A reliability testability collaborative modeling and optimization design method based on availability model

Abstract

This invention belongs to the field of reliability design analysis. It proposes a collaborative modeling and optimization design method for equipment reliability and testability based on an availability model. Key design indicators in reliability and testability design schemes are used as decision attribute data to evaluate the quality of the schemes. Weight coefficients for each decision attribute data are determined, and a multi-attribute decision-based reliability and testability design scheme optimization model is used to perform optimization analysis on various reliability and testability design schemes. This yields a ranking of multiple design schemes, and finally, the optimal reliability and testability design scheme is selected, thus realizing an overall collaborative modeling and optimization technique for reliability and testability.

Description

Technical Field

[0001] This invention belongs to the field of reliability design and analysis. Background Technology

[0002] Reliability refers to a product's ability to perform its intended function under specified conditions and within a specified time. However, no matter how reliable a system, device, or product is, it cannot function normally forever. Users and maintainers need to understand its health status and determine whether there are any faults or where they have occurred. This requires monitoring and testing, which involves testability. Testability is a design characteristic that allows a product to accurately and promptly determine its state (operable, inoperable, or degree of performance degradation) and isolate its internal faults. Both reliability and testability are important quality characteristics of equipment, and both technically and managerially, the testability of equipment is closely related to reliability. Therefore, it is necessary to establish an equipment performance optimization design method that can coordinate the relationship between equipment reliability design and testability design, providing an objective and scientific basis for the comprehensive performance analysis of equipment reliability and testability.

[0003] Traditional equipment reliability optimization design methods mainly focus on optimizing the level of a single general characteristic of the equipment, which has the problem of failing to meet the comprehensive requirements of multiple general characteristics of the equipment. Chinese Patent Publication No. CN105825274A discloses a "Reliability and Maintainability Optimization Design Method for Engineering Machinery Products," which includes the following steps: S1: Optimizing and allocating reliability and maintainability of the engineering machinery product design scheme; S2: Designing the product structure based on the optimized scheme in step S1; S3: Estimating the reliability and maintainability of the product structure design scheme in step S2; S4: If the design objectives are met, conducting product testing or use; if the design objectives are not met, adjusting the structural parameters and repeating steps S2 and S3; S5: Performing data statistical analysis, including calculating actual reliability and actual maintainability; S6: Based on the actual reliability and actual maintainability results calculated in step S5, if the design objectives are met, the process ends; if the design objectives are not met, returning to step S1 and repeating the subsequent steps.

[0004] The reliability and maintainability optimization design method in the aforementioned existing scheme is also a type of equipment reliability optimization design method. It optimizes the reliability and maintainability design of a product by adjusting its structural parameters, and judges whether the design meets the requirements by comparing the estimated and required values ​​of product reliability and maintainability. This solves the problem that traditional equipment reliability optimization design methods only optimize a single general characteristic level of the equipment, providing a more objective and scientific basis for equipment reliability and maintainability optimization design. However, in reality, to enhance the fault diagnosis capability of equipment and realize its testability design, certain hardware and software are needed as components of the equipment. But these hardware and software themselves can also fail, thus reducing the equipment's reliability. That is, there is a close relationship between equipment reliability and testability. It is difficult to achieve synergistic optimization design of equipment reliability and testability by designing a single general performance separately. Furthermore, modern equipment has multiple mission operation modes in complex environments. When conducting equipment reliability optimization design, it is also necessary to fully consider the comprehensive performance of equipment reliability and testability under multiple mission operation modes. However, existing equipment reliability optimization design methods lack a collaborative optimization model capable of analyzing the combined reliability and testability performance of equipment under various mission operating modes, and do not introduce an optimal model for ranking various equipment reliability and testability design schemes. Meanwhile, the applicant's practical research has revealed that equipment availability models can be used to comprehensively evaluate the reliability and testability parameters of equipment under various mission operating modes, providing a basis for collaborative optimization design of equipment reliability and testability, thereby enabling mission-oriented collaborative modeling and optimization design of equipment reliability and testability.

[0005] Therefore, how to design a collaborative modeling and optimization design method for equipment reliability and testability based on equipment availability model and scheme optimization model is an urgent technical problem to be solved. Summary of the Invention

[0006] To address the shortcomings of the existing technologies, the technical problem to be solved by this invention is: how to provide a collaborative modeling and optimization design method for equipment reliability and testability that can fully consider the characteristics of equipment operating in multi-task environments, enable collaborative analysis and optimization of equipment reliability and testability design schemes for multi-task environments based on availability models, and select the most suitable equipment reliability and testability design scheme based on equipment reliability and testability design scheme optimization models, thereby improving the accuracy and comprehensiveness of collaborative modeling and optimization design for equipment reliability and testability.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] S1: Combining reliability modeling and simulation with testability modeling and simulation, obtain key design variable data for the reliability and testability design schemes of the equipment under various mission operating modes.

[0009] S2: Sampling of key design index data for the reliability and testability design schemes of the equipment under various mission operating modes, and substituting the sampled data into the collaborative optimization model. Obtain multiple availability data A for the equipment under various task working modes;

[0010] S3: Determine the minimum required availability of equipment in each mission operating mode (A) min Determine whether multiple availability data A under each mission operating mode of the equipment meet the system requirements. When multiple availability data A under each mission operating mode are not less than the minimum availability requirement value A... min At that time, the reliability and testability design scheme met the requirements;

[0011] S4: If the reliability and testability design schemes meet the requirements, then the current reliability and testability design schemes are retained, meaning the collaborative modeling and optimization analysis of these schemes is completed. If the reliability and testability design schemes do not meet the requirements, then the reliability and testability parameters are optimized through adjustments to the reliability and testability design schemes. The key design performance data of the optimized reliability and testability design schemes are then sampled again, and the sampled data is substituted into the model. The process continues until the model's output meets the requirements, iterating multiple times to generate various reliability and testability design schemes and their key design performance data.

[0012] S5: Combining key design index data from multiple reliability and testability design schemes, and using a multi-attribute decision-making-based reliability and testability design scheme optimization model, the optimal reliability and testability design scheme is selected.

[0013] Preferably, in step S1, the key design variables for the reliability and testability design scheme include: reliability parameter Mean Time Between Failures (MTBF), maintainability parameter Mean Time Before Repair (MTTR), and testability parameter the sum of fault detection time and fault isolation time (T). D And the coverage parameter, Average Coverage Delay Time (MLDT).

[0014] Preferably, assuming that the failure rates of each component of the equipment follow an exponential distribution, the mean time between failures (MTBF) of the equipment under each mission operating mode is calculated using the following formula:

[0015]

[0016] In the formula: λ S This indicates the failure rate of the equipment in each mission's operating mode;

[0017] Among them, the mission reliability block diagram of the equipment under various mission operating modes and the failure rate λ of each component of the equipment are combined. i The failure rate λ of the equipment in each mission operating mode was obtained. S .

[0018] Preferably, the mean time to repair (MTTR) is calculated for the equipment under each mission operating mode using the following formula:

[0019]

[0020] In the formula: I represents the number of components of the equipment; λ i MTTR indicates the failure rate of each component of the equipment in each mission operating mode; i This indicates the repair time for each component of the equipment under each mission's operating mode.

[0021] Preferably, the sum of the fault detection time and fault isolation time of each component of the equipment is obtained by combining the fault detection time of each test point, the fault isolation time of fuzzy groups with different fuzzinesses, and the diagnostic strategy. The diagnostic strategy is obtained through test-based modeling and simulation. The diagnostic strategy includes: detecting a set of test points corresponding to the faults of each key component and the fuzziness of the corresponding ultimately detected fuzzy groups. The sum of the fault detection time and fault isolation time T of the b-th component of the equipment in each task operating mode is calculated using the following formula. Db :

[0022]

[0023] In the formula: A represents the number of test points used to detect whether the b-th component is faulty; t a t represents the fault detection time at the a-th test point in a set of test points for detecting whether the b-th component is faulty; b This represents the fault isolation time for detecting whether the b-th component is faulty;

[0024] The sum of the fault detection time and fault isolation time of all components of the equipment, T, is obtained. Db Based on this, the sum of the fault detection time and fault isolation time of each component is sampled according to the randomness of the failure of each component of the equipment, so as to obtain the sum of the fault detection time and fault isolation time of the equipment, T. D .

[0025] Preferably, the mean support delay time (MLDT) is used to obtain the supportability parameters of each component of the equipment under each mission operating mode.b The steps include:

[0026] S101: Specifies the factors affecting MLDT indicators: spare parts delay, support equipment delay, and support group occupancy delay;

[0027] S102: Specifies the set of levels for evaluating MLDT indicators and their influencing factors: {very short, short, medium, long, very long};

[0028] S103: Obtain the measurement values ​​of each influencing factor belonging to each evaluation level under each task operation mode of each component of the equipment. The measurement index adopts the 0.1-0.9 scaling method to obtain the fuzzy evaluation matrix R of each component of the equipment under each task operation mode.

[0029] S104: Obtain the weight vector W of each influencing factor of each component of the equipment under each task working mode;

[0030] S105: The comprehensive evaluation result S of the MLDT index of each component of the equipment under each mission working mode is calculated using the following formula:

[0031] S = W × R; where: W represents the weight vector of each influencing factor of each component of the equipment under each mission working mode; R represents the fuzzy evaluation matrix of each component of the equipment under each mission working mode.

[0032] S106: Based on the comprehensive evaluation results obtained in step S105, determine the evaluation level of the MLDT index of each component of the equipment under each task working mode and the corresponding maximum membership degree U according to the maximum membership principle. max (MLDT);

[0033] S107: Specifies the value range of the MLDT indicator corresponding to the MLDT indicator evaluation level:

[0034] S108: The MLDT index values ​​of each component of the equipment under each mission operating mode are calculated using the following formula:

[0035] MLDT=U max (MLDT)*(ba)+a;

[0036] In the formula: U max (MLDT) represents the maximum membership degree of the MLDT index obtained in step S107; a represents the maximum membership degree U. max (MLDT) represents the minimum value within the MLDT index range; b represents the maximum membership degree U. max The maximum value b in the MLDT indicator range where (MLDT) is located;

[0037] The mean time to maintenance (MLDT) for all components of the equipment is obtained. b Based on this, the average maintenance delay time of each component is sampled according to the randomness of failure of each component of the equipment, and the average maintenance delay time (MLDT) of the equipment is obtained.

[0038] Preferably, in step S5, the mean time to repair (MTTR) and mean time to failure (Tf) of the reliability and testability design scheme equipped under the same task operating mode are measured. D The average assurance delay time (MLDT) data was determined as the decision attribute data for reliability and testability design schemes.

[0039] Preferably, the steps for selecting the optimal model of the equipment reliability testability collaborative modeling and optimization design scheme based on the availability model include:

[0040] S501: Specifies the number of times n is sorted in a single operation, and determines the weighting coefficients ω1 for the average time before repair, ω2 for the sum of fault detection time and fault isolation time, and ω3 for the average support delay time.

[0041] S502: Sample one set of decision attribute data for each design scheme, including three types of data: Mean Time Before Repair (MTTR), Fault Detection Time (T), and Fault Isolation Time (T). D Average Detailed Delay Time (MLDT)

[0042] S503: Normalize the data of the same type from multiple sampled design schemes under the same task working mode using the following formula to obtain the normalized values ​​of various types of data for each design scheme:

[0043]

[0044] In the formula: I represents the number of design schemes; MTTR i MTTR represents the mean time to repair (MTTR) for the i-th design scheme. i ` represents the normalized value of the mean pre-repair time data for the i-th design scheme:

[0045]

[0046] In the formula: I represents the number of design schemes; T Di T represents the sum of fault detection time and fault isolation time for the i-th design scheme; Di ` represents the normalized value of the sum of fault detection time and fault isolation time for the i-th design scheme:

[0047]

[0048] In the formula: I represents the number of design schemes; MLDT i This represents the average assurance delay time data for the i-th design scheme; MLDT i ` represents the normalized value of the average guarantee delay time data for the i-th design scheme;

[0049] S504: Calculate the comprehensive ranking value A for each scheme using the following formula. i :

[0050] A i =ω1*MTTR i +ω2*T Di `+ω3*MLDT i `;

[0051] In the formula: A i ωi represents the overall ranking value of the i-th design scheme; ω1 represents the weighting coefficient of the mean time before repair; ω2 represents the weighting coefficient of the sum of fault detection time and fault isolation time; ω3 represents the weighting coefficient of the mean support delay time; MTTR i ` represents the normalized value of the mean pre-repair time data for the i-th design scheme; T Di ` represents the normalized value of the sum of fault detection time and fault isolation time for the i-th design scheme; MLDT i ` represents the normalized value of the average guarantee delay time data for the i-th design scheme;

[0052] S505: Perform a single sorting of multiple schemes based on the comprehensive sorting value, with the scheme with the smaller comprehensive sorting value ranked first, and record the sorting result;

[0053] S506: Determine if the number of sorting iterations for each scheme has reached the specified number of sorting iterations n. If not, return to step S502. If so, calculate the probability N of each scheme at each sorting position using the following formula. K :

[0054]

[0055] In the formula: a K This indicates the number of times the design scheme appears in position K; n represents the total number of sorting attempts.

[0056] S507: Compare the probability N of each scheme at the sorting position K. K The scheme with the greater size and probability is placed at position K. If the two schemes have the same reliability, the probability of the next position is compared until the final reliability and testability design scheme ranking structure is obtained.

[0057] Compared with existing technologies, the equipment reliability testability collaborative modeling and optimization design method in this invention has the following advantages:

[0058] In this invention, by introducing an equipment availability model, the constraints of equipment availability can be fully considered during the collaborative optimization of equipment reliability and testability, thereby improving the accuracy of collaborative modeling and optimization design for equipment reliability and testability. Simultaneously, based on the equipment availability model and key design variables of reliability and testability design schemes, the collaborative modeling and analysis process for equipment reliability and testability can be realized. Furthermore, by combining a multi-attribute decision-based reliability and testability design scheme optimization model, the optimal equipment reliability and testability design scheme can be selected, thus enhancing the comprehensiveness of the collaborative optimization method for equipment reliability and testability. Attached Figure Description

[0059] Figure 1 A logical block diagram for a collaborative modeling and optimization design method for equipment reliability testing oriented towards multiple tasks. Detailed Implementation

[0060] The following detailed explanation illustrates the specific implementation methods:

[0061] This embodiment discloses a reliability testability collaborative modeling and optimization design method based on an availability model.

[0062] like Figure 1 As shown, the collaborative modeling and optimization design method for equipment reliability testability oriented to multi-task includes the following steps:

[0063] S1: Combining reliability modeling and simulation with testability modeling and simulation, obtain key design variable data for the reliability and testability design schemes of the equipment under various mission operating modes.

[0064] S2: Sampling of key design index data for the reliability and testability design schemes of the equipment under various mission operating modes, and substituting the sampled data into the collaborative optimization model. Obtain multiple availability data A for the equipment under various task working modes;

[0065] S3: Determine the minimum required availability of equipment in each mission operating mode (A) min Determine whether multiple availability data A under each mission operating mode of the equipment meet the system requirements. When multiple availability data A under each mission operating mode are not less than the minimum availability requirement value A... min At that time, the reliability and testability design scheme met the requirements;

[0066] S4: If the reliability and testability design schemes meet the requirements, then the current reliability and testability design schemes are retained, meaning the collaborative modeling and optimization analysis of these schemes is completed. If the reliability and testability design schemes do not meet the requirements, then the reliability and testability parameters are optimized through adjustments to the reliability and testability design schemes. The key design performance data of the optimized reliability and testability design schemes are then sampled again, and the sampled data is substituted into the model. The process continues until the model's output meets the requirements, iterating multiple times to generate various reliability and testability design schemes and their key design performance data.

[0067] S5: Combining key design index data from multiple reliability and testability design schemes, and using a multi-attribute decision-making-based reliability and testability design scheme optimization model, the optimal reliability and testability design scheme is selected.

[0068] In this invention, by introducing an equipment availability model, the constraints of equipment availability can be fully considered during the collaborative optimization of equipment reliability and testability, thereby improving the accuracy of collaborative modeling and optimization design for equipment reliability and testability. Simultaneously, based on the equipment availability model and key design variables of reliability and testability design schemes, the collaborative modeling and analysis process for equipment reliability and testability can be realized. Furthermore, by combining a multi-attribute decision-based reliability and testability design scheme optimization model, the optimal equipment reliability and testability design scheme can be selected, thus enhancing the comprehensiveness of the collaborative optimization method for equipment reliability and testability.

[0069] In practical implementation, the key design variables for reliability and testability design schemes include: reliability parameter Mean Time Between Failures (MTBF), maintainability parameter Mean Time To Repair (MTTR), and testability parameter the sum of fault detection time and fault isolation time (T). D And the coverage parameter, Average Coverage Delay Time (MLDT).

[0070] Specifically, assuming that the failure rates of each component of the equipment follow an exponential distribution, the mean time between failures (MTBF) is calculated as follows for the equipment under each mission operating mode:

[0071]

[0072] In the formula: λ S This indicates the failure rate of the equipment in each mission's operating mode;

[0073] Among them, the mission reliability block diagram of the equipment under various mission operating modes and the failure rate λ of each component of the equipment are combined. iThe failure rate λ of the equipment in each mission operating mode was obtained. S .

[0074] Specifically, the mean time to repair (MTTR) is calculated for the equipment under each mission operating mode using the following formula:

[0075]

[0076] In the formula: I represents the number of components of the equipment; λ i MTTR indicates the failure rate of each component of the equipment in each mission operating mode; i This indicates the repair time for each component of the equipment under each mission's operating mode.

[0077] Specifically, the sum of the fault detection time and fault isolation time of each component of the equipment is obtained by combining the fault detection time of each test point, the fault isolation time of fuzzy groups with different fuzzinesses, and the diagnostic strategy. The diagnostic strategy is obtained through test-based modeling and simulation. The diagnostic strategy includes: detecting a set of test points corresponding to the faults of each key component and the fuzziness of the corresponding ultimately detected fuzzy groups. The sum of the fault detection time and fault isolation time T of the b-th component of the equipment under each task operating mode is calculated using the following formula. Db :

[0078]

[0079] In the formula: A represents the number of test points used to detect whether the b-th component is faulty; t a t represents the fault detection time at the a-th test point in a set of test points for detecting whether the b-th component is faulty; b This represents the fault isolation time for detecting whether the b-th component is faulty;

[0080] The sum of the fault detection time and fault isolation time of all components of the equipment, T, is obtained. Db Based on this, the sum of the fault detection time and fault isolation time of each component is sampled according to the randomness of the failure of each component of the equipment, so as to obtain the sum of the fault detection time and fault isolation time of the equipment, T. D .

[0081] Specifically, the steps for obtaining the mean support delay time (MLDT) for each component of the equipment under each mission operating mode include:

[0082] S101: Specifies the factors affecting MLDT indicators: spare parts delay, support equipment delay, and support group occupancy delay;

[0083] S102: Specifies the set of levels for evaluating MLDT indicators and their influencing factors: {very short, short, medium, long, very long};

[0084] S103: Obtain the measurement values ​​of each influencing factor belonging to each evaluation level under each task operation mode of each component of the equipment. The measurement index adopts the 0.1-0.9 scaling method to obtain the fuzzy evaluation matrix R of each component of the equipment under each task operation mode.

[0085] S104: Obtain the weight vector W of each influencing factor of each component of the equipment under each task working mode;

[0086] S105: The comprehensive evaluation result S of the MLDT index of each component of the equipment under each mission working mode is calculated using the following formula:

[0087] S = W × R; where: W represents the weight vector of each influencing factor of each component of the equipment under each mission working mode; R represents the fuzzy evaluation matrix of each component of the equipment under each mission working mode.

[0088] S106: Based on the comprehensive evaluation results obtained in step S105, determine the evaluation level of the MLDT index of each component of the equipment under each task working mode and the corresponding maximum membership degree U according to the maximum membership principle. max (MLDT);

[0089] S107: Specifies the value range of the MLDT indicator corresponding to the MLDT indicator evaluation level:

[0090] S108: The MLDT index values ​​of each component of the equipment under each mission operating mode are calculated using the following formula:

[0091] MLDT=U max (MLDT)*(ba)+a;

[0092] In the formula: U max (MLDT) represents the maximum membership degree of the MLDT index obtained in step S107; a represents the maximum membership degree U. max (MLDT) represents the minimum value within the MLDT index range; b represents the maximum membership degree U. max The maximum value b in the MLDT indicator range where (MLDT) is located;

[0093] Based on the mean maintenance delay time (MLDT) of all components of the equipment, the mean maintenance delay time (MLDT) of each component is sampled according to the randomness of failure of each component of the equipment, so as to obtain the mean maintenance delay time (MLDT) of the equipment.

[0094] This invention uses the following key design variables for reliability and testability design schemes: mean time between failures (MTBF), maintainability parameter (mean time to repair), testability parameter (sum of fault detection time and fault isolation time), and supportability parameter (mean time to support). Among these, MTBF and mean time to repair are deterministic data, the sum of fault detection time and fault isolation time is stochastic data, and mean time to support is fuzzy data. Simultaneously, by combining reliability modeling and simulation with testability modeling and simulation, the above key design variable data are obtained and used as input data for subsequent collaborative modeling and optimization of reliability and testability design schemes, as well as for the selection of optimal reliability and testability design schemes.

[0095] Based on the key design variable data of reliability and testability design schemes under various task working modes and the minimum usability requirements, this invention performs collaborative modeling analysis on multiple reliability and testability design schemes according to a collaborative optimization model to obtain the analysis results of whether the design schemes meet the usability requirements, and uses the design schemes that meet the requirements and the corresponding key design index data as input data for the optimization of reliability and testability design schemes.

[0096] In the specific implementation process, the mean time to repair (MTTR) and mean time to failure (Tf) of the reliability and testability design schemes equipped under the same mission operating mode will be used. D The average assurance delay time (MLDT) data was determined as the decision attribute data for reliability and testability design schemes.

[0097] Specifically, the steps for collaborative modeling and optimization design of equipment reliability testability based on multi-attribute decision-making include:

[0098] S501: Specifies the number of times n is sorted in a single operation, and determines the weighting coefficients ω1 for the average time before repair, ω2 for the sum of fault detection time and fault isolation time, and ω3 for the average support delay time.

[0099] S502: Sample one set of decision attribute data for each design scheme, including three types of data: Mean Time Before Repair (MTTR), Fault Detection Time (T), and Fault Isolation Time (T). D Average Detailed Delay Time (MLDT)

[0100] S503: Normalize the data of the same type from multiple sampled design schemes under the same task working mode using the following formula to obtain the normalized values ​​of various types of data for each design scheme:

[0101]

[0102] In the formula: I represents the number of design schemes; MTTR i MTTR represents the mean time to repair (MTTR) for the i-th design scheme. i ` represents the normalized value of the mean pre-repair time data for the i-th design scheme:

[0103]

[0104] In the formula: I represents the number of design schemes; T Di T represents the sum of fault detection time and fault isolation time for the i-th design scheme; Di ` represents the normalized value of the sum of fault detection time and fault isolation time for the i-th design scheme:

[0105]

[0106] In the formula: I represents the number of design schemes; MLDT i This represents the average assurance delay time data for the i-th design scheme; MLDT i ` represents the normalized value of the average guarantee delay time data for the i-th design scheme;

[0107] S504: Calculate the comprehensive ranking value A for each scheme using the following formula. i :

[0108] A i =ω1*MTTR i +ω2*T Di `+ω3*MLDT i `;

[0109] In the formula: A i ωi represents the overall ranking value of the i-th design scheme; ω1 represents the weighting coefficient of the mean time before repair; ω2 represents the weighting coefficient of the sum of fault detection time and fault isolation time; ω3 represents the weighting coefficient of the mean support delay time; MTTR i ` represents the normalized value of the mean pre-repair time data for the i-th design scheme; T Di ` represents the normalized value of the sum of fault detection time and fault isolation time for the i-th design scheme; MLDT i ` represents the normalized value of the average guarantee delay time data for the i-th design scheme;

[0110] S505: Perform a single sorting of multiple schemes based on the comprehensive sorting value, with the scheme with the smaller comprehensive sorting value ranked first, and record the sorting result;

[0111] S506: Determine if the number of sorting iterations for each scheme has reached the specified number of sorting iterations n. If not, return to step S502. If so, calculate the probability N of each scheme at each sorting position using the following formula. K :

[0112]

[0113] In the formula: a K This indicates the number of times the design scheme appears in position K; n represents the total number of sorting attempts.

[0114] S507: Compare the probability N of each scheme at the sorting position K. K The scheme with the greater size and probability is placed at position K. If the two schemes have the same reliability, the probability of the next position is compared until the final reliability and testability design scheme ranking structure is obtained.

[0115] This invention uses key design indicators in reliability and testability design schemes as decision attribute data to evaluate the quality of reliability and testability design schemes, determines the weight coefficients of each decision attribute data, and finally, performs optimization analysis on multiple reliability and testability design schemes based on a multi-attribute decision-making reliability and testability design scheme optimization model to obtain the ranking results of multiple design schemes, and finally selects the optimal reliability and testability design scheme, thereby realizing the overall reliability and testability collaborative modeling and optimization technology.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described with reference to preferred embodiments, those skilled in the art should understand that various changes in form and detail can be made without departing from the spirit and scope of the invention as defined in the appended claims. Furthermore, common knowledge such as specific structures and characteristics known in the embodiments is not described in detail here. Finally, the scope of protection claimed by the present invention should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A collaborative modeling and optimization design method for equipment reliability and testability based on an availability model, characterized by: S1: Combining reliability modeling and simulation with testability modeling and simulation, obtain key design variable data for the reliability and testability design schemes of the equipment under various mission operating modes; The key design variable data includes: reliability parameter Mean Time Between Failures (MTBF). MTBF Maintainability parameters: mean time before repair MTTR The sum of test parameter fault detection time and fault isolation time T D and the average coverage delay time as a guarantee parameter MLDT Assuming that the failure rates of all components of the equipment follow an exponential distribution, the mean time between failures (MTBF) is the reliability parameter of the equipment under each mission operating mode. MTBF Calculate using the following formula: ； In the formula: λ S This indicates the failure rate of the equipment under various mission operating modes, which includes a mission reliability block diagram combining the equipment's performance under each mission operating mode and the failure rate of each component of the equipment. λ i To obtain the failure rate of the equipment in each mission working mode. λ S The maintainability parameter of the equipment under each task operating mode: mean time to repair. MTTR Calculate using the following formula: In the formula: I Indicates the number of components in the equipment; λ i This indicates the failure rate of each component of the equipment in each mission operating mode; MTTR i This represents the repair time of each component of the equipment under each task operating mode. The sum of the fault detection time and fault isolation time of each component is obtained by combining the fault detection time at each test point, the fault isolation time of fuzzy groups with different fuzzinesses, and the diagnostic strategy. The diagnostic strategy is obtained through test-based modeling and simulation, and includes: detecting a set of test points corresponding to the faults of each key component and the fuzziness of the corresponding ultimately detected fuzzy group. The sum of the fault detection time and fault isolation time of the b-th component of the equipment under each task operating mode is... T Db Calculate using the following formula: ； In the formula: A represents the number of test points used to detect whether the b-th component is faulty; t a This represents the fault detection time of the a-th test point in a set of test points used to detect whether the b-th component is faulty. t b This represents the fault isolation time for detecting whether the b-th component is faulty; The sum of the fault detection time and fault isolation time for all components of the equipment is obtained. T Db Based on this, the sum of the fault detection time and fault isolation time of each component is sampled according to the randomness of the failure of each component of the equipment, thus obtaining the sum of the fault detection time and fault isolation time of the entire equipment. T D The average support delay time of each component of the equipment under each task operation mode; MLDT b Calculate using the following steps: S101: Impact of Regulations MLDT Factors affecting the indicator: spare parts delay, support equipment delay, and support team occupancy delay; S102: Evaluation Requirements MLDT The rating scale of the indicators and their influencing factors: {very short, short, medium, long, very long}; S103: Obtain the measurement values ​​of each influencing factor belonging to each evaluation level under each task operation mode of each component of the equipment. The measurement index adopts the 0.1-0.9 scaling method to obtain the fuzzy evaluation matrix R of each component of the equipment under each task operation mode. S104: Obtain the weight vector of each influencing factor for each component of the equipment under each mission operating mode. W ; S105: The following formula is used to calculate the operating conditions of each component of the equipment under each mission mode. MLDT The overall evaluation result S of the indicators: S=W×R ； In the formula: W This represents the weight vector of each influencing factor for each component of the equipment under each mission operating mode; R The fuzzy evaluation matrix represents the various components of the equipment under different mission operating modes; S106: Based on the comprehensive evaluation results obtained in step S105, determine the evaluation level and corresponding maximum membership degree of each component of the equipment in each task operation mode according to the maximum membership principle. U max (MLDT) ; S107: Regulations MLDT The corresponding indicator evaluation level MLDT Indicator value range: S108: The following formula is used to calculate the operating conditions of each component of the equipment under each mission mode. MLDT Indicator value: ； In the formula: U max (MLDT) This indicates the result obtained in step S107. MLDT The maximum membership degree of the indicator; 'a' represents the maximum membership degree. MLDT Location MLDT The minimum value in the range of indicator values; b represents the maximum membership degree. U max (MLDT) Location MLDT The maximum value b within the range of indicator values; The average maintenance delay time for all components of the equipment is obtained. MLDT b Based on this, the average maintenance delay time of each component is sampled according to the randomness of failure of each component of the equipment, and the average maintenance delay time of the equipment is obtained. MLDT ; S2: Sampling of key design index data for the reliability and testability design schemes of the equipment under various mission operating modes, and substituting the sampled data into the collaborative optimization model. This yields multiple availability data points for the equipment under various task operating modes. A； S3: Determine the minimum required availability of equipment in each mission's operational mode. A min Determine multiple availability data points for equipment under various mission operating modes. A Whether the system requirements are met depends on the availability data of multiple users in each task's working mode. A All are not less than the minimum usability requirement. A min At that time, the reliability and testability design scheme met the requirements; S4: If the reliability and testability design schemes meet the requirements, then the current reliability and testability design schemes are retained, meaning the collaborative modeling and optimization analysis of these schemes is completed. If the reliability and testability design schemes do not meet the requirements, then the reliability and testability parameters are optimized through adjustments to the reliability and testability design schemes. The key design performance data of the optimized reliability and testability design schemes are then sampled again, and the sampled data is substituted into the model. This process continues until the model's output meets the requirements, repeating multiple times to generate various reliability and testability design schemes and their key design performance data. S5: Combining key design index data from multiple reliability and testability design schemes, and using a multi-attribute decision-making-based reliability and testability design scheme optimization model, the optimal reliability and testability design scheme is selected.

2. The equipment reliability and testability collaborative modeling and optimization design method based on availability model according to claim 1, characterized in that: In step S4, the adjustment of the reliability design scheme and the testability design scheme includes: adjusting the hardware structure of the equipment, optimizing the reliability of the equipment components, adjusting the diagnostic strategy of the equipment, and optimizing the fault detection time of each test point of the equipment and the fault isolation time of each fuzzy group of the equipment.

3. The equipment reliability and testability collaborative modeling and optimization design method based on availability model according to claim 1, characterized in that: In step S5, the steps of collaborative modeling and optimization design scheme selection for equipment reliability testability based on multi-attribute decision-making include: S501: Specifies the number of sorting operations per operation for the scheme. n Determine the weighting coefficients for the mean time before repair. The weighting coefficient of the sum of fault detection time and fault isolation time Weighting coefficient for average guarantee delay time ; S502: Sample one set of decision attribute data for each design scheme, including three types of data: average time before repair. MTTR The sum of fault detection time and fault isolation time T D Average guarantee delay time MLDT ; S503: Normalize the data of the same type from multiple sampled design schemes under the same task working mode using the following formula to obtain the normalized values ​​of various types of data for each design scheme: In the formula: I represents the number of design schemes; MTTR i Indicates the first i Average pre-repair time data for each design scheme; Indicates the first i Normalized values ​​of the mean time before repair data for each design scheme; ； In the formula: I represents the number of design schemes; T Di Indicates the first i The sum of fault detection time and fault isolation time for each design scheme; Indicates the first i The normalized value of the sum of fault detection time and fault isolation time for each design scheme; ； In the formula: I represents the number of design schemes; MLDT i Indicates the first i Average support delay time data for each design scheme; Indicates the first i Normalized values ​​of the average guarantee delay time data for each design scheme; S504: Calculate the overall ranking value for each scheme using the following formula. A i : ； In the formula: A i Indicates the first i The overall ranking value of the design schemes; The weighting coefficients for the average time before repair are shown. The weighting coefficient representing the sum of fault detection time and fault isolation time; The weighting coefficient represents the average delay time in ensuring service availability; Indicates the first i Normalized values ​​of the mean time before repair data for each design scheme; Indicates the first i The normalized value of the sum of fault detection time and fault isolation time for each design scheme; Indicates the first i Normalized values ​​of the average guarantee delay time data for each design scheme; S505: Perform a single sorting of multiple schemes based on the comprehensive sorting value, with the scheme with the smaller comprehensive sorting value ranked first, and record the sorting result; S506: Determine whether the number of sorting iterations for each scheme has reached the specified number of sorting iterations n. If not, return to step S502; if so, calculate the probability of each scheme at each sorting position using the following formula. N K : ； In the formula: a K This indicates the position of the design scheme. K The number of times; n represents the total number of sorting attempts; S507: Compare the ranking positions of each solution K Possibility N K The larger the size, the higher the probability of success, the more likely the solution will be placed in the correct position. K If the reliability of the two schemes is equal, the probability of the next position is compared until the final reliability and testability design scheme ranking structure result is obtained.

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