Large complex system reliability test and evaluation method based on multiple distributions and related equipment

Abstract

The invention discloses a large-scale complex system reliability test and evaluation method based on multiple distributions and related equipment. The method comprises the following steps: determining a to-be-tested system based on a task unit of a large-scale system, and obtaining a reliability block diagram and type data of each product; determining the tightening system test duration of the tested sample machine in each product based on the tightening ratio; performing a laboratory reliability test on the large-scale system based on the test duration of the tightening system to obtain first test data; independently taking out the electronic and electrical part to carry out a laboratory reliability test, and carrying out a system-level bench test on a product of the electromechanical part or collecting live operation data to obtain second test data; and performing corresponding reliability evaluation on the products of different distribution types to obtain the reliability of the products of each distribution type, and finally converting according to the system reliability block diagram to obtain the system reliability. The reliability verification implementation problem of a large-scale system can be solved, and the method can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and related equipment for reliability testing and evaluation of large and complex systems based on multiple distributions. Background Technology

[0002] With the continuous development of production and technology, the requirements for equipment reliability indicators are also increasing. Using conventional standards for testing has significant limitations: on the one hand, the large size of the products makes testing difficult to implement; on the other hand, the testing costs are extremely high. Furthermore, conventional standards are only applicable to single-distribution scenarios and cannot cover systems with diverse distribution characteristics, let alone solve the evaluation problem of the smallest functional unit. Summary of the Invention

[0003] The main objective of this invention is to provide a method, apparatus, electronic device, storage medium, and program product for reliability testing and evaluation of large and complex systems based on multiple distributions, aiming to solve at least one problem of the prior art.

[0004] To achieve the above objectives, one aspect of this invention proposes a reliability testing and evaluation method for large and complex systems based on multiple distributions, the method comprising: Based on the task units of a large system, the system to be tested is determined, and the reliability block diagram of the system to be tested and the type data of each product are obtained. Among them, the type data includes product type and distribution type. Product type includes electronic and electrical parts and electromechanical parts, and distribution type includes exponential distribution, binomial distribution and normal distribution. Obtain the compression ratio for each product, and determine the test duration of the compression system of the sample tester for each product based on the compression ratio; Laboratory reliability tests were conducted on large systems based on the test duration of the compressed system, and the first test data was obtained. The electronic and electrical components within the mechanical systems of a large system are isolated for laboratory reliability testing. The electromechanical components performing the task are subjected to bench tests under normal temperature conditions along with the large system, or real-time operating data of the system is directly collected to obtain secondary test data. Among these, the mechanical structural components within the mechanical systems are not subject to laboratory reliability testing. Based on the first test data and / or the second test data, the corresponding reliability assessments are performed on products of different distribution types to obtain the reliability of each distribution type of product. Based on the reliability of each product, system reliability indicators are derived from the system reliability block diagram.

[0005] In some embodiments, obtaining the compression ratio for each product includes the following steps: Obtain the equipment composition and the number of sample testers for each product; The compression ratio of the corresponding product is determined by the ratio of the number of equipment components to the number of test samples.

[0006] In some embodiments, determining the compression system test duration for each product in the sample testing machine based on the compression ratio includes the following steps: The product of the original system test duration and the compression ratio corresponding to each product is taken as the compression system test duration of the sample machine for the corresponding product. The original system test duration is determined based on the ratio of the total test duration to the number of equipment units in the system to be tested. The total test duration is determined based on the product of the statistical scheme time coefficient and the equipment mean time between failures index.

[0007] In some embodiments, both the first test data and the second test data include the effective test time and the number of at-fault failures. When the distribution type is an exponential distribution, the reliability of products with different distribution types is evaluated accordingly to obtain the reliability of products with each distribution type, including the following steps: Based on the test duration of the compressed system, the number of responsible failures, and the preset confidence level, the average critical failure interval time is constructed by combining the chi-square distribution. The expression for the mean time between serious failures (MTBF) is as follows: ; In the formula, T Indicates the duration of the compression system test. r This represents the number of faults caused by negligence, and c represents the confidence level. Describing the degrees of freedom as The upper quantile of the (1-c) side of the chi-square distribution; The reliability of products with an exponential distribution is obtained by using the ratio of negative effective test time to mean time between serious failures as the exponential constant.

[0008] In some embodiments, when the distribution type is a binomial distribution, both the first and second test data include the number of tests and the number of failures. Reliability assessments are performed on products with different distribution types to obtain the reliability of each distribution type, including the following steps: When the number of failures is 0, the reliability of the binomial distribution product is constructed using the first formula based on the preset confidence level and the number of tests. The expression for the first formula is: ; In the formula, Indicates reliability. Represents the number of trials. Indicates the confidence level; When the number of failures is greater than or equal to 1, the reliability of the binomial distribution product is derived using the second formula based on the preset confidence level and the number of tests. The expression for the second formula is: ; In the formula, Indicates the number of failures. Indicates the first Number of failures It represents factorial.

[0009] In some embodiments, when the distribution type is a normal distribution, both the first and second experimental data include the number of samples and the sample value of each sample. Reliability assessments are performed on products with different distribution types to obtain the reliability of each distribution type, including the following steps: The average value of all samples is obtained by averaging all sample values ​​based on the sample size. The sample deviation is calculated based on the number of samples, the sample value of each sample, and the sample mean. The expression for the sample deviation is: ; In the formula, Indicates the sample deviation. Indicates the number of samples. Indicates the first The sample value of a sample. This represents the sample mean. The allowable limit coefficient is constructed based on the ratio of the absolute value of the difference between the preset technical indicator limit and the sample mean to the sample deviation. Based on the sample mean, sample deviation, and tolerance coefficient, the reliability of normally distributed products is obtained by looking up a table using a preset standard.

[0010] In some embodiments, based on the reliability of each product, a system reliability index is obtained by transforming the system reliability block diagram, including the following steps: The number of products is determined based on the system reliability block diagram, and then the system reliability index of the system under test is obtained by using the third formula in combination with the reliability of the corresponding products. The expression for the third formula is: ; In the formula, Indicates system reliability. Indicates the first The reliability of each product This indicates the number of products.

[0011] To achieve the above objectives, another aspect of the present invention proposes a reliability testing and evaluation device for large and complex systems based on multiple distributions. The device includes: The data acquisition module is used to determine the system to be tested based on the task units of the large system, and to acquire the reliability block diagram of the system to be tested and the type data of each product. Among them, the type data includes product type and distribution type. The product type includes electronic and electrical parts and electromechanical parts, and the distribution type includes exponential distribution, binomial distribution and normal distribution. The duration determination module is used to obtain the compression ratio of each product and determine the compression system test duration of the sample tester for each product based on the compression ratio. The first test module is used to conduct laboratory reliability tests on large systems based on the test duration of the compressed system, and obtain the first test data. The second test module is used to isolate the electronic and electrical components within the mechanical system of a large system for laboratory reliability testing. The electromechanical components performing the task are subjected to bench tests under normal temperature conditions along with the large system, or the system's real-time operating data is directly collected to obtain the second test data. Among these, the mechanical structural components within the mechanical system are not subject to laboratory reliability testing. The reliability device-level assessment module is used to perform corresponding reliability assessments on products of different distribution types based on the first test data and / or the second test data, and to obtain the reliability of each distribution type of product. The reliability system evaluation module is used to convert the system reliability index based on the reliability of each product and the system reliability block diagram.

[0012] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0013] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0014] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0015] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a method, apparatus, electronic device, storage medium, and program product for reliability testing and evaluation of large and complex systems based on multiple distributions. This scheme determines the system to be tested based on task units of the large system, obtains the reliability block diagram of the system to be tested, and type data for each product; wherein, the type data includes product type and distribution type, the product type includes electronic and electrical parts and electromechanical parts, and the distribution type includes exponential distribution, binomial distribution, and normal distribution; obtains the compression ratio of each product, and determines the compression system test duration of the test machine for each product based on the compression ratio; and adjusts the large system reliability test duration based on the compression system test duration. The system undergoes laboratory reliability testing to obtain first test data. The electronic and electrical components within the mechanical systems of the large system are then isolated for separate laboratory reliability testing. The electromechanical components performing the tasks undergo bench testing under ambient temperature conditions along with the large system, or real-time system operation data is collected directly to obtain second test data. Mechanical structural components within the mechanical systems are not subject to laboratory reliability testing. Based on the first and / or second test data, corresponding reliability assessments are performed on products of different distribution types to obtain the reliability of each distribution type. Based on the reliability of each product, a system reliability index is derived according to the system reliability block diagram. Since the mechanical structures that primarily function as the framework in a large system are not sensitive to comprehensive environmental stress and have high reliability indices, this embodiment of the invention conducts targeted tests on various product types within the task units of the large system (electronic and electrical components undergo separate laboratory reliability testing, while electromechanical components undergo bench testing along with the large system) to obtain test data. This overcomes the difficulty of implementing such tests in large systems, allowing for corresponding reliability assessments based on product distribution categories. Finally, the reliability of various products is summarized and transformed according to the system reliability block diagram to obtain the system reliability index. The embodiments of the present invention can effectively solve the technical problems of large equipment being limited in implementation due to its large size, lack of verification methods for the smallest functional unit system indicators, and the need for comprehensive reliability assessment of multi-distribution type systems by integrating multi-dimensional data. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of an implementation environment for a method for conducting reliability testing and evaluation of large and complex systems based on multiple distributions, as provided in an embodiment of the present invention. Figure 2 This is a flowchart illustrating a reliability testing and evaluation method for large and complex systems based on multiple distributions, provided by an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating an example of a system reliability block diagram provided in an embodiment of the present invention; Figure 4This is a schematic diagram of the structure of a reliability testing and evaluation device for large and complex systems based on multiple distributions provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0019] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0021] In related technologies, on the one hand, the large size of the product makes testing difficult to implement; on the other hand, the testing cost is extremely high. In addition, conventional standards are only applicable to single distribution scenarios and cannot cover systems with multiple distribution characteristics, let alone solve the evaluation problem of the smallest functional unit.

[0022] In view of this, this invention provides a method and related equipment for reliability testing and evaluation of large and complex systems based on multiple distributions. This method determines the system to be tested based on task units of the large system, obtains the reliability block diagram of the system to be tested and type data for each product. The type data includes product type and distribution type; product type includes electronic and electrical parts and electromechanical parts; distribution type includes exponential distribution, binomial distribution, and normal distribution. The method obtains the compression ratio of each product and determines the compression system test duration for each product based on the compression ratio. Based on the compression system test duration, a laboratory reliability test is conducted on the large system to obtain first test data. The electronic and electrical parts within the mechanical system of the large system are isolated for laboratory reliability testing, while the electromechanical parts performing the task are subjected to bench tests under normal temperature conditions along with the large system, or real-time system operation data is directly collected to obtain second test data. The mechanical structural components within the mechanical system are not subject to laboratory reliability testing. Based on the first test data and / or the second test data, corresponding reliability assessments are performed on products of different distribution types to obtain the reliability of each distribution type. Based on the reliability of each product, a system reliability index is obtained by converting the system reliability block diagram. Since the mechanical structures that primarily serve as the framework in large systems are not sensitive to comprehensive environmental stresses and have high reliability indicators, this invention provides targeted testing for various product types within the task units of large systems (electronic and electrical components undergo separate laboratory reliability testing, while electromechanical components undergo bench testing alongside the large system) to obtain test data. This addresses the challenge of implementation limitations in large systems, allowing for corresponding reliability assessments based on product distribution categories. Finally, the reliability of various products is summarized and transformed according to the system reliability block diagram to obtain system reliability indicators. This invention effectively solves the technical challenges of implementation limitations due to the large size of large equipment, the lack of verification methods for the smallest functional unit system indicators, and the integration of diverse data for comprehensive reliability assessment of systems with multiple distribution types.

[0023] It is understood that the reliability testing and evaluation method for large and complex systems based on multiple distributions provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiments is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0024] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0025] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0026] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0027] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0028] For example, based on Figure 1The implementation environment shown in this embodiment of the invention provides a reliability testing and evaluation method for large and complex systems based on multiple distributions. The following description uses the application of this reliability testing and evaluation method for large and complex systems based on multiple distributions in server 101 as an example. It can be understood that this reliability testing and evaluation method for large and complex systems based on multiple distributions can also be applied to terminal 102.

[0029] Reference Figure 2 , Figure 2 This is an optional flowchart of a reliability testing and evaluation method for large and complex systems based on multiple distributions provided in an embodiment of the present invention. The execution subject of this reliability testing and evaluation method for large and complex systems based on multiple distributions can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S600.

[0030] Step S100: Determine the system to be tested based on the task units of the large system, and obtain the reliability block diagram of the system to be tested and the type data of each product. Among them, the type data includes product type and distribution type. Product type includes electronic and electrical parts and electromechanical parts. Distribution type includes exponential distribution, binomial distribution and normal distribution. For example, in some specific implementations, the system reliability block diagram serves as the architectural basis for the final summary of system reliability assessment, while the distribution type of each product serves as the type benchmark for subsequent reliability assessments of different distribution types.

[0031] Step S200: Obtain the compression ratio of each product, and determine the test duration of the compression system of the sample tester for each product based on the compression ratio; It should be noted that, in some embodiments, obtaining the compression ratio of each product may include the following steps: obtaining the number of equipment components and the number of test samples for each product; and determining the compression ratio of the corresponding product based on the ratio of the number of equipment components to the number of test samples.

[0032] For example, in some specific implementations, the system composition is analyzed and the compression ratio is determined. This refers to the equipment reduction ratio.

[0033] It should be noted that, in some embodiments, determining the compression system test duration of the test sample machine for each product based on the compression ratio may include the following steps: multiplying the original system test duration by the compression ratio corresponding to each product as the compression system test duration of the test sample machine for the corresponding product; wherein, the original system test duration is determined based on the ratio of the total test duration to the number of equipment units in the system to be tested, and the total test duration is determined based on the product of the statistical scheme time coefficient and the equipment mean time between failures index.

[0034] For example, in some specific implementations, The confirmation is based on the principle of determining test time in the GJB899A-2009 Reliability Statistical Scheme. ,in The testing time required for a single unit (set) of equipment. For statistical scheme time coefficient, To equip equipment with MTBF (Mean Time Between Failures) metrics, The total duration of the experiment, The number of equipment units (sets) is used to determine the number of equipment units. The test duration can be allocated based on this number of equipment units. Thus, when the number of equipment units decreases, the corresponding test duration increases by a multiple, which determines the test duration of the compression system.

[0035] Examples of specific application scenarios are shown below (see Table 1): Table 1

[0036] Step S300: Conduct laboratory reliability tests on the large system based on the test duration of the compressed system to obtain the first test data; For example, in some specific implementations, since the mechanical structure is not sensitive to the overall environment and has a high reliability index, the electronic and electrical parts (exponentially distributed product devices) in the mechanical system are taken out separately to carry out laboratory reliability tests, thereby solving the problem that large systems cannot be implemented.

[0037] Step S400: Take out the electronic and electrical components of the mechanical system in the large system separately and conduct laboratory reliability tests. For the electromechanical components that perform the task, conduct bench tests under normal temperature conditions along with the large system or directly collect the system's real-time operating data to obtain the second test data. Laboratory reliability tests are not conducted on mechanical structural components within mechanical systems. For example, in some specific implementations, the binomial distribution (corresponding to success-failure type products) can be carried out based on the number of tasks within the task duration, and the total number of tests and the number of failures can be counted. Alternatively, historical data of the same technical state can be directly used. The normal distribution (corresponding to fatigue and wear-prone products) can be tested using n samples, and technical parameters can be recorded. Specifically, the system as a whole can carry out system-level bench tests or collect real-time operation data under normal temperature conditions. When testing or collecting real-time operation data for products with binomial and normal distributions, the relevant operations can be executed directly based on the preset task time, or the relevant operations can be implemented based on the time parameter index of the compressed system test duration corresponding to the corresponding product.

[0038] Step S500: Based on the first test data and / or the second test data, perform corresponding reliability assessments on products of different distribution types to obtain the reliability of each distribution type of product; The reliability assessment of the corresponding distribution type can be performed using either the first or the second experimental data, or the first and the second experimental data can be fused (e.g., by averaging the data or by weighted summation based on preset weights), and then the reliability assessment of the corresponding distribution type can be performed based on the fusion result.

[0039] It should be noted that both the first and second test data include the effective test time and the number of attributable failures. In some embodiments, when the distribution type is an exponential distribution, the reliability assessment of products with different distribution types is performed to obtain the reliability of each distribution type. This may include the following steps: constructing the mean critical failure interval (MCLE) based on the compressed system test duration, the number of attributable failures, and a preset confidence level, combined with a chi-square distribution; wherein, the expression for the mean critical failure interval is: ; In the formula, T Indicates the duration of the compression system test. r This represents the number of faults caused by negligence, and c represents the confidence level. Describing the degrees of freedom as The upper quantile of the (1-c) side of the chi-square distribution; The reliability of products with an exponential distribution is obtained by using the ratio of negative effective test time to mean time between serious failures as the exponential constant.

[0040] For example, in some specific implementations, the reliability assessment of the exponential distribution can be achieved as follows:

[0041]

[0042] In the formula, t: effective test time; Degrees of freedom are of Chi-square distribution upper quantile; Number of faults due to negligence; Confidence level: In the experiment, the confidence level can be set to 80% (this can be adjusted according to actual needs). : Reliability effective test time; MTBCF (Mean Time between Component Failures).

[0043] It should be noted that both the first and second test data include the number of tests and the number of failures. In some embodiments, when the distribution type is binomial distribution, the reliability assessment of products with different distribution types is performed to obtain the reliability of each distribution type. This may include the following steps: when the number of failures is 0, based on a preset confidence level and the number of tests, the reliability of the binomial distribution product is constructed using a first formula; wherein, the expression of the first formula is: ; In the formula, Indicates reliability. Represents the number of trials. Indicates the confidence level; When the number of failures is greater than or equal to 1, the reliability of the binomial distribution product is derived using the second formula based on a preset confidence level and the number of tests; the expression of the second formula is: ; In the formula, Indicates the number of failures. Indicates the first Number of failures It represents factorial.

[0044] For example, in some specific implementations, the reliability assessment of the binomial distribution can be achieved as follows:

[0045]

[0046] In the formula: —Number of trials; —Number of failures; —Confidence level; —Reliability.

[0047] It should be noted that when the distribution type is normal, both the first and second experimental data include the number of samples and the sample value of each sample. In some embodiments, the reliability assessment of products with different distribution types is performed to obtain the reliability of products with each distribution type, which may include the following steps: averaging all sample values ​​based on the number of samples to obtain the sample average; calculating the sample deviation based on the number of samples, the sample value of each sample, and the sample average; wherein, the expression for the sample deviation is: ; In the formula, Indicates the sample deviation. Indicates the number of samples. Indicates the first The sample value of a sample. This represents the sample mean. The tolerance limit coefficient is constructed based on the ratio of the absolute value of the difference between the preset technical indicator limit and the sample mean to the sample deviation; based on the sample mean, sample deviation and tolerance limit coefficient, the reliability of the normally distributed product is obtained by looking up a table using preset standards.

[0048] For example, in some specific implementations, the reliability assessment of the normal distribution can be achieved as follows:

[0049]

[0050]

[0051] In the formula: — Sample number; — Sample; —Sample mean; —Sample deviation; —Technical indicator limits; —Permissible limit coefficient.

[0052] The reliability R can be obtained by referring to the table in GB / T4885-2009.

[0053]

[0054] In the formula: Represents the normal distribution function. For reliability, specifically, the relationship between permissible limits and reliability (R) is mainly reflected in the statistical methods of quality control and reliability assessment. According to GB / T 4885-2009, permissible limits are usually used as a component of specification limits (such as two-sided or one-sided specification limits) to determine the confidence interval of product parameters. Specifically, permissible limits can be converted into reliability indicators using statistical methods (such as table lookup) to evaluate the functional stability of a product under different times and conditions.

[0055] Step S600: Based on the reliability of each product, the system reliability index is obtained by converting the system reliability block diagram. It should be noted that in some embodiments, step S600 may include the following steps: determining the number of products based on the system reliability block diagram, and then using the third formula to convert the reliability of the corresponding products to obtain the system reliability index of the system under test; wherein, the expression of the third formula is: ; In the formula, Indicates system reliability. Indicates the first The reliability of each product This indicates the number of products.

[0056] Exemplary examples, such as in some specific implementations, Figure 3 As shown, the system reliability (i.e., system reliability) can be calculated based on the system reliability block diagram. An example is shown below:

[0057] in, For system reliability, This indicates the reliability of product A. This represents the reliability of product B, and so on. It's important to note that products in different product distributions can be the same. For example, product C may have two product distributions, each with a dimension of 2. Simplified to .

[0058] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0059] First, it should be noted that the existing reliability testing and evaluation technologies include: 1) Test method: Conduct reliability tests by designing statistical schemes and profiles according to reliability test standards (common standard GJB899A-2009). If the test results meet the requirements, the test is considered successful; otherwise, it is not.

[0060] 2) Comprehensive evaluation method: The reliability level of the product is evaluated by overlaying historical usage data or data from similar products.

[0061] However, existing technologies have the following drawbacks: 1) Test method: GJB899A-2009 is only applicable to the exponential distribution and not to other distributions.

[0062] 2) Comprehensive evaluation cannot solve complex systems with multiple types of distributions.

[0063] 3) Unable to solve the following engineering problems: a) Existing testing equipment for large and complex systems is limited, making it impossible to conduct such tests; b) The system only provides some equipment (fully functional), i.e., the smallest functional unit, and no solution is given on how to implement it.

[0064] Therefore, for large and complex systems with multi-distribution characteristics, how to effectively conduct experiments, how to evaluate the smallest functional units, and how to establish a comprehensive evaluation system for multi-distribution systems have become key issues of significant engineering value. Against this backdrop, embodiments of the present invention propose a systematic and implementable solution. These embodiments provide a reliability test and evaluation method for large and complex systems based on multiple distributions, specifically implemented as follows: S1, Determination of test time.

[0065] Based on the fact that complex systems contain multiple types of distributed products, the specific decomposition by category is as follows: (1) Exponential distribution, such as in electronics and electrical engineering: S1-1, Calculate the required testing time for a single system: ,in For reliability scheme coefficients, The minimum acceptable value for MTBCF. The number of test kits. For task duration, This is a reliability indicator.

[0066] S1-2, Review the system composition, determine the compression ratio, and calculate the testing time required for each piece of equipment in a single minimum functional system. Examples are shown in Table 1 above.

[0067] S1-2-1, This refers to the equipment reduction ratio. The confirmation is based on the principle of determining test time in the GJB899A-2009 Reliability Statistical Scheme. Where T represents the testing time required for a single unit (set) of equipment. For statistical scheme time coefficient, To equip equipment with MTBF (Mean Time Between Failures) metrics, The total duration of the experiment, The number of equipment units (sets) is used to determine the number of equipment units. The test duration can be allocated based on this number of equipment units. Thus, when the number of equipment units decreases, the corresponding test duration increases by a multiple, which determines the test duration of the compression system.

[0068] (2) Binomial distribution, success-failure products: Conduct trials based on the duration and number of trials, and statistically analyze the total number of trials and the number of failures. Alternatively, historical data from the same technical state can be directly used.

[0069] (3) Normally distributed, fatigue- and wear-prone products: The experiment was conducted using n samples, and the technical parameters were recorded.

[0070] S2, conduct the test according to the test profile given in the standard or technical document. For large systems that are too large to be tested inside the enclosure, the following engineering solution is provided: S2-1, the system as a whole conducts system-level bench tests or collects real-time operation data under normal temperature conditions.

[0071] S2-2, based on the fact that the mechanical structure is not sensitive to the overall environment and has a high reliability index, the electronic and electrical parts in the mechanical system are separated to carry out laboratory reliability tests, thereby solving the problem that large systems cannot be implemented.

[0072] The valid data from S2-3, S2-1, and S2-2 are used together as a data source to evaluate reliability indicators. System-level data is then distributed to each subsystem to obtain the subsystem test duration. , , Then, the data from each subsystem is summed with the allocated data to obtain the total duration of each subsystem. System-level metrics are then calculated from the final data of each subsystem (S4 is one method).

[0073] S2-3-1, In special circumstances, the following engineering measures may be adopted: At the system level, a specified duration may be implemented ( For the bench test, only the electronic and electrical components other than the mechanical system in S2-2 need to be individually verified under high and low temperature conditions, excluding ambient temperature conditions. These ambient temperature conditions are derived from the typical integrated test profiles for various types of equipment given in GJB899A-2009. Since the ambient temperature conditions have already been verified on the bench, only the portion of the integrated test profile excluding the ambient temperature section needs to be verified.

[0074] S3, Data Processing. For systems with multiple distribution types, the following data processing is performed: S3-1, Exponential Distribution:

[0075]

[0076] In the formula, t: effective test time; Degrees of freedom are of Chi-square distribution upper quantile; Number of faults due to negligence; Confidence level: In the experiment, the confidence level can be set to 80% (this can be adjusted according to actual needs). : Reliability effective test time; MTBCF (Mean Time between Component Failures).

[0077] S3-2, binomial distribution:

[0078] In the formula: —Number of trials; —Number of failures; —Confidence level; —Reliability.

[0079] S3-3, Normal distribution:

[0080]

[0081]

[0082] In the formula: — Sample number; — Sample; —Sample mean; —Sample deviation; —Technical indicator limits; —Permissible limit coefficient.

[0083] The reliability R can be obtained by referring to the table in GB / T4885-2009.

[0084]

[0085] In the formula: Let R represent the normal distribution function, and R be the reliability.

[0086] S4, System Reliability Assessment. From S3-1, S3-2, and S3-3, reliability indices for products with exponential, binomial, and normal distributions can be obtained, corresponding to different subsystem indices, such as... Figure 3 As shown in the diagram, the system reliability can be calculated from the system reliability block diagram. An example is as follows:

[0087] In summary, this invention proposes a complete technical solution that effectively addresses the technical challenges of implementation limitations due to the large size of large equipment, the lack of verification methods for the minimum functional unit system indicators, and the integration of multi-dimensional data for comprehensive reliability assessment of multi-distributed systems. Specifically, this invention utilizes the minimum functional unit to assess system reliability indicators, thereby solving the engineering problem of the difficulty in conducting reliability tests on large systems due to limitations in commercially available equipment, and providing a reliability assessment scheme applicable to multi-distributed systems. Specifically, this invention incorporates a compaction ratio to assess the reliability indicators of the entire system based on the minimum functional unit. Furthermore, considering the sensitivity of electronic and electrical products to comprehensive environmental stress, while mechanical structural components are not, it provides a feasible solution for situations where it is impossible to conduct complete large-scale system tests in a laboratory. Simultaneously, based on the distribution characteristics of various products within the system, differentiated verification methods are employed to construct a comprehensive assessment system applicable to multi-type distributed systems.

[0088] Specifically, large systems typically include large mechanical structural components. Since mechanical structures are not sensitive to comprehensive environmental stresses and have high reliability indicators, this invention adopts the following engineering methods to conduct reliability tests on large and complex systems: the electronic and electrical components within the mechanical system are separated for laboratory reliability testing, while the mechanical structural components are not subjected to laboratory reliability testing; the electromechanical / mechanical components that perform actions / tasks are subjected to bench tests under normal temperature conditions along with the system, or the system's real-time operating data is directly collected; thereby resolving the problem that large systems cannot be implemented.

[0089] Compared with related technologies, the present invention provides a technical solution with the following beneficial effects: 1. Provide a method for evaluating and verifying system reliability indicators using the smallest functional unit; 2. Provide a solution for large equipment that cannot all be placed in the test chamber; 3. Provide a set of methods for reliability assessment of complex systems with multiple distribution types.

[0090] like Figure 4 As shown, this embodiment of the invention also provides a reliability testing and evaluation device 900 for large and complex systems based on multiple distributions, which can implement the above-described method. This device may include: The data acquisition module 910 is used to determine the system to be tested based on the task unit of the large system, and to acquire the reliability block diagram of the system to be tested and the type data of each product; wherein, the type data includes product type and distribution type, the product type includes electronic and electrical parts and electromechanical parts, and the distribution type includes exponential distribution, binomial distribution and normal distribution; The duration determination module 920 is used to obtain the compression ratio of each product and determine the compression system test duration of the sample machine for each product based on the compression ratio; The first test module 930 is used to conduct laboratory reliability tests on large systems based on the test duration of the compressed system to obtain the first test data; The second test module 940 is used to isolate the electronic and electrical components within the mechanical system of a large system for laboratory reliability testing. The electromechanical components performing the task are subjected to bench tests under normal temperature conditions along with the large system, or the system's real-time operating data is directly collected to obtain the second test data. Among these, the mechanical structural components within the mechanical system are not subject to laboratory reliability testing. The reliability device-level evaluation module 950 is used to perform corresponding reliability evaluations on products of different distribution types based on first test data and / or second test data, and to obtain the reliability of each distribution type of product. The reliability system evaluation module 960 is used to convert the system reliability index based on the reliability of each product and the system reliability block diagram.

[0091] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0092] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0093] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0094] like Figure 5 As shown, Figure 5 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0095] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0096] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0097] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0098] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0099] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0100] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0101] The present invention provides a method, apparatus, electronic device, storage medium, and program product for reliability testing and evaluation of large and complex systems based on multiple distributions. It determines the system under test by identifying task units based on the large system, and obtains a reliability block diagram and type data for each product. The type data includes product type and distribution type; product types include electronic / electrical components and electromechanical components; distribution types include exponential distribution, binomial distribution, and normal distribution. It obtains the compression ratio for each product and determines the compression system test duration for each product based on the compression ratio. Based on the compression system test duration, it conducts laboratory reliability testing on the large system to obtain first test data. It separately conducts laboratory reliability testing on the electronic / electrical components within the mechanical system of the large system, and conducts bench tests under normal temperature conditions or directly collects real-time system operation data for the electromechanical components performing the tasks, obtaining second test data. The mechanical structural components within the mechanical system are not subject to laboratory reliability testing. Based on the first and / or second test data, it performs corresponding reliability assessments on products of different distribution types to obtain the reliability of each distribution type. Based on the reliability of each product, it transforms the system reliability block diagram to obtain system reliability indicators. Since the mechanical structures that primarily serve as the framework in large systems are not sensitive to comprehensive environmental stresses and have high reliability indicators, this invention provides targeted testing for various product types within the task units of large systems (electronic and electrical components undergo separate laboratory reliability testing, while electromechanical components undergo bench testing alongside the large system) to obtain test data. This addresses the challenge of implementation limitations in large systems, allowing for corresponding reliability assessments based on product distribution categories. Finally, the reliability of various products is summarized and transformed according to the system reliability block diagram to obtain system reliability indicators. This invention effectively solves the technical challenges of implementation limitations due to the large size of large equipment, the lack of verification methods for the smallest functional unit system indicators, and the integration of diverse data for comprehensive reliability assessment of systems with multiple distribution types.

[0102] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0103] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0104] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0106] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A reliability testing and evaluation method for large and complex systems based on multiple distributions, characterized in that, The method includes the following steps: The task unit of the large system determines the system to be tested, and obtains the reliability block diagram of the system to be tested and the type data of each product; wherein, the type data includes product type and distribution type, the product type includes electronic and electrical parts and electromechanical parts, and the distribution type includes exponential distribution, binomial distribution and normal distribution; Obtain the compression ratio for each product, and determine the compression system test duration for each product in the sample testing machine based on the compression ratio; Based on the test duration of the compression system, a laboratory reliability test was conducted on the large system to obtain the first test data; The electronic and electrical components within the mechanical system of the large system are isolated for laboratory reliability testing. The electromechanical components performing the task are subjected to bench tests under normal temperature conditions along with the large system, or real-time system operation data is directly collected to obtain second test data. Among these, the mechanical structural components within the mechanical system are not subject to laboratory reliability testing. Based on the first test data and / or the second test data, a corresponding reliability assessment is performed on products of different distribution types to obtain the reliability of each distribution type of product. Based on the reliability of each product, the system reliability index is derived from the system reliability block diagram.

2. The method according to claim 1, characterized in that, Obtaining the compression ratio for each product includes the following steps: Obtain the equipment composition and number of sample testers for each product; The compression ratio of the corresponding product is determined based on the ratio of the number of equipment components to the number of test samples.

3. The method according to claim 1, characterized in that, The determination of the compression system test duration for each product based on the compression ratio includes the following steps: The product of the original system test duration and the compression ratio corresponding to each product is taken as the compression system test duration of the test sample machine in the corresponding product. The original system test duration is determined based on the ratio of the total test duration to the number of equipment units in the system to be tested, and the total test duration is determined based on the product of the statistical scheme time coefficient and the equipment mean time between failures index.

4. The method according to claim 1, characterized in that, Both the first and second test data include effective test time and the number of attributable failures. When the distribution type is the exponential distribution, the step of performing corresponding reliability assessments on products with different distribution types to obtain the reliability of each distribution type includes the following steps: Based on the test duration of the compression system, the number of responsible failures, and the preset confidence level, the average critical failure interval time is constructed using a chi-square distribution. The expression for the mean critical failure interval is: ； In the formula, T Indicates the duration of the compression system test. r This represents the number of faults caused by negligence, and c represents the confidence level. Describing the degrees of freedom as The upper quantile of the (1-c) side of the chi-square distribution; The reliability of the product with the exponential distribution is obtained by using the ratio of the negative effective test time to the mean critical failure interval as the exponent of the natural constant.

5. The method according to claim 1, characterized in that, When the distribution type is the binomial distribution, both the first and second test data include the number of tests and the number of failures. The step of performing corresponding reliability assessments on products with different distribution types to obtain the reliability of each distribution type includes the following steps: When the number of failures is 0, the reliability of the binomial distribution product is constructed using the first formula based on the preset confidence level and the number of tests. The expression for the first formula is: ； In the formula, Indicates reliability. Represents the number of trials. Indicates the confidence level; When the number of failures is greater than or equal to 1, the reliability of the binomial distribution product is derived using the second formula based on the preset confidence level and the number of tests. The expression for the second formula is: ； In the formula, Indicates the number of failures. Indicates the first Number of failures It represents factorial.

6. The method according to claim 1, characterized in that, When the distribution type is the normal distribution, both the first and second experimental data include the number of samples and the sample value of each sample. The step of performing corresponding reliability assessments on products with different distribution types to obtain the reliability of each distribution type includes the following steps: The average value of all the sample values ​​is obtained by averaging the sample size; Based on the number of samples, the sample value of each sample, and the sample average, the sample deviation is calculated. The expression for the sample deviation is as follows: ； In the formula, Indicates the sample deviation. Indicates the number of samples. Indicates the first The sample value of a sample. This represents the sample mean. An allowable limit coefficient is constructed based on the ratio of the absolute value of the difference between the preset technical indicator limit and the average value of the sample to the sample deviation. Based on the sample mean, the sample deviation, and the tolerance coefficient, the reliability of the normally distributed product is obtained by looking up a table using a preset standard.

7. The method according to any one of claims 1 to 6, characterized in that, The process of deriving system reliability indicators based on the reliability of each product and the system reliability block diagram includes the following steps: The number of products is determined based on the system reliability block diagram, and then the system reliability of the system under test is obtained by using the third formula in combination with the reliability of the corresponding products. The expression for the third formula is: ； In the formula, Indicates system reliability. Indicates the first The reliability of each product This indicates the number of products.

8. A reliability testing and evaluation device for large-scale complex systems based on multiple distributions, characterized in that, The device includes: The data acquisition module is used to determine the system to be tested based on the task units of the large system, and to acquire the reliability block diagram of the system to be tested and the type data of each product; wherein, the type data includes product type and distribution type, the product type includes electronic and electrical parts and electromechanical parts, and the distribution type includes exponential distribution, binomial distribution and normal distribution; The duration determination module is used to obtain the compression ratio of each product and determine the test duration of the compression system of the sample tester for each product based on the compression ratio; The first test module is used to conduct laboratory reliability tests on the large system based on the test duration of the compression system, and obtain first test data. The second test module is used to separately extract the electronic and electrical components within the mechanical system of the large system for laboratory reliability testing, and to conduct bench tests under normal temperature conditions on the electromechanical components performing the task along with the large system or to directly collect real-time system operation data to obtain the second test data; wherein, the mechanical structural components within the mechanical system are not subject to laboratory reliability testing; A reliability device-level evaluation module is used to perform corresponding reliability evaluations on products of different distribution types based on the first test data and / or the second test data, and to obtain the reliability of each distribution type of product. The reliability system evaluation module is used to convert the reliability of each product into a system reliability index based on the system reliability block diagram.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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