A Method and System for Evaluating the Storage Reliability of Shipborne Aviation Ammunition

Abstract

The present invention relates to a method and system for evaluating the storage reliability of shipborne aviation ammunition. The method includes obtaining shipborne storage environmental factors and a fault tree; determining weak components under the shipborne storage environment according to the shipborne storage environmental factors and the fault tree; determining corresponding performance degradation data or failure data based on the weak components; performing data fusion on the performance degradation data or failure data by using the analytic hierarchy process to determine a hierarchical model; determining the storage life value of each weak component according to the hierarchical model; determining the membership degree of the performance state of the corresponding weak component by using a membership function according to the storage life value of each weak component and the first life value and the second life value of the corresponding weak component; constructing a Bayesian network according to the membership degree of the performance state of each weak component under the shipborne storage environment and the fault tree; and determining the storage reliability of the shipborne aviation ammunition to be evaluated by using the Bayesian network. The present invention can improve the accuracy of the reliability evaluation of the whole ammunition.

Description

Technical Field

[0001] The present invention relates to the field of reliability assessment of shipborne aviation ammunition storage, and particularly to a method and system for assessing the storage reliability of shipborne aviation ammunition. Background Art

[0002] In recent years, events surrounding disputes over maritime rights and interests have been frequent. The scramble by various countries for the vast natural resources in the ocean has made the ocean the main battlefield of future wars. China has a coastline of 18,400 kilometers, and the task of safeguarding national maritime rights and interests is extremely onerous. In addition, due to the need for ocean-going operations, a large amount of aviation ammunition needs to be stored on aircraft carriers and other ships. Compared with the land storage environment, the storage environment of ammunition on ships is more severe. Shipborne aviation ammunition is affected by harsh environmental stresses such as temperature, humidity, vibration, radiation, and salt spray, and its storage reliability will gradually decrease with the increase of storage time. If the ammunition is not maintained regularly, not only will it be unable to complete combat readiness duty smoothly, but it may even cause major safety hazards and trigger major safety accidents.

[0003] The United States has carried out research on ammunition equipment storage since the 1950s. The basic strategy is to collect on-site storage data of various types of missiles across the country and combine it with a small amount of accelerated life tests to obtain a large amount of life data at the component and whole-machine levels of ammunition. The main difference in the relevant research in Russia from that in the United States is that it conducts missile reliability assessment through accelerated life test means supplemented by partial natural storage data. Among them, the Russian S-300 missile completed its life prediction through a 6-month accelerated life test. Since China began to develop various types of missiles in the 1960s, it has started planned research on missile reliability and has achieved quite rich test results.

[0004] Han Jianli of the Research Department of the Naval Aeronautical Engineering Institute gave the storage reliability of ammunition in air-conditioned warehouses and non-air-conditioned warehouses according to the regular batch inspection results of missiles in the storage state, adopting the principle of minimizing the "sum of squared residuals". Dai Zongliang of the Air Defense and Anti-Missile College of the Air Force Engineering University proposed a missile storage reliability prediction method based on the improved GM(1,1) model. By this method, a residual correction model of the reliability prediction model is established, thereby effectively reducing the influence of residuals on the results and improving the prediction accuracy. Zhao Jianzhong of the Naval Aviation University established a storage reliability prediction model for precision-guided ammunition that has exceeded its service life. The model reveals the relationship between regular inspection and maintenance and storage reliability, and can predict the storage reliability before the next inspection based on the previous historical inspection data. Zhao Xiaodong of the Army Engineering University conducted a step-stress accelerated life test with temperature as the test stress on the secondary power supply in a certain ammunition, and evaluated its life through the accelerated storage assessment method of the acceleration factor coefficient of variation, predicting the storage life of the secondary power supply under certain reliability requirements in the conventional environment.

[0005] Based on the above literature analysis, the existing research mainly focuses on the reliability of ammunition storage on land. By using the natural failure data of ammunition, a reliability prediction model is established. However, for shipborne aviation ammunition, there is a lack of research methods to comprehensively evaluate the reliability of the whole ammunition starting from the weak parts of the ammunition and combining with the storage environment analysis. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for evaluating the storage reliability of shipborne aviation ammunition, which can improve the accuracy of the reliability evaluation of the whole ammunition.

[0007] To achieve the above purpose, the present invention provides the following solutions:

[0008] A method for evaluating the storage reliability of shipborne aviation ammunition includes:

[0009] Obtaining the shipborne storage environment factors affecting the life of the shipborne aviation ammunition to be evaluated and the fault tree of the shipborne aviation ammunition to be evaluated; the shipborne storage environment factors include: temperature, humidity, salt spray, vibration, sway, and shock; the fault tree is used to analyze the failure mode and failure mechanism according to the failure forms of the weak parts in the shipborne aviation ammunition to be evaluated.

[0010] Determining the weak parts of the shipborne aviation ammunition to be evaluated under the shipborne storage environment according to the shipborne storage environment factors and the fault tree.

[0011] Determining the corresponding performance degradation data or failure data according to the weak parts under the shipborne storage environment.

[0012] Using the analytic hierarchy process to perform data fusion on the performance degradation data or failure data to determine the hierarchical model; the hierarchical model includes: the target layer, the criterion layer, and the scheme layer from top to bottom; the target layer is the reliability of the weak parts; the criterion layer includes: the reliability at the time of leaving the factory, the processing quality, and the storage environment; the scheme layer includes: accelerated experiment data, expert experience data, natural experiment data, and similar product data.

[0013] Determining the storage life value of each weak part under the shipborne storage environment according to the hierarchical model.

[0014] According to the storage life value of each weak part under the shipborne storage environment, as well as the first life value and the second life value of the corresponding weak part, using the membership function to determine the performance state membership degree of the corresponding weak part; when the storage life value is lower than the first life value, the corresponding weak part is in a failure state; when the storage life value is higher than the second life value, the corresponding weak part meets the usage requirements; the performance state includes: available and unavailable; the membership degree of the available performance state is 1; the membership degree of the unavailable performance state is 0.

[0015] Construct a Bayesian network based on the performance state membership degree of each weak component in the shipboard storage environment and the fault tree.

[0016] Use the Bayesian network to determine the storage reliability of the shipboard aviation ammunition to be evaluated.

[0017] Optionally, the obtaining of the shipboard storage environment factors affecting the life of the shipboard aviation ammunition to be evaluated and the fault tree of the shipboard aviation ammunition to be evaluated specifically includes:

[0018] Rank the shipboard storage environment factors by using the grey correlation entropy and Pearson data analysis methods; and determine the main environmental impact factors according to the ranking.

[0019] Construct the fault tree of the shipboard aviation ammunition to be evaluated according to the primary and secondary graph analysis method.

[0020] Optionally, the determination of the corresponding performance degradation data or failure data according to the weak components in the shipboard storage environment specifically includes:

[0021] Divide the corresponding weak components according to the positions of the weak components on the shipboard aviation ammunition to be evaluated to obtain externally exposed weak components and internal weak components.

[0022] Conduct natural storage tests or accelerated marine environment tests on the externally exposed weak components in the marine environment to determine the performance degradation data or failure data of the externally exposed weak components.

[0023] Conduct temperature and vibration acceleration tests or natural storage tests on the internal weak components to determine the performance degradation data or failure data of the internal weak components.

[0024] Optionally, the determination of the storage life value of each weak component in the shipboard storage environment according to the hierarchical model specifically includes:

[0025] Construct four matrices A, B, C, and D according to the hierarchical model; matrix A represents the judgment matrix of the scheme layer for the factory reliability of the criterion layer; matrix B represents the judgment matrix of the scheme layer for the processing quality of the criterion layer; matrix C represents the judgment matrix of the scheme layer for the working environment of the criterion layer; matrix D represents the judgment matrix of the criterion layer for the target layer.

[0026] Normalize the four matrices A, B, C, and D, and determine the weight coefficient of the scheme layer for the target layer.

[0027] Determine the storage life value of the corresponding weak component according to the life value determined from the performance degradation data or failure data and the corresponding weight coefficient.

[0028] A shipboard aviation ammunition storage reliability evaluation system includes:

[0029] A data acquisition module for acquiring the shipboard storage environment factors that affect the lifespan of the shipborne aviation ammunition to be evaluated and the fault tree of the shipborne aviation ammunition to be evaluated; the shipboard storage environment factors include: temperature, humidity, salt spray, vibration, sway, and shock; the fault tree is used to analyze the failure mode and failure mechanism according to the failure forms of the weak components in the shipborne aviation ammunition to be evaluated;

[0030] A weak component screening module for determining the weak components of the shipborne aviation ammunition to be evaluated in the shipboard storage environment according to the shipboard storage environment factors and the fault tree;

[0031] A data determination module for determining the corresponding performance degradation data or failure data according to the weak components in the shipboard storage environment;

[0032] A hierarchical model determination module for performing data fusion on the performance degradation data or failure data by using the analytic hierarchy process to determine the hierarchical model; the hierarchical model includes: the target layer, the criterion layer, and the scheme layer from top to bottom; the target layer is the reliability of the weak components; the criterion layer includes: the ex-factory reliability, the processing quality, and the storage environment; the scheme layer includes: accelerated experiment data, expert experience data, natural experiment data, and similar product data;

[0033] A storage lifespan value determination module for determining the storage lifespan value of each weak component in the shipboard storage environment according to the hierarchical model;

[0034] A performance state membership degree determination module for determining the performance state membership degree of the corresponding weak component by using the membership degree function according to the storage lifespan value of each weak component in the shipboard storage environment and the first lifespan value and the second lifespan value of the corresponding weak component; when the storage lifespan value is lower than the first lifespan value, the corresponding weak component is in a failure state; when the storage lifespan value is higher than the second lifespan value, the corresponding weak component meets the usage requirements; the performance state includes: available and unavailable; the membership degree of the available performance state is 1; the membership degree of the unavailable performance state is 0;

[0035] A Bayesian network construction module for constructing a Bayesian network according to the performance state membership degree of each weak component in the shipboard storage environment and the fault tree;

[0036] A storage reliability determination module for determining the storage reliability of the shipborne aviation ammunition to be evaluated by using the Bayesian network.

[0037] Optionally, the data acquisition module specifically includes:

[0038] A main environmental impact factor determination unit for ranking the shipboard storage environment factors by using the grey relational entropy and Pearson data analysis methods; and determining the main environmental impact factors according to the ranking;

[0039] A fault tree construction unit, configured to construct a fault tree of the shipborne aviation ammunition to be evaluated according to the primary and secondary diagram analysis method.

[0040] Optionally, the data determination module specifically includes:

[0041] A weak component division unit, configured to divide the corresponding weak components according to the positions of the weak components on the shipborne aviation ammunition to be evaluated, so as to obtain externally exposed weak components and internally weak components;

[0042] A first data determination unit, configured to perform a natural storage test or an accelerated ocean environment test on the externally exposed weak components to determine the performance degradation data or failure data of the externally exposed weak components;

[0043] A second data determination unit, configured to perform a temperature and vibration accelerated test or a natural storage test on the internally weak components to determine the performance degradation data or failure data of the internally weak components.

[0044] Optionally, the storage life value determination module specifically includes:

[0045] A matrix construction unit, configured to construct four matrices A, B, C, and D according to the hierarchical model; the A matrix represents the judgment matrix of the scheme layer for the factory reliability of the criterion layer; the B matrix represents the judgment matrix of the scheme layer for the processing quality of the criterion layer; the C matrix represents the judgment matrix of the scheme layer for the working environment of the criterion layer; the D matrix represents the judgment matrix of the criterion layer for the target layer;

[0046] A weight coefficient determination unit, configured to normalize the four matrices A, B, C, and D and determine the weight coefficient of the scheme layer for the target layer;

[0047] A storage life value determination unit, configured to determine the storage life value of the corresponding weak component according to the life value determined based on the performance degradation data or failure data and the corresponding weight coefficient.

[0048] According to the specific embodiments provided by the present invention, the following technical effects are disclosed:

[0049] A method and system for evaluating the storage reliability of shipborne aviation ammunition provided by the present invention divide weak components into two performance states: available and unavailable, and then use a membership function to calculate the state membership degree of the weak components, converting the life prediction value into a probability value representing the performance state. On this basis, an information fusion method is used to classify and organize the multi-source reliability data of weak components, establish a reliability information fusion database for ammunition, and fuse the data using a Bayesian network based on this database, so as to evaluate the overall reliability of the ammunition. Starting from the weak components of the ammunition and combining with the analysis of the storage environment, the present invention comprehensively evaluates the reliability of the whole ammunition, thereby improving the accuracy of the reliability evaluation of the whole ammunition. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a schematic flow diagram of a method for evaluating the storage reliability of shipborne aviation ammunition provided by the present invention;

[0052] Figure 2 It is a schematic diagram of the hierarchical model of the embodiment provided by the present invention;

[0053] Figure 3 It is a schematic diagram of the membership function;

[0054] Figure 4 It is a schematic diagram of the fault tree of a certain type of shipborne ammunition;

[0055] Figure 5 It is a schematic diagram of the fault tree structure of its components;

[0056] Figure 6 It is a schematic diagram of the specific process of reliability analysis;

[0057] Figure 7 It is a schematic diagram of the overall Bayesian network;

[0058] Figure 8 It is a schematic diagram of the structure of a system for evaluating the storage reliability of shipborne aviation ammunition provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0060] The purpose of the present invention is to provide a method and system for evaluating the storage reliability of shipborne aviation ammunition, which can improve the accuracy of the reliability evaluation of the entire ammunition.

[0061] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0062] Figure 1The schematic flow chart of a method for evaluating the storage reliability of shipborne aviation ammunition provided by the present invention is as follows. Figure 1 As shown, a method for evaluating the storage reliability of shipborne aviation ammunition provided by the present invention includes:

[0063] S101. Obtain the shipborne storage environmental factors affecting the life of the shipborne aviation ammunition to be evaluated and the fault tree of the shipborne aviation ammunition to be evaluated; the shipborne storage environmental factors include: temperature, humidity, salt spray, vibration, sway, and shock; the fault tree is used to analyze the failure mode and failure mechanism according to the failure forms of the weak components in the shipborne aviation ammunition to be evaluated.

[0064] S101 specifically includes:

[0065] Rank the shipborne storage environmental factors by using the grey relational entropy and Pearson data analysis methods; and determine the main environmental influencing factors according to the ranking.

[0066] Construct the fault tree of the shipborne aviation ammunition to be evaluated according to the primary and secondary graph analysis method.

[0067] S102. Determine the weak components of the shipborne aviation ammunition to be evaluated under the shipborne storage environment according to the shipborne storage environmental factors and the fault tree.

[0068] S103. Determine the corresponding performance degradation data or failure data according to the weak components under the shipborne storage environment.

[0069] S103 specifically includes:

[0070] Divide the corresponding weak components according to the positions of the weak components on the shipborne aviation ammunition to be evaluated to obtain external exposed weak components and internal weak components.

[0071] Conduct natural storage tests or accelerated tests in the marine environment on the external exposed weak components to determine the performance degradation data or failure data of the external exposed weak components.

[0072] Conduct temperature and vibration accelerated tests or natural storage tests on the internal weak components to determine the performance degradation data or failure data of the internal weak components.

[0073] S104. Use the analytic hierarchy process to perform data fusion on the performance degradation data or failure data to determine the hierarchical model; the hierarchical model includes: the target layer, criterion layer, and scheme layer from top to bottom; the target layer is the reliability of the weak components; the criterion layer includes: ex-factory reliability, processing quality, and storage environment; the scheme layer includes: accelerated experiment data, expert experience data, natural experiment data, and similar product data.

[0074] Data fusion of multi-source data of weak components is carried out by the Analytic Hierarchy Process (AHP). The Analytic Hierarchy Process refers to decomposing the elements related to the overall decision-making into levels such as objectives, criteria, and solutions, calculating the weight coefficients of each element through a qualitative index fuzzy quantification algorithm, and then allocating weights.

[0075] As Figure 2 shown, taking the sealing ring as an example, the reliability of the sealing ring is related to its inherent reliability, and is also related to its machining accuracy requirements and the quality of the working conditions. Based on this, a hierarchical model is established, which from top to bottom is the target layer, the criterion layer, and the solution layer;

[0076] The Analytic Hierarchy Process adopts a 1-5 scale method, and the scale is shown in Table 1. By combining literature research and consulting experts, the judgment matrix of the elements in the criterion layer relative to the target layer and the judgment matrix of the solution layer relative to the criterion layer are constructed.

[0077] Table 1

[0078]

[0079] Its judgment matrix is expressed as follows:

[0080]

[0081] The maximum eigenvalue λ is obtained according to the following formula max :

[0082] AW = λ max W;

[0083] The matrix consistency check CR is:

[0084]

[0085] In the formula: A is the judgment matrix; W is the eigenvector of the judgment matrix. After normalization, the weight coefficient vector of this level relative to the previous level is obtained. Finally, the rationality of the matrix is obtained through the consistency check. For a third-order judgment matrix, the RI generally takes a value of 0.52.

[0086] S105. Determine the storage life value of each weak component in the shipboard storage environment according to the hierarchical model;

[0087] S105 specifically includes:

[0088] Construct four matrices A, B, C, and D according to the hierarchical model; Matrix A represents the judgment matrix of the solution layer for the ex-factory reliability of the criterion layer; Matrix B represents the judgment matrix of the solution layer for the processing quality of the criterion layer; Matrix C represents the judgment matrix of the solution layer for the working environment of the criterion layer; Matrix D represents the judgment matrix of the criterion layer for the target layer;

[0089] Normalize the four matrices of A, B, C, and D, and determine the weight coefficients of the scheme layer with respect to the target layer;

[0090] Determine the storage life values of the corresponding weak components according to the life values determined from the performance degradation data or failure data and the corresponding weight coefficients.

[0091] S106. According to the storage life values of each weak component in the shipboard storage environment, as well as the first life value and the second life value of the corresponding weak component, use the membership function to determine the performance state membership degree of the corresponding weak component; when the storage life value is lower than the first life value, the corresponding weak component is in a failure state; when the storage life value is higher than the second life value, the corresponding weak component meets the usage requirements; the performance states include: available and unavailable; the membership degree of the available performance state is 1; the membership degree of the unavailable performance state is 0;

[0092] According to the life data of the weak components, use the fuzzy function to determine the performance state of the components, as Figure 3 shown. Since ammunition is a product that is stored for a long time and used once, the fuzzy language set can be described as {unavailable, available}, and the corresponding membership functions are μ A (t), μ B (t). In the figure, t1 is the minimum storage time, and t2 is the time to complete the storage task. The performance state of the weak components is judged through the membership function.

[0093] Similarly, taking the sealing ring as an example, by establishing a permanent deformation rate curve for it, its working life under the specified threshold can be obtained, and its life at room temperature of 25°C is 12.56 years. After literature search and expert consultation, the obtained life data is 13.8 years, and the life of similar products is 14.8 years. As shown in Table 2.

[0094] Table 2

[0095]

[0096] Based on the analytic hierarchy process, establish the following matrices:

[0097]

[0098]

[0099] Matrix A represents the judgment matrix of the scheme layer with respect to the factory reliability of the criterion layer; matrix B represents the judgment matrix of the scheme layer with respect to the processing quality of the criterion layer; matrix C represents the judgment matrix of the scheme layer with respect to the working environment of the criterion layer; matrix D represents the judgment matrix of the criterion layer with respect to the target layer. The matrix consistency tests are all less than 0.1. Their eigenvectors are as follows:

[0100] W A= [0.8468, 0.2565, 0.4660];

[0101] W B = [0.8920, 0.2810, 0.3540];

[0102] W C = [0.1862, 0.4881, 0.8527];

[0103] W D = [0.3762, 0.8957, 0.2370];

[0104] By calculation, the weight coefficients of the scheme layer with respect to the target layer can be obtained: ω1 = 0.3230, ω2 = 0.3291, ω3 = 0.3478. The fused evaluation life is:

[0105] T 总 = ω1×12.56 + ω2×14.8 + ω3×13.8 = 13.7272;

[0106] By the membership function, let t1 = 12 years and t2 = 18 years, then the calculation gives μ A = 0.7121, μ B = 0.2879;

[0107] t1 means that after the ammunition is below the life value t1, it is judged to be in a failure state, and t2 means that after the value higher than t2, the ammunition is judged to meet the usage requirements. After determining the estimated life Ttotal of the weak component, the farther its value is from t1 and the closer it is to t2, the higher the membership degree to the available state and the lower the membership degree to the unavailable state. μ A represents the membership degree belonging to the unreliable state, and μ B represents the membership degree of the reliable state. Depending on the calculation object and the different estimated lives of the calculation object, different membership degree values can be obtained.

[0108] S107. According to the performance state membership degree of each weak component in the shipboard storage environment and the fault tree, construct a Bayesian network;

[0109] Assume that the nodes in BN are: X = {x1, x2, …, x l }, according to the chain rule, x1 is equivalent to μ B The joint probability distribution P(X) of BN is expressed as follows:

[0110]

[0111] First, the node states can be assumed, such as available and unavailable, which are represented by 1 and 0 respectively. According to the characteristics of the system, it can be divided into a series system and a parallel system.

[0112] S108. Determine the storage reliability of the shipborne aviation ammunition to be evaluated using the Bayesian network.

[0113] The following is illustrated through specific embodiments:

[0114] The fault tree of a certain type of shipborne ammunition is as Figure 4 shown, and the fault tree of its components is as Figure 5 shown. For the structural characteristics and fault tree analysis results of this type of ammunition, the specific process of its reliability analysis is as Figure 6 shown; the storage life obtained from the analysis of the accelerated life test, natural storage test, and simulation test data of typical weak parts is shown in Table 3:

[0115] Table 3

[0116]

[0117] As Figure 7 shown, it is calculated through the Bayesian network that:

[0118] 1) When stored in the extreme environment on the deck, the ammunition has a minimum storage period of 1 month. When the storage period reaches 8 months to meet the storage requirements, the overall reliability of the ammunition is 63.9%. Through the reverse traceability of weak parts using the Bayesian network, the highest risk factor affecting the ammunition reliability is found to be the failure of alloy steel nails, followed by the thermal battery. When the maximum storage time is adjusted to 6 months, the reliability of the ammunition is 98.15%.

[0119] 2) When stored in the extreme environment without air conditioning in the cabin, the ammunition has a minimum storage period of 1 month. When the storage period reaches 2 years to meet the storage needs, the overall reliability of the ammunition is 89.8%. When the maximum storage time is adjusted to 1.9 years, the overall reliability of the ammunition is 94.9%. When it is determined that the ammunition fails at 100%, through the reverse traceability of weak parts using the Bayesian network, it can be concluded that the components with higher failure risks are thermal batteries.

[0120] 3) When stored in the extreme environment with air conditioning in the cabin, the minimum storage period of the ammunition is 2 years. When the storage period reaches 5 years to meet the storage needs, the overall reliability of the ammunition is 97.2%. Among them, the component with the highest failure risk is the electric detonator, followed by the thermal battery.

[0121] Through fault tree analysis, a Bayesian network of the ammunition is established, realizing the quantitative calculation of the overall failure probability of the ammunition; and through the reverse analysis of the Bayesian network, the weak parts prone to failure in different storage environments are obtained. Through comprehensive analysis, the thermal battery is a weak part inside the ammunition that is prone to failure. Its performance decays rapidly at higher temperatures and needs to be maintained regularly.

[0122] Figure 8The structural schematic diagram of a storage reliability evaluation system for shipborne aviation ammunition provided by the present invention is as follows: Figure 8 As shown, a storage reliability evaluation system for shipborne aviation ammunition provided by the present invention includes:

[0123] A data acquisition module 801, configured to acquire shipborne storage environmental factors affecting the life of the shipborne aviation ammunition to be evaluated and the fault tree of the shipborne aviation ammunition to be evaluated; the shipborne storage environmental factors include: temperature, humidity, salt spray, vibration, sway, and shock; the fault tree is used to analyze the failure mode and failure mechanism according to the failure forms of the weak components in the shipborne aviation ammunition to be evaluated.

[0124] A weak component screening module 802, configured to determine the weak components of the shipborne aviation ammunition to be evaluated in the shipborne storage environment according to the shipborne storage environmental factors and the fault tree.

[0125] A data determination module 803, configured to determine the corresponding performance degradation data or failure data according to the weak components in the shipborne storage environment.

[0126] A hierarchical model determination module 804, configured to perform data fusion on the performance degradation data or failure data by using the analytic hierarchy process to determine the hierarchical model; the hierarchical model includes: a target layer, a criterion layer, and a scheme layer from top to bottom; the target layer is the reliability of the weak components; the criterion layer includes: ex-factory reliability, processing quality, and storage environment; the scheme layer includes: accelerated test data, expert experience data, natural test data, and similar product data.

[0127] A storage life value determination module 805, configured to determine the storage life value of each weak component in the shipborne storage environment according to the hierarchical model.

[0128] A performance state membership determination module 806, configured to determine the performance state membership of the corresponding weak component by using the membership function according to the storage life value of each weak component in the shipborne storage environment and the first life value and the second life value of the corresponding weak component; when the storage life value is lower than the first life value, the corresponding weak component is in a failure state; when the storage life value is higher than the second life value, the corresponding weak component meets the use requirements; the performance state includes: available and unavailable; the membership of the available performance state is 1; the membership of the unavailable performance state is 0.

[0129] A Bayesian network construction module 807, configured to construct a Bayesian network according to the performance state membership of each weak component in the shipborne storage environment and the fault tree.

[0130] A storage reliability determination module 808, configured to determine the storage reliability of the shipborne aviation ammunition to be evaluated by using the Bayesian network.

[0131] The data acquisition module 801 specifically includes:

[0132] A main environmental impact factor determination unit, which is used to rank the shipboard storage environmental factors by using the grey correlation entropy and Pearson data analysis methods; and determine the main environmental impact factors according to the ranking;

[0133] A fault tree construction unit, which is used to construct a fault tree of the shipboard aviation ammunition to be evaluated according to the primary and secondary graph analysis method.

[0134] The data determination module 803 specifically includes:

[0135] A weak part division unit, which is used to divide the corresponding weak parts according to the positions of the weak parts on the shipboard aviation ammunition to be evaluated, and obtain externally exposed weak parts and internally weak parts;

[0136] A first data determination unit, which is used to conduct natural storage tests or accelerated marine environment tests on the externally exposed weak parts to determine the performance degradation data or failure data of the externally exposed weak parts;

[0137] A second data determination unit, which is used to conduct temperature and vibration acceleration tests or natural storage tests on the internally weak parts to determine the performance degradation data or failure data of the internally weak parts.

[0138] The storage life value determination module 805 specifically includes:

[0139] A matrix construction unit, which is used to construct four matrices A, B, C, and D according to the hierarchical model; Matrix A represents the judgment matrix of the scheme layer for the factory reliability of the criterion layer; Matrix B represents the judgment matrix of the scheme layer for the processing quality of the criterion layer; Matrix C represents the judgment matrix of the scheme layer for the working environment of the criterion layer; Matrix D represents the judgment matrix of the criterion layer for the target layer;

[0140] A weight coefficient determination unit, which is used to normalize the four matrices A, B, C, and D and determine the weight coefficient of the scheme layer for the target layer;

[0141] A storage life value determination unit, which is used to determine the storage life value of the corresponding weak part according to the life value determined from the performance degradation data or failure data and the corresponding weight coefficient.

[0142] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method part.

[0143] In this article, specific examples are used to illustrate the principles and implementation modes of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation modes and application scopes. To sum up, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for evaluating the storage reliability of shipborne aviation ammunition, characterized in that, Including: Obtaining shipboard storage environmental factors that affect the life of shipborne aviation ammunition to be evaluated and the fault tree of the shipborne aviation ammunition to be evaluated; The shipboard storage environmental factors include: temperature, humidity, salt spray, vibration, sway, and shock; the fault tree is used to analyze its failure mode and failure mechanism according to the failure forms of weak components in the shipborne aviation ammunition to be evaluated; Determining the weak components of the shipborne aviation ammunition to be evaluated in the shipboard storage environment according to the shipboard storage environmental factors and the fault tree; Determining corresponding performance degradation data or failure data according to the weak components in the shipboard storage environment; Using the analytic hierarchy process to perform data fusion on the performance degradation data or failure data to determine a hierarchical model; the hierarchical model includes: a target layer, a criterion layer, and a scheme layer from top to bottom; the target layer is the reliability of the weak component; the criterion layer includes: ex-factory reliability, processing quality, and storage environment; the scheme layer includes: accelerated test data, expert experience data, natural test data, and similar product data; Determining the storage life value of each weak component in the shipboard storage environment according to the hierarchical model; According to the storage life value of each weak component in the shipboard storage environment and the first life value and the second life value of the corresponding weak component, using a membership function to determine the performance state membership degree of the corresponding weak component; when the storage life value is lower than the first life value, the corresponding weak component is in a failure state; when the storage life value is higher than the second life value, the corresponding weak component meets the usage requirements; the performance state includes: available and unavailable; the membership degree of the available performance state is 1; the membership degree of the unavailable performance state is 0; Constructing a Bayesian network according to the performance state membership degree of each weak component in the shipboard storage environment and the fault tree; Using the Bayesian network to determine the storage reliability of the shipborne aviation ammunition to be evaluated.

2. The reliability assessment method for shipborne aviation ammunition storage according to claim 1, wherein The obtaining of the shipboard storage environmental factors that affect the life of the shipborne aviation ammunition to be evaluated and the fault tree of the shipborne aviation ammunition to be evaluated specifically includes: Ranking the shipboard storage environmental factors by using the grey relational entropy and Pearson data analysis methods; and determining the main environmental impact factors according to the ranking; Constructing the fault tree of the shipborne aviation ammunition to be evaluated according to the primary and secondary graph analysis method.

3. A method for evaluating the storage reliability of shipborne aviation ammunition according to claim 1, characterized in that The determining of the corresponding performance degradation data or failure data according to the weak components in the shipboard storage environment specifically includes: Dividing the corresponding weak components according to the positions of the weak components on the shipborne aviation ammunition to be evaluated to obtain externally exposed weak components and internally weak components; Conducting natural storage tests or accelerated tests in the marine environment on the externally exposed weak components to determine the performance degradation data or failure data of the externally exposed weak components; Conducting temperature and vibration accelerated tests or natural storage tests on the internally weak components to determine the performance degradation data or failure data of the internally weak components.

4. A method for evaluating the storage reliability of shipborne aviation ammunition according to claim 1, characterized in that, The determining of the storage life value of each weak component in the shipboard storage environment according to the hierarchical model specifically includes: Construct four matrices A, B, C, and D according to the hierarchical model; Matrix A represents the judgment matrix of the solution layer for the ex-factory reliability of the criterion layer; Matrix B represents the judgment matrix of the solution layer for the processing quality of the criterion layer; Matrix C represents the judgment matrix of the solution layer for the working environment of the criterion layer; Matrix D represents the judgment matrix of the criterion layer for the target layer; Normalize the four matrices A, B, C, and D, and determine the weight coefficient of the solution layer for the target layer; Determine the storage life value of the corresponding weak parts according to the life value determined from the performance degradation data or failure data and the corresponding weight coefficient.

5. A shipborne aviation ammunition storage reliability evaluation system, characterized in that, It includes: A data acquisition module, which is used to acquire the shipboard storage environment factors affecting the life of the shipboard aviation ammunition to be evaluated and the fault tree of the shipboard aviation ammunition to be evaluated; The shipboard storage environment factors include: temperature, humidity, salt spray, vibration, sway, and shock; the fault tree is used to analyze the failure mode and failure mechanism according to the failure form of the weak parts in the shipboard aviation ammunition to be evaluated; A weak part screening module, which is used to determine the weak parts of the shipboard aviation ammunition to be evaluated in the shipboard storage environment according to the shipboard storage environment factors and the fault tree; A data determination module, which is used to determine the corresponding performance degradation data or failure data according to the weak parts in the shipboard storage environment; A hierarchical model determination module, which is used to perform data fusion on the performance degradation data or failure data by using the analytic hierarchy process to determine the hierarchical model; the hierarchical model includes: a target layer, a criterion layer, and a solution layer from top to bottom; the target layer is the reliability of the weak parts; the criterion layer includes: ex-factory reliability, processing quality, and storage environment; the solution layer includes: accelerated test data, expert experience data, natural test data, and similar product data; A storage life value determination module, which is used to determine the storage life value of each weak part in the shipboard storage environment according to the hierarchical model; A performance state membership degree determination module, which is used to determine the performance state membership degree of the corresponding weak parts by using the membership degree function according to the storage life value of each weak part in the shipboard storage environment and the first life value and the second life value of the corresponding weak parts; when the storage life value is lower than the first life value, the corresponding weak part is in a failure state; when the storage life value is higher than the second life value, the corresponding weak part meets the use requirements; the performance state includes: available and unavailable; the membership degree of the available performance state is 1; the membership degree of the unavailable performance state is 0; A Bayesian network construction module, which is used to construct a Bayesian network according to the performance state membership degree of each weak part in the shipboard storage environment and the fault tree; A storage reliability determination module, which is used to determine the storage reliability of the shipboard aviation ammunition to be evaluated by using the Bayesian network.

6. The reliability evaluation system for shipborne aviation ammunition storage according to claim 5, characterized in that The data acquisition module specifically includes: A main environmental impact factor determination unit, which is used to rank the shipboard storage environment factors by using the grey relational entropy and Pearson data analysis methods; and determine the main environmental impact factors according to the ranking; A fault tree construction unit, which is used to construct the fault tree of the shipboard aviation ammunition to be evaluated according to the primary and secondary graph analysis method.

7. A shipborne aviation ammunition storage reliability evaluation system according to claim 5, characterized in that The data determination module specifically includes: The weak part division unit is used to divide the corresponding weak parts according to the positions of the weak parts on the shipborne aviation ammunition to be evaluated, so as to obtain externally exposed weak parts and internally weak parts; The first data determination unit is used to conduct natural storage tests or accelerated ocean environment tests on the externally exposed weak parts to determine the performance degradation data or failure data of the externally exposed weak parts; The second data determination unit is used to conduct temperature and vibration acceleration tests or natural storage tests on the internally weak parts to determine the performance degradation data or failure data of the internally weak parts.

8. A shipborne aviation ammunition storage reliability evaluation system according to claim 5, characterized in that, The storage life value determination module specifically includes: The matrix construction unit is used to construct four matrices A, B, C, and D according to the hierarchical model; Matrix A represents the judgment matrix of the solution layer for the factory reliability of the criterion layer; Matrix B represents the judgment matrix of the solution layer for the processing quality of the criterion layer; Matrix C represents the judgment matrix of the solution layer for the working environment of the criterion layer; Matrix D represents the judgment matrix of the criterion layer for the target layer; The weight coefficient determination unit is used to normalize the four matrices A, B, C, and D and determine the weight coefficient of the solution layer for the target layer; The storage life value determination unit is used to determine the storage life value of the corresponding weak part according to the life value determined from the performance degradation data or failure data and the corresponding weight coefficient.

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