prototype model of the switching power supply, the working stress of the switching power supply is input into the digital prototype model in a simulation and actual measurement mode, so that the working stress of the electronic component is obtained, and a working stress mapping model S (-) is established.
Meanwhile, a performance degradation model D of the electronic component is respectively established by using performance parameter degradation data and functional failure time data obtained in an electric, thermal and vibration accelerated degradation test of the electronic component i (s, t) and failure model K i (s, t), wherein the performance degradation model is of the form:
D i (s,t)＝e i (s)Λ i (t)+σ i C i (Λ i (t)) (3)；
wherein s = [ I = i ,V i ,T i ,a i ,f i ]，I i 、V i 、T i 、a i 、f i Respectively representing the effective values of current, voltage, surface average temperature, sinusoidal vibration acceleration and sinusoidal vibration frequency of the component i, e i (s) is a function of the stress vector s, Λ i (t) is a time scale transformation function, σ i As a drift coefficient, C i (Λ i (t)) as an uncertainty procedure, obeying a normal uncertainty distribution N (0, Λ) i (t)). The form of the functional failure model is:
K i (s,t)＝e iK (s)θ i (t)+ε i (4)
wherein s = [ I = i ,V i ,T i ,a i ,f i ]，I i 、V i 、T i 、a i 、f i Respectively representing the current effective value, the voltage effective value, the surface average temperature, the sinusoidal vibration acceleration and the sinusoidal vibration frequency of the component i,
is a function of the stress vector s, theta i (t) is a function of time, ε i For the perturbation factor, obey a mean of 0 and a variance of
Is normally distributed.
And then, determining the limit state of the output characteristic parameters according to the use requirements of the switching power supply, and describing a multi-input multi-output quantitative mapping relation between the performance degradation-prone parameters of the electronic components and the output characteristic parameters of the switching power supply by using a digital prototype model so as to construct a performance mapping model X (·). Then, a switching power supply reliability block diagram is constructed by using the result of the fault tree analysis, a logical mapping relation between the easy failure function of the electronic component and the switching power supply function is described, and a function mapping model G (-) is constructed according to the logical mapping relation, and has the following form:
wherein, F i And (t) is the failure time of the functional failure type electronic component i, wherein the front m electronic components are in a series structure, and the rear n-m electronic components are in a parallel structure. Then, the above models are connected as shown in fig. 7.
And finally, carrying out state iteration by using the reliability prediction model to obtain a reliability curve of the switching power supply. Firstly, the stress under the task section of the switching power supply is equivalently decomposed to obtain the predicted values of temperature, vibration and electric stress { V } used by the switching power supply S ,I S ,T S ,A S ,f S }. Inputting the predicted value into a working stress mapping model, and calculating to obtain the response values [ I ] of the temperature, vibration and electric stress of 87 electronic components in the switching power supply 1 ,V 1 ,T 1 ,a 1 ,f 1 ,…,I 87 ,V 87 ,T 87 ,a 87 ,f 87 ]＝S(V S ,I S ,T S ,A S ,f S ). Then, the response value is substituted into the performance degradation model D of the corresponding electronic component i (s, t) and failure model K i In (s, t), [ P ] can be obtained 1 (t),...,P 57 (t)]And [ F 1 (t),...,F 56 (t)]And sampling by Monte Carlo mode to obtain 5000 batches of performance parameters P 1 ′(t),…,P 5 ′ 7 (t)]And time to failure of function [ F 1 ′(t),…,F 5 ′ 6 (t)]. Then, the batch performance parameters of the electronic components are input into a performance mapping model of the switching power supply, and 5000 time-varying output characteristic parameter vectors P can be obtained S (t)＝X(P 1 ′(t),...,P 5 ′ 7 (t)), wherein P S (t) is a time-varying vector with distribution characteristics including voltage, ripple and efficiency, and is set according to a set output characteristic parameter threshold P th Counting P at any time t S (t) satisfies P th Probability of (R) P (t)＝Pr{P S (t)∈P th Get the reliability curve R of switch power performance degradation p (t), the results are shown in the curve (3) in FIG. 8. Meanwhile, the function failure time of the electronic component batch is input into the function mapping model of the switching power supply, and 5000 function failure times F of the corresponding switching power supply can be obtained S (t)＝G(F 1 ′(t),…,F 5 ′ 6 (t)), counting F at any time t S (t) probability of non-occurrence R f (t)＝Pr{F S (t) > t }, and then a reliability curve R of the switch power supply functional failure can be obtained f (t), the results are shown in the graph No. 2 in FIG. 8. And finally, according to the homology of the used performance degradation model and the function failure model, calculating by using a switching power supply performance degradation reliability curve and a function failure reliability curve according to the formula (6) to obtain a switching power supply reliability curve fusing performance degradation and function failure, wherein the result is shown as a curve (1) in fig. 8.
Wherein α = k/N s Total number of specifications N s At 50, the specification number k for both performance degradation and functional failure is 13.
Claims (5)
1. A switching power supply reliability prediction method based on a multi-physical-field digital prototype model is characterized by comprising the following steps:
s1, establishing a multi-physical-field digital prototype model of the switching power supply suitable for reliability prediction;
s2, analyzing and determining key failure modes and failure mechanisms of the switching power supply based on the digital prototype model established in the S1, and determining electronic components with weak reliability;
s3, establishing a switching power supply reliability prediction model fusing performance degradation and function failure based on the digital prototype model established in the step S1 and the electronic component with weak reliability determined in the step S2;
and S4, solving to obtain a reliability curve under a given predicted working environment based on the switching power supply reliability prediction model established in the step S3.
2. The method for predicting the reliability of the switching power supply based on the multi-physical-field digital prototype model according to claim 1, wherein the step S1 comprises the following steps:
s11, establishing a switching power supply circuit model through EDA software, and performing circuit simulation analysis;
s12, establishing a switching power supply structure model through three-dimensional modeling software and finite element analysis software, and performing thermal simulation analysis and vibration simulation analysis;
and S13, realizing data interaction and process control of the switch power supply electrothermal coupling simulation through the simulation coupling platform, further completing electrothermal coupling variable transmission, and realizing indirect coupling of multiple physical fields.
3. The method for predicting the reliability of the switching power supply based on the multi-physical-field digital prototype model according to claim 1, wherein the step S2 comprises the following steps:
s21, combing typical failure modes of the electronic components according to the types of the electronic components forming the switching power supply;
step S22, referring to the failure mode of the performance degradation type of the electronic component in the step S21, determining the electronic component and the sensitive parameter thereof which have great influence on the output performance of the switching power supply by using a sensitivity analysis method;
step S23, referring to the failure mode of the electronic component function failure type in the step S21, determining the electronic component which has the function failure to influence the function of the switching power supply by using a fault tree analysis method;
and step S24, integrating the electronic components analyzed and determined in step S22 and step S23, namely, the electronic components with weak reliability.
4. The method for predicting the reliability of the switching power supply based on the multi-physical-field digital prototype model according to claim 1, wherein the step S3 comprises the following steps:
step S31, according to the switching power supply digital prototype model established in the step S1, establishing a working stress mapping model [ I ] capable of describing the relation between the working condition of the switching power supply and the electric, thermal and vibration stress of the electronic component 1 ,V 1 ,T 1 ,a 1 ,f 1 ,…,I i ,V i ,T i ,a i ,f i ,…]＝S(V s ,I S ,T S ,A S ,f S ) Wherein, I i 、V i 、T i 、a i 、f i Respectively representing the effective values of current, voltage, surface average temperature, sinusoidal vibration acceleration and sinusoidal vibration frequency, V of the component i S 、I S 、T S 、A S 、f S Respectively representing an input voltage effective value, an output current effective value, an environment average temperature, equivalent sinusoidal vibration acceleration and equivalent sinusoidal vibration frequency of the switching power supply, wherein S (-) is a working stress mapping model;
step S32, establishing a time-varying performance degradation model P of the electronic component i according to performance parameter degradation data obtained by an electrical, thermal and vibration accelerated stress test of the electronic component i (t)＝D i (s, t), wherein s = [ I = i ,V i ,T i ,a i ,f i ]，D i (. Cndot.) is a model of performance degradation, and D i (s, t) has a distribution property, P i (t) a time-varying performance parameter vector having a distribution characteristic of the performance-degrading electronic component i;
step (ii) ofS33, establishing a functional failure model F of the electronic component i according to functional failure time data obtained by an electric, thermal and vibration accelerated stress test of the electronic component i (t)＝K i (s,t)，K i (. Is) a model of functional failure, and K i (s, t) has a distribution property, F i (t) is the failure time of the functional failure type electronic component i;
step S34, determining the output characteristic parameter limit state according to the use requirement of the switching power supply, and constructing a performance mapping model P capable of describing the relationship between the electronic component degradation and the output characteristic of the switching power supply by using a digital prototype model S (t)＝X(P 1 (t),…,P i (t) \ 8230;), wherein P S (t) is a switching power supply output characteristic parameter vector, and X (·) is a performance mapping model;
s35, constructing a reliability block diagram of the switching power supply by using the fault tree analysis result, and establishing a function mapping model F capable of describing the function failure corresponding relation between the electronic component and the switching power supply S (t)＝G(F 1 (t),…,F i (t) \8230whereinF S (t) is the failure time of the switch power supply function, and G (·) is a function mapping model;
step S36, the working stress mapping model S (-) inputs the stress to the electronic component performance degradation model D i (. O) and functional failure model K i (·)，D i (. O) and K i The outputs are respectively transmitted to a performance mapping model X (-) and a function mapping model G (-) according to X (-) and G (-) and an output characteristic parameter threshold vector P th Respectively obtaining R p (t) and R f (t), and describing the reliability R of the switching power supply at any time t through the formula (1) S (t), obtaining a reliability prediction model of the switching power supply:
wherein α = k/N s ，N s The total specification number of electronic components in the switching power supply is shown, and k represents that the electronic components are performance-degraded electronic components and function-failure electronic componentsPiece specification number.
5. The method for predicting the reliability of the switching power supply based on the multi-physical-field digital prototype model according to claim 4, wherein the step S4 comprises the following steps:
step S41, performing equivalent decomposition on the stress of the task section to obtain the predicted values of temperature, vibration and electric stress { V } used by the switching power supply S ,I S ,T S ,A S ,f S }；
Step S42, based on the switching power supply working stress mapping model established in step S31, converting { V into { V } S ,I S ,T S ,A S ,f S Substituting the obtained values into a calculation model to obtain temperature, vibration and electric stress response values (I) of all electronic components forming the switching power supply 1 ,V 1 ,P 1 ,T 1 ,a 1 ,f 1 ,…,I i ,V i ,P i ,T i ,a i ,f i ,…]；
Step S43, sequentially calculating the [ I ] of the electronic component I calculated in the step S42 i ,V i ,P i ,T i ,a i ,f i ]Substituting into D of the electronic component i i (. O) and K i In the model, a time-varying performance parameter vector P with distribution characteristics of an electronic component i is obtained i (t) and time to failure F i (t) sampling to obtain N individual performance parameters P 'of the electronic component i based on a Monte Carlo mode' i (t) and N functional failure times F i ′(t)；
Step S44, time-varying performance parameter [ P' 1 (t),…,P′ i (t),…]Substituting the mapping model X (-) into the switch power supply performance to obtain N time-varying output characteristic parameter vectors P of the corresponding switch power supply S (t), counting P at any time t S (t) satisfies P th Probability R of P (t)＝Pr{P S (t)∈P th Get the reliability curve R of switch power performance degradation p (t)；
Step S45, the electronic component batch obtained in the step S43Time to failure of subfunction [ F ] 1 ′(t),…,F i ′(t),…]Substituting the function mapping model G (-) of the switching power supply to obtain N corresponding function failure times F of the switching power supply S (t), counting F at any time t S (t) probability of non-occurrence R f (t)＝Pr{F S (t) > t }, and then a reliability curve R of the switch power supply functional failure can be obtained f (t)；
Step S46, according to the homology of the performance degradation model and the function failure model, calculating by the formula (1) to obtain a switching power supply reliability curve R fusing performance degradation and function failure S (t)。
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