CN111814360A - Method for evaluating use reliability of airborne electromechanical equipment - Google Patents
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Abstract
The invention discloses a reliability evaluation method for use of airborne electromechanical equipment, which comprises the steps of analyzing a fault mode of a specific product, counting fault data of mechanical and electronic components according to the fault mode, further fitting a product life distribution model, estimating distribution model parameters, respectively calculating fault weights of mechanical and electronic components of a system, then constructing a mixed distribution model of the system, finally drawing a system reliability curve of target equipment, then accurately reflecting the reliability level of the components, searching weak links of the system, accurately reflecting the overall reliability level of the system, and providing a basis for reducing the risk rate of the system, designing preventive maintenance cycles and optimizing maintenance cost.
Description
Technical Field
The invention relates to the technical field of reliability engineering, in particular to a method for evaluating the use reliability of airborne electromechanical equipment.
Background
The airborne electromechanical product is assisted by the severe environment, is easy to be subjected to the comprehensive action of various environmental stresses such as high temperature, high humidity and acid gas and various mechanical stresses such as vibration and impact, and has complex fault behaviors. In the design, development and production stages, due to various constraints such as cost, environment, test and calculation methods and the like, the reliability prediction and identification of the airborne product cannot completely reflect the reliability level of the product in the real environment, and huge hidden danger is caused to the reliability of the airborne electromechanical product after being put into use.
Currently, the reliability evaluation of the airborne electromechanical products is usually performed on the assumption that the airborne electromechanical products are subjected to a certain distribution, but the airborne products are generally formed by combining mechanical components and electronic components, and the fault distribution models of the mechanical components and the electronic components often do not belong to the same distribution type. Meanwhile, the existing reliability comprehensive method is often to simplify the system into a series model according to a reliability logic method, but the traditional reliability mathematical model of the series system has the defect that the reliability of the system is more conservative when being calculated, so that the reliability analysis result is not accurate enough.
Disclosure of Invention
The invention aims to provide a method for evaluating the use reliability of airborne electromechanical equipment, which can accurately reflect the reliability level of components, search weak links of a system, accurately reflect the overall reliability level of the system and provide a basis for reducing the risk rate of the system, designing preventive maintenance cycles and optimizing the maintenance cost.
The technical scheme adopted by the invention is as follows:
a method for evaluating the use reliability of airborne electromechanical equipment comprises the following steps:
s1: establishing a target equipment sample set; the method comprises the following specific steps:
s 1.1: counting fault data of target equipment, and constructing a fault sample database X ═ X1,…,xMM is the number of fault samples;
s 1.2: analyzing fault mode of fault data in fault sample database and comparing fault dataClassifying to form a mechanical sample database X1And an electronic sample database X2(ii) a The method specifically comprises the following steps:
s2: fitting a mechanical component service life distribution model and calculating a mechanical fault weight omega1：
S3: fitting an electronic component service life distribution model and calculating an electronic fault weight omega2；
S4: constructing a mixed distribution model of target equipment, and acquiring the system reliability R (t) of the model;
in formula (1), R (t) represents system reliability, F (t) represents system fault distribution function, F1(t) represents a mechanical assembly failure distribution function, F2(t) represents the electronic component failure distribution function, Φ1Representing a normal distribution function, t representing the running time, mu and sigma representing the mean value and variance of the mechanical assembly service life distribution model, alpha representing the shape parameter of the electronic assembly service life distribution model, and beta representing the scale parameter of the electronic assembly service life distribution model;
s5: and drawing a system reliability curve of the target equipment.
Further, the specific process of step s1.2 is as follows:
s 1.2.1: counting fault data of mechanical parts in target equipment, and constructing a mechanical sample database
M1Number of mechanical samples;
s 1.2.2: counting fault data of electronic components in target equipment, and constructing an electronic sample database
M2Number of electronic samples, M2＝M-M1。
Further, the step S2 specifically includes the following steps:
s 2.1: calculating distribution parameters of a service life distribution model of the mechanical assembly;
the service life of the mechanical assembly follows the log normal distribution, and the fault function of the mechanical assembly is as follows:
in the formula (2), phi1Representing a normal distribution function, t representing the operation time, and mu and sigma being the mean and variance of the life distribution function of the mechanical component;
the solution equations for μ and σ are as follows:
in the formula (3), lnL represents a log-likelihood function, t(i)For fault sample X1N is the total amount of observation samples, Z0＝(lnt0- μ)/σ as statistic, Φ (-Z)0) Is a standard normal distribution function, phi (Z)0) Is a standard normal distribution density function;
s 2.2: calculating a mechanical failure weight ω1；
In the formula (4), M is the total number of system faults, M1The number of mechanical samples.
Further, the step S3 specifically includes the following steps:
s 3.1: calculating distribution parameters of the electronic component service life distribution model;
the electronic component life follows a weibull distribution, and the electronic component failure function is as follows:
in the formula (5), alpha is a shape parameter, and beta is a scale parameter;
the shape parameter alpha and the scale parameter beta are solved through a likelihood equation, and the solving equation is as follows:
in the formula (6), t(i)For fault sample X2N is the total amount of observation samples, M2The number of failures of the electronic component;
s 3.2: calculating an electronic fault weight ω2；
ω2＝1-ω1(7)；
In the formula (7), ω1Is the mechanical failure weight.
Further, the fault sample database is established based on fault time.
The invention has the following beneficial effects:
the method comprises the steps of calculating fault data of mechanical and electronic components according to fault modes by analyzing the fault modes of specific products, further fitting a product life distribution model, calculating distribution model parameters, calculating fault weights of the mechanical and electronic components of a system respectively, then constructing a mixed distribution model of the system, finally drawing a system reliability curve of target equipment, accurately reflecting the reliability level of the components, searching weak links of the system, accurately reflecting the overall reliability level of the system, and providing a basis for reducing the risk rate of the system, designing preventive maintenance periods and optimizing the maintenance cost.
Drawings
FIG. 1 is a system reliability curve in an embodiment.
Detailed Description
The method for evaluating the use reliability of the airborne electromechanical equipment comprises the following steps: the method comprises the following steps:
s1: a sample set of target devices is established.
The method comprises the following specific steps:
s 1.1: counting fault data of target equipment, and constructing a fault sample database X ═ X1,…,xMM is the number of fault samples;
s 1.2: analyzing the fault mode of the fault data in the fault sample database and classifying the fault data to form a mechanical sample database X1And an electronic sample database X2(ii) a The method specifically comprises the following steps:
s 1.2.1: counting fault data of mechanical parts in target equipment, and constructing a mechanical sample database
M1Number of mechanical samples;
s 1.2.2: counting fault data of electronic components in target equipment, and constructing an electronic sample database
Number of electronic samples, M2＝M-M1。
S2: fitting a mechanical component service life distribution model and calculating a mechanical fault weight omega1。
The specific process is as follows:
s 2.1: calculating distribution parameters of a service life distribution model of the mechanical assembly;
the service life of the mechanical assembly follows the log normal distribution, and the fault function of the mechanical assembly is as follows:
in the formula (1), phi1Representing a normal distribution function, t representing the running time, and mu and sigma representing the mean and variance of a mechanical component life distribution model;
the solution equations for the distribution parameters μ and σ are as follows:
in equation (2), lnL is a log-likelihood function, t(i)For fault sample X1N is the total amount of observation samples, Z0＝(lnt0- μ)/σ as statistic, Φ (-Z)0) Is a standardNormal distribution function, phi (Z)0) Is a standard normal distribution density function;
s 2.2: calculating a mechanical failure weight ω1；
In formula (3), M is the total number of system faults, M1The number of mechanical samples.
S3: fitting an electronic component service life distribution model and calculating an electronic fault weight omega2。
The specific process is as follows:
s 3.1: calculating distribution parameters of the electronic component service life distribution model;
the electronic component life follows a weibull distribution, and the electronic component failure function is as follows:
in the formula (4), alpha is a shape parameter, and beta is a scale parameter;
the shape parameter alpha and the scale parameter beta are solved through a likelihood equation, and the solving equation is as follows:
in the formula (5), t(i)For fault sample X2N is the total amount of observation samples, M2Is the number of failures of the electronic component;
s 3.2: calculating an electronic fault weight ω2；
ω2＝1-ω1(6)；
In the formula (6), ω1Is the mechanical failure weight.
S4: constructing a mixed distribution model of the target equipment;
in the formula (7), R (t) represents the system reliability, F (t) represents the system fault distribution function, F1(t) represents a mechanical assembly failure distribution function, F2(t) represents the electronic component failure distribution function, Φ1Representing a normal distribution function, t representing the running time, mu and sigma representing the mean value and variance of the mechanical assembly service life distribution model, alpha representing the shape parameter of the electronic assembly Weibull distribution model, and beta representing the scale parameter of the electronic assembly service life distribution model;
s5: and drawing a system reliability curve of the target equipment.
For a better understanding of the present invention, the following embodiments are provided to further explain the technical solutions of the present invention.
The invention comprises the following steps:
s1: and (4) sorting and classifying system fault data in a service environment, and establishing a target equipment sample set.
The method comprises the following specific steps:
s 1.1: counting fault data of target equipment, and constructing a fault sample database X ═ X1,…,xMM is the number of fault samples;
s 1.2: analyzing the fault mode of the fault data in the fault sample database and classifying the fault data to form a mechanical sample database X1And an electronic sample database X2(ii) a The method specifically comprises the following steps:
s 1.2.1: counting fault data of mechanical parts in target equipment based on fault time, and constructing a mechanical sample database
M1Number of mechanical samples;
s 1.2.2: counting fault data of electronic components in target equipment based on fault time, and constructing an electronic sample database
M2Number of electronic samples, M2＝M-M1。
In this embodiment, a system sample 25 is observed, wherein a fault data sample 20 is present. The fault sample data is shown in table 1:
table 1:
s2: fitting a mechanical component service life distribution model and calculating a mechanical fault weight omega1。
The specific process is as follows:
s 2.1: and calculating distribution parameters of the mechanical component service life distribution model.
The service life of the mechanical assembly follows the log normal distribution, and the fault function of the mechanical assembly is as follows:
in the formula (2), phi1Normal distribution function is expressed, t represents the running time, and μ and σ are the mean and variance of the mechanical component life distribution model.
The solution equations for μ and σ are as follows:
in equation (3), lnL is a log-likelihood function, t(i)For fault sample X1N is the total amount of observation samples, Z0＝(lnt0- μ)/σ as statistic, Φ (-Z)0) Is a standard normal distribution function, phi (Z)0) Is a standard normal distribution density function. s 2.2: calculating a mechanical failure weight ω1。
In the formula (4), M is the total number of system faults, M1The number of mechanical samples.
The calculation result of this embodiment is as follows:
mechanical component failure function
Mechanical failure weight
The distribution parameters calculated in this embodiment are respectively:
s3: fitting an electronic component service life distribution model and calculating an electronic fault weight omega2。
The specific process is as follows:
s 3.1: calculating distribution parameters of the electronic component service life distribution model;
the electronic component life follows a weibull distribution, and the electronic component failure function is as follows:
in the formula (5), alpha is a shape parameter, and beta is a scale parameter;
the shape parameter alpha and the scale parameter beta are solved through a likelihood equation, and the solving equation is as follows:
in the formula (6), t(i)For fault sample X2N is the total amount of observation samples, M2Is the failure number of the electronic component;
s 3.2: calculating an electronic fault weight ω2；
ω2＝1-ω1(7)；
In the formula (7), ω1Is the mechanical failure weight.
The calculation results in this example are as follows:
electronic component failure function
Shape parameter alpha is 0.931, and scale parameter beta is 9.304
Electronic fault weighting
S4: and constructing a mixed distribution model of the target equipment.
In the formula (1), r (t) represents the system reliability, and t represents the operating time.
S5: and calculating the mean time between failures of the system, and drawing a system reliability curve of the target equipment as shown in figure 1.
According to the method, service life distribution models of the mechanical component and the electronic component are respectively fitted based on fault data generated by the airborne electromechanical equipment in the service process, then a new system reliability model is constructed based on the mixed distribution model on the basis of considering the fault weight of each component, and finally a system reliability curve of the target equipment is drawn, so that the reliability level of the component is accurately reflected, weak links of the system are searched, the overall reliability level of the system can be accurately reflected, and a basis is provided for reducing the risk rate of the system, designing a preventive maintenance period and optimizing the maintenance cost.
Claims (5)
1. The use reliability assessment method of the airborne electromechanical equipment is characterized by comprising the following steps: the method comprises the following steps:
s1: establishing a target equipment sample set; the method comprises the following specific steps:
s 1.1: counting fault data of target equipment, and constructing a fault sample database X ═ X1,…,xMM is the number of fault samples;
s 1.2: analyzing the fault mode of the fault data in the fault sample database and classifying the fault data to form a mechanical sample database X1And electronsSample database X2(ii) a The method specifically comprises the following steps:
s2: fitting a mechanical component service life distribution model and calculating a mechanical fault weight omega1：
S3: fitting an electronic component service life distribution model and calculating an electronic fault weight omega2；
S4: constructing a mixed distribution model of target equipment, and acquiring the system reliability R (t) of the model;
in formula (1), R (t) represents system reliability, F (t) represents system fault distribution function, F1(t) represents a mechanical assembly failure distribution function, F2(t) represents the electronic component failure distribution function, Φ1Representing a normal distribution function, t representing the running time, mu and sigma representing the mean value and variance of the mechanical assembly service life distribution model, alpha representing the shape parameter of the electronic assembly service life distribution model, and beta representing the scale parameter of the electronic assembly service life distribution model;
s5: and drawing a system reliability curve of the target equipment.
2. The airborne electromechanical device usage reliability assessment method according to claim 1, characterized in that: the specific process of step s1.2 is as follows:
s 1.2.1: counting fault data of mechanical parts in target equipment, and constructing a mechanical sample database
M1Number of mechanical samples;
s 1.2.2: counting fault data of electronic components in target equipment, and constructing an electronic sample database
M2Number of electronic samples, M2＝M-M1。
3. The airborne electromechanical device usage reliability assessment method according to claim 1, characterized in that: the specific process of step S2 is as follows:
s 2.1: calculating distribution parameters of a service life distribution model of the mechanical assembly;
the service life of the mechanical assembly follows the log normal distribution, and the fault function of the mechanical assembly is as follows:
in the formula (2), phi1Representing a normal distribution function, t representing the operation time, and mu and sigma being the mean and variance of the life distribution function of the mechanical component;
the solution equations for μ and σ are as follows:
in the formula (3), lnL represents a log-likelihood function, t(i)For fault sample X1N is the total amount of observation samples, Z0＝(lnt0- μ)/σ as statistic, Φ (-Z)0) Is a standard normal distribution function, phi (Z)0) Is a standard normal distribution density function;
s 2.2: calculating a mechanical failure weight ω1；
In the formula (4), M is the total number of system faults, M1The number of mechanical samples.
4. The airborne electromechanical device usage reliability assessment method according to claim 1, characterized in that: the specific process of step S3 is as follows:
s 3.1: calculating distribution parameters of the electronic component service life distribution model;
the electronic component life follows a weibull distribution, and the electronic component failure function is as follows:
in the formula (5), alpha is a shape parameter, and beta is a scale parameter;
the shape parameter alpha and the scale parameter beta are solved through a likelihood equation, and the solving equation is as follows:
in the formula (6), t(i)For fault sample X2N is the total amount of observation samples, M2The number of failures of the electronic component;
s 3.2: calculating an electronic fault weight ω2；
ω2＝1-ω1(7)；
In the formula (7), ω1Is the mechanical failure weight.
5. The airborne electromechanical device usage reliability assessment method according to claim 1, characterized in that: the specific process of step S3 is as follows: the fault sample database is established based on fault time.
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