CN115526080A - Reliability Prediction Method of Switching Power Supply Based on Multiphysics Digital Prototyping Model - Google Patents

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

Reliability Prediction Method of Switching Power Supply Based on Multiphysics Digital Prototyping Model

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-physics field digital prototype model.

Background technique

In order to improve the reliability of switching power supply, it is particularly important to predict the reliability of switching power supply accurately. Most of the current reliability prediction methods for switching power supplies are based on mathematical statistics. This method is based on the failure data in reliability prediction manuals or standards, and uses empirical formulas including factors such as quality grades and service conditions to calculate the failure of electronic components. Rate, and then predict the reliability of electronic components and switching power supply. This method is out of touch with the failure mechanism and does not consider the impact of the degradation process on reliability, resulting in poor prediction accuracy.

Contents of the invention

Aiming at the above-mentioned problems existing in the prior art, the present invention provides a method for predicting the reliability of a switching power supply based on a multi-physics digital prototype model. This method can improve the accuracy of switching power supply reliability prediction, and then provide strong support for improving the reliability of switching power supply.

The purpose of the present invention is achieved through the following technical solutions:

A method for predicting the reliability of a switching power supply based on a multi-physics digital prototype model, comprising the following steps:

Step S1, establishing a digital prototype model of electrical, thermal and vibrational multi-physics fields of switching power supply suitable for reliability prediction;

Step S2, based on the digital prototype model established in step S1, analyze and determine the key failure mode and failure mechanism of the switching power supply, and determine the electronic components with weak reliability;

Step S3, based on the digital prototype model established in step S1 and the electronic components with weak reliability determined in step S2, establish a reliability prediction model of switching power supply that integrates performance degradation and functional failure;

Step S4 , based on the reliability prediction model of the switching power supply established in step S3 , solve to obtain a reliability curve under a given predicted working environment.

Compared with the prior art, the present invention has the following advantages:

The invention utilizes the digital prototype model and considers the influence factors of function failure and performance degradation of the key components of the switching power supply under the conditions of electrical, thermal and vibration stresses on the reliability, which can improve the reliability prediction accuracy of the switching power supply, and contribute to improving the reliability of the switching power supply. Sex provides strong support.

Description of drawings

Fig. 1 is a flow chart of the reliability prediction method of switching power supply based on multi-physics digital prototype model;

Fig. 2 is the topological diagram of switching power supply circuit in the embodiment;

Fig. 3 is the structural model of switching power supply in the embodiment;

Fig. 4 is the data interaction process of the iSIGHT platform realizing the electrothermal coupling simulation of the switching power supply in the embodiment;

Fig. 5 is the switching power supply sensitivity analysis result in the embodiment;

Fig. 6 is the switching power supply fault tree analysis result in the embodiment;

Fig. 7 is the flow chart of the reliability prediction modeling of the switching power supply considering the performance degradation and functional failure of the electronic components in the embodiment;

Fig. 8 is the switching power supply reliability curve and its function reliability curve and performance reliability curve in the embodiment.

detailed description

The technical solution of the present invention will be further described below in conjunction with the accompanying drawings, but it is not limited thereto. 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 should be covered by the present invention. within the scope of protection.

The present invention provides a method for predicting the reliability of a switching power supply based on a multi-physics digital prototype model. As shown in Figure 1, the method includes the following steps:

Step S1, establishing a digital prototype model of electrical, thermal, and vibrational multi-physics fields of switching power supply suitable for reliability prediction, the specific steps are as follows:

Step S11, establishing a switching power supply circuit model through EDA software, and performing circuit simulation analysis;

Step S12, establishing a structural model of the switching power supply through three-dimensional modeling software and finite element analysis software, and performing thermal simulation analysis and vibration simulation analysis;

Step S13 , realizing data interaction and process control of the electrothermal coupling simulation of the switching power supply through the simulation coupling platform, and then completing the transfer of the electrothermal coupling variables and realizing the indirect coupling of multiple physical fields.

Step S2, based on the digital prototype model established in step S1, analyze and determine the key failure mode and failure mechanism of the switching power supply, and determine the electronic components with weak reliability, the specific steps are as follows:

Step S21, sort out the typical failure modes according to the types of electronic components that make up the switching power supply;

Step S22, referring to the failure modes of the performance degradation types of electronic components in step S21, using the sensitivity analysis method to determine the electronic components and their sensitive parameters that have a greater impact on the output performance of the switching power supply;

Step S23, referring to the failure mode of the failure type of the electronic component function in step S21, using the method of fault tree analysis to determine the electronic component whose function failure will affect the function of the switching power supply;

The electronic components analyzed and determined in step S24 and integrated step S22 and step S23 are electronic components with weak reliability.

Step S3, based on the digital prototype model established in step S1 and the electronic components with weak reliability determined in step S2, establish a switching power supply reliability prediction model that integrates performance degradation and functional failure, and the specific steps are as follows:

Step S31, according to the switching power supply digital prototype model established in step S1, in combination with various data acquisition methods such as simulation and actual measurement, establish a working stress mapping model [I 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 ), where, I _i , V _i , T _i , a _i , and f _i represent the current effective value, voltage effective value, surface average temperature, sinusoidal vibration acceleration, and sinusoidal vibration frequency of component i respectively, and V _S , I _S , T _S , A _S , and f _S represent the input voltage effective value, output current effective value, ambient average temperature, equivalent sinusoidal vibration acceleration, and equivalent sinusoidal vibration frequency of the switching power supply, respectively, and S(·) is the working stress mapping model;

Step S32: Establish a time-varying performance degradation model P _i (t)=D _i (s, t) for electronic component i based on performance parameter degradation data obtained from electrical, thermal and vibration accelerated stress tests of electronic components, where s ＝[I _i ,V _i ,T _i ,a _i ,f _i ], D _i (·) is a performance degradation model, and D _i (s,t) has distribution characteristics, P _i (t) is a performance degradation electronic Time-varying performance parameter vector with distribution characteristics of component i;

Step S33, according to the functional failure time data (lifetime data) obtained from the electrical, thermal and vibration accelerated stress tests of electronic components, establish a functional failure model F _i (t) = K _i (s, t) of electronic component i, K _i (·) is the functional failure model, and K _i (s,t) has distribution characteristics, and F _i (t) is the failure time of electronic component i with functional failure;

Step S34, determine the limit state of the output characteristic parameters according to the requirements of the switching power supply, use the digital prototype model to construct a performance mapping model P _S (t)=X(P ₁ (t), which can describe the relationship between the degradation of electronic components and the output characteristics of the switching power supply, …,P _i (t),…), where P _S (t) is the output characteristic parameter vector of switching power supply, and X(·) is the performance mapping model;

Step S35, using the fault tree analysis results to construct a reliability block diagram of the switching power supply, and establishing a function mapping model F _S (t)=G(F ₁ (t),...,F _i that can describe the corresponding relationship between electronic components and switching power supply function failures (t),…), where, F _S (t) is the failure time of switching power supply function, G( ) is the function mapping model;

Step S36, the working stress mapping model S(·) inputs the stress to the electronic component performance degradation model D _i (·) and the functional failure model K _i (·), D _i (·) and K _i (·) respectively The output is passed to the performance mapping model X( ) and function mapping model G( ), according to X( ) and G( ) and the output characteristic parameter threshold vector P _th , R _p (t) and R _f ( t), and then use formula (1) to describe the reliability R _S (t) of the switching power supply at any time t, that is, the reliability prediction model of the switching power supply is obtained:

Among them, α=k/N _s , N _s represents the total number of specifications of electronic components in the switching power supply, and k represents the number of specifications of both performance-degraded electronic components and functional failure electronic components.

Step S4, based on the reliability prediction model of the switching power supply established in step S3, solve to obtain the reliability curve under the given expected working environment, the specific steps are as follows:

Step S41, performing an equivalent decomposition on the stress of the mission section, and obtaining the estimated values {V _S , I _S , T _S , A _S , f _S } of the temperature, vibration, and electrical stress used by the switching power supply;

Step S42, based on the working stress mapping model of the switching power supply established in step S31, substituting {V _S , I _S , T _S , A _S , f _S } into it to calculate the temperature, vibration, Electrical stress response value [I 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 substituting [I _i , V _i , P _i , T _i , a _i , f _i ] of electronic component i calculated in step S42 into D _i ( ) and K _{i of electronic component i} (·) In the model, the time-varying performance parameter vector P _i (t) and functional failure time F _i (t) of electronic component i with distribution characteristics are obtained, and N pieces of electronic component i are obtained based on Monte Carlo sampling. Performance parameters P _i ′(t) and N function failure times F _i ′(t);

Step S44, substituting the time-varying performance parameters [P ₁ ′(t),…,P _i ′(t),…] of each batch of electronic components obtained in step S43 into the switching power supply performance mapping model X( ), to obtain Corresponding to the N time-varying output characteristic parameter vectors P _S (t) of the switching power supply, the probability that P _S (t) satisfies P _th at any time t is counted R _P (t)=Pr{P _S (t)∈P _th }, the performance degradation reliability curve R _p (t) of the switching power supply can be obtained;

Step S45, substituting the electronic component batch function failure time [F ₁ ′(t),…,F _i ′(t),…] obtained in step S43 into the switching power supply function mapping model G( ) to obtain the corresponding Switching power supply N function failure time F _S (t), count the probability of F _S (t) not occurring at any time t R _f (t) = Pr{F _S (t)>t}, you can get the switching power supply function Failure reliability curve R _f (t);

Step S46 , according to the homology of the used performance degradation model and functional failure model, the reliability curve R _S (t) of the switching power supply integrating performance degradation and functional failure is calculated by formula (1).

Example:

The engineering object in this embodiment is a switching power supply, whose main function is energy conversion, which is the basic guarantee for the normal operation of electrical equipment. The switching power supply used in this embodiment contains 50 specifications and 87 electronic components. Using the digital prototype model of the switching power supply, considering the functional failure and performance degradation of the internal electronic components of the switching power supply under the three stress environments of electricity, heat and vibration, the process of predicting the reliability of the switching power supply mainly includes the following aspects :

First, establish the digital prototype model of the switching power supply. According to the working principle of the switching power supply, its circuit topology can be established, as shown in Figure 2, where Q1-Q6 represent MOSFETs. Afterwards, based on the circuit topology diagram, use Saber software to build a switching power supply circuit model, and conduct circuit simulation analysis to obtain the power loss of each electronic component in the switching power supply. At the same time, the structural composition of the switching power supply is analyzed, and the structural model of the switching power supply is built using SolidWorks. The results are shown in Figure 3. Then, the structural model is imported into ANSYS, and vibration simulation and thermal simulation are performed through model simplification, material property setting, subnetting, and boundary condition setting. Among them, thermal simulation needs to substitute the power consumption conversion of electronic components into the internal heat generation power in the thermal simulation model based on the iSIGHT platform, and then obtain the thermal field distribution of the switching power supply. The specific process is shown in Figure 4. Afterwards, the parameters of electronic components are obtained through the functional relationship between temperature and device parameters, and passed to the electrical simulation model. Iterating in this way until the iterative difference is less than 0.1°C, it is considered that the electrothermal coupling simulation results converge at this time, and the steady-state response of the switching power supply circuit and thermal field can be obtained.

Second, determine the reliability of the switching power supply and weak electronic components. Based on the above-mentioned digital prototype model, the types of electronic components and their typical failure modes are sorted out, and the results are shown in Table 1.

Table 1

Referring to the failure modes of the performance degradation types of the above-mentioned electronic components, use the sensitivity analysis method to determine the electronic components that have a greater impact on the output performance of the switching power supply. The relative sensitivity is obtained by the ratio of the relative change rate of the technical index P (mainly including 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 the relative sensitivity L _i is higher, it means that the relative change of the parameter i of the component can affect the voltage, ripple and efficiency of the circuit more. For the switching power supply studied in this embodiment, a sensitivity analysis is carried out, and it is determined that there are 57 performance-degraded electronic components in the switching power supply, and some results are shown in FIG. 5 .

Refer to the failure modes of the failure types of electronic components, and use the method of fault tree analysis to determine the electronic components that have functional failures that will affect the function of the switching power supply. Take the switching power supply output fault as the top event of the fault tree, find out all the possible factors and reasons that lead to this event, and connect them with the specified logic symbols to analyze the fault tree. Starting from the top event, analyze down to the middle event, and analyze layer by layer until the basic cause of the event is found, that is, the bottom event of the fault tree. Then the minimum cut set of the fault tree is solved to determine the electronic components that affect the failure of the switching power supply. It is finally determined that the switching power supply contains 56 functional failure electronic components, and some results of the fault tree analysis are shown in Figure 6.

Based on the results of the above sensitivity analysis and fault tree analysis, the union of them is the electronic components with weak reliability of the switching power supply, and it is determined that the number of electronic components with both performance degradation and functional failure is 13.

Then, the reliability prediction model of switching power supply considering the performance degradation and function failure of components is established, and the specific process is shown in Figure 7. First, according to the digital prototype model of the switching power supply, the working stress of the switching power supply is input into the digital prototype model through simulation and actual measurement, so as to obtain the working stress of the electronic components, and establish the working stress mapping model S(·).

At the same time, using the performance parameter degradation data and functional failure time data obtained in the electrical, thermal and vibration accelerated degradation tests of electronic components, the performance degradation model D _i (s, t) and functional failure model K _i ( s,t), where the form of the performance degradation model is:

D _i (s, t) = e _i (s) Λ _i (t) + σ _i C _i (Λ _i (t)) (3);

Among them, s=[I _i , V _i , T _i , a _i , f _i ], I _i , V _i , T _i , a _i , and f _i represent the current effective value, voltage effective value, and surface value of component i respectively. Average temperature, sinusoidal vibration acceleration, sinusoidal vibration frequency, e _i (s) is the function of stress vector s, Λ _i (t) is the time scale conversion function, σ _i is the drift coefficient, C _i (Λ _i (t)) is Uncertain process, subject to normal uncertain distribution N(0,Λ _i (t)). The functional failure model has the form:

K _i (s,t)=e _iK (s)θ _i (t)+ε _i (4)

是应力向量s的函数，θ_i(t)为时间函数，ε_i为扰动因子，服从均值为0方差为

的正态分布。Among them, s=[I _i , V _i , T _i , a _i , f _i ], I _i , V _i , T _i , a _i , and f _i represent the current effective value, voltage effective value, and surface value of component i respectively. Average temperature, sinusoidal vibration acceleration, sinusoidal vibration frequency,

is the function of the stress vector s, θ _i (t) is the time function, ε _i is the disturbance factor, the mean value is 0 and the variance is

normal distribution of .

Afterwards, according to the requirements of the switching power supply, the limit state of the output characteristic parameters is determined, and the digital prototype model is used to describe the multi-input and multi-output quantitative mapping relationship between the performance degradation parameters of electronic components and the output characteristic parameters of the switching power supply, so as to build a performance mapping model X(·). Then, using the results of the fault tree analysis, the reliability block diagram of switching power supply is constructed to describe the logical mapping relationship between the failure-prone function of electronic components and the function of switching power supply, so as to construct the function mapping model G( ), which has the following form:

Among them, F _i (t) is the failure time of functional failure electronic component i, the first m electronic components are in series structure, and the last nm electronic components are in parallel structure. Then, as shown in Figure 7, connect the above model.

Finally, the reliability prediction model is used for state iteration to obtain the reliability curve of the switching power supply. Firstly, the equivalent decomposition of the stress under the task profile of the switching power supply is carried out to obtain the predicted values {V _S , I _S , T _S , A _S , f _S } of the temperature, vibration, and electrical stress of the switching power supply. Input the expected value into the working stress mapping model, and calculate the temperature, vibration and electrical stress response values of the 87 electronic components inside the switching power supply [I 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 ). Afterwards, the response value is substituted into the performance degradation model D _i (s,t) and functional failure model K _i (s,t) of the corresponding electronic components, and [P ₁ (t),...,P ₅₇ ( t)] and [F ₁ (t),...,F ₅₆ (t)], and get 5000 batch performance parameters [P ₁ ′(t),…,P ₅ ′ ₇ ( t)] and functional failure time [F ₁ ′(t),…,F ₅ ′ ₆ (t)]. Then, input the batch performance parameters of electronic components into the switching power supply performance mapping model, and 5000 time-varying output characteristic parameter vectors P _S (t)=X(P ₁ ′(t),...,P ₅ ′ ₇ (t)), where P _S (t) is a time-varying vector with distribution characteristics including voltage, ripple, and efficiency, and then according to the set output characteristic parameter threshold _{P th} , count PS (t at any time t ) satisfies the probability of P _th R _P (t) = Pr{P _S (t)∈P _th }, then the performance degradation reliability curve R _p (t) of the switching power supply can be obtained, and the result is shown as ③ in Figure 8 curve. At the same time, inputting the function failure time of batches of electronic components into the switching power supply function mapping model, the corresponding function failure time of 5000 switching power supplies can be obtained F _S (t)=G(F ₁ ′(t),…,F ₅ ′ ₆ (t)), counting the probability of F _S (t) not occurring at any time t R _f (t) = Pr{F _S (t)>t}, you can get the failure reliability curve of the switching power supply function R _f (t ), the result is the curve ② shown in Figure 8. Finally, according to the homology of the used performance degradation model and functional failure model, using the performance degradation reliability curve and functional failure reliability curve of the switching power supply, the reliability curve of the switching power supply integrating performance degradation and functional failure is calculated by formula (6) , and the result is shown in the curve ① in Figure 8.

Wherein, α=k/N _s , the total number of specifications N _s is 50, and the number k of specifications with both performance degradation and function failure is 13.

Claims (5)

1. A method for predicting reliability of switching power supply based on multi-physics digital prototype model, characterized in that said method comprises the steps: Step S1, establishing a digital prototype model of electrical, thermal and vibrational multi-physics fields of switching power supply suitable for reliability prediction; Step S2, based on the digital prototype model established in step S1, analyze and determine the key failure mode and failure mechanism of the switching power supply, and determine the electronic components with weak reliability; Step S3, based on the digital prototype model established in step S1 and the electronic components with weak reliability determined in step S2, establish a reliability prediction model of switching power supply that integrates performance degradation and functional failure; Step S4 , based on the reliability prediction model of the switching power supply established in step S3 , solve to obtain a reliability curve under a given predicted working environment. 2. the switching power supply reliability prediction method based on the multi-physics field digital prototype model according to claim 1, is characterized in that the concrete steps of described step S1 are as follows: Step S11, establishing a switching power supply circuit model through EDA software, and performing circuit simulation analysis; Step S12, establishing a structural model of the switching power supply through three-dimensional modeling software and finite element analysis software, and performing thermal simulation analysis and vibration simulation analysis; Step S13 , realizing data interaction and process control of the electrothermal coupling simulation of the switching power supply through the simulation coupling platform, and then completing the transfer of the electrothermal coupling variables and realizing the indirect coupling of multiple physical fields. 3. the method for predicting reliability of switching power supply based on multi-physics field digital prototype model according to claim 1, is characterized in that the concrete steps of described step S2 are as follows: Step S21, sort out the typical failure modes according to the types of electronic components that make up the switching power supply; Step S22, referring to the failure modes of the performance degradation types of electronic components in step S21, using the sensitivity analysis method to determine the electronic components and their sensitive parameters that have a greater impact on the output performance of the switching power supply; Step S23, referring to the failure mode of the failure type of the electronic component function in step S21, using the method of fault tree analysis to determine the electronic component whose function failure will affect the function of the switching power supply; The electronic components analyzed and determined in step S24 and integrated step S22 and step S23 are electronic components with weak reliability. 4. the method for predicting reliability of switching power supply based on multi-physics field digital prototype model according to claim 1, is characterized in that the concrete steps of described step S3 are as follows: Step S31, according to the switching power supply digital prototype model established in step S1, establish a working stress mapping model [I 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 , _AS ,f _S ), where I _i ,V _i ,T _i , a _i , and f _i represent the current effective value, voltage effective value, surface average temperature, sinusoidal vibration acceleration, and sinusoidal vibration frequency of component i respectively; V _S , I _S , T _S , A _S , and f _S represent the switch The effective value of the input voltage of the power supply, the effective value of the output current, the average temperature of the environment, the equivalent sinusoidal vibration acceleration, and the equivalent sinusoidal vibration frequency, S(·) is the working stress mapping model; Step S32: Establish a time-varying performance degradation model P _i (t)=D _i (s, t) for electronic component i based on performance parameter degradation data obtained from electrical, thermal and vibration accelerated stress tests of electronic components, where s ＝[I _i ,V _i ,T _i ,a _i ,f _i ], D _i (·) is a performance degradation model, and D _i (s,t) has distribution characteristics, P _i (t) is a performance degradation electronic Time-varying performance parameter vector with distribution characteristics of component i; Step S33, according to the functional failure time data obtained from the electrical, thermal and vibration accelerated stress tests of electronic components, establish a functional failure model F _i (t) = K _i (s, t) of electronic component i, K _i (·) is the functional failure model, and K _i (s, t) has distribution characteristics, and F _i (t) is the failure time of electronic component i with functional failure; Step S34, determine the limit state of the output characteristic parameters according to the requirements of the switching power supply, use the digital prototype model to construct a performance mapping model P _S (t)=X(P ₁ (t), which can describe the relationship between the degradation of electronic components and the output characteristics of the switching power supply, …,P _i (t),…), where P _S (t) is the output characteristic parameter vector of switching power supply, and X(·) is the performance mapping model; Step S35, using the fault tree analysis results to construct a reliability block diagram of the switching power supply, and establishing a function mapping model F _S (t)=G(F ₁ (t),...,F _i that can describe the corresponding relationship between electronic components and switching power supply function failures (t),…), where, F _S (t) is the failure time of switching power supply function, G( ) is the function mapping model; Step S36, the working stress mapping model S(·) inputs the stress to the electronic component performance degradation model D _i (·) and the functional failure model K _i (·), D _i (·) and K _i (·) respectively The output is passed to the performance mapping model X( ) and function mapping model G( ), according to X( ) and G( ) and the output characteristic parameter threshold vector P _th , R _p (t) and R _f ( t), and then use formula (1) to describe the reliability R _S (t) of the switching power supply at any time t, that is, the reliability prediction model of the switching power supply is obtained:

Among them, α=k/N _s , N _s represents the total number of specifications of electronic components in the switching power supply, and k represents the number of specifications of both performance-degraded electronic components and functional failure electronic components. 5. the switching power supply reliability prediction method based on the multi-physics field digital prototype model according to claim 4, is characterized in that the concrete steps of described step S4 are as follows: Step S41, performing an equivalent decomposition on the stress of the mission section, and obtaining the estimated values {V _S , I _S , T _S , A _S , f _S } of the temperature, vibration, and electrical stress used by the switching power supply; Step S42, based on the working stress mapping model of the switching power supply established in step S31, substituting {V _S , I _S , T _S , A _S , f _S } into it to calculate the temperature, vibration, Electrical stress response value [I 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 substituting [I _i , V _i , P _i , T _i , a _i , f _i ] of electronic component i calculated in step S42 into D _i ( ) and K _{i of electronic component i} (·) In the model, the time-varying performance parameter vector P _i (t) and functional failure time F _i (t) of electronic component i with distribution characteristics are obtained, and N pieces of electronic component i are obtained based on Monte Carlo sampling. Performance parameters P′ _i (t) and N functional failure times F _i ′(t); Step S44, substituting the time-varying performance parameters [P' ₁ (t), ..., P' _i (t), ...] of each batch of electronic components obtained in step S43 into the switching power supply performance mapping model X ( ), to obtain Corresponding to the N time-varying output characteristic parameter vectors P _S (t) of the switching power supply, the probability that P _S (t) satisfies P _th at any time t is counted R _P (t)=Pr{P _S (t)∈P _th }, the performance degradation reliability curve R _p (t) of the switching power supply can be obtained; Step S45, substituting the electronic component batch function failure time [F ₁ ′(t),…,F _i ′(t),…] obtained in step S43 into the switching power supply function mapping model G( ) to obtain the corresponding Switching power supply N function failure time F _S (t), count the probability of F _S (t) not occurring at any time t R _f (t) = Pr{F _S (t)>t}, you can get the switching power supply function Failure reliability curve R _f (t); Step S46 , according to the homology of the used performance degradation model and functional failure model, the reliability curve R _S (t) of the switching power supply integrating performance degradation and functional failure is calculated by formula (1).

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