CN115455596A - Particle swarm algorithm-based polyurethane rubber sealing member storage reliability evaluation method - Google Patents
Particle swarm algorithm-based polyurethane rubber sealing member storage reliability evaluation method Download PDFInfo
- Publication number
- CN115455596A CN115455596A CN202211135332.1A CN202211135332A CN115455596A CN 115455596 A CN115455596 A CN 115455596A CN 202211135332 A CN202211135332 A CN 202211135332A CN 115455596 A CN115455596 A CN 115455596A
- Authority
- CN
- China
- Prior art keywords
- electric connector
- measured
- group
- leakage rate
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Geometry (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Connector Housings Or Holding Contact Members (AREA)
Abstract
The invention discloses a particle swarm algorithm-based polyurethane adhesive sealing element storage reliability evaluation method. The commonly used parameter estimation method comprises a maximum likelihood method and a least square method, but when the two methods are used for solving, the estimated result can only obtain single optimal solution, cannot obtain integral optimal solution, and the calculation speed is slow. According to the invention, the accelerated test data of the polyurethane rubber sealing element for the electric connector is collected, the test data is preprocessed and substituted into the reliability evaluation model, the unknown parameters of the reliability evaluation model are estimated by adopting the particle swarm algorithm, the storage reliability level of the polyurethane rubber sealing element for the electric connector is evaluated according to the parameter estimation result, and the storage reliability evaluation efficiency and accuracy of the polyurethane rubber sealing element for the electric connector are greatly improved.
Description
Technical Field
The invention belongs to the technical field of reliability evaluation of electric connector sealing elements, and particularly relates to a method for evaluating storage reliability of a polyurethane rubber sealing element based on a particle swarm algorithm.
Background
The electric connector is an indispensable electronic component in various aerospace devices and weapon systems, is widely applied to various parts of the systems, bears an important mission of information transmission, and can cause system operation errors when one electric connector fails.
The reliability of electrical connectors during long term storage depends on the performance of components such as contacts, insulators and seals. The sealing element is used as a key component of the electric connector and is mainly used for blocking substance exchange between devices and preventing harmful gas from entering the electric connector, so that components in the electric connector are protected from normal operation. However, under a long-term storage environment, the performance of the sealing member may be degraded, so that moisture or harmful gas in the outside air may enter the inside of the electrical connector, and the insulation performance of the electrical connector may be reduced, or even a short circuit may cause a safety accident. However, reliability studies for electrical connectors are currently focused on contacts and insulators, and the study of sealing performance is seldom concerned.
Polyurethane glue is one of sealing element materials commonly used in an electric connector, and has the advantages of high adhesion, aging resistance and the like. At present, many scholars at home and abroad research the performance of polyurethane adhesive and analyze the reasons of the performance reduction of the polyurethane adhesive under different stress conditions, but most of the scholars only pay attention to the polyurethane adhesive, do not combine the performance reduction of the polyurethane adhesive with specific products for analysis, and start from the premise condition of environmental stress really applied to the product during storage and work to research the real performance expression of the polyurethane adhesive product during work and evaluate the reliability level of the product.
The commonly used parameter estimation method comprises a maximum likelihood method and a least square method, and the optimal solution is determined by an optimization algorithm such as a Newton method and random gradient descent. However, when the two methods are used for solving, the estimated result can only obtain single optimal solution, and cannot obtain the overall optimal solution, and the calculation speed is slow. A group of particles in the particle swarm algorithm represent a parameter, in the process of solving calculation, parameter estimation values can be searched by comparing values of all parameters, so that the overall error is minimum, meanwhile, the particle swarm algorithm can rapidly judge the range of estimation results, the efficiency of parameter estimation is greatly improved, and the shortage of a maximum likelihood estimation method and a least square estimation method is effectively solved.
Therefore, in order to solve the above technical problems, it is necessary to provide a method for evaluating storage reliability of a polyurethane sealant for an electrical connector based on a particle swarm optimization.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a reliability evaluation method of a polyurethane rubber sealing element for an electric connector, which comprises the steps of collecting accelerated test data of the polyurethane rubber sealing element for the electric connector, preprocessing the test data, substituting the preprocessed test data into a reliability evaluation model, estimating unknown parameters of the reliability evaluation model by adopting a particle swarm algorithm, and evaluating the storage reliability level of the polyurethane rubber sealing element for the electric connector according to the parameter estimation result, thereby greatly improving the storage reliability evaluation efficiency and accuracy of the polyurethane rubber sealing element for the electric connector.
The invention relates to a method for evaluating the storage reliability of a polyurethane rubber sealing element based on a particle swarm algorithm, which comprises the following steps of:
step one, testing the sealing performance of the electric connector to obtain helium leakage rate data of the electric connector.
And step two, eliminating abnormal data exceeding the range of (mu-3 sigma, mu +3 sigma) in the helium leakage rate measured value of each electric connector, wherein mu represents the average value of the helium leakage rate measured values of each electric connector in each group at the same moment, and sigma represents the standard deviation of the helium leakage rate measured values of each electric connector in each group at the same moment.
Step three, establishing a reliability evaluation model and carrying out sorting and simplification, wherein the method specifically comprises the following steps:
establishing a reliability evaluation model:
in the formula, Q t For testing the ambient temperature T at time T 0 Measured helium leakage rate, Q, of the electrical connector 0 Is initially at measuring ambient temperature T 0 Measuring the helium leakage rate of the electric connector, G is a leakage degradation parameter, gamma is the gas diffusion randomness,
seal surface area A = π r for mean pressure of helium leak in the time period before time t 1 2 -N 1 ·π·r 2 2 ，r 1 Is the outer diameter of the seal member, r 2 Is the pin radius, N 1 The total circumference L of the sealing member is equal to the number of the pins 3 ＝2πr 1 +N 1 ·2πr 2 Delta P is the difference between the internal pressure and the external pressure of the helium leak detector, eta is the viscosity of helium gas, h is the thickness of a sealing element, and K B Is the Boltzmann constant, m 0 Mass of a single helium molecule, H seal bond interface roughness, σ n For elastic stress of the seal, R S Sealing coefficient, RH test environmental humidity, E a For reaction activation energy, T is test ambient temperature, σ 0 Initial stress of sealing element when electric connector leaves factory, and polyurethane integral material coefficient K t ＝κ·E 0 ，E 0 Is the initial modulus of elasticity of the polyurethane, χ is the viscosity coefficient of the polyurethane, Z 1 、Z 2 、Z 3 、Z 4 、Z 5 Is a scale factor.
Let unknown parameters to be estimated
Y 6 ＝Z 5 、Y 7 = χ; let unknown variable X 1 ＝L 3 ,X 2 = gamma, wherein L 3 The machining accuracy is considered to be a variable.
Thereby obtaining:
wherein:
wherein the content of the first and second substances,
is X 1 The average value of (a) of (b),
is X 1 The variance of (a) is determined,
is X 2 The average value of (a) of (b),
is X 2 The variance of (c).
Combining the terms in formula (2) to obtain:
and step four, performing parameter estimation of the reliability evaluation model by adopting a particle swarm algorithm.
Step five, evaluating the storage reliability of the electric connector, which comprises the following steps:
for a single electric connector, estimating the optimal parameter value of the reliability evaluation model obtained by the four-parameter estimation, the sealing member thickness h of the electric connector and the initial measured environment temperature T of the electric connector 0 Lower measured helium leak rate Q of electrical connector 0 Substituting formula (5) with the actual storage ambient temperature for T and the actual storage ambient humidity for RH in formula (5) to obtain the electrical connector stored under the conditions of the actual storage ambient temperature and the actual storage ambient humidity and measured ambient temperature T 0 The measured helium leak rate is plotted against time.
Due to Q t Obeying normal distribution, sampling the reliability estimation of the polyurethane adhesive sealing element for the electric connector at T moment under the actual storage environment temperature and the actual storage environment humidity by adopting a Monte Carlo sampling method, combining the storage of the electric connector under the conditions of the actual storage environment temperature and the actual storage environment humidity, and measuring the environmental temperature T 0 And obtaining a reliability function of the polyurethane rubber sealing member for the electric connector at the t moment according to the relation between the measured helium leakage rate and time.
Preferably, the electric connector samples are divided into n groups, each group is m, each electric connector in each group is tested under the conditions of the same test environment temperature, test environment humidity and thickness of the polyurethane adhesive sealing element for the electric connector, a helium leakage instrument is adopted to measure helium leakage rate of the electric connector at different times, but the test environment temperature, the test environment humidity and the thickness of the polyurethane adhesive sealing element for the electric connector in different groups are different during testing.
Preferably, the parameter estimation of the reliability evaluation model in the fourth step is performed by the following specific process:
4.1 build fitness function:
wherein the content of the first and second substances,
and Q representing the corresponding time t of each electric connector of each group when the ith helium leakage rate is measured t The values of the fit are determined,
and (4) representing the average value of the helium leakage rate measured by the ith electric connector of each group, and N representing the number of times of measuring the helium leakage rate of each electric connector of each group.
4.2 given Y 1 、Y 2 、Y 3 、Y 4 、Y 5 、Y 6 、Y 7 、X 1 And X 2 Setting initial values of the number of particles and the number of evolutionary times in the particle swarm algorithm, and substituting the average helium leakage rate value measured by each group of electric connectors at different moments, the reliability evaluation model of each group of electric connectors and the fitness function of each group of electric connectors into the particle swarm algorithm for updating calculation; in iteration, the quality of the particles is measured according to the adaptive value calculated by the fitness function, and the flight speed and the value of the particles are updated.
4.3 obtaining a set p of k parameter optima for each iteration g ＝(p g1 ,p g2 ,···,p gk ) Then, adding 1 to the value of c; if c reaches 10, the final p with higher accuracy is obtained g Step 4.4 is performed, otherwise the parameter range of the kth parameter is changed to
The number of particles and the number of evolutionary times are increased to 5 times of the original number, and then the step 4.2 is returned; wherein p is gk The initial value of c is 0, which is the optimal value of the kth parameter.
4.4 obtaining the optimal values of the parameters of each group of electric connectors under the conditions corresponding to the test environment temperature, the test environment humidity and the thickness of the polyurethane rubber sealing element for the electric connectors, and selecting the optimal value of the parameter with the minimum fitness function value as the optimal parameter value finally considering the different test environment temperatures, the test environment humidity and the thickness of the polyurethane rubber sealing element for the electric connectors after the fitness function value under the conditions is obtained.
4.5 Standard deviation of the seal Overall perimeter for Each set of Electrical connectors
And standard deviation of randomness of gas diffusion
The parameter estimation is as follows:
x of each group of electric connectors obtained in step 4.3 1 I.e. the average value of the whole perimeter of each electric connector sealing element in each group
Resulting X for each set of electrical connectors 2 I.e. the random mean value of the gas diffusion of each electric connector in each group
Standard deviation of the overall perimeter of the seal of the final electrical connector
And standard deviation of randomness of gas diffusion
All using formula
Calculating, wherein X (q) represents the whole perimeter of the sealing member of the q-th electric connector of the group currently subjected to calculation or the gas diffusion randomness, and when X (q) represents the gas diffusion randomness, the calculation is also carried out through the steps of 4.1, 4.2 and 4.3, the average value of the measured helium leakage rates is replaced by the measured helium leakage rate of the q-th electric connector of the group currently subjected to calculation in the estimation process, and each electric connector of each group is replaced by the q-th electric connector of the group currently subjected to calculation; sigma 1 2 To represent
Or
μ 1 To represent
Or
Solving for
When, mu 1 Using the current calculated overall perimeter mean of each electrical connector seal of the set
Substitution, X (q) is substituted with the seal member overall perimeter of the qth electrical connector of the set currently being calculated, solving
When, mu 1 Using the currently calculated mean value of the gas diffusion randomness of each electrical connector of the group
Substitution, X (q) is substituted randomly with the gas diffusion of the qth electrical connector of the set currently being calculated.
More preferably, the reliability function at time t of the polyurethane adhesive seal for an electrical connector is as follows:
in the formula: a is t Number of failures of stored electrical connector sampled for Monte Carlo at time t, N t Total number of stored electrical connectors sampled for Monte Carlo at time t, P () representing probability, Q D To measureAmbient temperature T 0 Helium leak Rate failure threshold of Q The mean value of helium leakage rate sigma at t moment of the electric connector under the actual storage environment temperature and the actual storage environment humidity Q The standard deviation of the helium leakage rate of the electric connector at the t moment under the actual storage environment temperature and the actual storage environment humidity is shown; wherein u is Q The value is stored by adopting the electric connector under the conditions of actual storage environment temperature and actual storage environment humidity and measured environment temperature T 0 Q calculated by the relation of the measured helium leakage rate and time t ，σ Q The value is that the electric connector is stored under the conditions of actual storage environment temperature and actual storage environment humidity and is measured at the environment temperature T 0 X in the relation of the measured helium leakage rate and time 1 And X 2 Respectively replaced by the estimation of step 4.5
And
and Q is calculated t 。
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a storage reliability evaluation method of a polyurethane rubber sealing element for an electric connector, which is characterized in that an unknown parameter estimation value of a performance degradation track model of the polyurethane rubber sealing element for the electric connector is obtained by processing test data, the storage reliability level of the polyurethane rubber sealing element for the electric connector is finally evaluated, and the storage reliability evaluation efficiency and accuracy of the polyurethane rubber sealing element for the electric connector are greatly improved.
Drawings
FIG. 1 is a flow chart of a polyurethane sealant storage reliability evaluation method based on a particle swarm optimization provided by the invention;
fig. 2 is a calculation flowchart of the particle swarm algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments and the accompanying drawings.
As shown in fig. 1, the method for evaluating the storage reliability of the polyurethane sealant based on the particle swarm optimization comprises the following specific steps:
step one, testing the sealing performance of the electric connector to obtain helium leakage rate data of the electric connector.
Dividing the electrical connector samples into n groups of m; according to a GB/T34986-2017 product accelerated test method or a GJB1217A-2009 electric connector test method, each electric connector of each group is tested under the conditions of the same test environment temperature, test environment humidity and the thickness of the polyurethane adhesive sealing element for the electric connector, and a helium leakage instrument is adopted to measure the helium leakage rate of the electric connector at different times, but the test environment temperature, the test environment humidity and the thickness of the polyurethane adhesive sealing element for the electric connector are different when different groups of electric connectors are tested.
And step two, preprocessing a helium leakage rate measured value.
And rejecting abnormal data which exceed the range of (mu-3 sigma, mu +3 sigma) in the helium leakage rate measured value of each electrical connector according to the 3 sigma criterion, wherein mu represents the average value of the helium leakage rate measured values of each electrical connector of each group at the same moment, and sigma represents the standard deviation of the helium leakage rate measured values of each electrical connector of each group at the same moment. Abnormal values measured at each moment of each group of the electric connectors can be eliminated through the 3 sigma rule, so that the whole operation result is prevented from being influenced.
And step three, establishing a reliability evaluation model and carrying out sorting and simplification.
The reliability evaluation model is established as follows:
in the formula, Q t For testing the ambient temperature T at time T 0 Measured helium leakage rate, Q, of the electrical connector 0 Is initially at measuring ambient temperature T 0 Electrical connection measured at the bottomHelium leakage rate of the connector, G is leakage degradation parameter, gamma is gas diffusion randomness,
seal surface area A = π r for mean pressure of helium leak in the time period before time t 1 2 -N 1 ·π·r 2 2 ，r 1 Is the outer diameter of the seal member, r 2 Is the pin radius, N 1 The total circumference L of the sealing member is equal to the number of the pins 3 ＝2πr 1 +N 1 ·2πr 2 Delta P is the difference between the internal pressure and the external pressure of the helium leak detector, eta is the viscosity of helium gas, h is the thickness of a sealing element, and K B Is the Boltzmann constant, m 0 Mass of a single helium molecule, H seal bond interface roughness, σ n For elastic stress of the seal, R S Sealing coefficient, RH test environmental humidity, E a For reaction activation energy, T is test ambient temperature, σ 0 Initial stress of sealing element when electric connector leaves factory, and polyurethane integral material coefficient K t ＝κ·E 0 ，K t Is set in consideration of the change of the material properties caused by the oxidation and hydrolysis of polyurethane in the environment, E 0 Is the initial modulus of elasticity of the polyurethane, χ is the viscosity coefficient of the polyurethane, Z 1 、Z 2 、Z 3 、Z 4 Is a proportionality coefficient, Z 5 Is a proportionality coefficient associated with the hydrolysis reaction of the polyurethane.
Sealing coefficient R S Proportionality factor Z 1 、Z 2 、Z 3 、Z 4 Coefficient of proportionality Z relating to the hydrolysis reaction of the polyurethane 5 Mean pressure of helium leak during time period before time t
(since the variation is small, it is considered to be a constant value here), the surface area of the seal member A, the difference Δ P between the internal pressure and the external pressure of the helium leak detector (the difference between the internal pressure and the atmospheric pressure can be maintained at a value close to that of the helium leak detector and can be considered to be a constant value), the viscosity η of the helium gas, and the Boltzmann constant K B MeasuringAmbient temperature T 0 Mass m of a single helium molecule 0 Roughness H of bonding interface of sealing member and activation energy E of reaction a Initial stress σ of the seal when the electrical connector is shipped 0 The polyurethane viscosity coefficient χ is a fixed value, although some parameters cannot be directly measured, the parameters can be combined, and then parameter estimation is carried out through experimental data; therefore, the unknown parameter to be estimated can be obtained
Y 6 ＝Z 5 、Y 7 (= χ). Let unknown variable X 1 ＝L 3 ,X 2 = γ, wherein L 3 The variable is set in consideration of the machining accuracy.
Thereby obtaining:
wherein:
namely X 1 、X 2 Obey a normal distribution, therefore, Q t Also obey a normal distribution; wherein the content of the first and second substances,
is X 1 The average value of (a) of (b),
is X 1 The variance of (a) is determined,
is X 2 The average value of (a) of (b),
is X 2 The variance of (c).
Combining the terms in formula (2) to obtain:
and step four, estimating parameters of the reliability evaluation model.
Parameter Y of reliability evaluation model by particle swarm optimization 1 、Y 2 、Y 3 、Y 4 、Y 5 、Y 6 、Y 7 、
The core idea of the estimation, as shown in fig. 2, is to preset a range for each parameter (a suitable initial value range is not known in advance), and then randomly select a value in each preset range for each parameter, and share the values with other unknown parameters after selecting the values. After each parameter is selected, the error between the fitting value and the actual value is compared, and a fitness function is established. After each parameter value is taken, the error comparison is carried out, each parameter is selected in the direction that the error is smaller in the next value taking, the whole error can be kept in a stable small range through reciprocating iteration, the optimal values of the parameters of each group of electric connectors under the conditions of corresponding test environment temperature, test environment humidity and the thickness of the polyurethane rubber sealing element for the electric connectors are obtained, and after the value of the fitness function under the conditions is obtained, the optimal value of the parameter which enables the fitness function value to be minimum is selected as the final optimal parameter value. Finally, the standard deviation of the whole circumference of the sealing member of the electric connector is carried out
And standard deviation of randomness of gas diffusion
The parameter estimation of (2).
The specific process is as follows:
4.1 build fitness function:
wherein the content of the first and second substances,
and Q representing the corresponding time t of each electric connector of each group when the ith helium leakage rate is measured t The values of the fit are determined,
and the average value of the helium leakage rate measured by each group of the electric connectors at the ith time is shown, and N is the number of times of measuring the helium leakage rate of each group of the electric connectors.
4.2 given Y 1 、Y 2 、Y 3 、Y 4 、Y 5 、Y 6 、Y 7 、X 1 And X 2 Given a larger range of-1000,1000 in the present embodiment]Setting initial values of the number of particles D and the number of evolutions in the particle swarm optimization, which are 50 and 100 in this embodiment, let x k ＝(x k1 ,x k2 ,x k3 ,···,x kd ,···,x kD ) Is Y 1 、Y 2 、Y 3 、Y 4 、Y 5 、Y 6 、Y 7 、X 1 And X 2 D-dimensional vector of the kth parameter, k =1,2, …,9, D =1,2, …, D, x k Wherein each particle number constitutes an arithmetic progression and x k1 And x kD Respectively taking the minimum value and the maximum value of the k parameter initial parameter range; the average helium leak rate of each set of electrical connectors measured at different times, a reliability evaluation model for each set of electrical connectors (which set of electrical connectors is evaluated and which set of temperature, humidity and thickness values are substituted into the model), and a fitness function for each set of electrical connectorsSubstituting the number into a particle swarm algorithm to carry out updating calculation; in the iteration, x is calculated according to the fitness function fit k And (3) measuring the quality of the particles according to the current adaptive value, and updating the flight speed and the value of the particles according to the formula (7):
where j denotes the number of iterations, each element takes the initial value when j =0, c 1 And c 2 Are acceleration factors, and the random number rand satisfies: range is more than 0 and less than 1; v. of k ＝(v k1 ,v k2 ,···,v kd ,···,v kD ) Set of flight velocities (numerical variations) v for each particle of the kth parameter k Setting the initial value of each element to be 0; p is a radical of k ＝(p k1 ,p k2 ,···,p kd ,···,p kD ) The set of optimum values, p, searched for each particle of the kth parameter kd X searched for the d-th particle kd An optimal value; p is a radical of g ＝(p g1 ,p g2 ,···,p gk ) For the searched k parameter optimum value sets, p gk Is the optimum value of the k parameter, i.e. p k The optimal value of comparison among the elements in the sequence; p is a radical of k Each element in (1) and p g The initial value of each element in the series is 0.
4.3 obtaining p per iteration g ＝(p g1 ,p g2 ,···,p gk ) Then, adding 1 to the value of c; if c reaches 10, the final p with higher accuracy is obtained g Step 4.4 is executed, otherwise the parameter range of the kth parameter is changed to
The number of particles and the number of evolutionary times are improved to 5 times of the original number, and then the step 4.2 is returned; wherein the initial value of c is 0; the method has the advantages that the time of the whole particle swarm algorithm operation can be effectively saved, and the result can be obtained quickly.
4.4 choosing the best parameter value
And obtaining the optimal values of all parameters (the optimal values of the parameters under different conditions are not greatly different) of all groups of electric connectors under the conditions corresponding to the test environment temperature, the test environment humidity and the thickness of the polyurethane rubber sealing element for the electric connector and the values of the fitness function fit under the conditions by a particle swarm algorithm, and then selecting a group of optimal values of the parameters which enable the fitness function value to be minimum as the optimal parameter values finally considering the different test environment temperatures, the test environment humidity and the thickness of the polyurethane rubber sealing element for the electric connector.
4.5 Standard deviation of the seal Overall perimeter for Each set of Electrical connectors
And standard deviation of randomness of gas diffusion
The parameter estimation is as follows:
x of each group of electric connectors obtained in step 4.3 1 I.e. the average value of the whole circumferences of the electric connector sealing parts of each group
Resulting X for each set of electrical connectors 2 I.e. the random mean value of the gas diffusion of each electric connector of each group
Standard deviation of overall perimeter of seal of final electrical connector
And standard deviation of randomness of gas diffusion
All using formula
Calculated, where X (q) represents the seal overall perimeter or gas diffusion randomness of the qth electrical connector of the set currently being calculated, and X (q) represents the gas diffusion randomness, also estimated by step 4.1, step 4.2, and step 4.3Replacing the measured helium leakage rate average value with the helium leakage rate measured by the q-th electric connector of the group currently calculated in the estimation process, and replacing each electric connector of each group with the q-th electric connector of the group currently calculated; sigma 1 2 To represent
Or
μ 1 Represent
Or
Solving for
When, mu 1 Using the average value of the overall perimeter of each electrical connector seal of the currently calculated set
Substitution, X (q) is substituted with the seal overall perimeter of the qth electrical connector of the set currently being calculated, solving
When, mu 1 Using the current calculated random mean value of gas diffusion for each electrical connector of the set
Substituted, X (q) is substituted randomly with the gas diffusion of the qth electrical connector of the set currently being calculated.
And step five, evaluating the storage reliability of the electric connector.
For a single electrical connector, the optimum parameter values obtained in step 4.4, the seal thickness h of the electrical connector and the initial measured ambient temperature T of the electrical connector 0 Measured Down electric connector helium bleedDew rate Q 0 Substituting formula (5) with the actual storage ambient temperature for T and the actual storage ambient humidity for RH in formula (5) to obtain the electrical connector stored under the conditions of actual storage ambient temperature and actual storage ambient humidity and measured ambient temperature T 0 The measured helium leak rate is plotted against time.
Due to Q t The reliability of the polyurethane adhesive sealing element for the electric connector at the t moment under the actual storage environment temperature and the actual storage environment humidity cannot be directly obtained, so that the reliability estimation of the polyurethane adhesive sealing element for the electric connector at the t moment under the actual storage environment temperature and the actual storage environment humidity is sampled by adopting a Monte Carlo sampling method, a performance degradation curve is generated according to the sampled data, the failure probability F (t) of the polyurethane adhesive sealing element for the electric connector at the t moment under the actual storage environment temperature and the actual storage environment humidity is calculated to be the ratio of the failure number of the storage electric connector sampled by the Monte Carlo to the total number of the storage electric connectors sampled by the Monte Carlo, and the reliability of the polyurethane adhesive sealing element for the electric connector at the t moment under the actual storage environment temperature and the actual storage environment humidity is 1-F (t).
The reliability function of the polyurethane rubber sealing member for the electric connector at the moment t can be expressed by the formula (8):
in the formula: a is t Number of failures of stored electrical connectors for Monte Carlo sampling at time t, N t Total number of electrical connectors stored for Monte Carlo sampling at time t, P () representing probability, Q D For measuring ambient temperature T 0 Helium leak Rate failure threshold of Q The mean value of helium leakage rate sigma at t moment of the electric connector under the actual storage environment temperature and the actual storage environment humidity Q The standard deviation of the helium leakage rate of the electric connector at the t moment under the actual storage environment temperature and the actual storage environment humidity is shown; wherein u is Q The value is that the electric connector is adopted to be at the actual storage environment temperature and the actual storage environment humidityStored at ambient temperature and measured at ambient temperature T 0 Q calculated by the relation of the measured helium leakage rate and time t ，σ Q The value is to store the electrical connector at an actual storage ambient temperature and an actual storage ambient humidity and to measure the ambient temperature T 0 X in the relation of the helium leakage rate and the time measured 1 And X 2 Respectively replaced by the estimation of step 4.5
And
and calculated Q t 。
Recording and measuring ambient temperature T 0 Helium leak rate failure threshold Q D ＝10 -7 pa·m 3 S, mixing Q D The numerical value is substituted into the formula (8), and the sealing reliability of the polyurethane rubber sealing element for the electric connector with the thickness of h at the time of storage t under the conditions of actual storage environment temperature and actual storage environment humidity can be obtained.
The above is only a preferred embodiment of the present invention, and various modifications and variations of the present invention will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The method for evaluating the storage reliability of the polyurethane rubber sealing element based on the particle swarm optimization is characterized by comprising the following steps of: the method specifically comprises the following steps:
testing the sealing performance of the electric connector to obtain helium leakage rate data of the electric connector;
step two, eliminating abnormal data exceeding the range of (mu-3 sigma, mu +3 sigma) in the helium leakage rate measured value of each electric connector, wherein mu represents the average value of the helium leakage rate measured values of each electric connector in each group at the same moment, and sigma represents the standard deviation of the helium leakage rate measured values of each electric connector in each group at the same moment;
step three, establishing a reliability evaluation model and carrying out sorting and simplification, wherein the method specifically comprises the following steps:
establishing a reliability evaluation model:
in the formula, Q t For testing the ambient temperature T at time T 0 Measured helium leakage rate, Q, of the electrical connector 0 Is initially at measuring ambient temperature T 0 Measuring the helium leakage rate of the electric connector, G is a leakage degradation parameter, gamma is the gas diffusion randomness,
seal surface area A = π r for mean pressure of helium leak in the time period before time t 1 2 -N 1 ·π·r 2 2 ，r 1 Is the outer diameter of the sealing member r 2 Is the pin radius, N 1 The total circumference L of the sealing member is equal to the number of the pins 3 ＝2πr 1 +N 1 ·2πr 2 Delta P is the difference between the internal pressure and the external pressure of the helium leak detector, eta is the viscosity of helium gas, h is the thickness of a sealing element, and K B Is the Boltzmann constant, m 0 Mass of a single helium molecule, H seal bond interface roughness, σ n For elastic stress of the seal, R S Sealing coefficient, RH test environmental humidity, E a For reaction activation energy, T is test ambient temperature, σ 0 Initial stress of the sealing element when the electric connector leaves the factory, and the coefficient K of the polyurethane integral material t ＝κ·E 0 ，E 0 Is the initial modulus of elasticity of the polyurethane, χ is the viscosity coefficient of the polyurethane, Z 1 、Z 2 、Z 3 、Z 4 、Z 5 Is a proportionality coefficient;
let unknown parameters to be estimated
Y 6 ＝Z 5 、Y 7 = χ; let unknown variable X 1 ＝L 3 ,X 2 = gamma, wherein L 3 Is set as a variable in consideration of the machining accuracy;
thereby obtaining:
wherein:
wherein the content of the first and second substances,
is X 1 The average value of (a) of (b),
is X 1 The variance of (a) is determined,
is X 2 The average value of (a) is calculated,
is X 2 The variance of (a);
combining the terms in formula (2) to obtain:
fourthly, performing parameter estimation of the reliability evaluation model by adopting a particle swarm algorithm;
step five, evaluating the storage reliability of the electric connector, specifically comprising the following steps:
for a single electric connector, estimating the optimal parameter value of the reliability evaluation model obtained by the four-parameter estimation, the sealing member thickness h of the electric connector and the initial measured environment temperature T of the electric connector 0 Measured helium leakage rate Q of electric connector 0 Substituting formula (5), wherein T in formula (5) is substituted by actual storage environment temperature, and RH is substituted by actual storage environment humidity to obtain the electric connector which is stored under the conditions of actual storage environment temperature and actual storage environment humidity and is subjected to measurement environment temperature T 0 The relation between the helium leakage rate and the time is measured;
due to Q t Obeying normal distribution, sampling the reliability estimation of the polyurethane adhesive sealing element for the electric connector at T moment under the actual storage environment temperature and the actual storage environment humidity by adopting a Monte Carlo sampling method, storing the electric connector under the conditions of the actual storage environment temperature and the actual storage environment humidity and measuring the environment temperature T 0 And obtaining a reliability function of the polyurethane rubber sealing member for the electric connector at the t moment according to the relation between the measured helium leakage rate and time.
2. The method for evaluating the storage reliability of the polyurethane rubber sealing member based on the particle swarm optimization algorithm according to claim 1, wherein the method comprises the following steps: the electric connector samples are divided into n groups, each group is m, each electric connector of each group is tested under the conditions of the same test environment temperature, test environment humidity and the thickness of the polyurethane adhesive sealing piece for the electric connector, a helium leakage instrument is adopted to measure the helium leakage rate of the electric connector at different times, but the test environment temperature, the test environment humidity and the thickness of the polyurethane adhesive sealing piece for the electric connector are different when different groups of electric connectors are tested.
3. The particle swarm algorithm-based polyurethane adhesive sealing member storage reliability evaluation method according to claim 1, wherein: step four, estimating parameters of the reliability evaluation model, wherein the specific process is as follows:
4.1 build fitness function:
wherein the content of the first and second substances,
and Q representing the corresponding time t of each electric connector of each group when the ith helium leakage rate is measured t The values of the fit are determined,
the average value of the helium leakage rate measured by each electric connector of each group at the ith time is represented, and N represents the measurement times of the helium leakage rate of each electric connector of each group;
4.2 given Y 1 、Y 2 、Y 3 、Y 4 、Y 5 、Y 6 、Y 7 、X 1 And X 2 Setting initial values of the number of particles and the number of evolutionary times in the particle swarm algorithm, and substituting the average helium leakage rate value measured by each group of electric connectors at different moments, the reliability evaluation model of each group of electric connectors and the fitness function of each group of electric connectors into the particle swarm algorithm for updating calculation; in iteration, according to the adaptive value calculated by the fitness function, the quality of the particles is measured, and the flight speed and the value of the particles are updated;
4.3 obtaining a set p of k parameter optima for each iteration g ＝(p g1 ,p g2 ,···,p gk ) Then, adding 1 to the value of c; if c reaches 10, the final p with higher accuracy is obtained g Step 4.4 is performed, otherwise the parameter range of the kth parameter is changed to
The number of particles and the number of evolutionary times are improved to 5 times of the original number, and then the step 4.2 is returned; wherein p is gk The initial value of c is 0;
4.4 obtaining the optimal values of all parameters of all groups of electric connectors under the conditions corresponding to the test environment temperature, the test environment humidity and the thickness of the polyurethane rubber sealing element for the electric connectors, and selecting a group of optimal values of the parameters which enable the fitness function value to be minimum as the optimal parameter values finally considering the different test environment temperatures, the test environment humidity and the thickness of the polyurethane rubber sealing element for the electric connectors after the fitness function value under the conditions is obtained;
4.5 Standard deviation of the seal Overall perimeter for Each set of Electrical connectors
And standard deviation of randomness of gas diffusion
The parameter estimation of (2) is as follows:
x of each group of electric connectors obtained in step 4.3 1 I.e. the average value of the whole perimeter of each electric connector sealing element in each group
Resulting X for each set of electrical connectors 2 I.e. the random mean value of the gas diffusion of each electric connector of each group
Standard deviation of the overall perimeter of the seal of the final electrical connector
And standard deviation of randomness of gas diffusion
All using formula
Calculated, where X (q) denotes the qth of the group currently under calculationWhen the overall perimeter or the gas diffusion randomness of the sealing member of the electric connector is represented by X (q), the gas diffusion randomness is also obtained through the estimation of the step 4.1, the step 4.2 and the step 4.3, the average value of the measured helium leakage rates is replaced by the helium leakage rate measured by the q-th electric connector of the currently calculated group in the estimation process, and each electric connector of each group is replaced by the q-th electric connector of the currently calculated group; sigma 1 2 To represent
Or
μ 1 To represent
Or
Solving for
When, mu 1 Using the current calculated overall perimeter mean of each electrical connector seal of the set
Substitution, X (q) is substituted with the seal member overall perimeter of the qth electrical connector of the set currently being calculated, solving
When, mu 1 Using the current calculated random mean value of gas diffusion for each electrical connector of the set
Substitution, X (q) is substituted randomly with the gas diffusion of the qth electrical connector of the set currently being calculated.
4. The method for evaluating the storage reliability of the polyurethane rubber sealing member based on the particle swarm optimization algorithm according to claim 3, wherein the method comprises the following steps: the reliability function of the polyurethane rubber sealing member for the electric connector at the moment t is as follows:
in the formula: a is t Number of failures of stored electrical connector sampled for Monte Carlo at time t, N t Total number of stored electrical connectors sampled for Monte Carlo at time t, P () representing probability, Q D For measuring ambient temperature T 0 Helium leak Rate failure threshold of Q The mean value of helium leakage rate sigma at t moment of the electric connector under the actual storage environment temperature and the actual storage environment humidity Q The standard deviation of the helium leakage rate of the electric connector at the t moment under the actual storage environment temperature and the actual storage environment humidity is shown; wherein u is Q The value is stored by adopting the electric connector under the conditions of actual storage environment temperature and actual storage environment humidity and measured environment temperature T 0 Q calculated by the relation of the measured helium leakage rate and time t ，σ Q The value is that the electric connector is stored under the conditions of actual storage environment temperature and actual storage environment humidity and is measured at the environment temperature T 0 X in the relation of the helium leakage rate and the time measured 1 And X 2 Respectively replaced by the estimation of step 4.5
And
and Q is calculated t 。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211135332.1A CN115455596A (en) | 2022-09-19 | 2022-09-19 | Particle swarm algorithm-based polyurethane rubber sealing member storage reliability evaluation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211135332.1A CN115455596A (en) | 2022-09-19 | 2022-09-19 | Particle swarm algorithm-based polyurethane rubber sealing member storage reliability evaluation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115455596A true CN115455596A (en) | 2022-12-09 |
Family
ID=84305654
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211135332.1A Pending CN115455596A (en) | 2022-09-19 | 2022-09-19 | Particle swarm algorithm-based polyurethane rubber sealing member storage reliability evaluation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115455596A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116151695A (en) * | 2023-04-20 | 2023-05-23 | 青岛工学院 | System and method for evaluating processing quality of rubber and plastic sealing element structure |
-
2022
- 2022-09-19 CN CN202211135332.1A patent/CN115455596A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116151695A (en) * | 2023-04-20 | 2023-05-23 | 青岛工学院 | System and method for evaluating processing quality of rubber and plastic sealing element structure |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110414117B (en) | Method for predicting sealing reliability of soft package lithium ion battery | |
| CN108037460B (en) | Real-time capacity evaluation method for lithium ion batteries produced in batches | |
| CN111859658B (en) | Product storage life and reliability assessment method | |
| CN108549047A (en) | A kind of electric energy meter lifetime estimation method based on accelerated degradation test | |
| CN109359432A (en) | A kind of momenttum wheel life-span prediction method | |
| CN114035086B (en) | Multi-fault diagnosis method for battery pack based on signal processing | |
| CN115455596A (en) | Particle swarm algorithm-based polyurethane rubber sealing member storage reliability evaluation method | |
| CN115962797B (en) | Sensor reliability test method and system based on temperature stress | |
| CN112883550A (en) | Degradation equipment residual life prediction method considering multiple uncertainties | |
| CN115219918A (en) | Lithium ion battery life prediction method based on capacity decline combined model | |
| Gu et al. | An Early minor-fault diagnosis method for lithium-ion battery packs based on unsupervised learning | |
| CN113761755A (en) | Accelerated life analysis method considering cognitive uncertainty under temperature and humidity dual stress | |
| CN113433468A (en) | Early fault fusion diagnosis method and safety early warning system for energy storage battery | |
| CN109375143A (en) | A kind of method of determining intelligent electric energy meter remaining life | |
| CN116773239A (en) | Intelligent gas meter controller reliability life prediction method | |
| CN107561452A (en) | A kind of appraisal procedure of batteries health degree | |
| CN113405743B (en) | New energy electric vehicle production and manufacturing test data analysis processing method and system based on cloud computing and storage medium | |
| CN117805616A (en) | Abnormal cell identification method for lithium ion energy storage system | |
| CN113672658B (en) | Power equipment online monitoring error data identification method based on complex correlation coefficient | |
| CN114398770A (en) | Service life prediction method for liquid particle counting sensor | |
| CN114626248B (en) | Spiral spring reliability evaluation method based on multi-stress accelerated degradation data | |
| CN114239424A (en) | Method for predicting service life of epoxy resin adhesive by using small sample data | |
| CN113465930B (en) | Gas turbine multi-sensor fault detection method based on hybrid method | |
| CN116500456B (en) | Ternary lithium battery capacity detection method based on machine learning | |
| CN114545264A (en) | Method for evaluating storage trend of battery |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |