Correlation evaluation method and system for performance design and reliability design of airborne products

Abstract

The present invention belongs to the field of product reliability, and discloses a method and system for evaluating the correlation between performance design and reliability design of airborne products. The method includes: obtaining basic evaluation data; obtaining the project integration degree of reliability design; obtaining the control level of key components; obtaining the reliability completion degree; obtaining the reliability growth coefficient; determining the weight of the performance design and reliability design correlation evaluation index; obtaining the performance design and reliability design correlation evaluation results; and analyzing the performance design and reliability design correlation evaluation. The system of the present invention includes: a database, a project integration degree module for reliability design, a key component control level module, a reliability completion degree module, a reliability growth coefficient module, a weight module, a correlation evaluation module, and a correlation analysis module. The present invention performs analysis based on multi-source data and processes of reliability design and reliability test, so that the results of the performance design and reliability design correlation evaluation are more real and accurate.

Description

Technical Field

[0001] The present invention belongs to the field of equipment reliability, and in particular relates to a method and system for evaluating the correlation between performance design and reliability design of an airborne product. Background Art

[0002] The development of domestic airborne products has gradually shifted from surveying and mapping to independent research and development. The traditional airborne product development method is driven by the realization of performance indicators and ignores reliability indicators, which leads to the risk of reliability indicators not being achieved and frequent early failures. Therefore, accurately evaluating the correlation between the performance design and reliability design of airborne products is an inevitable trend. The correlation between the performance design and reliability design of airborne products refers to the degree to which performance design work and reliability design work are organically combined. It is used to ensure that reliability work is implemented in the entire process of product development and that performance requirements and reliability requirements are achieved at the same time. Therefore, considering the correlation between performance design and reliability design can improve the shortcomings of the traditional design process, so that reliability requirements and the results obtained from design analysis work are embedded in the development process as equally important constraints to carry out related design work, thereby promoting the development of independent research and development of my country's airborne products.

[0003] Evaluation is typically categorized into qualitative and quantitative methods. Using a single method alone can fail to objectively reflect relevance or be overly mechanistic. Therefore, a comprehensive evaluation approach is generally used, integrating different evaluation methods based on their characteristics and applying them to a single problem, making the evaluation results more accurate and effective. However, currently, there is a lack of specific evaluation methods for the degree of integration between performance design and reliability design for airborne products. Therefore, it is necessary to establish a reasonable, comprehensive, systematic, and engineering-applicable evaluation method based on the relevance of performance design and reliability design to accurately and objectively assess the degree of integration between the two. Summary of the Invention

[0004] In response to the shortcomings of existing technologies, the present invention provides a method and system for evaluating the correlation between the performance design and reliability design of airborne products. This method uses the correlation between the performance design and reliability design of airborne products as the evaluation object, evaluates the degree of integration between performance design and reliability design in engineering applications, and can then provide corresponding improvement suggestions. The method proposed in the present invention can be carried out according to the airborne product design process to evaluate the degree to which airborne products integrate reliability design into their performance design. It can also be used to identify the part of the airborne product's performance design that is currently least integrated with reliability design. When resources are limited, this part can be improved first, providing supervision and control support for the integration of reliability design into the performance design of airborne products.

[0005] To achieve the above objectives, the present invention discloses a method for evaluating the correlation between performance design and reliability design of airborne products based on multi-source data fusion, which includes the following steps:

[0006] Step 1: Obtain basic evaluation data;

[0007] Basic evaluation data consists of four parts obtained from two sources. The first source is obtained based on the reliability design of airborne products, including project integration and key component control level. The second source is obtained based on reliability experiments, including reliability completion and reliability growth coefficient.

[0008] Step 2: Obtain the project integration degree of reliability design;

[0009] When the number of reliability design projects is n, the calculation formula for the project integration degree Z of reliability design is:

[0010]

[0011] Among them, Z is the project integration degree of reliability design, n is the number of reliability design projects, a represents one of the reliability design projects, a=1,2…,n; n is a positive integer, Z a The integration degree of a single project;

[0012] Step 3: Obtain the control level of key parts;

[0013] The key component control level F∈(0,1) is:

[0014]

[0015] Among them, V is the average level of control level of key items, F is the control level of key items, F∈(0,1), P ij represents the evaluation score of the i-th expert on the j-th indicator, i = 1, 2, ..., m, m is the number of experts, j = 1, 2, ..., 6;

[0016] Step 4: Get reliability completion;

[0017] The estimated MTBF for airborne products is:

[0018]

[0019] Where t is time, f(t) is the lifetime distribution density function after fusion;

[0020] The reliability completion degree Q is:

[0021]

[0022] Among them, Q is the reliability completion degree, MTBF is the estimated value of the reliability of the airborne product in the reliability test. 要求 is the target value of the reliability MTBF indicator;

[0023] Step 5: Obtain the reliability growth coefficient;

[0024] Taking 0.7 as the growth limit for normalization, the reliability growth coefficient U∈(0,1) of the performance design after reliability design is:

[0025]

[0026] Among them, U1 is the improvement degree of reliability growth test; U2 is the improvement degree of reliability development test;

[0027] Step 6: Determine the weight of the performance design and reliability design correlation evaluation indicators;

[0028] Let the project integration degree, key component control level, reliability growth coefficient and reliability completion of reliability design be evaluation indicators 1, 2, 3 and 4 respectively. The three-scale method is used to establish the judgment matrix A=(a ij ) 4×4 ,but:

[0029]

[0030] in,

[0031]

[0032] Normalize the matrix by column and establish the normalized matrix B=(b ij ) 4×4 , sum the rows of matrix B and perform normalization to establish the judgment matrix w=(w i ) 4×1 , the indicator weights of reliability design project integration degree, key component control level, reliability growth coefficient, and reliability completion degree are obtained as w1, w2, w3, and w4 respectively;

[0033] Step 7: Obtain the performance design and reliability design correlation evaluation results;

[0034] The correlation evaluation value R between performance design and reliability design is:

[0035] R=Z×w1+S×w2+U×w3+Q×w4;

[0036] The larger the correlation evaluation value R between performance design and reliability design, the higher the support degree of reliability design for performance design.

[0037] Preferably, in step 1, the four parts of data include data related to project integration and key component control level, reliability completion and reliability growth coefficient, specifically:

[0038] (1) Obtain data related to the project integration degree of reliability design, including: reliability prediction, failure mode impact and criticality analysis, formulation of reliability design criteria, fault tree analysis, finite element analysis and durability analysis, and the number of times each project should be carried out during the development process (m) a1 、The actual number of times m a2 , the number of iterations to design improvement m a3 , the number of times the design has not been improved a4 , the number of times no improvement was provided to the design m a5 and the number of times the design is supported but not iterated a6 , where m a1 、m a2 、m a3 、m a4 、m a5 、m a6 a represents one of the reliability design items, a=1,2…,n; n is the number of reliability design items, n is a positive integer;

[0039] (2) Obtain relevant data on the control level of key items, including six indicators: key control level, improvement control level, distribution control level, quality control level, inspection control level, and procurement control level;

[0040] (3) Obtain relevant data on reliability completion, including: test time, number of responsible failures and confidence information during the reliability identification test, life test, and reliability acceptance test, as well as reliability index requirements;

[0041] (4) Obtain relevant data on the reliability growth factor, including the occurrence time of each failure observed during the reliability growth test, and the MTBF values ​​of the first and last tests of the reliability development test.

[0042] Preferably, in the step 2, the project integration degree of the reliability design is obtained:

[0043] The design support of item a in reliability design is:

[0044]

[0045] The completion degree of reliability design project a is:

[0046]

[0047] The integration degree Z of a single item of reliability design project a a , the formula is:

[0048]

[0049] Among them, w1 a Design support for item a designed for reliability, w1 a ∈(0,1),m a2 The number of times the project a designed for actual reliability is performed, m r5 The number of times that a project designed for reliability does not provide an improvement to the design; Y a The degree of completion of project a designed for reliability, Y a ∈(0,+∞),m a2 The number of times the project a designed for actual reliability is performed, m a1 The number of times that a project designed for reliability should be carried out during the development process.

[0050] Preferably, in step 4, the specific steps for obtaining the fused life distribution density function f(t) are as follows:

[0051] First, for a single reliability experiment, the lower confidence limit at a given confidence level c is θ L ; Then the life distribution density function of airborne products is:

[0052] f(t)=λe -λt

[0053] in, t is time;

[0054] If the reliability experiment is of m types, then m life distribution density functions are fused, and the support vector of the mutual support degree between the reliability acceptance test and other tests is established with the reliability acceptance test as the center point, that is:

[0055] S=(S 11 S 12 … S 1m )

[0056] in, f1(t) represents the life distribution density function obtained from the reliability acceptance test; f i (t) represents the life distribution density function of the i-th type reliability experiment, S 1i represents f1(t) and f i (t) the distance between the two;

[0057] Determine the weight w21 of the reliability acceptance test as:

[0058]

[0059] Among them, L p and L q They represent the lengths of the confidence intervals of the life distribution density function obtained from the reliability acceptance test at the confidence levels p and q, respectively, p>q;

[0060] After obtaining the weight of w21, the weight of the reliability experiment of type i, w2 i for:

[0061]

[0062] Among them, S 1i w2 represents the distance between the life distribution density function obtained from the reliability acceptance test and the life distribution density function of the i-th type reliability experiment; i is the weight of the reliability experiment of type i;

[0063] The final fusion model of life distribution density function is:

[0064]

[0065] Among them, f(t) is the lifetime distribution density function after fusion, w2 i is the weight of the reliability experiment of type i; f i (t) represents the life distribution density function of the i-th type reliability experiment, and m is the number of reliability experiment types.

[0066] Preferably, in step 5, the specific steps for obtaining the reliability growth test improvement degree U1 and the reliability development test improvement degree U2 are as follows:

[0067] The reliability growth curve is the AMSAA model, and its expression is:

[0068] E[N(t)]=at b

[0069] Where N(t) is the cumulative number of failures that occur during the cumulative test time t; a is the scale parameter; b is the growth shape parameter, and E[N(t)] is the mathematical expectation of N(t);

[0070] The reliability growth test improvement degree U1 is:

[0071]

[0072] Where N is the total number of observed faults; t i is the cumulative test time when the i-th fault occurs; T is the total cumulative test time; M is the first parameter, and its value satisfies

[0073] The improvement degree Y2 of reliability development test is:

[0074]

[0075] Where t1 is the first MTBF obtained from the test, and t2 is the last MTBF obtained from the test.

[0076] Preferably, in step 6, after establishing the judgment matrix w, w also needs to pass a consistency test, specifically:

[0077] Judgment matrix w=(w i ) 4×1 The largest characteristic root of Among them, (Bw) i Represents the i-th row element after matrix B is multiplied by judgment matrix w;

[0078] Perform consistency test on the judgment matrix w, and the consistency index CI is expressed as:

[0079]

[0080] Random consistency index RI, when the number of indicators n is equal to 4, RI = 0.89, the consistency ratio CR is If CR<0t1, the eigenvector of the judgment matrix w is used to represent the weight vector;

[0081] If the consistency check fails, it means that the setting of matrix A is unreasonable, and the values ​​in matrix A need to be reset.

[0082] Preferably, the step seven further includes:

[0083] According to the value range of the performance design and reliability design correlation evaluation value R, segmentation is performed so that the performance design and reliability design correlation evaluation R corresponds to evaluation results of different levels.

[0084] Preferably, the method further includes step eight of analyzing the correlation evaluation between performance design and reliability design, specifically:

[0085] 1) Sort Z, F, Q, and U by score from low to high to obtain a ranking table. The lowest-scoring indicator is the weak link in the combination of performance design and reliability design.

[0086] 2) For the project integration degree Z, according to the reliability design analysis work in item a, the project integration degree Z i Sort by scores from low to high and output a ranking table of project integration shortcomings;

[0087] 3) For the control level F of key items, sort them from low to high according to the scoring results of the key item control level indicators to obtain a ranking table of the short board of the control level;

[0088] 4) For the reliability completion degree Q, if Q>0, it means that the specified reliability index requirements have been met; if Q<0, it means that the specified reliability index requirements have not been met;

[0089] 5) For the reliability growth coefficient U, based on the comparison of U1 and U2, a ranking table of the short board in terms of reliability growth degree is obtained;

[0090] From 1) to 5), the lower the score, the more improvement is needed.

[0091] The present invention also discloses a system for evaluating the correlation between performance design and reliability design of airborne products, comprising:

[0092] Database, reliability design project integration module, key component control level module, reliability completion module, reliability growth coefficient module, weight module and correlation evaluation module, wherein the reliability design project integration module, key component control level module, reliability completion module and reliability growth coefficient module are connected to the database, weight module and correlation evaluation module respectively;

[0093] The database is used to store the data required for the performance reliability integrated design degree evaluation, including a reliability design project sub-database, a key component control level sub-database, a reliability completion sub-database, and a reliability growth factor sub-database; wherein the reliability design project sub-database includes the number of times m that reliability prediction, failure mode effect and criticality analysis, formulation of reliability design criteria, fault tree analysis, finite element analysis, and durability analysis projects should be carried out during the development process. a1 , the number of iterations to design improvement m a3 , the number of times no improvement was provided to the design m a5 , the number of times the design is supported but not iterated again m a6 The key component control level sub-database includes six indicator values, namely, key control level, improvement control level, allocation control level, quality control level, inspection control level, and procurement control level, obtained by expert scoring. The reliability completion sub-database includes the test time, number of responsible failures, confidence information, and reliability indicator requirements during the reliability identification test, life test, and reliability acceptance test. The reliability growth factor sub-database includes the occurrence time of each failure observed during the reliability growth test, as well as the MTBF values ​​of the first and last tests of the reliability development test.

[0094] The project integration degree module of reliability design is used to obtain the project integration degree Z of reliability design, which is achieved by the method in step 2;

[0095] The critical component control level module is used to obtain the critical component control level F, which is implemented in the manner of step 3;

[0096] The reliability completion module is used to obtain the reliability completion degree Q, which is implemented using the method in step 4;

[0097] The reliability growth coefficient module is used to obtain the reliability growth coefficient U, which is implemented using the method in step 5;

[0098] The weight module is used to establish a judgment matrix using the three-scale method to obtain the weights of project integration degree Z, key component control level F, reliability completion degree Q and reliability growth coefficient U;

[0099] The correlation evaluation module obtains the correlation evaluation value of performance design and reliability design according to the weight output by the weight module, the project integration degree Z output by the project integration degree module of reliability design, the key component control level F output by the key component control level module, the reliability completion degree Q output by the reliability completion degree module, and the reliability growth coefficient U output by the reliability growth coefficient module, using the step seven method.

[0100] Preferably, a correlation analysis module is further included, and the correlation analysis module is connected to the project integration module of reliability design, the key component control level module, the reliability completion module, the reliability growth coefficient module and the correlation evaluation module respectively.

[0101] Compared with the prior art, the present invention has the following beneficial effects:

[0102] a) Analyze the multi-source data of reliability design and reliability test in the whole development stage to make the results of the correlation evaluation between performance design and reliability design more real and accurate.

[0103] b) Comprehensively consider the relevant factors of performance design and reliability design, provide reliability improvement measures, provide design references, judge whether the specified performance requirements are met, and determine the correlation evaluation indicators between performance design and reliability design, so that the results of the correlation evaluation are more comprehensive.

[0104] c) This method establishes a performance design and reliability design correlation evaluation process, taking into account the support effect of reliability work for design work and the performance and reliability indicators required after the product design is finalized. It uses a multi-source data fusion method to extract four types of indicators to express the degree of correlation between performance design and reliability design, making the evaluation more scientific.

[0105] d) The results of this invention can quantitatively describe the degree of integration of performance design and reliability design during the development process, and output a ranking table of shortcomings of various indicators, which can be used to discover problems, correct them in time, and provide direction for integrated control. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] Figure 1 Flowchart of correlation evaluation method for performance design and reliability design of airborne products;

[0107] Figure 2 A multi-source data system diagram for the performance design and reliability design correlation evaluation method of airborne products;

[0108] Figure 3 A data relationship diagram showing the integration of performance design and reliability design;

[0109] Figure 4 Schematic diagram of the correlation evaluation system structure for performance design and reliability design of airborne products. DETAILED DESCRIPTION

[0110] The exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0111] The present invention provides a method and system for evaluating the correlation between performance design and reliability design of airborne products. The method uses data obtained from reliability design and reliability test as a data source and uses a multi-source data fusion method to evaluate the correlation between performance design and reliability design of airborne products. Figure 1 As shown, it includes the following steps:

[0112] Step 1: Obtain basic evaluation data.

[0113] Basic evaluation data sources such as Figure 2 As shown in the figure, it consists of four parts of data obtained from two sources. The first source is obtained based on the reliability design of airborne products, including project integration and key component control level. The second source is obtained based on reliability experiments, including reliability completion and reliability growth coefficient. The following is a detailed description of the four parts of data:

[0114] (1) Obtain data related to the project integration of reliability design. The projects of reliability design in this application include reliability prediction, failure mode effect and criticality analysis, formulation of reliability design criteria, fault tree analysis, finite element analysis and durability analysis. Therefore, the data related to the project integration of reliability design include: the number of times m that the projects such as reliability prediction, failure mode effect and criticality analysis, formulation of reliability design criteria, fault tree analysis, finite element analysis or durability analysis should be carried out in the development process. a1 , the number of iterations to design improvement m a3 , the number of times no improvement was provided to the design m a5, the number of times the design is supported but not iterated again m a6 , the number of iterations not reaching the design improvement is m a4 The number of times no improvement is provided to the design m a5 and the number of times the design is supported but not iterated a6 Additive composition, that is, m a4 =m a5 +m a6 , the actual reliability design project a is carried out m times a2 The number of iterations to design improvement m a3 and the number of times m that the design is not improved a4 Additive composition, that is, m a2 =m a3 +m a4 , specifically Figure 3 As shown. a1 、m a2 、m a3 、m a4 、m a5 、m a6 a represents one of the reliability design items, a=1,2…,n; n is the number of reliability design items, n is a positive integer. For example, when a=1, the item is reliability prediction, then m 11 Indicates the number of times reliability is expected to be carried out during the development process, m 12 This represents the number of times the actual reliability design is expected to be performed. The above numbers refer to the number of times the project is completed. In a fault tree analysis, a fault tree analysis of all failure modes at the same agreed level is counted as one. Data related to the project integration degree of reliability design can be obtained by looking up reliability design reports, performance design reports, or by asking designers.

[0115] (2) Obtain relevant data on the control level of key parts. The control level of key parts includes six indicators: key control level, improvement control level, distribution control level, quality control level, inspection control level, and procurement control level. The control level of key parts is determined by expert scoring. Key parts refer to key parts and important parts.

[0116] Among them, the key control level, improvement control level, and allocation control level are evaluation indicators in design control, and the quality control level, inspection control level, and procurement control level are evaluation indicators in process control. The following is an overview of these six evaluation indicators. The overview of the evaluation indicators is shown in Table 1:

[0117] Table 1 Evaluation indicators and overview of the control level of key items

[0118]

[0119]

[0120] Each evaluation indicator is graded into five levels. A higher level indicates better reliability control of critical components, a higher degree of reference for performance design to reliability design, and a higher degree of correlation between performance design and reliability design. Table 2 explains the various levels of the evaluation indicators.

[0121] Table 2 Evaluation criteria for key component control level evaluation indicators

[0122]

[0123]

[0124] (3) Obtain relevant data on reliability completion. Reliability experiments include reliability identification tests, life tests, and reliability acceptance tests. Therefore, relevant data on reliability completion include: the test time, number of responsible failures, and confidence level of the reliability identification test, life test, and reliability acceptance test during the process. Reliability index requirements also need to be clarified.

[0125] (4) Obtain relevant data on the reliability growth factor, including the occurrence time of each failure observed during the reliability growth test, and the MTBF (Mean Time Between Failure) values ​​of the first and last tests of the reliability development test.

[0126] Step 2: Obtain the project integration degree of reliability design.

[0127] The degree to which reliability design project a helps performance design is defined as design support, denoted as w1 a ∈(0,1), its calculation formula is:

[0128]

[0129] Among them, w1 a Design support for item a designed for reliability, m a2 The number of times the project a designed for actual reliability is performed, m r5 The number of times a project designed for reliability provides no improvement to the design.

[0130] Define the degree of completion of reliability design project a as completion degree Y a ∈(0,+∞), its calculation formula is:

[0131]

[0132] Among them, Y a Completion of project a designed for reliability, ma2 The number of times the project a designed for actual reliability is performed, m a1 The number of times that a project designed for reliability should be carried out during the development process.

[0133] Define the integration degree of reliability design project a and performance design as the single project integration degree Z a , and its calculation formula is:

[0134]

[0135] When the number of reliability design projects that should be carried out during the development of airborne products is n, where n is a positive integer, the calculation formula for the reliability design project integration degree Z is:

[0136]

[0137] Where Z is the project integration degree of reliability design, n is the number of reliability design projects, a represents one of the reliability design projects, a=1,2…,n; Z a The integration degree of a single project.

[0138] Step 3: Get the control level of key parts.

[0139] The key control level indicators include: key control level, improvement control level, distribution control level, quality control level, inspection control level, and procurement control level, which are set as indicator 1, indicator 2, indicator 3, ..., indicator 6 respectively. Assume that the scoring results of m experts are as follows:

[0140]

[0141] Among them, P ij represents the evaluation score of the i-th expert on the j-th indicator, i = 1, 2, ..., m, m is the number of experts, j = 1, 2, ..., 6, assuming that the weights of m experts are the same, the key control level F∈(0, 1), its calculation formula is:

[0142]

[0143] Among them, V is the average level of control of key parts, t is the control level of key parts, and the relative score between the average level of control of key parts and the highest level 5, F∈(0,1), represents the degree to which key parts are effectively controlled in the design process of airborne products.

[0144] Step 4: Get the reliability completion degree.

[0145] Reliability testing includes reliability identification testing, life testing, and reliability acceptance testing. Based on the test time T and the number of responsible failures that occur during the reliability test, the system's MTBF (mean time between failures) can be evaluated.

[0146] First, for a single reliability experiment, the lower confidence limit at a given confidence level c is:

[0147]

[0148] Among them, θ L is the lower confidence limit, T is the test duration, r is the number of responsible failures that occurred, It is a chi-square distribution with 2r+2 degrees of freedom, which is a statistic and can be obtained by looking up the table.

[0149] Therefore, the life distribution function and density function of airborne products are:

[0150] F(t)=1-e -λt

[0151] f(t)=λe -λt

[0152] in, t is time.

[0153] Secondly, to fuse the data of different reliability tests, it is necessary to determine the support weight of each type of test data for the results. Since each type of test has a certain correlation in the test conditions, it is assumed that the life distribution density functions obtained by different tests also have a certain correlation. The weight of the life distribution density function of each type of test is obtained according to the degree of mutual support between different tests. Assuming that the life distribution density functions obtained by the two types of tests are f p (t) and f q (t), then the mutual support between the life distribution density functions can be expressed by D(f p ||f q ) to define:

[0154]

[0155] Among them, the definition If D(f p ||f q ) is larger, which means that the distance between the life distribution density functions obtained by the two types of experiments is smaller, and the degree of mutual support between the two is higher.

[0156] If the reliability experiment is of m types, then m life distribution density functions are fused. First, the mutual support degree of different reliability experiments is calculated. Since the reliability acceptance test in the reliability experiment is the test in the final stage of the development process, the reliability acceptance test is taken as the center point to establish the support vector of the mutual support degree between the reliability acceptance test and other tests, namely:

[0157] S=(S 11 S 12 … S 1m )

[0158] in, f1(t) represents the life distribution density function obtained from the reliability acceptance test; f i (t) represents the life distribution density function of the i-th type reliability test. In this embodiment, m=3, f2(t) and f3(t) are the life distribution density functions of the airborne product obtained from the reliability evaluation test and life test, respectively. 1i represents f1(t) and f i (t) The distance between the two, if S 1i The larger it is, the smaller the distance is, that is, the greater the support level is, S 1i Inversely proportional to the level of support.

[0159] Determine the weight of the reliability acceptance test. Since there is a certain gap between the life distribution obtained by the reliability acceptance test and the actual life distribution of the product, the credibility ρ of the life distribution density function f1(t) is used as its weight, that is, the weight w21 of the reliability acceptance test is:

[0160]

[0161] Among them, L p and L q They represent the lengths of the confidence intervals of the life distribution density function obtained from the reliability acceptance test at confidence levels p and q, respectively, where p>q. In engineering, a 50% confidence lower limit is often used as the point estimate of the reliability parameter, while the confidence level of the reliability parameter estimate obtained from physical test data is 80%. Therefore, we generally have:

[0162]

[0163] Among them, L 0t8 and L 0t5 Respectively represent the length of the confidence interval of the life distribution parameter obtained from the reliability acceptance test at 80% and 50% confidence levels. After obtaining the weight of w21, the weight w2 of the reliability experiment of type i is i for:

[0164]

[0165] Among them, S 1i w2 represents the distance between the life distribution density function obtained from the reliability acceptance test and the life distribution density function of the i-th type reliability experiment; i is the weight of the reliability experiment of type i;

[0166] The final fusion model of life distribution density function is:

[0167]

[0168] Among them, f(t) is the lifetime distribution density function after fusion, w2 i is the weight of the reliability experiment of type i; f i (t) represents the life distribution density function of the i-th type reliability experiment, and m is the number of reliability experiment types.

[0169] Finally, the estimated MTBF for the airborne product is:

[0170]

[0171] The reliability completion degree Q is defined as the ratio of the estimated reliability index value of the product in the reliability experiment to the required reliability index value. In this case, the required reliability index is MTBF. Using the target value in the parameters, the calculation formula is:

[0172]

[0173] Among them, Q is the reliability completion degree, MTBF is the estimated value of the reliability of the airborne product in the reliability test. 要求 It is the target value of the reliability MTBF indicator.

[0174] Step 5: Get the reliability growth coefficient.

[0175] First, collect reliability growth test data and continuously record the cumulative number of product failures N(t) and the cumulative test time t. Assuming that the reliability growth curve is the AMSAA (Army Material Systems Analysis Activity) model, its expression is:

[0176] E[N(t)]=at b

[0177] Where N(t) is the cumulative number of failures that occur during the cumulative test time t; a is the scale parameter; b is the growth shape parameter, and E[N(t)] is the mathematical expectation of N(t).

[0178] The estimate for the growth shape parameter b is:

[0179]

[0180] Where N is the total number of observed faults; t i is the cumulative test time when the i-th fault occurs; T is the total cumulative test time; M is the first parameter, and its value satisfies

[0181] According to the above formula, the design improvement degree of the reliability growth test is derived and recorded as the reliability growth test improvement degree U1:

[0182]

[0183] Secondly, consider the design improvement effect brought about by the reliability development test. Assuming that the first MTBF obtained by the test is t1 and the last MTBF obtained by the test is t2, the reliability improvement degree of the design improvement of the reliability development test is obtained, which is recorded as the reliability development test improvement degree U2:

[0184]

[0185] Assuming that the reliability growth test and the reliability development test have the same impact on reliability growth, and according to the relevant provisions of GJB1407, a reliability growth rate below 0.3 indicates that the corrective measures are ineffective, and between 0.6-0.7 indicates that strong and effective fault analysis and corrective measures have been taken. Therefore, the overall reliability growth coefficient is processed and normalized with 0.7 as the growth limit. The reliability growth coefficient U∈(0,1) of the performance design after reliability design work is:

[0186]

[0187] Step 6: Determine the weights of the performance design and reliability design correlation evaluation indicators.

[0188] According to the process of steps 2, 3, 4 and 5, a multi-source data system for performance design and reliability design correlation evaluation is constructed, as shown in the attached Figure 2 , and finally obtained four performance design and reliability design correlation evaluation indicators, their meanings and types are shown in Table 3:

[0189] Table 3 Classification and meaning of performance design and reliability design correlation evaluation indicators

[0190]

[0191] Let the project integration degree, key component control level, reliability growth coefficient and reliability completion of reliability design be evaluation indicators 1, 2, 3 and 4 respectively. The evaluation indicators are established by the three-scale method to establish the judgment matrix A=(a ij ) 4×4 ,but:

[0192]

[0193] in,

[0194]

[0195] In order to obtain the maximum eigenvalue and corresponding eigenvector of the judgment matrix, it is necessary to normalize the matrix by column and establish a normalized matrix B = (b ij ) 4×4 ,but:

[0196]

[0197] in,

[0198] Sum the rows of matrix B and perform normalization to establish the judgment matrix w=(w i ) 4×1 ,but

[0199] w=(w1,w2,w3,w4) T

[0200] in,

[0201] Calculate the maximum eigenvalue, that is

[0202] Among them, (Bw) i Represents the i-th row element after matrix B is multiplied by judgment matrix w.

[0203] Perform consistency test on the judgment matrix w, and the consistency index CI is expressed as:

[0204]

[0205] Random consistency index RI, when the number of indicators n is equal to 4, RI = 0.89, RI is obtained by looking up the average random consistency index RI table. The consistency ratio CR is defined as According to the regulations, if CR<0t1, the weight vector is represented by the eigenvector of the judgment matrix w.

[0206] If the consistency test fails, it means that the setting of matrix A is unreasonable and the values ​​in matrix A need to be reset: the consistency test is to determine whether there will be inconsistent errors in the matrix: for example, for indicators a, b and c, assuming that a>b, b>c are set in the matrix, but c>a exists, then the consistency test will fail.

[0207] Therefore, when the consistency test is passed, the indicator weights of the reliability design project integration degree, key parts control level, reliability growth coefficient, and reliability completion degree are w1, w2, w3, and w4 respectively.

[0208] Step 7: Obtain the evaluation results of the correlation between performance design and reliability design.

[0209] Based on the index evaluation results obtained in steps 2, 3, 4, and 5 and the index weights obtained in step 6, the correlation evaluation of performance design and reliability design is obtained, and the output result is R:

[0210] R=Z×w1+S×w2+U×w3+Q×w4.

[0211] The larger the R value of the correlation evaluation between performance design and reliability design, the higher the support degree of reliability design for performance design.

[0212] Furthermore, the value ranges of the four evaluation indicators are analyzed. The value ranges of the key component control level and the reliability growth coefficient are between (0, 1); the reliability completion degree will be greater than 1 if the designed and produced product exceeds the target value, and will be between (0, 1) if it does not exceed the target value but exceeds the threshold value, and will gradually decrease with the actual MTBF value; the project integration degree of reliability design is generally between (0, 1). If the number of iterations of design improvement is large, its range will be greater than 1, but a large number of iterations also indicates that the reliability design work has a high degree of support for the design work; Based on the above analysis, the performance reliability integrated design degree R is graded into five levels, and the corresponding evaluation results are output according to the score range, as shown in Table 4.

[0213] Table 4 Analysis of the evaluation results of the correlation between performance design and reliability design

[0214]

[0215] Step 8. Analyze the correlation evaluation between performance design and reliability design.

[0216] Analyze the above output content to find the shortcomings in the combination of performance design and reliability design, and provide a reference for the combination of performance design and reliability design.

[0217] 1) First, Z, F, Q, and U should be sorted from low to high according to their scores to obtain a ranking table. The lowest score is the shortcoming indicator item that combines performance design and reliability design.

[0218] 2) Further analyze the project integration degree Z, and the project integration degree Z of the reliability design analysis work of item a i Sort by scores from low to high, output a ranking table of project integration shortcomings, and analyze the reasons for the low integration with performance design from the two aspects of reliability design support and completion.

[0219] 3) Further analyze the control level F of key items, sort the scoring results of the key item control level indicators from low to high, obtain a ranking table of the shortcomings of the control level, and analyze the shortcomings of the work items in the key item control process.

[0220] 4) Further analyze the reliability completion degree Q. If Q>0, it means that the specified reliability index requirements have been met. If Q<0, it means that the specified reliability index requirements have not been met, indicating that the degree of integration between performance design and reliability design is very poor.

[0221] 5) Further analyze the reliability growth coefficient U, compare U1 and U2, and obtain a ranking table of shortcomings according to the degree of reliability growth.

[0222] In the above analysis, the lower the score, the more improvement is needed, and the degree of improvement of each indicator can be controlled according to the corresponding ranking table.

[0223] The present invention also provides a system for evaluating the performance design and reliability design correlation of airborne products. Figure 4 As shown, it includes a database 1, a project integration module 2 for reliability design, a key component control level module 3, a reliability completion module 4, a reliability growth coefficient module 5, a weight module 6, a correlation evaluation module 7 and a correlation analysis module 8, wherein the project integration module 2 for reliability design, the key component control level module 3, the reliability completion module 4 and the reliability growth coefficient module 5 are connected to the database 1, the weight module 6, the correlation evaluation module 7 and the correlation analysis module 8 respectively, and the correlation evaluation module 6 is also connected to the weight module 5 and the correlation analysis module 7 respectively.

[0224] Among them, database 1 is used to store the data required for the performance reliability integrated design degree evaluation, including reliability design project sub-database 11, key component control level sub-database 12, reliability completion sub-database 13 and reliability growth factor sub-database 14. Among them, the reliability design project sub-database includes reliability prediction, failure mode effect and criticality analysis, formulation of reliability design criteria, fault tree analysis, finite element analysis and durability analysis, etc. The number of times they should be carried out in the development process is m. a1 , the number of iterations to design improvement m a3 , the number of times no improvement was provided to the design m a5 , the number of times the design is supported but not iterated again m a6 . The key parts control level sub-database includes six indicator values, namely key control level, improvement control level, allocation control level, quality control level, inspection control level and procurement control level obtained by expert scoring. The reliability completion sub-database includes the test time, number of responsible failures, confidence information, and reliability index requirements during the reliability identification test, life test and reliability acceptance test. The reliability growth coefficient sub-database includes the occurrence time of each failure observed during the reliability growth test, as well as the MTBF (Mean Time Between Failure) values ​​of the first and last tests of the reliability development test.

[0225] The project integration degree module 2 of reliability design is used to obtain the project integration degree Z of reliability design, which is implemented using the method in step 2.

[0226] The critical item control level module 3 is used to obtain the critical item control level F, which is achieved using the method in step three.

[0227] The reliability completion module 4 is used to obtain the reliability completion Q, which is implemented in the manner of step 4.

[0228] The reliability growth coefficient module 5 is used to obtain the reliability growth coefficient U, which is implemented in the manner of step five.

[0229] The weight module 6 is used to establish a judgment matrix using the three-scale method to obtain the weights of the project integration degree Z, the key component control level F, the reliability completion degree Q and the reliability growth coefficient U.

[0230] The correlation evaluation module 7 obtains the performance design and reliability design correlation evaluation score according to the weight output by the weight module, the project integration degree Z output by the project integration degree module of the reliability design, the key component control level F output by the key component control level module, the reliability completion degree Q output by the reliability completion degree module, and the reliability growth coefficient U output by the reliability growth coefficient module, using the method of step seven.

[0231] The correlation analysis module 8 is used to determine the parts that need to be improved in the performance design to increase the relevance of reliability, specifically including: obtaining the evaluation results based on the performance design and reliability design relevance evaluation score R output by the correlation evaluation module, sorting the short board index items according to the project integration degree Z, key component control level F, reliability completion degree Q and reliability growth coefficient U; and sorting the project integration degree Z for the reliability design analysis work item a in the reliability design project integration degree module. i According to the ranking from low to high scores, the reasons for the low integration with performance design can be further analyzed from the two aspects of reliability design support and completion; according to the distance S between the life distribution density function of the reliability experiment of type i and the reliability acceptance test in the key component control level module 1i Sorting is performed to obtain the shortcomings in the key parts control process; according to the reliability completion degree Q output by the reliability completion degree module, if it is greater than 0, the specified reliability index requirements are met, otherwise it is not met; according to the comparison of the reliability growth test improvement degree U1 and the reliability development test improvement degree U2 in the reliability growth coefficient module, the shortcomings of the reliability growth degree are obtained.

[0232] The following takes the development of a certain type of helicopter actuator as an example to implement the performance design and reliability design correlation evaluation method of the transmission system. The details are as follows:

[0233] Step 1: Obtain basic evaluation data.

[0234] Identify the required data and collect the data. The required data include the following:

[0235] (1) Obtain data related to the project integration degree of reliability design, including: reliability prediction, failure mode impact and criticality analysis, formulation of reliability design criteria, fault tree analysis, finite element analysis or durability analysis, etc. The number of times each project should be carried out in the development process is m a1 、The actual number of times m a2 , the number of iterations to design improvement m a3 , the number of times the design has not been improved a4 , the number of times no improvement was provided to the design m a5 , the number of times the design is supported but not iterated again m a6 , where a represents the item of corresponding reliability design.

[0236] (2) Obtain relevant data on the control level of key items, including: experts' scoring data on the six indicators of key control level, improvement control level, distribution control level, quality control level, inspection control level and procurement control level.

[0237] (3) Obtain relevant data on reliability completion, including: the test time, number of responsible failures, confidence level, and other information during the reliability identification test, life test, and reliability acceptance test, as well as reliability index requirements.

[0238] (4) Obtain relevant data on the reliability growth factor, including: the occurrence time of each failure observed during the reliability growth test, and the MTBF value of the first and last tests of the reliability development test.

[0239] Step 2: Obtain the project integration degree of reliability design.

[0240] Based on the obtained project integration data of reliability design, the design support, completion, single project integration and project integration were calculated. The results are shown in Table 5.

[0241] Table 5 Project integration degree of reliability design

[0242]

[0243] Step 3: Get the control level of key parts.

[0244] According to the scores given by the five invited experts on the six indicators of key control level, improvement control level, distribution control level, quality control level, inspection control level and procurement control level of the transmission system, the expert scoring results were obtained, as shown in Table 6. The key parts control level was then obtained based on the expert scoring results.

[0245] Table 6: Scoring table for control level of important items

[0246]

[0247]

[0248] As can be seen from Table 6, the average level V of the control level of important items is 3.33. Compared with the highest level 5, the control level F of important items is 0.67.

[0249] Step 4: Get the reliability completion degree.

[0250] Based on the data from different reliability experiments, the life distribution density function and weight obtained for each type of test are obtained. The fused life distribution density function is obtained through fusion, and then the estimated value of the transmission system reliability MTBF is obtained. It is then compared with the target value of the reliability MTBF indicator, and the final reliability completion degree Q is 1.02.

[0251] Step 5: Get the reliability growth coefficient.

[0252] According to the occurrence time of each failure during the reliability growth test and the MTBF values ​​of the first and last tests of the reliability development test, the comprehensive reliability growth coefficient U is 0.442.

[0253] Step 6: Determine the weights of the performance design and reliability design correlation evaluation indicators.

[0254] Let the project integration degree, key component control level, reliability growth coefficient and reliability completion of reliability design be evaluation indicators 1, 2, 3 and 4 respectively. The evaluation indicators are constructed using the three-scale method to establish a judgment matrix:

[0255]

[0256] After normalization according to step 6, the project integration degree, key component control level, reliability completion degree and reliability growth coefficient weights of reliability design are obtained as follows:

[0257] Step 7: Obtain the evaluation results of the correlation between performance design and reliability design.

[0258] Based on the formula in step 7 and the results from steps 2, 3, 4, 5, and 6, the performance design and reliability design correlation R output is 0.813. The evaluation result is "Good." The degree of integrated design is high, and the reliability indicators generally meet the requirements. Sorting and analyzing the results of each indicator reveals a low reliability growth coefficient, indicating that the corrective measures have been generally effective.

[0259] Integration degree Z of reliability design analysis work item a i Sorting the scores from low to high yields the ranking of the shortcomings in the integration of reliability design project a, as shown in Table 7. Three key tasks require special attention: "Developing Reliability Design Guidelines," "Failure Mode Effects and Criticality Analysis," and "Fault Tree Analysis." While work on the reliability design guidelines has been carried out but has not been incorporated into the design, the failure mode effects and criticality analysis tasks have low completion and design support, providing limited support for the design.

[0260] Table 7: Ranking of shortcomings of project integration

[0261] Project Name Project integration Develop reliability design guidelines 0.00 Failure Mode Effects and Criticality Analysis 0.5 Fault Tree Analysis 0.5 Reliability Prediction 0.67 Finite element analysis 1.00 Durability analysis 1.00

[0262] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for evaluating the correlation between performance design and reliability design of an airborne product, characterized by: It includes: Step 1: Obtain basic evaluation data; Basic evaluation data consists of four parts obtained from two sources. The first source is obtained based on the reliability design of airborne products, including project integration and key component control level. The second source is obtained based on reliability experiments, including reliability completion and reliability growth coefficient. Step 2: Obtain the project integration degree of reliability design; When the number of reliability design projects is n, the calculation formula for the project integration degree Z of reliability design is: Among them, Z is the project integration degree of reliability design, n is the number of reliability design projects, a represents one of the reliability design projects, a=1,2…,n; n is a positive integer, Z a The integration degree of a single project; Step 3: Obtain the control level of key parts; The key component control level F∈(0,1) is: Among them, V is the average level of control level of key items, F is the control level of key items, F∈(0,1), P ij represents the evaluation score of the i-th expert on the j-th indicator, i = 1, 2, ..., m, m is the number of experts, j = 1, 2, ..., 6; Step 4: Get reliability completion; The estimated MTBF for airborne products is: Where t is time, f(t) is the lifetime distribution density function after fusion; The reliability completion degree Q is: Among them, Q is the reliability completion degree, MTBF is the estimated value of the reliability of the airborne product in the reliability test. 要求 is the target value of the reliability MTBF indicator; Step 5: Obtain the reliability growth coefficient; Taking 0.7 as the growth limit for normalization, the reliability growth coefficient U∈(0,1) of the performance design after reliability design is: Among them, U1 is the improvement degree of reliability growth test; U2 is the improvement degree of reliability development test; Step 6: Determine the weight of the performance design and reliability design correlation evaluation indicators; Let the project integration degree, key component control level, reliability growth coefficient and reliability completion of reliability design be evaluation indicators 1, 2, 3 and 4 respectively. The three-scale method is used to establish the judgment matrix A=(a ij ) 4×4 ,but: in, Normalize the matrix by column and establish the normalized matrix B=(b ij ) 4×4 , sum the rows of matrix B and perform normalization to establish the judgment matrix w=(w i ) 4×1 , the indicator weights of reliability design project integration degree, key component control level, reliability growth coefficient, and reliability completion degree are obtained as w1, w2, w3, and w4 respectively; Step 7: Obtain the performance design and reliability design correlation evaluation results; The correlation evaluation value R between performance design and reliability design is: R=Z×w1+S×w2+U×w3+Q×w4; The larger the correlation evaluation value R between performance design and reliability design, the higher the support degree of reliability design for performance design.

2. The method for evaluating the correlation between performance design and reliability design of an airborne product according to claim 1, characterized in that: In step 1, the four parts of data include project integration and key component control levels, reliability completion, and reliability growth coefficient, specifically: (1) Obtain data related to the project integration degree of reliability design, including: reliability prediction, failure mode impact and criticality analysis, formulation of reliability design criteria, fault tree analysis, finite element analysis and durability analysis, and the number of times each project should be carried out during the development process (m) a1 、The actual number of times m a2 , the number of iterations to design improvement m a3 , the number of times the design has not been improved a4 , the number of times no improvement was provided to the design m a5 and the number of times the design is supported but not iterated a6 , where m a1 、m a2 、m a3 、m a4 、m a5 、m a6 a represents one of the reliability design items, a=1,2…,n; n is the number of reliability design items, n is a positive integer; (2) Obtain relevant data on the control level of key items, including six indicators: key control level, improvement control level, distribution control level, quality control level, inspection control level, and procurement control level; (3) Obtain relevant data on reliability completion, including: test time, number of responsible failures and confidence information during the reliability identification test, life test, and reliability acceptance test, as well as reliability index requirements; (4) Obtain relevant data on the reliability growth factor, including the occurrence time of each failure observed during the reliability growth test, and the MTBF values ​​of the first and last tests of the reliability development test.

3. The method for evaluating the correlation between performance design and reliability design of an airborne product according to claim 1, characterized in that: In the step 2, the project integration degree of reliability design is obtained: The design support of item a in reliability design is: The completion degree of reliability design project a is: The integration degree Z of a single item of reliability design project a a , the formula is: Among them, w1 a Design support for item a designed for reliability, w1 a ∈(0,1),m a2 The number of times the project a designed for actual reliability is performed, m r5 The number of times that a project designed for reliability does not provide an improvement to the design; Y a The degree of completion of project a designed for reliability, Y a ∈(0,+∞),m a2 The number of times the project a designed for actual reliability is performed, m a1 The number of times that a project designed for reliability should be carried out during the development process.

4. The method for evaluating the correlation between performance design and reliability design of an airborne product according to claim 1, wherein: In step 4, the specific steps for obtaining the fused lifetime distribution density function f(t) are as follows: First, for a single reliability experiment, the lower confidence limit at a given confidence level c is θ L ; Then the life distribution density function of airborne products is: f(t)=λe -λt in, t is time; If the reliability experiment is of m types, then m life distribution density functions are fused, and the support vector of the mutual support degree between the reliability acceptance test and other tests is established with the reliability acceptance test as the center point, that is: S=(S 11 S 12 … S 1m ) in, f1(t) represents the life distribution density function obtained from the reliability acceptance test; f i (t) represents the life distribution density function of the i-th type reliability experiment, S 1i represents f1(t) and f i (t) the distance between the two; Determine the weight w21 of the reliability acceptance test as: Among them, L p and L q They represent the lengths of the confidence intervals of the life distribution density function obtained from the reliability acceptance test at the confidence levels p and q, respectively, p>q; After obtaining the weight of w21, the weight of the reliability experiment of type i, w2 i for: Among them, S 1i w2 represents the distance between the life distribution density function obtained from the reliability acceptance test and the life distribution density function of the i-th type reliability experiment; i is the weight of the reliability experiment of type i; The final fusion model of life distribution density function is: Among them, f(t) is the lifetime distribution density function after fusion, w2 i is the weight of the reliability experiment of type i; f i (t) represents the life distribution density function of the i-th type reliability experiment, and m is the number of reliability experiment types.

5. The method for evaluating the correlation between performance design and reliability design of an airborne product according to claim 1, characterized in that: In step 5, the specific steps for obtaining the reliability growth test improvement degree U1 and the reliability development test improvement degree U2 are as follows: The reliability growth curve is the AMSAA model, and its expression is: E[N(t)]=at b Where N(t) is the cumulative number of failures that occur during the cumulative test time t; a is the scale parameter; b is the growth shape parameter, and E[N(t)] is the mathematical expectation of N(t); The improvement degree U1 of the reliability growth test is: Where N is the total number of observed faults; t i is the cumulative test time when the i-th fault occurs; T is the total cumulative test time; M is the first parameter, and its value satisfies The reliability development test improvement degree U2 is: Where t1 is the first MTBF obtained from the test, and t2 is the last MTBF obtained from the test.

6. The method for evaluating the correlation between performance design and reliability design of an airborne product according to claim 1, characterized in that: In step 6, after establishing the judgment matrix w, w also needs to pass the consistency test, specifically: Judgment matrix w=(w i ) 4×1 The largest characteristic root of Among them, (Bw) i Represents the i-th row element after matrix B is multiplied by judgment matrix w; Perform consistency test on the judgment matrix w, and the consistency index CI is expressed as: Random consistency index RI, when the number of indicators n is equal to 4, RI = 0.89, the consistency ratio CR is If CR<0.1, the eigenvector of the judgment matrix w is used to represent the weight vector; If the consistency check fails, it means that the setting of matrix A is unreasonable, and the values ​​in matrix A need to be reset.

7. The method for evaluating the correlation between performance design and reliability design of an airborne product according to claim 1, characterized in that: The step seven further includes: According to the value range of the performance design and reliability design correlation evaluation value R, segmentation is performed so that the performance design and reliability design correlation evaluation R corresponds to evaluation results of different levels.

8. The method for evaluating the correlation between performance design and reliability design of an airborne product according to claim 1, characterized in that: Also includes: Step 8: Analyze the correlation between performance design and reliability design, specifically: 1) Sort Z, F, Q, and U by score from low to high to obtain a ranking table. The lowest-scoring indicator is the weak link in the combination of performance design and reliability design. 2) For the project integration degree Z, according to the reliability design analysis work in item a, the project integration degree Z i Sort by scores from low to high and output a ranking table of project integration shortcomings; 3) For the control level F of important items, sort them from low to high according to the scoring results of the control level index of important items to obtain a ranking table of the shortcomings of the control level; 4) For the reliability completion degree Q, if Q>0, it means that the specified reliability index requirements have been met; if Q<0, it means that the specified reliability index requirements have not been met; 5) For the reliability growth coefficient U, based on the comparison of U1 and U2, a ranking table of the short board in terms of reliability growth degree is obtained; From 1) to 5), the lower the score, the more improvement is needed.

9. A system using a method for evaluating the correlation between performance design and reliability design of an airborne product, characterized in that: It includes: Database, reliability design project integration module, key component control level module, reliability completion module, reliability growth coefficient module, weight module and correlation evaluation module, wherein the reliability design project integration module, key component control level module, reliability completion module and reliability growth coefficient module are connected to the database, weight module and correlation evaluation module respectively; The database is used to store the data required for the performance reliability integrated design degree evaluation, including a reliability design project sub-database, a key component control level sub-database, a reliability completion sub-database, and a reliability growth factor sub-database; wherein the reliability design project sub-database includes the number of times m that reliability prediction, failure mode effect and criticality analysis, formulation of reliability design criteria, fault tree analysis, finite element analysis, and durability analysis projects should be carried out during the development process. a1 , the number of iterations to design improvement m a3 , the number of times no improvement was provided to the design m a5 , the number of times the design is supported but not iterated again m a6 The key component control level sub-database includes six indicator values, namely, key control level, improvement control level, allocation control level, quality control level, inspection control level, and procurement control level, obtained by expert scoring. The reliability completion sub-database includes the test time, number of responsible failures, confidence information, and reliability indicator requirements during the reliability identification test, life test, and reliability acceptance test. The reliability growth factor sub-database includes the occurrence time of each failure observed during the reliability growth test, as well as the MTBF values ​​of the first and last tests of the reliability development test. The project integration degree module of reliability design is used to obtain the project integration degree Z of reliability design, which is achieved by the method in step 2; The critical component control level module is used to obtain the critical component control level F, which is implemented in the manner of step 3; The reliability completion module is used to obtain the reliability completion degree Q, which is implemented using the method in step 4; The reliability growth coefficient module is used to obtain the reliability growth coefficient U, which is implemented using the method in step 5; The weight module is used to establish a judgment matrix using the three-scale method to obtain the weights of project integration degree Z, key component control level F, reliability completion degree Q and reliability growth coefficient U; The correlation evaluation module obtains the correlation evaluation value of performance design and reliability design according to the weight output by the weight module, the project integration degree Z output by the project integration degree module of reliability design, the key component control level F output by the key component control level module, the reliability completion degree Q output by the reliability completion degree module, and the reliability growth coefficient U output by the reliability growth coefficient module, using the step seven method.

10. The system using the performance design and reliability design correlation evaluation method of an airborne product according to claim 9, characterized in that: Also includes: Correlation analysis module, The correlation analysis module is connected with the project integration module of reliability design, the key component control level module, the reliability completion module, the reliability growth coefficient module and the correlation evaluation module respectively.

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