Aviation security spare part configuration optimization method for constructing reference comparison system data model

Abstract

The invention provides an aviation guarantee spare part configuration optimization method for constructing a reference comparison system data model, and relates to the field of equipment maintenance guarantee, and the method comprises the following steps: S1, determining a reference comparison system construction demand; s2, comprehensively measuring reliability and maintainability of the equipment and a spare part and guarantee equipment configuration scheme according to requirements; s3, carrying out multi-source data acquisition according to a measurement result, and constructing a reference comparison system for comprehensive optimization of spare parts and support equipment; s4, analyzing the optimization parameters based on a reference comparison system, and S5, constructing a comprehensive optimization model of the spare parts and the support equipment according to an analysis result. According to the method, the variable interval of each optimization parameter is determined from the quantitative perspective through a method based on sensitivity analysis depending on a typical task scene of the equipment, and a comprehensive optimization model of spare parts and support equipment is further constructed based on a marginal analysis method considering importance levels, so that multi-source similar equipment data can be effectively utilized, and the optimization efficiency is improved. And the configuration optimization of the spare parts and the support equipment is more accurate.

Description

Technical Field

[0001] This invention relates to the field of equipment maintenance and support, and specifically to a method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model. Background Technology

[0002] Spare parts are spare components, parts, or assemblies reserved for maintenance and replacement. When a part of equipment or a system fails, wears out, or reaches the end of its lifespan, spare parts are used to replace it and restore its original function. Support equipment consists of the tools, equipment, instruments, and devices necessary for the installation, testing, calibration, maintenance, repair, and overhaul of main equipment. It is not itself a component of the main equipment but serves the main equipment throughout its entire lifecycle. As two important support resources, the optimal configuration of spare parts and support equipment plays a crucial role in the capability of the support system. Their adequacy directly affects the combat readiness and operational effectiveness of equipment, and is key to ensuring the timely, economical, and effective establishment of a support system when equipment is delivered to the troops.

[0003] Existing analytical methods for aviation spare parts and support equipment can be used to construct data models for aviation equipment supportability benchmark comparison systems, supporting supportability data collection. These methods have already considered support characteristic trade-offs and optimization, and extensive research has been conducted on life cycle modeling. Relatively rich methods exist for spare parts and support equipment demand estimation and forecasting. However, shortcomings remain: 1. Inaccurate resource allocation: When proposing spare parts and support equipment requirements during the development phase, the impact of the equipment's mission scenarios and support characteristics was not fully considered, resulting in a discrepancy between the support analysis personnel's control over support resource requirements and the actual situation.

[0004] 2. Insufficient data: Due to the imperfect data sharing mechanism and the non-standard and untimely data entry, the data often fails to fully cover the entire life cycle of equipment production, inventory, supply, maintenance and other aspects. As a result, the multi-source heterogeneous data of spare parts and support equipment has not been effectively utilized, and benchmark comparison has not been systematically implemented for support resource analysis and calculation.

[0005] Therefore, there is an urgent need to build a benchmark comparison system that combines analysis of typical equipment mission scenarios and covers similar system mission data, characteristic parameters, spare parts and support equipment configuration data. Summary of the Invention

[0006] Based on the shortcomings of the existing technology, this invention provides a method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model. This method addresses the problems in the existing technology where equipment data cannot fully cover the entire life cycle and multi-source heterogeneous data is not effectively utilized. This invention applies the benchmark comparison system to the field of spare parts and support equipment analysis, which can effectively utilize multi-source data of similar equipment, solve the problem of insufficient data in traditional spare parts and support equipment optimization, and improve the accuracy of spare parts and support equipment configuration optimization.

[0007] Specifically, the present invention provides a method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model, comprising the following steps: A method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model includes the following steps: S1. Based on the typical mission scenarios of the equipment to be analyzed, and according to the support characteristics of the equipment to be analyzed and the data requirements of the support system, determine the construction requirements of the benchmark comparison system. S2. Conduct a comprehensive measurement of equipment reliability, maintainability, and spare parts and support equipment configuration schemes, and construct a comprehensive optimization model, which includes the following sub-steps: S21. Based on the typical mission scenarios of the equipment to be analyzed, construct a mission availability model: ; in, Indicates execution of the first Working time for a typical task. Indicates task availability. Indicating the task Task availability at any given time express Expected shortage of spare parts at all times Indicates the quantity of equipment required for the mission. Indicates spare parts and support equipment exist Time availability; S22. Based on the components of factors affecting equipment mission availability during the life cycle, construct a mission factor model: ; Among them, task factors These are key factors influencing mission availability, including the support system and its characteristics. Indicates the supply factor. Indicates the usage and maintenance guarantee factor; These respectively represent the spare parts factor, support equipment factor, and maintenance factor involved in the support system during use and maintenance; S23. Establish a comprehensive optimization model with the maximum task availability in typical task scenarios as the objective and the maximum task factor as the constraint: ; in, Indicates the maximum task factor; S3. Based on the comprehensive optimization model constructed in step S2, conduct multi-source data acquisition and construct a benchmark comparison system for comprehensive optimization of spare parts and support equipment; S4. Based on the benchmark comparison system constructed in step S3, conduct sensitivity analysis on the optimization parameters, determine the weight of the impact of changes in each optimization parameter on availability, and the range and area of ​​variable values ​​when implementing trade-off analysis and optimization. S5. Based on the results of the sensitivity analysis of the optimization parameters in step S4, and using the comprehensive optimization model based on the marginal analysis method that considers the importance level, the optimization analysis results for multiple stages of equipment support characteristics and support resources are obtained.

[0008] Furthermore, step S3 specifically includes the following sub-steps: S31. Determine the data sources and select the following as basic data inputs: equipment life cycle reliability and maintainability requirements demonstration report, test report, spare parts and support equipment analysis report, supply data record form, maintenance procedure, maintenance work card, field maintenance record, equipment accident report, and support professional books. S32. Process the basic data input in step S31 and organize it into the data required for the comprehensive measurement of equipment support characteristics and support resources. S33. Store the data required for the comprehensive measurement of equipment support characteristics and support resources obtained in step S32, and construct a benchmark comparison system data model for the comprehensive optimization of spare parts and support equipment.

[0009] Furthermore, step S4 specifically includes the following sub-steps: S41. Based on the benchmark comparison system data, preliminarily determine the value range of each optimization parameter, including the optimal value and the worst value; S42. For each optimization parameter, while keeping other parameter values ​​fixed, the value of the optimized parameter being analyzed approaches the optimal value from the worst value obtained in S41, and the task availability value under the corresponding parameter value is obtained. The sensitivity is determined based on the slope of the tangent line of the parameter value function curve. S43. Based on the sensitivity measured in S42, formulate the criteria for determining the significant sensitivity interval of each parameter, determine the significant sensitivity interval of each parameter, determine the range of parameter values, and optimize the variable region of the optimization parameter.

[0010] Furthermore, step S5 specifically includes the following sub-steps: S51. Determine the importance level based on the sensitivity of reliability, maintainability, spare parts and support equipment configuration quantity determined in step S4; S52. Based on the importance level determined in step S51, obtain the comprehensive optimization model of spare parts and support equipment, and analyze it step by step to obtain the optimal configuration scheme of all optimization parameters among multiple optimization indicators that meet the maximum material consumption constraint, and generate the task availability-task factor curve.

[0011] Furthermore, the parameter calculation of the task factor model in step S22 specifically includes the following sub-steps: S221. Calculate the supply factor based on the proportion of specific tasks within the equipment's service life. ; S222. Calculate the spare parts factor based on equipment supply and support data. ; S223. Calculate the support equipment factor by taking into account the development cycle of the support equipment. ; S224. Calculate the maintenance factor by considering corrective maintenance after a failure. .

[0012] Further: Step S221 obtains the supply factor The method is as follows: ; in, Indicates the total service life of the equipment. Indicates the supply response time of a single piece of equipment. This indicates the number of components required for the mission deployed on the equipment. Indicates the quantity of equipment required for the mission.

[0013] Further: Step S222 obtains the spare parts factor The method is as follows: ; in, Indicates the first in the equipment Number of LRU-like configurations Indicates the development cycle of spare parts. Indicates transportability. Indicates the dimensions of the spare parts.

[0014] Further: Step S223 obtains the support equipment factor The method is as follows: ; in, Indicates the first equipment Number of protection devices configured Indicates the research and development cycle.

[0015] Further: Step S224 obtains the maintenance factor The method is as follows: ; in, The value is the influence coefficient, MTTR is the mean repair time, and MTBF is the mean time between failures.

[0016] The present invention provides a method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model, which has the following advantages compared with the prior art: (1) Based on the similar characteristic parameter data of equipment, determine the construction requirements of the benchmark comparison system, carry out multi-source data collection, and determine the variable range of each optimization parameter from a quantitative perspective through the method based on sensitivity analysis. This can effectively utilize multi-source similar equipment data and solve the problem of insufficient data in the optimization of traditional spare parts and support equipment.

[0017] (2) Based on typical equipment mission scenarios, and further based on the marginal analysis method that considers the importance level, a comprehensive optimization model for spare parts and support equipment is constructed. The results of multi-stage optimization analysis on equipment support characteristics and support resources are obtained. This can alleviate the decision-making difficulties caused by many influencing factors and variables in the configuration of spare parts and support equipment, make the configuration optimization of spare parts and support equipment more accurate, and provide a relatively simple solution for actual engineering problems.

[0018] (3) The calculation of four optimization parameters, namely supply factor, spare parts factor, support equipment factor and maintenance factor, provides technical support for the balance and optimization of parameters such as equipment reliability, maintainability design and support resource allocation. Attached Figure Description

[0019] Figure 1 This is the overall flowchart of the aviation support spare parts configuration optimization method for constructing a benchmark comparison system data model according to the present invention; Figure 2 This is the sensitivity analysis result of the reliability parameter MTBF of this invention; Figure 3 This is the result of a sensitivity analysis of the maintainability parameter MTTF of this invention; Figure 4 This is the result of a sensitivity analysis of the number of spare parts configured in this invention; Figure 5 This is the result of a sensitivity analysis of the number of devices configured in this invention. Detailed Implementation

[0020] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

[0021] In this invention, the factor represents the amount of material consumed, such as the task factor representing the amount of material consumed to complete the task.

[0022] Figure 1 The present invention presents a flowchart of the aviation support spare parts configuration optimization method for constructing a benchmark comparison system data model, comprising the following steps: S1. For typical mission scenarios of the equipment to be analyzed, determine the construction requirements of the benchmark comparison system based on the support characteristics of the equipment to be analyzed and the data requirements of the support system.

[0023] S11. Determine the typical mission scenario for the equipment to be analyzed: The typical mission of a certain aviation equipment is a 5-day continuous loitering patrol mission. During the mission time, it will conduct all-day, all-weather patrol and surveillance of the target area. The mission requires 8 aircraft. The equipment has a total of 5 types of field replaceable units (LRUs) and 2 types of portable repair equipment. Its reliability, maintainability parameters and support system parameters are shown in Tables 1 and 2 below: Table 1 LRU Parameter Table Table 2. Correspondence between Spare Parts and Support Equipment Consider the constraint as the task factor, i.e., the maximum material consumption. =55.6.

[0024] S12. Determine the construction requirements of the benchmark comparison system. The benchmark comparison system is essentially a database of the usage parameters of various aviation spare parts and support equipment under typical mission scenarios of the equipment to be analyzed. Determine its construction requirements, that is, determine the data required to carry out the analysis process.

[0025] S2. Construct a comprehensive optimization model to comprehensively measure equipment reliability, maintainability, and the configuration scheme of spare parts and support equipment.

[0026] S21. Based on the typical mission scenarios of the equipment to be analyzed, construct a mission availability model: ; In the formula, Indicates task availability. Indicating the task Task availability at any given time express Time of the first Expected shortage of LRU spare parts Indicates the quantity of equipment required for the mission. Indicates the first Repair equipment carried with LRU spare parts The availability of spare parts at any given time, including the expected number of spare parts shortages and the availability of repair equipment carried by the spare parts, are parameters specific to aviation support spare parts.

[0027] S22. Based on the composition of factors in the life cycle, construct a task factor model: ; in, Indicates task factor, , These represent the supply factor, usage factor, and guarantee factor, respectively. These represent spare parts factor, support equipment factor, and maintenance factor, respectively.

[0028] S221. The supply factor is calculated based on the proportion of the specific mission within the equipment's service life. According to the mission parameters, the total working time of this typical mission is Tm = 960 flight hours. Therefore, the mission supply factor for this mission can be further calculated as follows: ; In the formula, Indicates the total service life of the equipment. Indicates the supply response time of a single piece of equipment. This indicates the number of components required for the mission deployed on the equipment. This indicates the quantity of equipment required for the mission, where the equipment supply factor is... The supply response time is measured based on the equipment reliability parameter Mean Time Between Failures (MTBF) and the maintainability parameter Mean Time To Repair (MTTR). When the equipment adopts different reliability and maintainability design schemes, the supply response time will vary greatly.

[0029] S222, Spare parts factor is mainly calculated based on equipment supply and support data, and the method for obtaining it is as follows: ; In the formula, Indicates the first in the equipment Number of LRU-like configurations The development cycle of the spare parts is indicated by the value taken from Table 1 in step S11.

[0030] S223, the support equipment factor mainly refers to the support equipment development cycle, and the method for obtaining it is as follows: ; In the formula, Indicates the first equipment Number of protection devices configured The research and development cycle is indicated by values ​​taken from Table 2 in step S11.

[0031] S224. The maintenance factor mainly considers corrective maintenance after a failure. The impact of reliability parameters and maintainability parameters on the maintenance factor is obtained as follows: In the formula, 0.824, 2.264, 1.318, 2.198, and 1.977 are... As an influence factor, MTTR is the mean repair time, and MTBF is the mean time between failures.

[0032] S23. Establish a comprehensive optimization model with the maximum task availability in typical task scenarios as the objective and the maximum material consumption of task factors as the constraint: ; ; In the formula, Task availability represents typical task scenarios. This represents the material consumption of the task factor, with 55.6 being the maximum material consumption.

[0033] S3. By acquiring data from multiple sources, construct a benchmark comparison system for comprehensive optimization of spare parts and support equipment: S31. Determine the data source. Based on the data resources and knowledge processing requirements of typical equipment mission scenarios, design the data structure required for the analysis and storage of spare parts and support equipment. Select the equipment's full life cycle reliability and maintainability requirements demonstration and test reports, spare parts and support equipment analysis reports, data record forms, equipment accident reports, and support professional books as data inputs.

[0034] S32. Perform data processing, merge multimodal data into documents to be labeled, carry out manual data labeling and rule design, and then use a computer to extract attributes at multiple granularities for characters, phrases and paragraphs to obtain the data required for the comprehensive optimization model's guarantee characteristics and comprehensive measurement of guarantee resources.

[0035] S33. Conduct data storage, and in the form of a database, input the processed data in a prescribed structure format to build a benchmark comparison system for the comprehensive optimization of spare parts and support equipment.

[0036] S4. Based on benchmark comparison system data, conduct sensitivity analysis on the optimization parameters of the comprehensive optimization model to determine the magnitude of the impact of changes in parameters such as reliability, maintainability, spare parts and support equipment configuration quantity on availability, and determine the variable value range and area of ​​each parameter when realizing trade-off analysis and optimization.

[0037] S41. Based on the benchmark comparison system data, the range of values ​​for the optimization parameters is initially determined, including the optimal and worst values, as shown in Table 3 below: Table 3. Value range of each optimization parameter S42. Based on the value ranges of each parameter initially determined above, a sensitivity analysis is performed on each factor. For reliability and maintainability parameters, the analysis is conducted by increasing the value by 20% each time. For the quantity of spare parts and support equipment, the analysis is conducted by increasing the value by 1 each time. The final MTBF sensitivity analysis results for each LRU are as follows: Figures 2-5 As shown.

[0038] S43. Determine the significant sensitivity intervals and corresponding parameter ranges for each optimization parameter. By establishing a determination criterion, the significant sensitivity intervals for each parameter are obtained. Based on this, the value range of the parameters is initially determined, and the variable region of the optimization parameters is further narrowed for optimization. The analysis results are shown in Table 4 below: Table 4. Significant sensitivity intervals and corresponding parameter ranges for each optimization parameter. S5. Based on the comprehensive optimization model of the marginal analysis method that takes into account the importance level, the configuration scheme with the highest task availability is obtained under the requirement of the highest material consumption of the maximum factor.

[0039] S51. Based on the analysis results of step S4, the sensitivity of each optimization index is further determined, and the following can be obtained: k MTBF =0.0482, k MTTR =0.0465, k s =0.0177, k r =0.2829, so the importance level of each optimization index can be determined according to the sensitivity. The order from low to high is: spare parts configuration quantity, MTTR, MTBF, and support equipment configuration quantity, and the corresponding importance levels are recorded as 0, 1, 2, and 3.

[0040] S52. Obtain the configuration scheme with the highest task availability under the requirement of maximum material consumption of the maximum factor. Based on the importance level determined in step S51 and combined with the regeneration task availability of the given configuration scheme, obtain the comprehensive optimization model of spare parts and support equipment. Analyze step by step to obtain the optimal configuration scheme of all optimization parameters among multiple optimization indicators that meet the maximum material consumption constraint. Generate the task availability-task factor curve and obtain the four-stage optimization analysis results for equipment reliability, maintainability, and other support characteristics and support resources, as shown in Table 5 below: Table 5. Results of Comprehensive Optimization Analysis of Guarantee Characteristics and Guarantee Resources A convexity analysis of all the configuration schemes given in the table above shows that there is no "non-convexity". By comparing all the alternative schemes that meet the conditions, it is easy to see that scheme 9 is the optimal scheme in terms of reliability, maintainability measurement parameters and the number of spare parts and support resources that maximizes the task availability target.

[0041] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model, characterized in that: It includes the following steps: S1. Based on the typical mission scenarios of the equipment to be analyzed, and according to the support characteristics of the equipment to be analyzed and the data requirements of the support system, determine the construction requirements of the benchmark comparison system. S2. Conduct a comprehensive measurement of equipment reliability, maintainability, and spare parts and support equipment configuration schemes, and construct a comprehensive optimization model, which includes the following sub-steps: S21. Based on the typical mission scenarios of the equipment to be analyzed, construct a mission availability model: ； in, Indicates execution of the first Working time for a typical task. Indicates task availability. Indicating the task Task availability at any given time express Expected shortage of spare parts at all times Indicates the quantity of equipment required for the mission. Indicates spare parts and support equipment exist Time availability; S22. Based on the components of factors affecting equipment mission availability during the life cycle, construct a mission factor model: ； Among them, task factors These are key factors influencing mission availability, including the support system and its characteristics. Indicates the supply factor. Indicates the usage and maintenance guarantee factor; These respectively represent the spare parts factor, support equipment factor, and maintenance factor involved in the support system during use and maintenance; S23. Establish a comprehensive optimization model with the maximum task availability in typical task scenarios as the objective and the maximum task factor as the constraint: ； in, Indicates the maximum task factor; S3. Based on the comprehensive optimization model constructed in step S2, conduct multi-source data acquisition and construct a benchmark comparison system for comprehensive optimization of spare parts and support equipment; S4. Based on the benchmark comparison system constructed in step S3, conduct sensitivity analysis on the optimization parameters, determine the weight of the impact of changes in each optimization parameter on availability, and the range and area of ​​variable values ​​when implementing trade-off analysis and optimization. S5. Based on the results of the sensitivity analysis of the optimization parameters in step S4, and using the comprehensive optimization model based on the marginal analysis method that considers the importance level, the optimization analysis results for multiple stages of equipment support characteristics and support resources are obtained.

2. The method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model as described in claim 1, characterized in that, Step S3 specifically includes the following sub-steps: S31. Determine the data sources and select the following as basic data inputs: equipment life cycle reliability and maintainability requirements demonstration report, test report, spare parts and support equipment analysis report, supply data record form, maintenance procedure, maintenance work card, field maintenance record, equipment accident report, and support professional books. S32. Process the basic data input in step S31 and organize it into the data required for the comprehensive measurement of equipment support characteristics and support resources. S33. Store the data required for the comprehensive measurement of equipment support characteristics and support resources obtained in step S32, and construct a benchmark comparison system data model for the comprehensive optimization of spare parts and support equipment.

3. The method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model as described in claim 1, characterized in that, Step S4 specifically includes the following sub-steps: S41. Based on the benchmark comparison system data, preliminarily determine the value range of each optimization parameter, including the optimal value and the worst value; S42. For each optimization parameter, while keeping other parameter values ​​fixed, the value of the optimized parameter being analyzed approaches the optimal value from the worst value obtained in S41, and the task availability value under the corresponding parameter value is obtained. The sensitivity is determined based on the slope of the tangent line of the parameter value function curve. S43. Based on the sensitivity measured in S42, formulate the criteria for determining the significant sensitivity interval of each parameter, determine the significant sensitivity interval of each parameter, determine the range of parameter values, and optimize the variable region of the optimization parameter.

4. The method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model as described in claim 1, characterized in that, Step S5 specifically includes the following sub-steps: S51. Determine the importance level based on the sensitivity of reliability, maintainability, spare parts and support equipment configuration quantity determined in step S4; S52. Based on the importance level determined in step S51, obtain the comprehensive optimization model of spare parts and support equipment, and analyze it step by step to obtain the optimal configuration scheme of all optimization parameters among multiple optimization indicators that meet the maximum material consumption constraint, and generate the task availability-task factor curve.

5. The method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model as described in claim 1, characterized in that, The parameter calculation of the task factor model in step S22 specifically includes the following sub-steps: S221. Calculate the supply factor based on the proportion of specific tasks within the equipment's service life. ; S222. Calculate the spare parts factor based on equipment supply and support data. ; S223. Calculate the support equipment factor by taking into account the development cycle of the support equipment. ; S224. Calculate the maintenance factor by considering corrective maintenance after a failure. .

6. The method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model as described in claim 5, characterized in that, Step S221 Obtain the supply factor The method is as follows: ； in, Indicates the total service life of the equipment. Indicates the supply response time of a single piece of equipment. This indicates the number of components required for the mission deployed on the equipment. Indicates the quantity of equipment required for the mission.

7. The method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model as described in claim 5, characterized in that, Step S222 Obtain spare parts factor The method is as follows: ； in, Indicates the first in the equipment Number of LRU-like configurations Indicates the development cycle of spare parts. Indicates transportability. Indicates the dimensions of the spare parts.

8. The method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model as described in claim 5, characterized in that, Step S223 Obtain the support equipment factor The method is as follows: ； in, Indicates the first equipment Number of protection devices configured Indicates the research and development cycle.

9. The method for optimizing the configuration of aviation support spare parts by constructing a benchmark comparison system data model as described in claim 5, characterized in that, Step S224: Obtain the maintenance factor The method is as follows: ； in, The value is the influence coefficient, MTTR is the mean repair time, and MTBF is the mean time between failures.

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