Power supply system multistage reliability prediction method based on fault precursor parameters

Abstract

The invention discloses a power supply system multistage reliability prediction method based on fault precursor parameters, belongs to the technical field of reliability prediction of a power supply system, and aims to solve the problem that the reliability of a multi-level and multi-task power supply system is difficult to effectively assess by an existing reliability modeling method. According to the method, a system dynamic fault tree is constructed according to a signal hierarchical relationship to represent a system function structure, external excitation is converted into component stress through a sub-circuit multi-stress field simulation model, and output parameters including performance degradation and function failure characteristics are determined in combination with a failure physical model of key parameters. Transmission from the component layer to the sub-circuit layer is completed; a system-level dynamic simulation model is established based on the system dynamic fault tree, functional unit output parameters under equal stress are obtained, and transmission from the sub-circuit layer to the functional unit layer is completed; and based on the function unit output parameter reliability, completing parameter transmission from the function unit to the system task section layer and the system general task layer through the weight coefficient, and predicting the final reliability.

Description

Technical Field

[0001] The present invention belongs to the technical field of reliability prediction of power supply systems, and in particular relates to a multi-level reliability prediction method for power supply systems based on fault precursor parameters. Background Art

[0002] From the perspective of reliability design and health management, the reliability of the output parameters of the power supply system is an important information for evaluating the reliability of the system, and its modeling accuracy determines the accuracy of the system reliability prediction. The power supply system will perform various power supply, distribution, and transfer tasks in operation, and the weighted results of the execution reliability under each system task profile will be used as the comprehensive evaluation parameter of the system reliability during its life cycle. However, complex power supply systems have the characteristics of multi-level and multi-task circuits, numerous components, mutual coupling of signals between units, and complex and changeable task timing. Most of the existing system-level reliability prediction methods only consider the calculation of unit-level input and output parameters, and the prediction accuracy and analysis depth of system reliability are insufficient. The system reliability prediction method based on digital models relies too much on the underlying component information as input, and there is insufficient research on building a comprehensive system reliability characterization model for systems with limited underlying information. In addition, the dynamic timing changes of the system under the working tasks and the multi-stress coupling phenomenon have higher requirements for modeling accuracy. Summary of the invention

[0003] The present invention aims to solve the problem that the existing reliability modeling method is difficult to effectively evaluate the reliability of a multi-level and multi-task power supply system.

[0004] A method for predicting the multi-level reliability of a power supply system based on fault precursor parameters comprises the following steps:

[0005] S1. Characterize the system functional structure based on the dynamic fault tree of the power system;

[0006] The power system dynamic fault tree includes a component layer, a subcircuit layer, a functional unit layer, a system task profile layer and a system total task layer. The directed edges represent the state parameter transmission mode of each level. The component state parameter A l The multi-stress field simulation model is passed to the sub-circuit layer, and the sub-circuit state parameter B m The system-level dynamic simulation model is passed to the functional unit layer, and the functional unit state parameter C p By weight coefficient W i,p Passed to the system task profile layer, the system task profile state parameter D i Through the weight coefficient W sys,i Pass it to the system's overall task layer;

[0007] S2. For each sub-circuit, a multi-stress field simulation model is established to convert external excitation into component stress, and the component state parameter A is calculated byl Sensitivity analysis to determine the key parameter A k ; Establish a failure physical model of key parameters in combination with component stress, substitute the degradation curve and failure time samples obtained by sampling into the sub-circuit multi-stress field simulation model to obtain the sub-circuit output parameters that can represent the performance degradation characteristics and functional failure characteristics; realize the transmission from the component layer to the sub-circuit layer;

[0008] S3. Based on the key parameters of the subcircuit and the output parameters of the subcircuit, the parameters that affect the failure of the power supply system, i.e., the system fault precursor parameters, are determined through correlation coefficient analysis; a system-level dynamic simulation model is established based on the dynamic fault tree of the power supply system, and the fault precursor parameters are substituted into the system-level dynamic simulation model to obtain the output parameters of the functional unit under equal stress, so as to realize the transmission from the subcircuit layer to the functional unit layer; the output parameters of the functional unit can be expressed as a degradation curve with distribution, i.e., the degradation curve of the output parameters of the functional unit;

[0009] S4. Determine the functional unit degradation model based on the functional unit output parameter degradation curve under equal stress, and determine the reliability R of the functional unit output parameter according to the functional unit degradation model and failure threshold out,p (t), and through the weight coefficient W i,p Realize the transfer of functional units to the system task profile layer;

[0010] S5. System mission profile state parameter D i Through the weight coefficient W sys,i It is passed to the system's overall task layer to ultimately determine reliability characteristics such as the system's overall task reliability.

[0011] Furthermore, the specific process of step S2 includes the following steps:

[0012] S21. Establish X sub The multi-stress field simulation model corresponding to each sub-circuit converts external excitation into component stress;

[0013] S22, based on the multi-stress field simulation model corresponding to each sub-circuit, the component state parameter A is calculated for each sub-circuit. l Conduct sensitivity analysis to determine key parameters A k ;

[0014] S23, determine the failure physical model of key parameters, including characterization of key parameters A k Time-dependent degradation model A k (t) = G P (t,Τ l (t),E l (t)) and the key characterization parameter A k Failure model F of the time to hard failure state k(t) = G F (t,Τ l (t),E l (t)), G P (·) and G F (·) are functions used to represent degradation model and failure model respectively;

[0015] Through random sampling, the degradation curve and failure time sample corresponding to each key parameter are generated respectively, and the data corresponding to the degradation curve and the data corresponding to the open circuit and short circuit after reaching the failure time are substituted into the multi-stress field simulation model to obtain the sub-circuit output parameters. The sub-circuit output parameters can represent the performance degradation characteristics and functional failure characteristics.

[0016] Furthermore, the multi-stress field simulation model includes a component 1 electrical simulation model E l (t) = G E (t,S,Τ) and thermal simulation model Τ l (t) = G T (t,E,M), and the electrical and thermal simulation models can transfer parameters to each other;

[0017] The electrical simulation model converts the external electrical stress S and temperature stress Τ into the component electrical stress E based on time t. l (t);

[0018] The thermal simulation model converts the ambient temperature stress M and the electrical stress of all components of the subcircuit into the thermal stress T on the surface of each component l. l (t).

[0019] Furthermore, step S22 includes:

[0020] During the life cycle, l Perform Sobol first- and second-order sensitivity analysis to analyze the impact of single parameter changes on subcircuit outputs and the coupling effects of any two parameter changes on subcircuit outputs; identify parameters whose first- and second-order sensitivity values ​​are higher than the sensitivity threshold as key parameters A k , key parameter A k Contains component intrinsic parameters R k , key node parameters V k .

[0021] Furthermore, the multi-stress field simulation model: [V n (t),I n (t)]=G M (t,S,A k (t),F k (t)), where G M (·) is the function used to represent the corresponding multi-stress field simulation model, Vn (t), I n (t) is the voltage and current parameters of the sub-circuit output node n degraded over time t, V n (t), I n (t) Capable of expressing performance degradation characteristics and functional failure characteristics.

[0022] Furthermore, the specific process of step S3 includes the following steps:

[0023] S31, based on the key parameters of the sub-circuit and the sub-circuit output parameters, through Pearson correlation coefficient analysis, select the parameters that affect the failure of the power supply system from the key parameters determined in S22, that is, the system failure precursor parameters, the system failure precursor parameters A S Including sensitive components intrinsic parameters R S , sensitive key node parameters V S ;

[0024] S32, based on the topological structure of the dynamic fault tree of the power supply system, a dynamic logic gate component is formed by simulation components, wherein the simulation components include: a timing switch, a programmable switch, a register, a basic logic gate, etc., a system-level dynamic simulation model is established based on the dynamic logic gate component, an interface between the subcircuit level and the functional unit level is obtained, and a qualitative power supply system dynamic fault tree model is converted into a quantitative simulation model;

[0025] S33, firstly obtain the system fault precursor parameter A based on historical data S , including: R based on historical data fitting under equal stress S , V corresponding to historical test data under equal stress S ; The non-isostatic stress historical test data V corresponding to the sensitive key node parameters test As a supplement, multiply it by the correction factor μ to obtain V′ S =(1-μ)V S +μV test ;

[0026] Will be V S Corrected to V′ S The corresponding system fault precursor parameter A S Substitute it into the system-level dynamic simulation model. During the calculation, the goal is to minimize the error between the simulation value and the actual value of the system-level dynamic simulation model. The value of this factor is changed through the optimization algorithm to correct the correction factor μ. The output parameters of the functional unit under equal stress are obtained through simulation. The output parameters of the functional unit can be expressed as a degradation curve with distribution, that is, the degradation curve of the functional unit output parameters.

[0027] Furthermore, the process of determining the functional unit degradation model based on the functional unit output parameter degradation curve under equal stress described in step S4 includes:

[0028] The degradation model of the pth functional unit is expressed as:

[0029] D p (t) = d(t; Φ) + ξ (2)

[0030] Among them, D p (t) is the cumulative degradation amount, d(t; Φ) is the degradation trajectory function representing the degradation curve, Φ is the coefficient in the function; ξ is the uncertainty parameter that obeys the normal distribution.

[0031] Furthermore, the process of determining the reliability of the functional unit according to the functional unit degradation model and the failure threshold in step S4 includes:

[0032] D p (t)>Failure threshold D f Failure occurs at time t, and the failure probability function F of the pth functional unit at time t is p (t) is:

[0033]

[0034] Among them G f is the standard normal cumulative distribution function, with mean μ and standard deviation σ;

[0035] The reliability R of the output parameter of the pth functional unit is calculated based on the failure probability function. out,p (t) = 1-F p (t).

[0036] Furthermore, the weight coefficients for implementing the transfer of the functional unit to the system task profile layer in step S4 are as follows:

[0037]

[0038] Among them, W i,p is the weight coefficient of the output parameter of the pth functional unit in the i-th system mission profile; t 第p个功能单元时长 represents the working time of the pth functional unit in the i-th system task profile; t 第i个系统任务剖面总时长 Represents the total duration corresponding to the i-th system task profile.

[0039] Furthermore, the specific process of step S5 includes the following steps:

[0040] Determine the weight coefficient Among them, W sys,i is the weight coefficient of the total system mission of the i-th system mission profile in the whole life cycle; t 第i个任务剖面出现次数对应的时长 represents the total duration of the i-th system task profile calculated by the number of occurrences, t 系统总任务 Indicates the duration of the total system task;

[0041] Reliability R based on output parameters of functional units out，p Determine the total mission reliability R over the entire life cycle sys :

[0042]

[0043] Based on the total task reliability R sys Conduct reliability prediction.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] The present invention performs hierarchical dynamic modeling on system-level circuits, combines system fault precursor parameters, and calculates comprehensive characterization parameters of system total task reliability based on weights of different system task profiles, thereby solving the problem that existing reliability modeling methods are difficult to effectively evaluate the reliability of multi-level and multi-task power supply systems. Sensitivity analysis and correlation analysis methods are used to extract various key failure precursor parameters to reduce the amount of information required at the component layer, thereby improving the ability to achieve power supply system reliability prediction modeling under limited information. Historical test data is also integrated to perform system-level reliability calculations to ensure prediction accuracy. The present invention also provides a new idea for the combination of failure physics models and system-level reliability prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a multi-level reliability prediction method of a power system based on fault precursor parameters;

[0047] Figure 2 It is the system layering and parameter transfer flow chart;

[0048] Figure 3 It is the dynamic fault tree of the power system;

[0049] Figure 4 This is the multi-stress field simulation diagram of subcircuit B4;

[0050] Figure 5 It is the result of Sobol first-order and second-order sensitivity analysis;

[0051] Figure 6 It is a sub-circuit degradation simulation platform based on iSIGHT;

[0052] Figure 7 It is the data sampling form of degradation and failure model;

[0053] Figure 8 It is a system-level dynamic simulation model;

[0054] Fig. 9 It is the degradation curve of the fault precursor parameters of A7 resistor, A8 chip, etc. and the output parameters of functional unit C2;

[0055] Fig.10 is the reliability curve of functional unit C2;

[0056] Fig.11 It is the system comprehensive evaluation parameter system total task reliability curve. DETAILED DESCRIPTION

[0057] In view of the problems existing in the prior art, the present invention provides a multi-level reliability prediction method for a power supply system based on fault precursor parameters. This method is based on the key parameter degradation and failure model. By establishing a parameter transfer relationship between five functional levels and extracting the system fault precursor parameters through quantitative analysis, the reliability prediction of complex electronic system functions under limited information is achieved. And through the comprehensive characterization parameter index of system reliability, the ability to predict the reliability of the power supply system under various task processes is improved. The following is an explanation in conjunction with a specific implementation method.

[0058] Specific implementation method 1: Combination Figure 1 To explain this embodiment,

[0059] A method for predicting the multi-level reliability of a power supply system based on fault precursor parameters comprises the following steps:

[0060] Step S1: Divide the power supply system into five levels: component level, subcircuit level, functional unit level, system task profile level and system total task level (output reliability characteristics) according to the signal level relationship; and construct a dynamic fault tree of the power supply system to characterize the system functional structure and determine the state parameter transmission mode of each level; specifically, including:

[0061] Step S11: The power system is referred to as the system, and the system is divided into five levels based on the directed acyclic graph construction method. Each level is regarded as a network node. The system includes X comp +X sub +X func +X sys +1 node: X comp Components, X sub Subcircuits, X func Functional units, X sys System task profiles, 1 system total task. The system network node state set is Ω = {A l ,B m ,C p ,D i ,R sys}, where A l (l=1,2,…,X comp ) represents the component status parameter; B m (m=1,2,…,X sub) represents the sub-circuit state parameter; C p (p=1,2,…,X func ) represents the functional unit state parameter; D i (i=1,2,…,X sys ) represents the system mission profile state parameter; R sys It represents the reliability characteristics of the system's total mission (reliability, failure rate, etc.); the constructed directed acyclic graph with 5 levels is a hierarchical construction of the dynamic fault tree of the power system;

[0062] Step S12: Using the above hierarchically constructed power system dynamic fault tree to characterize the functional structure of the system, the directed edges characterize the state parameter transmission mode of each level: A l Through the multi-stress field simulation model, it is transmitted to B m , B m Pass the system-level dynamic simulation model to C p , C p By weight coefficient W i,p Pass to D i , and finally D i Through the weight coefficient W sys,i Pass to R sys .

[0063] Step S2, establish a sub-circuit multi-stress field simulation model, convert external excitation into component stress, and determine key parameters through sensitivity analysis; establish a failure physical model of key parameters in combination with component stress, substitute the degradation curve and failure time sample obtained by random sampling into the sub-circuit multi-stress field simulation model to obtain the output parameters of the sub-circuit containing performance degradation and functional failure characteristics, and complete the transfer from the component layer to the sub-circuit layer; the specific process includes the following steps:

[0064] Step S21: Establish X sub Each sub-circuit has its own corresponding multi-stress field simulation model, which converts external excitation into component stress. The multi-stress field simulation model includes component electrical simulation model E l (t) = G E (t,S,Τ) and thermal simulation model Τ l (t) = G T (t,E,M), and the electrical and thermal simulation models can transfer parameters to each other.

[0065] The electrical simulation model combines the external electrical stress S and the temperature stress Τ (where T = {T1, ..., T a ,…}, T a represents the temperature of the ath component) is converted into the electrical stress E of the component l based on time t l (t);

[0066] The thermal simulation model combines the ambient temperature stress M and the electrical stress E of all components of the subcircuit (where E = {V1, I1, P1, ... V b ,I b ,P b ,},V b ,I b , P b The voltage, current, and power of the bth component are converted into the thermal stress T on the surface of each component l. l (t);

[0067] Step S22: Based on the multi-stress field simulation model corresponding to each sub-circuit, the component state parameter A is calculated for each sub-circuit. l Conduct sensitivity analysis to determine the key parameter A k , the steps are:

[0068] During the life cycle, l Sobol first- and second-order sensitivity analysis was performed to analyze the impact of a single parameter change of ±20% on the sub-circuit output (i.e., the simulation results of the multi-stress field simulation model), and the coupling impact of any two parameter changes of ±20% on the sub-circuit output; parameters with first- and second-order sensitivity values ​​higher than ±1% were identified as key parameters A. k , key parameter A k Contains component intrinsic parameters R k , key node parameters V k , which can be expressed as A k = {R k ,V k}.

[0069] Step S23: Establish a physical failure model (PoF) of key parameters, including characterization of key parameters A k Time-dependent degradation model A k (t) = G P (t,Τ l (t),E l (t)) and the key characterization parameter A k Failure model F of the time of hard fault (open circuit, short circuit) state k (t) = G F (t,Τ l (t),E l (t)), G P (·) and G F (·) are functions used to represent degradation model and failure model respectively;

[0070] Through Monte Carlo random sampling, the degradation curve (the corresponding data on the degradation curve) and the failure time sample corresponding to each key parameter are generated respectively, and the data corresponding to the degradation curve and the data corresponding to the open circuit and the short circuit after reaching the failure time are substituted into the multi-stress field simulation model to obtain the sub-circuit output parameters, which can represent the performance degradation characteristics and functional failure characteristics; the multi-stress field simulation model in this embodiment: [V n (t),I n (t)]=G M (t,S,A k (t),F k (t)), where G M (·) is the function used to represent the corresponding multi-stress field simulation model, V n (t), I n (t) is the voltage and current parameters of the sub-circuit output node n degraded over time t, V n (t), I n (t) Capable of expressing performance degradation characteristics and functional failure characteristics.

[0071] Step S3, determine the parameters that have a significant impact on system failure, i.e., system fault precursor parameters, through Pearson correlation coefficient analysis. Establish a system-level dynamic simulation model based on the dynamic fault tree of the power system, substitute the fault precursor parameters into the system-level dynamic simulation model, obtain the output parameters of the functional unit under equal stress, and complete the transfer from the sub-circuit layer to the functional unit layer; the specific process includes the following steps:

[0072] Step S31: Through Pearson correlation coefficient analysis, select parameters that have a significant impact on power system failure from the key parameters determined in S22, that is, system failure precursor parameters:

[0073] The Pearson correlation coefficient r is calculated for the input and output of S23, where:

[0074]

[0075] Among them, x m ,y m They are the input value of the key parameters of the sub-circuit and the output value of the output parameters of the sub-circuit (simulation value). and is the corresponding mean, s x and y is the corresponding standard deviation, and N is the number of data points.

[0076] The parameter whose correlation coefficient r with the unit output curve is greater than a given threshold Z is determined as the system fault precursor parameter A. S , System failure precursor parameter A S Including sensitive components intrinsic parameters R S, sensitive key node parameters V S , which can be expressed as A S = {R S ,V S};

[0077] Step S32: Based on the topological structure of the dynamic fault tree of the power supply system in step S12, a dynamic logic gate component is formed by simulation components, and the simulation components include: timing switches, programmable switches, registers, basic logic gates, etc. A system-level dynamic simulation model is established based on the dynamic logic gate component, and an interface between the sub-circuit level and the functional unit level is obtained to realize the conversion of the qualitative power supply system dynamic fault tree model into a quantitative simulation model.

[0078] Step S33: First, obtain the system fault precursor parameter A based on historical data. S , including: R based on historical data fitting under equal stress S , V corresponding to historical test data under equal stress S ; The non-isostatic stress historical test data V corresponding to the sensitive key node parameters test As a supplement, multiply it by the correction factor μ (0<μ<1) to obtain V′ S =(1-μ)V S +μV test ;

[0079] Will be V S Corrected to V′ S The corresponding system fault precursor parameter A S Substitute it into the system-level dynamic simulation model. During the calculation, the goal is to minimize the error between the simulation value and the actual value of the system-level dynamic simulation model. The value of this factor is changed through the optimization algorithm to correct the correction factor μ. The output parameters of the functional unit under equal stress are obtained through simulation. The output parameters of the functional unit can be expressed as a degradation curve with distribution, that is, the degradation curve of the functional unit output parameters.

[0080] The above process can make V test More accurate approximation to the data table under constant stress.

[0081] It should also be noted that the function unit output parameter is a simulation result, and the certain situation of the function unit reflected by this result is called the function unit state;

[0082] Step S4: Determine the functional unit degradation model based on the functional unit output parameter degradation curve under equal stress, determine the functional unit reliability according to the functional unit degradation model and the failure threshold, and i,p Realize the transfer of functional units to the system task profile layer. The specific process includes:

[0083] Step S41, determining a functional unit degradation model based on the functional unit output parameter degradation curve under equal stress, and analyzing the reliability of the functional unit according to the functional unit degradation model and the failure threshold;

[0084] The pth (p=1,2,...,X func )The functional unit degradation model is expressed as:

[0085] D p (t) = d(t; Φ) + ξ (2)

[0086] Among them, D p (t) is the cumulative degradation amount, d(t; Φ) is the degradation trajectory function representing the degradation curve, Φ is the coefficient in the function (electrical, thermal stress, etc.); ξ is the uncertainty parameter that obeys the normal distribution (related to individual tolerance differences and environmental noise).

[0087] Parameter D p (t) is a continuously differentiable monotonically increasing function, when D p (t)>D f Failure occurs at time t. Then the failure probability function F of the pth functional unit at time t is p (t) is:

[0088]

[0089] Among them G f is the standard normal cumulative distribution function with mean μ and standard deviation σ.

[0090] The reliability R of the output parameter of the pth functional unit is calculated based on the failure probability function. out,p :

[0091] R out,p (t) = 1-F p (t) (4)

[0092] Step S42: Solve C according to the working conditions p To D i The weight coefficient W i,p is the weight coefficient of the output parameter of the pth functional unit in the i-th system mission profile; t 第p个功能单元时长 represents the working time of the pth functional unit in the i-th system task profile; t 第i个系统任务剖面总时长 Represents the total duration corresponding to the i-th system task profile.

[0093] The reliability of the i-th system mission profile is calculated by the reliability of the p functional unit output parameters according to W i,p Weighted and normalized determination.

[0094] S5. System mission profile state parameter Di Through the weight coefficient W sys,i Pass it to the system's overall task layer, and finally determine the reliability characteristics such as the system's overall task reliability; including the following steps:

[0095] Determine D i to R sys The weight coefficient Among them, W sys,i is the weight coefficient of the total system mission of the i-th system mission profile in the whole life cycle; t 第i个任务剖面出现次数对应的时长 represents the total duration of the i-th system task profile calculated by the number of occurrences, t 系统总任务 Indicates the duration of the total system task;

[0096] The total mission reliability of the whole life cycle system is calculated by the reliability of the i system mission profiles according to W sys,i Weighted and normalized determination.

[0097] Reliability R based on output parameters of functional units out，p , Comprehensive evaluation index of the whole life cycle System total mission reliability R sys Calculate by formula (5) and get its reliability curve.

[0098]

[0099] Based on reliability, other reliability characteristics such as unreliability, failure rate, and reliable life can be obtained.

[0100] Example

[0101] The object in this embodiment is a certain aircraft power supply system, whose main function is to perform voltage conversion of different timings and sizes according to various preset task processes, and is a basic guarantee system for the normal operation of aircraft electrical equipment. The aircraft power supply system is a multi-level system, including 6 sub-circuit units with mutual influence, which are used to realize various functions such as power supply, control, and communication. By using system stratification and power supply system dynamic fault tree establishment, the reliability of the aircraft power supply system is predicted based on the characteristics of complex electronic systems with multi-level circuits and multi-tasks, considering the functional failure, performance degradation and multi-level influence of its internal key components.

[0102] First, according to the aircraft power system schematic diagram, the system is divided into five levels: component level, subcircuit level, functional unit level, system mission profile level, and system total mission level (output reliability characteristics) according to the signal hierarchy relationship. The hierarchical system parameter transfer relationship is as follows: Figure 2 In reality, the reliability information that can be obtained is often weakened level by level. By considering each level as a network node based on the directed acyclic graph construction method, the system includes X comp +X sub +Xfunc +X sys +1 node: X comp Components, X sub Subcircuits, X func Functional units, X sys System task profiles, 1 system total task. The system network node state set is Ω = {A l ,B m ,C p ,D i ,R sys}, where A l (l=1,2,...,X comp ) represents the component status parameter, B m (m=1,2,...,X sub ) represents the subcircuit state parameter, C p (p=1,2,...,X func ) represents the functional unit status parameter, D i (i=1,2,...,X sys ) represents the system mission profile state parameter, R sys Represents the overall mission reliability characteristics of the system (reliability, failure rate, etc.).

[0103] The multi-level power supply system (including multiple sub-circuit modules for realizing various functions such as power supply, control, communication, etc. and having mutual influence) is represented by the dynamic fault tree method. The functional structure of the power supply is characterized as follows Figure 3 As shown. And the state parameter transmission mode of each level is represented by the directed edge, l The multi-stress field simulation model is transmitted to B m , B m Pass the system-level dynamic simulation model to C p , C p By weight coefficient W i,p Pass to D i , and finally D i Through the weight coefficient W sys,i Pass to R sys .

[0104] For this system, the system's total mission reliability characteristic R sys The evaluation index is the weighted result of the output parameters in the three functional units of C1 monitoring, C2 driving, and C3 power supply. C1 includes independent sub-circuit units such as B1 control sub-circuit and B2 communication sub-circuit, C2 includes B3 control sub-circuit and B4 power supply sub-circuit, and C4 includes B5 battery module sub-circuit and B6 control sub-circuit.

[0105] Secondly, taking the B4 power supply subcircuit as an example, subcircuit level modeling is carried out to realize the transfer from the component layer to the subcircuit. The multi-stress field simulation model of the B4 power supply subcircuit is established through EDA software. Figure 4 , converting external excitation into component stress. First, a simulation model of the thermal-sensitive component is constructed. Based on the test data of the electrical parameter values ​​of the component at different ambient temperatures, a model of the component parameters changing with temperature is established. For example, the power MOSFET is modeled based on the main electrical parameter values ​​at 25°C and 175°C. The electrical simulation model of the component is E l (t) = G E (t,S,Τ), the thermal simulation model is Τ l (t) = G T (t, E, M). The electrical simulation model combines the external electrical stress S and the temperature stress Τ (where T = {T1, ..., T a ,…}, T a represents the temperature of the ath component) is converted into the electrical stress E of the component l based on time t l (t); The thermal simulation model combines the ambient temperature stress M and the electrical stress E of all components of the subcircuit (where E = {V1, I1, P1, ... V b ,I b ,P b ,…}, Vb, Ib, Pb are the voltage, current, and power of the bth component) converted into the thermal stress T on the surface of each component l l (t);

[0106] Perform Sobol first-order and second-order sensitivity analysis to determine key parameters. Based on the simulation model of B4, parameter A l The input variables A1 to A9 are labeled. During the life cycle, the influence of a single parameter on the sub-circuit output signal V4 and the coupled influence of two parameters on the sub-circuit output signal V4 are analyzed respectively when the single parameter changes by ±20%. The analysis results are shown in Figure 5 As shown in Figure 1, if the first-order and second-order sensitivities are both higher than ±1%, it means that the effect of this parameter on the output during the life cycle will not be overwhelmed by noise and can be identified as a key parameter A. k = {R k ,V k}, including the intrinsic parameters of the components R k , key node parameters V k Since the first-order and second-order sensitivities of A7, A8, and A3 are all higher than ±1%, they are identified as key parameters.

[0107] Establish the PoF model of key parameters, including: Degradation model A that characterizes key parameters k (t) = G P (t,Τ l (t),E l(t)). The specific form is: conventional devices such as resistors, capacitors, and transistors use the Arrhenius physical model, such as the A7 resistor resistance degradation model as shown in formula (1), which satisfies the normal distribution; for physical models such as DC / DC converters that cannot be covered, the empirical degradation model is used as a supplement, for example: the degradation curve of the circuit node signal A3 can be extended by the Irene model as shown in formula (2):

[0108]

[0109] Among them, R0 is the initial value of the intrinsic parameter of the component (the resistance value in A7), R k (t) is the degradation value of the intrinsic parameters of the components over time. V0 is the initial value of the key node parameters (output voltage in A3), V k (t) The degradation value of key node parameters over time. V l For the set E l Voltage stress in (t), T l For the set l The thermal stress in (t) is in Kelvin, t is time, k is the Boltzmann constant 8.617×10 -5 eV / K, ε1 and ε2 are random quantities. Activation energy E a The other parameters m, p, a, b are unknown coefficients obtained by fitting the experimental data. Specifically for A7:

[0110] R7(t)=R0~N(3400,33.12 2 )

[0111]

[0112] Failure model F that characterizes the hard fault (open circuit, short circuit) state of key parameters k (t) = G F (t,Τ l (t),E l (t)). The specific form is: For example, the failure model of the A8 power chip is as shown in formula (3), which satisfies the normal distribution

[0113]

[0114] Among them, F k (t) is the component failure life. l For the set E l (t) is the current stress, and ε3 is a random quantity.

[0115] In fact, A k (t) = G P (t,Τ l (t),E l(t)) is a generalized expression of formula (1) and formula (2), F k (t) = G F (t,Τ l (t),E l (t)) is a generalized expression of formula (3).

[0116] Based on parameter optimization software, such as iSIGHT, a sub-circuit degradation simulation calculation platform is established, such as Figure 6 Through Monte Carlo random sampling, the degradation curve and failure (life end) time samples are generated respectively. The degradation curve data and the open circuit and short circuit data after reaching the failure time are substituted into the simulation model, such as Figure 7 In the B4 subcircuit, the power MOSFET works in the PWM state and adjusts the power of each output load according to the drive signal. Finally, the subcircuit output parameters are obtained: [V n (t),I n (t)]=G M (t,S,A k (t),F k (t)), where V n (t), I n (t) is the voltage and current parameters of the sub-circuit output node n degraded over time t, which can represent the performance degradation characteristics and functional failure characteristics. For example, V n ,I n is a degenerate characteristic, and when t is longer, V n ,I n Similarly, use EDA software to build models of each subcircuit included in the power system and perform simulation analysis.

[0117] Then, the Pearson correlation coefficient was further analyzed to find the key parameter A k = {R k ,V k} (the subcircuit is A7, A8, A3) to determine the parameters that have a significant impact on the failure of the power system, that is, the system failure precursor parameters. The Pearson correlation coefficient r is as shown in formula (4):

[0118]

[0119] x m and m are the input values ​​of the key parameters of the sub-circuit and the values ​​of the simulation output, and is the corresponding mean, s x and yis the standard deviation, and N is the number of data points. After analysis, the Pearson correlation coefficients of A7, A8, and A3 curves relative to V4 are 0.891, 0.853, and 0.716, respectively. A7 and A8 are greater than Z (in this analysis scenario, it is considered that greater than Z = 0.85 meets the correlation requirement), which is the weakest parameter of B4 and is determined as the system failure precursor parameter. Similarly, the system failure precursor parameters of other sub-circuits are determined.

[0120] Establish a system dynamic model. Based on the location and type of fault precursor parameters, construct dynamic logic gate components in the EDA software from simulation components: timing switches, programmable switches, registers, basic logic gates, etc., to achieve the conversion of qualitative dynamic fault tree models to quantitative simulation models. For example, in the model, some analog sub-circuits based on power supplies are integrated. When the multi-channel power supply voltage is lower than a certain threshold, it will cause the failure of subsequent sub-circuits. This simulation is achieved through voltage-controlled switch components. Connect the various sub-circuits according to the system connection method, and the final system dynamic model is as follows: Figure 8 .

[0121] Substitute A7 and A8 of B4 subcircuit into the dynamic simulation model in combination with the system fault precursor parameters of other subcircuits, including: R based on historical data fitting under equal stress S 、Historical test data V S In addition, the non-isostatic stress historical test data V of sensitive key node parameters test As a supplement, multiply it by the correction factor μ (0 < μ < 1) before substitution, and the default value is 0.5, that is, V′ S =0.5V S +0.5V test , so that the weight of this parameter can be adjusted in the subsequent calibration of reliability prediction to improve the accuracy of reliability prediction. In the calculation process, the value of this factor is changed through the optimization algorithm, and the correction is performed with the goal of minimizing the error between the simulation value and the actual value. The output parameters of the functional unit under equal stress (including the output degradation curve with distribution, etc.) are obtained through simulation, among which the output parameter degradation curve of the functional unit C2 is as follows Fig. 9 ;

[0122] Finally, the obtained degradation curve of the functional unit output parameters under equal stress is used to analyze the reliability in combination with the functional unit degradation model and failure threshold. The output voltage is a key output parameter that determines the life of C2. It is in series with other output parameters and directly determines whether the C2 control module can work. The degradation model of the functional unit C2 is expressed as:

[0123] D2(t)=d(t;Φ)+ξ (5)

[0124] Where D2(t) is the cumulative degradation, d(t; Φ) is the degradation trajectory function representing the degradation curve, Φ is the coefficient in the function; ξ is the uncertainty parameter that obeys the normal distribution (related to individual tolerance differences and environmental noise). The parameter D2(t) is a continuously differentiable monotonically increasing function. When D2(t)>D f Failure occurs at time t. Then the failure probability function F2(t) at time t is:

[0125]

[0126] Among them G f is the standard normal cumulative distribution function, with a mean of μ and a standard deviation of σ. The reliability R of the output parameter of the functional unit C2 is calculated based on the life distribution function. out,2 , such as:

[0127] R out,2 (t) = 1 - F2(t) (7)

[0128] According to this calculation process, the reliability curve of functional unit C2 under equal stress is obtained as follows: Fig.10 Similarly, the reliability curves of other functional units are obtained. According to the existing test data, the life of the functional units are all within the reliability prediction range of the 95% confidence level, and the model accuracy meets the requirements.

[0129] Then, solve C according to the working conditions p To D i The weight coefficient For example, the weight coefficient W of the output parameter of the second functional unit C2 in the first system task profile D1 is 1,2 =0.6. Solve for D i to R sys The weight coefficient For example, the weight coefficient W of the first system mission profile D1 in the total mission of the system in the whole life cycle is sys,1 =0.2. The final system comprehensive evaluation coefficient calculation model is as follows:

[0130]

[0131] 0.2(0.1R out,1 +0.6R out,2 +0.3R out,3 ), 0.2 means that the execution frequency of the task profile D1 of the system accounts for 20%, R out,1 , R out,2 , R out,3The reliability of the output parameters of the functional units C1, C2, and C3 are 0.1, 0.6, and 0.3, respectively, which are the normalized values ​​of the weight coefficients of the working time of C1, C2, and C3 in the total duration of the first system task profile. Finally, the comprehensive evaluation parameter system total task reliability curve of the power supply system is obtained as follows: Fig.11 , the life of the power system is about 1790 days at a reliability of 0.9. In addition, other reliability characteristics can be obtained through this weighted method. The model prediction results can be corrected by coupling the parameters of the multi-stress field simulation model and adaptively adjusting the correction factor of the supplementary system fault precursor parameters. Compared with the historical test data of the same type of equipment, the life prediction accuracy can reach more than 90%, which is relatively accurate.

[0132] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-level reliability prediction method for a power supply system based on fault precursor parameters, characterized in that: The following steps are involved: S1. Characterize the system functional structure based on the dynamic fault tree of the power system; The power system dynamic fault tree includes a component layer, a subcircuit layer, a functional unit layer, a system task profile layer and a system total task layer. The directed edges represent the state parameter transmission mode of each level. The component state parameter A l The multi-stress field simulation model is passed to the sub-circuit layer, and the sub-circuit state parameter B m The system-level dynamic simulation model is passed to the functional unit layer, and the functional unit state parameter C p By weight coefficient W i,p Passed to the system task profile layer, the system task profile state parameter D i Through the weight coefficient W sys,i Pass it to the system's overall task layer; S2. For each sub-circuit, a multi-stress field simulation model is established to convert external excitation into component stress, and the component state parameter A is calculated by l Sensitivity analysis to determine the key parameter A k ; Establish a failure physical model of key parameters in combination with component stress, substitute the degradation curve and failure time samples obtained by sampling into the sub-circuit multi-stress field simulation model to obtain the sub-circuit output parameters that can represent the performance degradation characteristics and functional failure characteristics; realize the transmission from the component layer to the sub-circuit layer; S3. Based on the key parameters of the subcircuit and the output parameters of the subcircuit, the parameters that affect the failure of the power supply system, i.e., the system fault precursor parameters, are determined through correlation coefficient analysis; a system-level dynamic simulation model is established based on the dynamic fault tree of the power supply system, and the fault precursor parameters are substituted into the system-level dynamic simulation model to obtain the output parameters of the functional unit under equal stress, so as to realize the transmission from the subcircuit layer to the functional unit layer; the output parameters of the functional unit can be expressed as a degradation curve with distribution, i.e., the degradation curve of the output parameters of the functional unit; S4. Determine the functional unit degradation model based on the functional unit output parameter degradation curve under equal stress, and determine the reliability R of the functional unit output parameter according to the functional unit degradation model and failure threshold out,p (t), and through the weight coefficient W i,p Realize the transfer of functional units to the system task profile layer; S5. System mission profile state parameter D i Through the weight coefficient W sys,i It is passed to the system's overall task layer to ultimately determine reliability characteristics such as the system's overall task reliability.

2. A method for constructing a multi-level reliability prediction model of a power supply system based on fault precursor parameters according to claim 1, characterized in that: The specific process of step S2 includes the following steps: S21. Establish X sub The multi-stress field simulation model corresponding to each sub-circuit converts external excitation into component stress; S22, based on the multi-stress field simulation model corresponding to each sub-circuit, the component state parameter A is calculated for each sub-circuit. l Conduct sensitivity analysis to determine key parameters A k ; S23, determine the failure physical model of key parameters, including characterization of key parameters A k Time-dependent degradation model A k (t) = G P (t,Τ l (t),E l (t)) and the key characterization parameter A k Failure model F of the time to hard failure state k (t) = G F (t,Τ l (t),E l (t)), G P (·) and G F (·) are functions used to represent degradation model and failure model respectively; Through random sampling, the degradation curve and failure time sample corresponding to each key parameter are generated respectively, and the data corresponding to the degradation curve and the data corresponding to the open circuit and short circuit after reaching the failure time are substituted into the multi-stress field simulation model to obtain the sub-circuit output parameters. The sub-circuit output parameters can represent the performance degradation characteristics and functional failure characteristics.

3. The method for constructing a multi-level reliability prediction model of a power supply system based on fault precursor parameters according to claim 2, characterized in that: The multi-stress field simulation model includes a component 1 electrical simulation model E l (t) = G E (t,S,Τ) and thermal simulation model Τ l (t) = G T (t,E,M), and the electrical and thermal simulation models can transfer parameters to each other; The electrical simulation model converts the external electrical stress S and temperature stress Τ into the component electrical stress E based on time t. l (t); The thermal simulation model converts the ambient temperature stress M and the electrical stress of all components of the subcircuit into the thermal stress T on the surface of each component l. l (t).

4. The method for constructing a multi-level reliability prediction model of a power supply system based on fault precursor parameters according to claim 3, characterized in that: The steps of step S22 include: During the life cycle, l Perform Sobol first- and second-order sensitivity analysis to analyze the impact of single parameter changes on subcircuit outputs and the coupling effects of any two parameter changes on subcircuit outputs; identify parameters whose first- and second-order sensitivity values ​​are higher than the sensitivity threshold as key parameters A k , key parameter A k Contains component intrinsic parameters R k , key node parameters V k .

5. A method for constructing a multi-level reliability prediction model of a power supply system based on fault precursor parameters according to claim 4, characterized in that: The multi-stress field simulation model: [V n (t),I n (t)]=G M (t,S,A k (t),F k (t)), where G M (·) is the function used to represent the corresponding multi-stress field simulation model, V n (t), I n (t) is the voltage and current parameters of the sub-circuit output node n degraded over time t, V n (t), I n (t) Capable of expressing performance degradation characteristics and functional failure characteristics.

6. A method for constructing a multi-level reliability prediction model of a power supply system based on fault precursor parameters according to any one of claims 1 to 5, characterized in that: The specific process of step S3 includes the following steps: S31, based on the key parameters of the sub-circuit and the sub-circuit output parameters, through Pearson correlation coefficient analysis, select the parameters that affect the failure of the power supply system from the key parameters determined in S22, that is, the system failure precursor parameters, the system failure precursor parameters A S Including sensitive components intrinsic parameters R S , sensitive key node parameters V S ; S32, based on the topological structure of the dynamic fault tree of the power supply system, a dynamic logic gate component is formed by simulation components, wherein the simulation components include: a timing switch, a programmable switch, a register, a basic logic gate, etc., a system-level dynamic simulation model is established based on the dynamic logic gate component, an interface between the subcircuit level and the functional unit level is obtained, and a qualitative power supply system dynamic fault tree model is converted into a quantitative simulation model; S33, firstly obtain the system fault precursor parameter A based on historical data S , including: R based on historical data fitting under equal stress S , V corresponding to historical test data under equal stress S ; The non-isostatic stress historical test data V corresponding to the sensitive key node parameters test As a complement, multiply it by the correction factor μ to obtain V S ′=(1-μ)V S +μV test ; Will be V S Corrected to V S ′The corresponding system fault precursor parameter AS is substituted into the system-level dynamic simulation model. In the calculation, the goal is to minimize the error between the simulation value and the actual value of the system-level dynamic simulation model. The value of this factor is changed through the optimization algorithm to correct the correction factor μ. The output parameters of the functional unit under equal stress are obtained through simulation. The output parameters of the functional unit can be expressed as a degradation curve with distribution, that is, the degradation curve of the functional unit output parameter.

7. A method for constructing a multi-level reliability prediction model of a power supply system based on fault precursor parameters according to claim 6, characterized in that: The process of determining the functional unit degradation model based on the functional unit output parameter degradation curve under equal stress described in step S4 includes: The degradation model of the pth functional unit is expressed as: D p (t)=d(t;Φ)+ξ (2) Among them, D p (t) is the cumulative degradation amount, d(t; Φ) is the degradation trajectory function representing the degradation curve, Φ is the coefficient in the function; ξ is the uncertainty parameter that obeys the normal distribution.

8. A method for constructing a multi-level reliability prediction model of a power supply system based on fault precursor parameters according to claim 7, characterized in that: The process of determining the reliability of the functional unit according to the functional unit degradation model and the failure threshold in step S4 includes: D p (t)>Failure threshold D f Failure occurs at time t, and the failure probability function F of the pth functional unit at time t is p (t) is: Among them G f is the standard normal cumulative distribution function, with mean μ and standard deviation σ; The reliability R of the output parameter of the pth functional unit is calculated based on the failure probability function. out,p (t) = 1-F p (t).

9. A method for constructing a multi-level reliability prediction model of a power supply system based on fault precursor parameters according to claim 8, characterized in that: The weight coefficients for transferring the functional unit to the system task profile layer in step S4 are as follows: Among them, W i,p is the weight coefficient of the output parameter of the pth functional unit in the i-th system mission profile; t 第p个功能单元时长 represents the working time of the pth functional unit in the i-th system task profile; t 第i个系统任务剖面总时长 Represents the total duration corresponding to the i-th system task profile.

10. A method for constructing a multi-level reliability prediction model of a power supply system based on fault precursor parameters according to claim 9, characterized in that: The specific process of step S5 includes the following steps: Determine the weight coefficient Among them, W sys,i is the weight coefficient of the total system mission of the i-th system mission profile in the whole life cycle; t 第i个任务剖面出现次数对应的时长 represents the total duration of the i-th system task profile calculated by the number of occurrences, t 系统总任务 Indicates the duration of the total system task; Reliability R based on output parameters of functional units out，p Determine the total mission reliability R over the entire life cycle sys : Based on the total task reliability R sys Conduct reliability prediction.

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