Method for predicting reliability of flight control system of unmanned aerial vehicle based on secondary screening data of components

Abstract

The invention belongs to the technical field of unmanned aerial vehicles, and particularly discloses an unmanned aerial vehicle flight control system reliability prediction method based on component secondary screening data, which comprises the following steps: establishing a component secondary screening data matrix of an unmanned aerial vehicle flight control system, and performing component reliability model parameter estimation; obtaining reliability model parameters of various types of components in the unmanned aerial vehicle flight control system; dividing key function modules of the unmanned aerial vehicle flight control system and constructing a reliability block diagram of the unmanned aerial vehicle flight control system; and performing reliability prediction of the unmanned aerial vehicle flight control system by using a Monte Carlo sampling method based on reliability model parameters of various types of components in the unmanned aerial vehicle flight control system, key function modules of the unmanned aerial vehicle flight control system and a reliability block diagram structure of the unmanned aerial vehicle flight control system. According to the method, the problems of low data utilization rate, poor model relevance, high modeling complexity and insufficient random processing capability of an existing method are solved, and comprehensive, scientific and efficient reliability prediction of the flight control system of the unmanned aerial vehicle is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a reliability prediction method for an unmanned aerial vehicle flight control system based on secondary screening data of components. Background Art

[0002] Drone technology has developed rapidly in recent years, and its application scenarios have gradually expanded from traditional military purposes to logistics and transportation, agricultural monitoring, environmental protection, disaster relief and other fields. However, as the core part of the drone, the reliability of the drone flight control system directly determines the safety, stability and service life of the drone. The electronic components in the flight control system are the key factors affecting reliability. Due to the complex and changeable working environment of drones (such as high temperature, low pressure, vibration, etc.), the risk of component failure increases significantly, so it is particularly important to perform reliability prediction in the flight control system design stage.

[0003] At present, component reliability screening (including primary and secondary screening) is an important means to ensure component performance. The screening results usually include component failure data under different accelerated conditions. However, these data are only used for preliminary screening of components and are not further utilized in system design. In addition, traditional system reliability analysis methods are often based on empirical values ​​or reliability parameters of a single component, which makes it difficult to fully reflect the actual reliability of complex flight control systems. This situation makes it an important requirement for technological development to accurately predict the reliability of flight control systems in the early stages of design.

[0004] At present, the mainstream methods of system reliability analysis include the following: 1. Fault tree analysis-based method: By analyzing the possible failure modes and their impacts of the system layer by layer, the system reliability is derived. However, this method has low analysis efficiency for complex systems and it is difficult to use dynamic component data for reliability modeling.

[0005] 2. Reliability block diagram-based method: By constructing a reliability block diagram of the system, the overall reliability of the system is calculated. However, this method usually assumes that the reliability of components is known, ignoring the randomness and complex failure modes of components in actual environments.

[0006] 3. Data model based on accelerated life test of components: Existing research mostly focuses on the accelerated life model analysis of a single component (such as the Arrhenius model), but fails to combine accelerated test data with system-level reliability modeling.

[0007] Existing technologies have many deficiencies in the reliability analysis of UAV flight control systems, mainly in that they fail to fully utilize the secondary screening data of components for system-level reliability modeling. These screening data are usually only used to determine whether components are qualified, and there is a lack of standardized processing methods to convert them into reliability models, which makes it impossible for valuable failure data to serve system-level reliability analysis.

[0008] In addition, there is insufficient correlation between system-level reliability analysis and component failure data. Existing methods usually assume that component reliability parameters are fixed values, ignoring the dynamic changes of component accelerated test data and environmental impacts. The topology of the UAV flight control system is complex, including a variety of series, parallel and hybrid structures. Existing modeling methods are inefficient in processing these complex logics, especially in the reliability calculation of redundant structures and parallel modules, which easily leads to inaccurate analysis or incomplete modeling.

[0009] At the same time, the existing technology has limited ability to handle the randomness of component failure time, and generally adopts deterministic analysis methods, which are difficult to truly reflect the random characteristics of component failure. In particular, the lack of random modeling tools such as Monte Carlo simulation makes it impossible to conduct comprehensive sampling and statistical analysis of the failure time of a large number of components in the flight control system.

[0010] Finally, existing methods usually perform reliability assessment based on actual test data only after the system design is completed, and it is difficult to provide reliable prediction results in the early stages of design. The lack of reliability modeling methods based on component screening data and design logic in the early stages of design makes reliability prediction unable to meet the high standards of safety and stability of drone flight control systems. Summary of the invention

[0011] The purpose of the present invention is to solve the problems of low data utilization, poor model correlation, high modeling complexity and insufficient randomness processing ability of existing methods, and proposes a reliability prediction method for UAV flight control system based on component secondary screening data.

[0012] The technical solution of the present invention is: a method for predicting the reliability of a UAV flight control system based on component secondary screening data, comprising the following steps: S1. Establish the second screening data matrix of components of the UAV flight control system; S2. Estimating the reliability model parameters of components according to the second screening data matrix of components, and obtaining the reliability model parameters of each type of components in the UAV flight control system; S3. Divide the key functional modules of the UAV flight control system and construct a reliability block diagram of the UAV flight control system; S4. Based on the reliability model parameters of various types of components in the UAV flight control system, the key functional modules of the UAV flight control system and the reliability block diagram structure of the UAV flight control system, the Monte Carlo sampling method is used to predict the reliability of the UAV flight control system.

[0013] The beneficial effects of the present invention are: 1. The present invention is based on the second screening data of components. By establishing the second screening data matrix of components and combining the Monte Carlo sampling method, a reliability model from the component level to the system level is constructed and reliability prediction is performed, thereby making up for the defects of low data utilization, poor model correlation, high modeling complexity and insufficient randomness processing capability in the prior art, and providing a scientific basis for the safety and stability of the UAV system.

[0014] 2. By utilizing the second screening data of components and the reliability block diagram model, the present invention can predict the reliability of the UAV flight control system in the early stage of design, filling the gap in the reliability analysis capability of the prior art in the design stage.

[0015] As a preference, the second screening data matrix of the components in step S1 The expression formula is:

[0016] in, Indicates i The second screening data matrix of the model components, , Indicates the number of component models of the drone flight control system; The expression formula is:

[0017] in, Indicates i The first part of the component j Number of test components for the second screen, Indicates i The first part of the component j Number of failed components in the second-screen test, Indicates i The first part of the component j Total duration of the second screening test, Indicates i The first part of the component j Temperature conditions for the second sieve test: , Indicates i The number of second screening data for each model of components.

[0018] Preferably, the step S2 specifically includes the following sub-steps: S21. Calculate the equivalent test duration of the components of the UAV flight control system under the target operating temperature conditions based on the temperature accelerated life model and the second screening data matrix of the components; S22. Convert the component second screening data matrix into a left-censored data matrix and a right-censored data matrix to obtain the censored data of all types of components in the UAV flight control system; S23. Set reliability models for various types of components in the UAV flight control system; S24. Based on the missing data of all types of components, the maximum likelihood estimation method is used to estimate the estimated values ​​of the reliability model parameters of each type of component in the UAV flight control system.

[0019] Preferably, the calculation formula for the equivalent test time of the components of the UAV flight control system under the target operating temperature conditions in step S21 is:

[0020] in, Indicates the equivalent test time of the components of the UAV flight control system under the target operating temperature conditions. represents the activation energy, represents the Boltzmann constant, k= 8.617×10 −5  eV / K, Indicates the target operating temperature, Represents the natural base.

[0021] Preferably, the step S22 specifically includes the following steps: For i The first part of the component j Two-screen data, in the left-censored data matrix Increase indivual , in the right-censored data matrix Increase indivual ; Sequentially filter the data from the first to the second The second screening data is processed to obtain i Left-censored data matrix of components of different models and the right-censored data matrix , and its expression formula is:

[0022] make , , for i Left-censored data matrix of components of different models and the right-censored data matrix Simplifying, we get:

[0023] in, correspond Equivalent test duration for left-center censored data; correspond Equivalent test duration for middle and right censored data; Repeat the above steps until the component missing data conversion is completed for all types of components, and the component missing data of all types in the UAV flight control system is obtained.

[0024] As a preference, in step S23, according to the failure characteristics of components of various types, the reliability models of components of various types in the UAV flight control system are set, specifically: If the failure characteristics of components are random failures, the reliability model of each type of components in the UAV flight control system is an exponential distribution, and its expression formula is:

[0025] in, represents the exponentially distributed cumulative failure distribution function, represents the reliability function of the exponential distribution, represents the natural base, Indicates time, represents the exponential distribution parameter, ; If the failure characteristic of the component is wear-out failure, the reliability model of each type of component in the UAV flight control system is a log-normal distribution, and its expression formula is:

[0026] in, represents the cumulative failure distribution function of the lognormal distribution, represents the reliability function of the lognormal distribution, represents the logarithmic mean, , represents the logarithmic standard deviation, , represents the standard normal distribution, Represents natural base Logarithmic function with base ; If the failure characteristics of components are early failure, random failure or wear-out failure, the reliability model of each type of component in the UAV flight control system is Weibull distribution, and its expression formula is:

[0027] in, represents the cumulative failure distribution function of the Weibull distribution, represents the reliability function of the Weibull distribution, represents the shape parameter of the Weibull distribution, , represents the scale parameter of the Weibull distribution, .

[0028] As a preference, the reliability model parameters of each type of components in the UAV flight control system in step S24 include exponential distribution parameters , log mean , logarithmic standard deviation , shape parameter of the Weibull distribution and the scale parameter of the Weibull distribution ; The step S24 specifically includes the following sub-steps: S241. Establish the maximum likelihood function of the reliability model parameters of various types of components in the UAV flight control system ; S242. The maximum likelihood function Convert to log maximum likelihood function; S243. Using the optimization method, the maximum logarithmic maximum likelihood function The goal is to obtain the estimated values ​​of the reliability model parameters of various types of components.

[0029] Preferably, the maximum likelihood function The expression formula is:

[0030] in, represents the distribution parameter vector, Indicates i The cumulative failure distribution function of the reliability model of the components of each model, Indicates i The reliability function of the reliability model of the component model is Indicates i Model Component No. p left-censored equivalent test time, Indicates i Model Component No. q right-censored equivalent test time; The logarithmic maximum likelihood function The calculation formula is:

[0031] in, Represents natural base The logarithmic function with base .

[0032] Preferably, the step S4 specifically includes the following sub-steps: S41. Based on the component list and position number information of the key functional modules in the UAV flight control system, as well as the reliability model parameters of various types of components in the UAV flight control system, the Monte Carlo sampling method is used to obtain the failure time matrix of all components in the key functional modules in the UAV flight control system. , and its expression formula is:

[0033] in, Indicates i 1 Components j 1 The failure time sampling results of Monte Carlo sampling are: , , Indicates the total number of components. represents the number of Monte Carlo sampling; S42. Based on the failure time matrix of all components of key functional modules in the UAV flight control system , select the minimum failure time of all components of each key functional module as the failure time of the key functional module, and obtain the failure time matrix of all key functional modules in the UAV flight control system , and its expression formula is:

[0034] in, Indicates Key functional modules The failure time results of the Monte Carlo sampling, , Indicates the total number of key functional modules; S43. According to the reliability block diagram of the UAV flight control system, for the parallel structure in the reliability block diagram, the maximum failure time of all modules or substructures in the parallel structure is selected as the failure time of the parallel structure; for the series structure in the reliability structure block diagram, the minimum failure time of all modules or substructures in the series structure is selected as the failure time of the series structure, and the failure time vector of the UAV flight control system is calculated. , and its expression formula is:

[0035] in, Indicates the UAV flight control system Failure time results for Monte Carlo sampling; S44. The failure time vector of the UAV flight control system Sorting in ascending order, we get the failure time sequence statistics of the UAV flight control system, which is expressed as:

[0036] in, represents the number of failure times that are less than the expected service time, , The number of expiration times that represent the expected usage time The corresponding failure time of the UAV flight control system; S45. Based on the failure time sequence statistics of the UAV flight control system, find the number of failure times that are less than the expected use time , and then calculate the reliability of the UAV flight control system and the average failure time of the UAV flight control system to complete the reliability prediction of the UAV flight control system.

[0037] Preferably, the calculation formula of the reliability of the UAV flight control system in step S45 is:

[0038] in, Indicates the reliability of the UAV flight control system; The calculation formula for the average failure time of the UAV flight control system is:

[0039] in, Indicates the average failure time of the UAV flight control system.

[0040] The beneficial effects of the above preferred solution are: 1. The above preferred solution is based on the second screening data of components and parts, constructs the second screening data matrix of components and parts, and converts the screening data into equivalent test time through the temperature accelerated life model, which solves the problem of low utilization rate of screening data in the prior art; 2. By converting the missing data of components into a reliability distribution model and combining it with the logical relationship of the functional modules of the flight control system, a unified reliability block diagram model was established, which solved the technical defect that the system-level analysis in the existing technology failed to effectively combine the failure data of components; 3. The reliability block diagram modeling method is suitable for complex systems and can clearly express the logical relationship of series, parallel and hybrid structures in the UAV flight control system, thereby improving the accuracy and operability of the modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Shown is a flow chart of a method for predicting the reliability of a UAV flight control system based on secondary screening data of components.

[0042] Figure 2 Shown is a schematic diagram of the reliability block diagram of the UAV flight control system. DETAILED DESCRIPTION

[0043] Now, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are only exemplary and are intended to explain the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0044] Example: like Figure 1 As shown, a method for predicting the reliability of a UAV flight control system based on component secondary screening data includes the following steps: S1. Collect the list of components of the UAV flight control system, including the number of all components of the flight control motherboard, attitude sensor, servo drive unit, power control unit, etc., and establish the second screening data matrix of the components of the UAV flight control system; S2. Estimating the reliability model parameters of components according to the second screening data matrix of components, and obtaining the reliability model parameters of each type of components in the UAV flight control system; S3. Divide the key functional modules of the UAV flight control system and construct a reliability block diagram of the UAV flight control system; S4. Based on the reliability model parameters of various types of components in the UAV flight control system, the key functional modules of the UAV flight control system and the reliability block diagram structure of the UAV flight control system, the Monte Carlo sampling method is used to predict the reliability of the UAV flight control system.

[0045] In this embodiment, the second screening data matrix of the components in step S1 The expression formula is:

[0046] in, Indicates i The second screening data matrix of the model components, , Indicates the number of component models of the drone flight control system; The expression formula is:

[0047] in, Indicates i The first part of the component j Number of test components for the second screen, Indicates i The first part of the component j Number of failed components in the second-screen test, Indicatesi The first part of the component j Total duration of the second screening test, Indicates i The first part of the component j Temperature conditions for the second sieve test: , Indicates i The number of second-screen data of components of each model; the second-screen data matrix of components of the UAV flight control system is shown in Table 1.

[0048] Table 1 Second screening data matrix of components of UAV flight control system

[0049]

[0050]

[0051] In this embodiment, step S2 specifically includes the following sub-steps: S21. Calculate the equivalent test duration of the components of the UAV flight control system under the target operating temperature conditions based on the temperature accelerated life model and the second screening data matrix of the components; S22. Convert the component second screening data matrix into a left-censored data matrix and a right-censored data matrix to obtain the censored data of all types of components in the UAV flight control system; S23. Set reliability models for various types of components in the UAV flight control system; S24. Based on the missing data of all types of components, the maximum likelihood estimation method is used to estimate the estimated values ​​of the reliability model parameters of each type of component in the UAV flight control system.

[0052] In this embodiment, the Arrhenius temperature accelerated life model is adopted, and its expression formula is:

[0053] in, represents the degradation reaction rate, represents the life proportionality factor constant under calibration conditions, represents the activation energy in eV, represents the Boltzmann constant, k= 8.617×10 −5  eV / K, Indicates temperature in K. Represents the natural base; Therefore, the calculation formula for the equivalent test time of the components of the UAV flight control system under the target operating temperature conditions in step S21 is:

[0054] in, Indicates the equivalent test time of the components of the UAV flight control system under the target operating temperature conditions. represents the Boltzmann constant, k= 8.617×10 −5  eV / K, Indicates the target operating temperature; the equivalent test duration of the UAV flight control system components under the target operating temperature conditions is shown in Table 2.

[0055] Table 2 Equivalent test duration under target operating temperature conditions

[0056]

[0057]

[0058] In this embodiment, step S22 specifically includes the following steps: For i The first part of the component j The second screening data, Increase indivual , Increase indivual ; Sequentially, the second screening data from the first to the The second screening data can be processed to obtain i Left-censored data matrix of components of different models and the right-censored data matrix , and its expression formula is:

[0059] make , , for i Left-censored data matrix of components of different models and the right-censored data matrix Simplifying, we get:

[0060] in, correspond Equivalent test duration for left-center censored data; correspond Equivalent test duration for middle and right censored data; Repeat the above steps until the component missing data conversion is completed for all types of components, and the missing data of all types of components in the UAV flight control system are obtained, as shown in Table 3.

[0061] Table 3 Missing data of all types of components in the UAV flight control system

[0062] In this embodiment, the reliability model of each type of component in the UAV flight control system is set according to the failure characteristics of each type of component in step S23, specifically: If the failure characteristics of components are random failures, and the failure rate is independent of time, the reliability model of each type of component in the UAV flight control system is an exponential distribution, and its expression formula is:

[0063] in, represents the exponentially distributed cumulative failure distribution function, represents the reliability function of the exponential distribution, represents the natural base, Indicates time, represents the exponential distribution parameter, ; If the failure characteristic of the component is wear-out failure, the reliability model of each type of component in the UAV flight control system is a log-normal distribution, and its expression formula is:

[0064] in, represents the cumulative failure distribution function of the lognormal distribution, represents the reliability function of the lognormal distribution, represents the logarithmic mean, , represents the logarithmic standard deviation, , represents the standard normal distribution, Represents natural base Logarithmic function with base ; If the failure characteristics of components are early failure, random failure or wear-out failure, the reliability model of each type of component in the UAV flight control system is Weibull distribution, and its expression formula is:

[0065] in, represents the cumulative failure distribution function of the Weibull distribution, represents the reliability function of the Weibull distribution, represents the shape parameter of the Weibull distribution, , represents the scale parameter of the Weibull distribution, .

[0066] In this embodiment, the reliability model parameters of each type of components in the UAV flight control system in step S24 include exponential distribution parameters , log mean , logarithmic standard deviation , shape parameter of the Weibull distribution and the scale parameter of the Weibull distribution ; The step S24 specifically includes the following sub-steps: S241. Establish the maximum likelihood function of the reliability model parameters of various types of components in the UAV flight control system , and its expression formula is:

[0067] in, represents a distribution parameter vector (e.g., exponential distribution parameter , log mean , logarithmic standard deviation , shape parameter of the Weibull distribution and the scale parameter of the Weibull distribution ), Indicates i The cumulative failure distribution function of the reliability model of the components of each model, Indicates i The reliability function of the reliability model of the component model is Indicates i Model Component No. p left-censored equivalent test time, Indicates i Model Component No. q right-censored equivalent test time; S242. The maximum likelihood function Convert to log maximum likelihood function , the conversion formula is:

[0068] in, Represents natural base Logarithmic function with base ; S243. Using optimization methods, such as the least squares method, to maximize the logarithmic maximum likelihood function The goal is to obtain the estimated values ​​of the reliability model parameters of various types of components; the estimated values ​​of the reliability model parameters of various types of components in the UAV flight control system are shown in Table 4.

[0069] Table 4 Estimated values ​​of reliability model parameters of various types of components in the UAV flight control system

[0070] In this embodiment, in step S3, key functional modules are divided according to the functions and roles of each module of the UAV flight control system, such as control module (flight control mainboard and sensor interface unit), execution module (servo drive unit and power control unit), perception module (attitude sensor unit and GPS unit), communication module (wireless transmission unit and signal relay unit) and power module (battery management unit and power supply unit), etc. The attitude sensor unit is a triple-mode redundant attitude sensor; According to the working logic of the UAV flight control system and the functional coupling relationship of each module, the reliability block diagram of the UAV flight control system is determined. The reliability block diagram structure of the UAV flight control system is as follows: Figure 2 As shown, the flight control mainboard, triple-mode redundant attitude sensor, servo drive unit, and power control unit are in a series structure, and the triple-mode redundant attitude sensor is a parallel structure including three attitude sensors, thereby forming a reliability block diagram structure of a hybrid structure UAV flight control system.

[0071] In this embodiment, step S4 specifically includes the following sub-steps: S41. Based on the component list and position number information of the key functional modules in the UAV flight control system, as well as the reliability model parameters of various types of components in the UAV flight control system, the Monte Carlo sampling method is used to obtain the failure time matrix of all components in the key functional modules in the UAV flight control system. , and its expression formula is:

[0072] in, Indicates the total number of components. , represents the number of Monte Carlo sampling, Indicates i 1 Components j 1 The failure time sampling results of Monte Carlo sampling are: , The random sampling results of the failure time of all components of the key functional modules in the UAV flight control system can be obtained, as shown in Table 5.

[0073] Table 5 Random sampling results of component failure time in UAV flight control system (hours)

[0074]

[0075]

[0076] S42. Based on the failure time matrix of all components of key functional modules in the UAV flight control system , select the minimum failure time of all components of each key functional module as the failure time of the key functional module, and obtain the failure time matrix of all key functional modules in the UAV flight control system , and its expression formula is:

[0077] in, Indicates Key functional modules The failure time results of the Monte Carlo sampling, , Indicates the total number of key functional modules; the failure time of all key functional modules in the UAV flight control system is shown in Table 6; Table 6 Failure time of all key functional modules in the UAV flight control system

[0078] S43. According to the reliability block diagram of the UAV flight control system, for the parallel structure in the reliability block diagram, the maximum failure time of all modules or substructures in the parallel structure is selected as the failure time of the parallel structure; for the series structure in the reliability structure block diagram, the minimum failure time of all modules or substructures in the series structure is selected as the failure time of the series structure, and the failure time vector of the UAV flight control system is calculated. , and its expression formula is:

[0079] in, Indicates the UAV flight control system Failure time results for Monte Carlo sampling; S44. The failure time vector of the UAV flight control system Sorting in ascending order, we get the failure time sequence statistics of the UAV flight control system, which is expressed as:

[0080] in, represents the number of failure times that are less than the expected service time, , The number of expiration times that represent the expected usage time The corresponding failure time of the UAV flight control system; For the parallel structure in the reliability block diagram, in this example, the maximum failure time of the first attitude sensor, the second attitude sensor and the third attitude sensor in the parallel structure is selected as the failure time of the triple-mode redundant attitude sensor parallel structure, and the failure time of the triple-mode redundant attitude sensor is shown in Table 7.

[0081] Table 7 Triple-mode redundant attitude sensor failure time (hours)

[0082] For the series structure in the reliability structure diagram, the minimum failure time of the flight control mainboard, triple-mode redundant attitude sensor, servo drive unit, and power control unit in the series structure is selected as the failure time of the series structure, that is, the failure time of the UAV flight control system, as shown in Table 8. The failure time sequence statistics of the UAV flight control system are shown in Table 9.

[0083] Table 8 Failure time of UAV flight control system (hours)

[0084] Table 9 Statistics of the failure time sequence of the UAV flight control system (hours)

[0085] S45. According to the statistics of the failure time sequence of the UAV flight control system, the expected use time Hours of reliability prediction, find less than expected usage time The number of failure times , and then calculate the reliability of the UAV flight control system and the average failure time of the UAV flight control system to complete the reliability prediction of the UAV flight control system; the UAV flight control system reliability for: ; The average failure time of the UAV flight control system for: .

[0086] In the early stage of UAV flight control system design, the present invention constructs a system-level reliability model and performs reliability prediction based on the second screening data of components, thereby making up for the defects of low data utilization, poor model correlation, high modeling complexity and insufficient randomness processing capability in the prior art, and providing a scientific basis for the safety and stability of the UAV system.

[0087] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for predicting the reliability of a UAV flight control system based on secondary screening data of components, characterized in that: The following steps are involved: S1. Establish the second screening data matrix of components of the UAV flight control system; S2. Estimating the reliability model parameters of components according to the second screening data matrix of components, and obtaining the reliability model parameters of each type of components in the UAV flight control system; S3. Divide the key functional modules of the UAV flight control system and construct a reliability block diagram of the UAV flight control system; S4. Based on the reliability model parameters of various types of components in the UAV flight control system, the key functional modules of the UAV flight control system and the reliability block diagram structure of the UAV flight control system, the Monte Carlo sampling method is used to predict the reliability of the UAV flight control system.

2. The reliability prediction method of the UAV flight control system based on the second screening data of components according to claim 1 is characterized in that: The second screening data matrix of components in step S1 The expression formula is: in, Indicates i The second screening data matrix of the model components, , Indicates the number of component models of the drone flight control system; The expression formula is: in, Indicates i The first part of the component j Number of test components for the second screen, Indicates i The first part of the component j Number of failed components in the second-screen test, Indicates i The first part of the component j Total duration of the second screening test, Indicates i The first part of the component j Temperature conditions for the second sieve test: , Indicates i The number of second screening data for each model of components.

3. The reliability prediction method of the UAV flight control system based on the second screening data of components according to claim 2 is characterized in that: The step S2 specifically includes the following sub-steps: S21. Calculate the equivalent test duration of the components of the UAV flight control system under the target operating temperature conditions based on the temperature accelerated life model and the second screening data matrix of the components; S22. Convert the component second screening data matrix into a left-censored data matrix and a right-censored data matrix to obtain the censored data of all types of components in the UAV flight control system; S23. Set reliability models for various types of components in the UAV flight control system; S24. Based on the missing data of all types of components, the maximum likelihood estimation method is used to estimate the estimated values ​​of the reliability model parameters of each type of component in the UAV flight control system.

4. The reliability prediction method of the UAV flight control system based on the second screening data of components according to claim 3 is characterized in that: The calculation formula for the equivalent test time of the components of the UAV flight control system under the target operating temperature conditions in step S21 is: in, Indicates the equivalent test time of the components of the UAV flight control system under the target operating temperature conditions. represents the activation energy, represents the Boltzmann constant, k= 8.617×10 −5  eV / K, Indicates the target operating temperature, Represents the natural base.

5. The method for predicting the reliability of a UAV flight control system based on component secondary screening data according to claim 4 is characterized in that: The step S22 specifically includes the following steps: For i The first part of the component j Two-screen data, in the left-censored data matrix Increase indivual , in the right-censored data matrix Increase indivual ; Sequentially filter the data from the first to the second The second screening data is processed to obtain i Left-censored data matrix of components of different models and the right-censored data matrix , and its expression formula is: make , , for i Left-censored data matrix of components of different models and the right-censored data matrix Simplifying, we get: in, correspond Equivalent test duration for left-center censored data; correspond Equivalent test duration for middle and right censored data; Repeat the above steps until the component missing data conversion is completed for all types of components, and the component missing data of all types in the UAV flight control system is obtained.

6. The method for predicting the reliability of a UAV flight control system based on component secondary screening data according to claim 3 is characterized in that: In step S23, according to the missing data of all types of components, the reliability model of each type of component in the UAV flight control system is set, specifically: If the failure characteristics of components are random failures, the reliability model of each type of components in the UAV flight control system is an exponential distribution, and its expression formula is: in, represents the exponentially distributed cumulative failure distribution function, represents the reliability function of the exponential distribution, represents the natural base, Indicates time, represents the exponential distribution parameter, ; If the failure characteristic of the component is wear-out failure, the reliability model of each type of component in the UAV flight control system is a log-normal distribution, and its expression formula is: in, represents the cumulative failure distribution function of the lognormal distribution, represents the reliability function of the lognormal distribution, represents the logarithmic mean, , represents the logarithmic standard deviation, , represents the standard normal distribution, Represents natural base Logarithmic function with base ; If the failure characteristics of components are early failure, random failure or wear-out failure, the reliability model of each type of component in the UAV flight control system is Weibull distribution, and its expression formula is: in, represents the cumulative failure distribution function of the Weibull distribution, represents the reliability function of the Weibull distribution, represents the shape parameter of the Weibull distribution, , represents the scale parameter of the Weibull distribution, .

7. The reliability prediction method of the UAV flight control system based on the second screening data of components according to claim 6 is characterized in that: The reliability model parameters of each type of components in the UAV flight control system in step S24 include exponential distribution parameters , log mean , logarithmic standard deviation , shape parameter of the Weibull distribution and the scale parameter of the Weibull distribution ; The step S24 specifically includes the following sub-steps: S241. Establish the maximum likelihood function of the reliability model parameters of various types of components in the UAV flight control system ; S242. The maximum likelihood function Convert to log maximum likelihood function; S243. Using the optimization method, the maximum logarithmic maximum likelihood function The goal is to obtain the estimated values ​​of the reliability model parameters of various types of components.

8. The method for predicting the reliability of a UAV flight control system based on component secondary screening data according to claim 7 is characterized in that: The maximum likelihood function The expression formula is: in, represents the distribution parameter vector, Indicates i The cumulative failure distribution function of the reliability model of the components of each model, Indicates i The reliability function of the reliability model of the component model is Indicates i Model Component No. p left-censored equivalent test time, Indicates i Model Component No. q right-censored equivalent test time; The logarithmic maximum likelihood function The calculation formula is: in, Represents natural base The logarithmic function with base .

9. The method for predicting the reliability of a UAV flight control system based on component secondary screening data according to claim 1 is characterized in that: The step S4 specifically includes the following sub-steps: S41. Based on the component list and position number information of the key functional modules in the UAV flight control system, as well as the reliability model parameters of various types of components in the UAV flight control system, the Monte Carlo sampling method is used to obtain the failure time matrix of all components in the key functional modules in the UAV flight control system. , and its expression formula is: in, Indicates i 1 component j The failure time sampling results of 1 Monte Carlo sampling, , , Indicates the total number of components. represents the number of Monte Carlo sampling; S42. Based on the failure time matrix of all components of key functional modules in the UAV flight control system , select the minimum failure time of all components of each key functional module as the failure time of the key functional module, and obtain the failure time matrix of all key functional modules in the UAV flight control system , and its expression formula is: in, Indicates Key functional modules The failure time results of the Monte Carlo sampling, , Indicates the total number of key functional modules; S43. According to the reliability block diagram of the UAV flight control system, for the parallel structure in the reliability block diagram, the maximum failure time of all modules or substructures in the parallel structure is selected as the failure time of the parallel structure; for the series structure in the reliability structure block diagram, the minimum failure time of all modules or substructures in the series structure is selected as the failure time of the series structure, and the failure time vector of the UAV flight control system is calculated. , and its expression formula is: in, Indicates the UAV flight control system Failure time results for Monte Carlo sampling; S44. The failure time vector of the UAV flight control system Sorting in ascending order, we get the failure time sequence statistics of the UAV flight control system, which is expressed as: in, represents the number of failure times that are less than the expected service time, , The number of expiration times that represent the expected usage time The corresponding UAV flight control system failure time; S45. Based on the failure time sequence statistics of the UAV flight control system, find the number of failure times that are less than the expected use time , and then calculate the reliability of the UAV flight control system and the average failure time of the UAV flight control system to complete the reliability prediction of the UAV flight control system.

10. The method for predicting the reliability of a UAV flight control system based on component secondary screening data according to claim 9 is characterized in that: The calculation formula of the reliability of the UAV flight control system in step S45 is: in, Indicates the reliability of the UAV flight control system; The calculation formula for the average failure time of the UAV flight control system is: in, Indicates the average failure time of the UAV flight control system.

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