Electronic component reliability prediction method and system

Abstract

The invention relates to the technical field of electronic component reliability prediction, in particular to an electronic component reliability prediction method and system, a product structure tree of a target component is constructed, the product structure tree comprises superior nodes and subordinate nodes, each node stores a feature data set, the superior nodes comprise at least one subordinate node, and the subordinate nodes comprise at least one feature data set; based on the failure rate type of the subordinate node, calculating the failure rate of the subordinate node by adopting the feature data set, collecting the working condition data of the target component in real time, correcting the failure rate of the subordinate node based on the working condition data to obtain the corrected failure rate of the subordinate node, and calculating the failure rate of the superior node by adopting the corrected failure rate of the subordinate node. And inputting the failure rate of the superior node into a preset analysis function to obtain a reliability index value, and obtaining a reliability evaluation report based on a comparison result of a preset target threshold standard and the reliability index value. The problem of low reliability prediction precision of the target component in the prior art is solved, so that the prediction precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic component reliability prediction, and in particular to an electronic component reliability prediction method and system. BACKGROUND

[0002] With the rapid development of electronic technology, electronic equipment as the core carrier of modern industrial system, its reliability level is directly related to the stability and service life of system operation, and affects the safe operation and intelligent development of key fields of national economy. The reliability index of electronic components is an important basis for judging the reliability state of equipment and preventing system failure. Therefore, higher requirements are put forward for the accuracy, dynamic adaptability and engineering applicability of electronic component reliability prediction.

[0003] At present, in order to solve the problem of electronic equipment reliability prediction, the following means are often used in traditional methods, one is to calculate the failure rate based on the statistical model of the reliability prediction manual of electronic equipment, but this method does not consider the specific failure mechanism and environmental stress influence, two is to independently apply the failure physical model, and to establish a physical model for a specific failure mode, but it lacks the fusion ability of system level reliability; Three is to use the data fusion method such as Bayesian framework, which has the advantage of multi-parameter fusion in theory, but it lacks the support of real-time sensor monitoring data, and it is difficult to realize the dynamic correction of the model, so that the prediction result is out of touch with the actual working condition, resulting in low reliability prediction accuracy. SUMMARY

[0004] The present application aims to provide an electronic component reliability prediction method and system, which has the characteristics of improving the reliability prediction accuracy of target components.

[0005] The above invention of the present application is realized by the following technical scheme: An electronic component reliability prediction method, comprising: Constructing a product structure tree of the target component, the product structure tree comprising upper nodes and lower nodes, each node storing a feature data set, wherein the upper nodes comprise at least one lower node; Based on the failure rate type of the lower node, the feature data set is used to calculate the failure rate of the lower node; Real-time acquisition of working condition data of the target component, correction of the failure rate of the lower node based on the working condition data, to obtain the corrected failure rate of the lower node; Using the corrected failure rate of the lower node to calculate the failure rate of the upper node; Inputting the failure rate of the upper node and the failure rate of the lower node into a preset analysis function to obtain a reliability index value; Based on a comparison result of the preset target threshold standard and the reliability index value, a reliability evaluation report is obtained.

[0006] By adopting the technical scheme, the product structure tree containing the upper and lower nodes is constructed, the initial failure rate of the lower node is calculated based on the feature data set and the failure rate type, the failure rate of the lower node is dynamically corrected through the real-time collected working condition data, the failure rate of the upper node is determined according to the corrected failure rate of the lower node, the reliability index is obtained through the failure rate of the upper node, the preset target threshold standard is compared, and the report is generated, so that the accuracy and efficiency of the electronic component reliability prediction are improved.

[0007] In a preferred example, the application can be further configured to: Based on the failure rate type of the lower node, the feature data set is used to calculate the failure rate of the lower node, including: The non-working state environment parameters, the classification parameters and the duty cycle are obtained, wherein the duty cycle is the working time ratio of the target electronic component in the task cycle; When the failure rate type is the preset type, the preset failure rate value is used as the failure rate of the lower node; When the failure rate type is the calculation type, the initial failure rate is calculated based on the classification parameters and the feature data set; Based on the non-working state environment parameters, the initial failure rate and the duty cycle are used to calculate the failure rate of the lower node.

[0008] By adopting the technical scheme, the initial failure rate is calculated from the feature data set based on the classification parameters for the calculation type, and the actual stress influence in the task cycle is quantified by combining the non-working state environment parameters and the duty cycle to calculate the failure rate of the lower node, so that the calculation feasibility is ensured, and the accuracy of the reliability prediction is improved.

[0009] In a preferred example, the application can be further configured to: When the failure rate type is the calculation type, the initial failure rate is calculated based on the classification parameters and the feature data set, including: The initial failure rate includes a first failure rate and a second failure rate; The feature data set includes environment category parameters, part category parameters, part attribute parameters, failure parameters and failure method parameters; when the classification parameters are conventional, the part category parameters are input into a coupling analysis model to obtain the first failure rate; The second failure rate is calculated by using the correction coefficient and the first failure rate, wherein the correction parameter is determined based on the corresponding relationship between the environment category parameters and the non-working state environment parameters; When the classification parameter is the preset database, a corresponding failure rate value is matched from the preset database according to the part attribute parameter as the first failure rate, and the second failure rate is a preset value. When the classification parameter is self-defined, the failure parameter is input into a preset failure function based on the failure method parameter to determine the first failure rate, and the second failure rate is a preset value.

[0010] By adopting the above technical solutions, the application designs differentiated calculation paths for three types of classification parameters, namely, conventional, preset database and self-defined, to meet flexible calculation requirements in different scenarios and improve the accuracy of initial failure rate calculation under different classification parameters.

[0011] In a preferred example, the application can be further configured as: When the classification parameter is self-defined, the failure parameter is input into a preset failure function based on the failure method parameter to determine the first failure rate, and the second failure rate is a preset value. The failure method parameter includes failure rate method, test data extrapolation method, risk probability method and Arrhenius method. When the failure method parameter is the failure rate method, a preset failure rate is used as the first failure rate. When the failure method parameter is the test data extrapolation method, the failure parameter is input into a first preset failure function to obtain the first failure rate. When the failure method parameter is the risk probability method, the failure parameter is input into a second preset failure function to obtain the first failure rate; and when the failure method parameter is the Arrhenius method, the failure parameter is input into a third preset failure function to obtain the first failure rate.

[0012] By adopting the above technical solutions, the application directly determines the first failure rate as a preset value according to the failure rate method, respectively calls corresponding preset failure functions for the test data extrapolation method, the risk probability method and the Arrhenius method, and maps the failure parameter into the preset functions, thereby flexibly adapting to diversified failure analysis requirements in user-defined scenarios and covering failure rate calculation scenarios under different failure mechanisms, so as to improve the adaptability and accuracy of failure rate calculation for self-defined classified parts.

[0013] In a preferred example, the application can be further configured as: Based on the non-working state environmental parameter, the initial failure rate and the duty cycle are used to calculate the failure rate of the lower-level node, including: When the non-working state environmental parameter has a value, the first failure rate, the second failure rate and the duty cycle are used to calculate the failure rate of the lower-level node. When the non-working state environmental parameter has no value, the first failure rate and the duty cycle are used to calculate the failure rate of the lower-level node.

[0014] By adopting the technical scheme, according to whether the non-working state environment parameter has a value, corresponding calculation modes are flexibly selected, when the non-working state environment parameter exists, the first failure rate, the second failure rate and the duty cycle are comprehensively calculated, different environment influences of the target component in working and non-working states are considered, when the non-working state environment parameter does not exist, the first failure rate and the duty cycle are only used for calculation, the calculation process in the scene without related data is simplified, and the accuracy and applicability of the failure rate calculation of the lower-level node are effectively improved.

[0015] The application can be further configured as in a preferred example: Real-time working condition data of the target component are collected, the failure rate of the lower-level node is corrected based on the working condition data, and the corrected failure rate of the lower-level node is obtained, including: The real-time working condition data includes temperature parameters, vibration parameters, voltage parameters and current parameters; The target correction factors are calculated by using the target working condition parameters; The comprehensive correction factor is calculated by using the target correction factors; The corrected node failure rate is calculated by using the failure rate of the lower-level node and the comprehensive correction factor.

[0016] By adopting the technical scheme, the working condition data collected in real time are preprocessed to obtain target correction factors, the target correction factors are used to calculate a comprehensive correction factor, and the failure rate of the lower-level node is adaptively corrected by using the comprehensive correction factor, so that the prediction error caused by ignoring the actual environment is effectively reduced, and the reliability prediction accuracy of the target component is improved.

[0017] The application can be further configured as in a preferred example: The failure rate of the upper-level node is calculated by using the corrected failure rate of the lower-level node, including: The corrected failure rates of all lower-level nodes contained in the upper-level node and corresponding quantities are obtained; The corrected failure rates of all lower-level nodes and corresponding quantities are weighted and summed to obtain the failure rate of the upper-level node.

[0018] By adopting the technical scheme, the application carries out reliability prediction calculation from bottom to top based on the product structure tree, and the failure rates of all lower-level nodes contained in the upper-level node and the quantities are weighted and summed to obtain the failure rate of the upper-level node, so that the calculation of the failure rate of the upper-level node is more close to the actual situation.

[0019] The application can be further configured as in a preferred example: The superior node failure rate and the inferior node failure rate are input into a preset analysis function to obtain a reliability index value, including: The reliability index value includes a first index value, a second index value and a third index value; The superior node failure rate is input into a first preset analysis function for calculation to obtain a first evaluation value; A task duration parameter is obtained, and the task duration parameter and the superior node failure rate are input into a second preset analysis function for calculation to obtain a second evaluation value; A maintenance characteristic parameter is obtained, and based on the maintenance characteristic parameter, the inferior node failure rate is input into a third preset analysis function for calculation to obtain a third evaluation value.

[0020] By adopting the above technical solution, the first evaluation value reflecting the reliability is directly calculated by the first preset analysis function based on the superior node failure rate, then the task duration parameter is introduced, the second evaluation value reflecting the reliability in a certain time dimension is calculated by the second preset analysis function in combination with the failure rate, and then the third evaluation value is calculated by the third preset analysis function by fusing the maintenance characteristic parameter and the inferior node failure rate, so that the reliability performance in different scenarios is quantified from multiple aspects to ensure the accuracy of the reliability evaluation.

[0021] In a preferred example, the application can be further configured as: Based on the comparison result of the preset target threshold standard and the reliability index value, a reliability evaluation report is obtained, including: The first index value, the second index value and the third index value are compared with the corresponding preset target threshold standard to obtain multiple reliability evaluation results; When the multiple reliability comparison results all meet the preset target threshold standard, the reliability evaluation report is obtained; When any reliability evaluation result does not meet the preset target threshold standard, the weak link data of the inferior node is located based on the part data of the product structure tree to obtain a reliability evaluation report containing the weak link.

[0022] By adopting the above technical solution, the reliability evaluation report is generated after the multiple reliability index values are compared with the preset target threshold standard, when all the indexes meet the standard, the reliability evaluation report is directly output, if any index does not meet the standard, the problem source is quickly traced based on the product structure tree, the specific inferior node and the weak link data causing the insufficient reliability are accurately located and the reliability evaluation report containing the weak link is output, so that the practicality of the reliability evaluation is improved.

[0023] The second object of the application is to provide an electronic component reliability prediction system with the characteristics of improving the target component reliability prediction accuracy.

[0024] The second application purpose of the present application is achieved by the following technical solutions. The initialization module is configured to construct a product structure tree of the target component, the product structure tree comprising upper nodes and lower nodes, each node storing a feature data set, wherein the upper nodes comprise at least one lower node; The first calculation module is configured to calculate the failure rate of the lower nodes based on the feature data set and the failure rate type of the lower nodes; and the correction module is configured to collect working condition data of the target component in real time, correct the failure rate of the lower nodes based on the working condition data, and obtain the corrected failure rate of the lower nodes. The second calculation module is configured to calculate the failure rate of the upper nodes based on the corrected failure rate of the lower nodes. The analysis module is configured to input the failure rate of the upper nodes into a preset analysis function to obtain a reliability index value. The output module is configured to obtain a reliability evaluation report based on a comparison result of a preset target threshold standard and the reliability index value.

[0025] By using the above technical solutions, the product structure tree comprising upper and lower nodes is initialized and constructed, the first calculation module calculates the initial failure rate of the lower nodes based on the feature data set and the failure rate type, the correction module dynamically corrects the failure rate of the lower nodes based on the working condition data collected in real time, the second calculation module determines the failure rate of the upper nodes based on the corrected failure rate of the lower nodes, the analysis module obtains the reliability index value based on the failure rate of the upper nodes, and the preset target threshold standard is compared to generate a report, so as to improve the accuracy and efficiency of the reliability prediction of the electronic component.

[0026] In summary, the present application has at least one of the following beneficial technical effects: 1.A method for predicting reliability of an electronic component, comprising: constructing a product structure tree of the electronic component, the product structure tree comprising upper nodes and lower nodes, each node storing a feature data set, wherein the upper nodes comprise at least one lower node, calculating a failure rate of the lower node based on a type of the failure rate of the lower node using the feature data set of the lower node, collecting working condition data of the electronic component in real time, correcting the failure rate of the lower node based on the working condition data to obtain a corrected failure rate of the lower node, calculating a failure rate of the upper node using the corrected failure rate of the lower node, inputting the failure rate of the upper node into a preset analysis function to obtain a reliability index value, and obtaining a reliability evaluation report based on a comparison result of a preset target threshold and the reliability index value. The product structure tree comprising the upper and lower nodes is constructed to provide a clear hierarchical framework for reliability analysis of the electronic component, and the failure rate of the lower node is dynamically corrected using the real-time working condition data to ensure the accuracy of the calculation of the failure rate of the lower node and the adaptability to the actual scene, and then the failure rate of the upper node is derived using the corrected failure rate of the lower node to realize the reliability transmission from the local to the whole, thereby effectively improving the accuracy and practicability of the reliability prediction of the electronic component.

[0027] 2.According to the difference in failure rate type, the preset value is directly called for the preset type to improve the calculation convenience, the initial failure rate is calculated from the feature data set based on the classification parameters for the calculation type, and then the initial failure rate is corrected in combination with the non-working state environmental parameters, and the failure rate of the lower node is obtained by quantifying the actual stress influence in the task cycle through the duty cycle, thereby ensuring the calculation feasibility and improving the accuracy of the reliability prediction.

[0028] 3.The method is designed with different calculation paths for the three types of classification parameters, i.e., the conventional, preset database and self-defined, to meet the flexible calculation requirements in different scenarios and improve the accuracy of the calculation of the initial failure rate under different classification parameters. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a step flowchart of a method for predicting reliability of an electronic component according to an embodiment of the present application.

[0030] Figure 2 is a structural block diagram of a system for predicting reliability of an electronic component according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] Embodiments of the present application provide a method and system for predicting reliability of an electronic component to solve the technical problem of low prediction accuracy of the reliability of the electronic component in the prior art.

[0032] In order to make the application purposes, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings of the embodiments of the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of the present application.

[0033] Embodiment one: Please refer to Figure 1 The embodiment of the present application provides an electronic component reliability prediction method, comprising: S1, constructing a product structure tree of a target component, the product structure tree comprising upper nodes and lower nodes, each node storing a feature data set, wherein the upper nodes comprise at least one lower node.

[0034] The feature data set refers to a parameter set stored in each node of the product structure tree of the target component, which is used to support reliability analysis.

[0035] The structure tree of the target component presents the composition of the target component through the hierarchical relationship of the upper nodes and the lower nodes, wherein the upper nodes are component-level units, each upper node contains at least one lower node, the lower nodes are part-level units, and the two form a "component-part" hierarchical structure through a parent-child relationship. It can be understood that the upper node can be a circuit board, and the lower nodes thereof can be electronic components such as resistors and / or capacitors on the circuit board.

[0036] It is worth mentioning that the product structure tree supports reliability prediction from bottom to top based on the BOM structure, has basic editing functions such as insertion, deletion, copying, cutting and pasting of nodes, and supports import and export of BOM, so as to effectively improve the query and processing efficiency of data.

[0037] In the embodiment of the present application, the product structure tree of the target component is constructed, the product structure tree comprises component parent nodes and part child nodes, each parent node comprises at least one child node, and each node stores a feature data set.

[0038] S2, calculating the failure rate of the lower node based on the feature data set according to the failure rate type of the lower node.

[0039] The failure rate type refers to a way for determining the failure rate of a node in reliability prediction calculation.

[0040] The failure rate of the lower node refers to the probability of failure of the lower node in the product structure tree per unit time under certain conditions in reliability prediction calculation.

[0041] In the embodiments of the present application, in the reliability prediction calculation, the failure rate of the sub-node is calculated by judging different failure rate types and combining the feature data set of the sub-node.

[0042] Preferably, S2 comprises the following sub-steps: S201, obtaining non-working state environment parameters, classification parameters and duty cycles, wherein the duty cycle is the working time ratio of the target electronic component in a task cycle.

[0043] The non-working state environment parameter refers to the environment-related parameter of the target electronic component in the non-working state.

[0044] The classification parameter refers to the parameter for classifying the target electronic component.

[0045] In the embodiments of the present application, the non-working state environment parameters, classification parameters and duty cycles in the node are obtained.

[0046] S202, when the failure rate type is a preset type, a preset failure rate value is used as the failure rate of the lower-level node.

[0047] The preset type includes two cases: First, the preset type is "specified MTBF", that is, the average failure interval time preset by the user. MTBF is a key indicator in reliability engineering, which is used to represent the reliability level of the node under certain conditions, and its reciprocal directly reflects the failure rate of the node.

[0048] Specifically, the failure rate of the lower-level node is obtained by the following formula:

[0049] It can be understood that the user presets the average failure interval time (MTBF) of the node as 20000 hours, so the failure rate of the node does not need to be calculated additionally, and the data can be directly substituted into the above formula to calculate the failure rate of the lower node as 0.00005 / hour.

[0050] Second, the preset type is "specified failure rate", that is, the node failure rate value directly set by the user, which is suitable for the case that the failure rate data of a specific component or assembly in actual application is known.

[0051] Specifically, the failure rate of the lower-level node = specified failure rate.

[0052] It can be understood that the user can determine the failure rate of the node as 80x10 -6 / hour based on various reports, so the value preset by the user is directly used as the failure rate of the lower-level node, and the failure rate of the lower-level node is 80x10 -6 / hour.

[0053] In the embodiment of the present application, by judging whether the failure rate type of the node belongs to "specified MTBF" or "specified failure rate", the corresponding calculation logic is executed, and the user can select different preset types according to the data source to adapt to the reliability analysis requirements of different scenes, thereby effectively improving the flexibility of reliability analysis, and at the same time, avoiding repeated calculation, directly utilizing known data to improve the analysis speed, and effectively improving the calculation efficiency.

[0054] S203, when the failure rate type is a calculation type, an initial failure rate is calculated based on a classification parameter and a feature data set.

[0055] The classification parameter refers to a key parameter for distinguishing different calculation methods when the failure rate type is a calculation type, and the classification parameter includes a regular, a preset database and a custom.

[0056] The initial failure rate refers to an initial failure probability of the node under a specific condition obtained by a corresponding calculation model based on the classification parameter and the feature data set when the failure rate type is a calculation type.

[0057] In the embodiment of the present application, when the failure rate type is a calculation type, different classification parameters are judged, and the initial failure rate is obtained by calculating the feature data set according to the calculation method corresponding to the different classification parameters.

[0058] Preferably, S203 includes the following sub-steps: S2031, the initial failure rate includes a first failure rate and a second failure rate.

[0059] The first failure rate refers to the failure rate of the target component in the working state calculated based on the selected calculation model.

[0060] The second failure rate refers to the failure rate of the target component in the non-working state.

[0061] In the embodiment of the present application, the initial failure rate includes two kinds of data, which are the first failure rate and the second failure rate.

[0062] S2032, the feature data set includes an environment category parameter, a part category parameter, a part attribute parameter, a failure parameter and a failure method parameter.

[0063] The environment category parameter is used to describe the parameter of the environment condition in which the electronic component works or does not work, and the environment category includes but is not limited to ground, aviation, etc.

[0064] The part category parameter refers to the parameter for classifying the target component, and the part category includes but is not limited to capacitor, circuit board, connector, etc., and different categories have differences in failure rate calculation.

[0065] The part attribute parameter refers to relevant characteristic information capable of being uniquely matched to a corresponding part from a preset database, including but not limited to a model, a specification and other attributes related to the identity of a specific part.

[0066] The failure parameter refers to various input parameters used for calculating the first failure rate under different failure methods.

[0067] The failure method parameter refers to a parameter used for determining the failure rate calculation method.

[0068] In the embodiment of the present application, the environment category parameter, the part category parameter, the part attribute parameter, the failure parameter and the failure method parameter are obtained.

[0069] S2033, when the classification parameter is regular, the part category parameter is input into the coupling analysis model to obtain the first failure rate.

[0070] The coupling analysis model includes a GJB299C stress analysis method, a GJB299C counting method, a MIL-HDBK-217FN2 stress analysis method or a MIL-HDBK-217FN2 counting method.

[0071] Taking the GJB299C stress analysis method as an example of the coupling analysis model: When the part is classified as a regular resistor, referring to the GJB299C standard, the coupling analysis model of the first failure rate λp is λp=λb×πE×πQ×πT×πV, the general failure rate λb is determined through the part category, and the corresponding environmental factor πE, the quality factor πQ, the temperature stress coefficient πT and the voltage stress coefficient πV are determined by querying the table in the relevant appendix of the GJB299C standard, combined with the actual working environment, the quality grade, the temperature and the voltage stress, and substituted into the coupling analysis model, so as to calculate the first failure rate.

[0072] It can be understood that the first failure rate calculation methods using the GJB299C counting method, the MIL-HDBK-217FN2 stress analysis method and the MIL-HDBK-217FN2 counting method are consistent with the GJB299C stress analysis method, and all are calculated according to the part category and the corresponding π factor, which will not be described here.

[0073] In the embodiment of the present application, the regular electronic components are selected to calculate the first failure rate according to different part category parameters by selecting the corresponding coupling analysis model, without manually deriving complex physical formulas, effectively reducing the time consumption of single failure rate calculation.

[0074] S2034, a second failure rate is calculated using a correction coefficient and the first failure rate, wherein the correction parameter is determined based on the correspondence between the environment category parameter and the non-working state environment parameter.

[0075] The correction parameter refers to a non-working state failure rate correction factor K, which is used to adjust the failure rate in the non-working state.

[0076] The non-working state environment parameter refers to environment category information of the node in the non-working state.

[0077] The correction factor K is selected based on the correspondence between the environment category parameter and the non-working state environment parameter, as shown in Table 1 below, which shows the correction factor values of different part categories under different combinations of environment categories and non-working state environments.

[0078] Table 1 Non-working state correction factor table In the above table, the correspondence between the environment category of GJB299C and the working environment is shown in Table 2 below: Table 2 Environment category correspondence table Table 3 Non-working state category correspondence table 217 Standard Environment Active Environment Class GB - Ground Based - Good Control Ground GF - Ground Based - Fixed - No Control Ground GM - Ground Based - Mobile Ground NS - Shipboard - In Cabin Ship NU - Shipboard - Outside Cabin Ship AIC - Airplane - Transport Cabin Air AIF - Airplane - Fighter Cabin Air AUC - Airplane - Transport Unmanned Cabin Air AUF - Airplane - Fighter Unmanned Cabin Air ARW - Helicopter Air SF - Space Flight Space MF - Missile Flight Air ML - Missile Launch Air CL - Cannon Launch Air After determining the correction factor K, the second failure rate is determined in combination with the correction factor K and the first failure rate, wherein the calculation method of the second failure rate is as follows: second failure rate = first failure rate * K, wherein K is the correction factor.

[0079] It can be understood that when the node is an inductor, the environment category parameter is ground, the non-working state environment parameter is ground, and the value of the first failure rate is 0.01*10 -6 / h, then the correction factor K value 0.2 is obtained by querying Table 1, therefore, the second failure rate = 0.01*10 -6 *0.2 = 0.002*10 -6 / h.

[0080] In the embodiment of the present application, by introducing the correction factor and determining the second failure rate according to the ratio of the first failure rate and the correction factor, the influence of different environmental conditions on the non-working state failure rate of the component can be targetedly reflected, the calculation of the non-working failure rate is more in line with the actual scene, and thus the accuracy of the reliability analysis is improved. At the same time, the value of the correction factor is clearly corresponding to the “environment category” and “environment, non-working state”, forming a standardized calculation rule, which not only simplifies the calculation process of the non-working failure rate, avoids the deviation caused by subjective estimation, but also ensures the consistency and comparability of the non-working failure rate calculation under different components and different scenes, effectively improving the accuracy of reliability prediction analysis.

[0081] S2035, when the classification parameter is a preset database, a corresponding failure rate value is matched from the preset database as the first failure rate according to the part attribute parameter, and the second failure rate is a preset value.

[0082] The preset database includes an NPRD or an EPRD database, the NPRD database refers to a mechanical component failure mode and distribution ratio database, and the EPRD database refers to an electronic component failure mode and distribution ratio database.

[0083] Preferably, the preset value of the second failure rate is 0.

[0084] Taking a target component "high-frequency transistor" as an example, the specific implementation manner is as follows: The "classification parameter" in the part table of the high-frequency transistor is marked as "NPRD", and the part is directly inserted from the NPRD preset database by the system, and the part attribute parameter included in the characteristic data set is: model "2N3904". When the first failure rate is calculated, based on the above part attribute parameter, matching is performed in the NPRD preset database, the corresponding entry is accurately located through the model "2N3904", the working failure rate value "0.008 times / 10 6 hours" recorded in the database is obtained, and the value is taken as the first failure rate.

[0085] It can be understood that the manner of obtaining the first failure rate by using the EPRD is consistent with the manner of obtaining the first failure rate by using the NPRD, and details are not repeated here.

[0086] In the embodiment of the application, when the classification parameter is the NPRD or EPRD database, a query condition is input through a query interface or an interface provided by the database, so that a corresponding failure rate value is matched as the first failure rate, and the value of the second failure rate is 0. By using this calculation manner, the failure rate data verified by a large amount of actual measurement and statistics in the database can be directly called, without complex model calculation and parameter iteration, so that the failure rate calculation process of the target component is simplified, and the reliability analysis and prediction accuracy is improved.

[0087] S2036, when the classification parameter is self-defined, the failure parameter is input into a preset failure function based on the failure method parameter to determine the first failure rate, and the second failure rate is a preset value.

[0088] In the embodiment of the application, when the classification parameter is self-defined, the failure parameter is input into a preset failure function according to different failure method parameters to calculate the first failure rate, and the value of the second failure rate is 0.

[0089] Preferably, S2036 includes the following sub-steps: S2101, the failure method parameter includes a failure rate method, a test data extrapolation method, a risk probability method, and an Arrhenius method.

[0090] In the embodiment of the application, the failure method parameter is divided into four types, which are the failure rate method, the test data extrapolation method, the risk probability method, and the Arrhenius method. Different calculation methods are determined according to different failure method parameters, which greatly improves the prediction accuracy of reliability analysis.

[0091] S2102, when the failure method parameter is the failure rate method, a preset failure rate is used as the first failure rate.

[0092] It can be understood that the user directly inputs a preset failure rate value according to the historical test data of the target component, industry experience or design requirements, which will be used as the first failure rate.

[0093] In the embodiment of the application, when the failure method parameter is the failure rate method, a preset failure rate is used as the first failure rate.

[0094] S2103, when the failure method parameter is the test data extrapolation method, the failure parameter is input into a first preset failure function to obtain the first failure rate.

[0095] The failure parameter includes a failure number, a number of test devices, a test time, a confidence level, an activation energy, a working temperature, and an aging temperature.

[0096] The failure number refers to the number of components that fail during reliability testing, which is basic data reflecting the failure of the test sample.

[0097] The number of test devices refers to the total number of components participating in reliability testing, which is used to reflect the size of the test sample.

[0098] The test time refers to the duration of reliability testing of the components.

[0099] The confidence level refers to the credibility of the failure rate result obtained by test data extrapolation, which is usually expressed in percentage.

[0100] The activation energy refers to the parameter of the difficulty of chemical reaction or physical process related to the failure of the component.

[0101] The working temperature refers to the environmental temperature in which the component actually works.

[0102] The aging temperature refers to the test temperature set in the accelerated aging test, which is usually higher than the actual working temperature, and the purpose is to accelerate the aging process of the component.

[0103] The first preset failure function is: In the formula, Chi2(a, b) is the chi-square value at the confidence level and the degree of freedom, the value of a depends on the type of confidence limit, for the lower limit, a = confidence level / 100, for the upper limit, a = 1-(confidence level) / 100, the degree of freedom b = 2x(failure number+1), AF is the acceleration factor, the number of test devices is the total number of components or equipment participating in reliability testing, and the test time is the number of hours of reliability testing of these equipment.

[0104] Wherein, the calculation method of AF is as follows: In the formula, e is a natural constant, E is the activation energy (eV), K is the Boltzmann constant (8.617342E-5 eV / Degrees K), T1 is the converted operating temperature, and T2 is the converted aging temperature.

[0105] It is worth mentioning that the operating temperature is determined by the target component operating shell temperature and temperature rise, and the operating shell temperature refers to the temperature of the shell of the electronic component, which is the measured temperature of the shell when the component is actually working, and the temperature rise refers to the temperature difference between the internal core area and the shell of the component when it is working. Since the obtained shell temperature and temperature rise are set temperatures, the Celsius temperature is converted to Kelvin temperature before the calculation of the AF acceleration factor.

[0106] The calculation method of the converted operating temperature T1 is as follows: The calculation method of the converted aging temperature T2 is as follows: It can be understood that, taking the number of test devices = 50, test time = 1000 hours, failure number = 2, confidence level = 90%, activation energy E = 0.9 eV, operating temperature = 125℃, and aging temperature = 150℃ as an example, the calculation process of the first failure rate is as follows: The degree of freedom b = 2x(2+1) = 6, when the confidence level is 90%, a = 1-90 / 100 = 0.1, chi2(0.1, 6) = 10.645 is obtained by looking up the chi-square distribution table, T1 = 125+273.15 = 398.15 K, and T2 = 150+273.15 = 423.15 K Acceleration factor The first failure rate = 10.645 / (2x50x1000x3.2)≈3.33x10 -6 / h.

[0107] In the embodiment of the present application, the activation energy, the Boltzmann constant, the converted working temperature and the converted aging temperature are input into the calculation formula of the acceleration factor to determine the specific value of the acceleration factor, and then the failure number, the number of test devices, the test time and the confidence level are input into the first preset function to determine the first failure rate.

[0108] S2104, when the failure method parameter is the risk probability method, the failure parameter is input into the second preset failure function to obtain the first failure rate.

[0109] The failure parameter is obtained, wherein the failure parameter further includes a shape parameter, a life characteristic parameter, a time point of evaluating the hazard degree and a minimum life parameter.

[0110] The shape parameter is a key parameter in the Weibull distribution and is used to describe the change trend of the failure probability of the component with time.

[0111] The life characteristic parameter is a parameter in the Weibull distribution and is used to represent the life level of the component.

[0112] The time point of evaluating the hazard degree refers to a specific time at which the failure hazard degree of the component needs to be evaluated, that is, a time at which the user is concerned, and is used to calculate the failure rate at the time point.

[0113] The minimum life parameter represents the earliest time at which the component may start to fail, and the target component will not fail before the minimum life parameter.

[0114] The second preset failure function is as follows: In the formula, β is the shape parameter, η is the life characteristic parameter, t is the time point of evaluating the hazard degree, and t0 is the minimum life parameter.

[0115] It can be understood that, taking the shape parameter β = 1.8, the scale parameter η = 5000 hours, the evaluation time t = 1000 hours and the fault-free time t0 = 0 as examples: The first failure rate = (1.8 / 5000) * [(1000-0) / 5000]^(1.8-1) ≈ 1.27 * 10 -4 / h.

[0116] In the embodiment of the present application, when the failure parameter is the Weibull risk probability method, the shape parameter, the life characteristic parameter, the time point of evaluating the hazard degree and the minimum life parameter are input into the second preset failure function to determine the first failure rate.

[0117] S2105, when the failure method parameter is the Arrhenius method, the failure parameter is input into the third preset failure function to obtain the first failure rate.

[0118] The failure parameter also includes an empirical constant. The third preset failure function is specifically as follows: In the formula, A is an empirical constant, E is an activation energy, K is a Boltzmann constant, and T is an absolute temperature.

[0119] It is worth mentioning that A is an empirical constant, a constant related to the characteristics of the part itself, test conditions, etc., which needs to be determined through experiments or reference to industry data, E is an activation energy, K is a Boltzmann constant, and T is an absolute temperature, which is the same as the working temperature acquisition method described above and will not be repeated here. In this method, the absolute temperature T can also be a user-defined temperature.

[0120] In the embodiment of the application, when the failure parameter is the risk probability method, the failure rate parameters of the empirical constant, the activation energy, the Boltzmann constant and the absolute temperature are input into the third preset failure function to determine the first failure rate.

[0121] S204, based on the non-working state environmental parameter, the initial failure rate and the duty cycle are used to calculate the failure rate of the lower level node.

[0122] In the embodiment of the application, whether there is a non-working state parameter is judged, and then the initial failure rate and the duty cycle are used to calculate the failure rate of the lower level node according to different judgment results.

[0123] Preferably, step S204 includes the following sub-steps: S2041, when the non-working state environmental parameter has a value, the first failure rate, the second failure rate and the duty cycle are used to calculate the failure rate of the lower level node.

[0124] The calculation formula for calculating the failure rate of the lower level node based on the first failure rate, the second failure rate and the duty cycle is as follows: Lower level node failure rate = first failure rate × duty cycle / 100 + second failure rate × (100-duty cycle) / 100.

[0125] It can be understood that, assuming that the classification parameter of the capacitor is regular, the first failure rate is calculated to be 0.02×10 -6 / h by GJB299C stress analysis method, and then the correction factor corresponding to the environmental category parameter and the non-working state environmental parameter is determined to be 0.2, so the second failure rate = 0.02×10 -6 / h×0.2 = 0.004×10 -6 / h, the task cycle is 10 hours, the node working time is 10 hours, so the duty cycle is 6 / 10×100% = 60%, and these parameters are substituted into the above formula to obtain the failure rate of the lower level node.

[0126] In the embodiment of the present application, when the non-working state environmental parameter has a value, the first failure rate, the second failure rate and the duty cycle are input into the pre-designed calculation formula to calculate the failure rate of the lower-level node.

[0127] S2042, when the non-working state environmental parameter has no value, the first failure rate and the duty cycle are used to calculate the failure rate of the lower-level node.

[0128] The formula for calculating the failure rate of the lower-level node based on the first failure rate and the duty cycle is as follows: Lower-level node failure rate = first failure rate x duty cycle / 100 It can be understood that, assuming that no non-working state parameter of the part sub-node is monitored, when the failure parameter method is the test data extrapolation method, the failure parameter is input into the first pre-set function to obtain a value of the first failure rate of 0.05x10 -6 / h, the part sub-node works for 8 hours per day, and the total duration is 24 hours, so the duty cycle = 8 / 24x100%≈33.33%, thus, input into the above calculation formula, the lower node failure rate = 0.05x10 -6 / hx33.33 / 100≈0.0167x10 -6 / h.

[0129] In the embodiment of the present application, when the non-working state environmental parameter has no value, the first failure rate and the duty cycle are input into the pre-set formula to calculate the failure rate of the lower-level node.

[0130] S3, real-time acquisition of working condition data of the target component, correction of the failure rate of the lower-level node based on the working condition data, and obtaining of the corrected failure rate of the lower-level node.

[0131] The working condition data refers to the data collected in the actual working state of the target component.

[0132] It is worth mentioning that the working condition data of the target component is collected in real time by a sensor according to a pre-set monitoring period, wherein the sensor is a temperature sensor, a vibration sensor, a voltage sensor and a current sensor, the temperature sensor includes but is not limited to a thermocouple, an infrared temperature sensor and the like, the vibration sensor includes but is not limited to a piezoelectric vibration sensor and the like, the voltage sensor includes but is not limited to a direct current voltage sensor, an alternating current voltage sensor and the like, and the current sensor includes but is not limited to a Hall current sensor and the like.

[0133] Preferably, the pre-set monitoring period is 10 minutes.

[0134] In the embodiment of the present application, the target component working condition data is collected by the sensor according to a preset detection period, and the lower node failure rate is dynamically corrected by the working condition data to obtain the corrected lower node failure rate, so that the calculation of the failure rate is more in line with the actual working condition.

[0135] Preferably, S3 comprises the following sub-steps: S301, pre-processing real-time working condition parameters to obtain a plurality of target working condition parameters, wherein the real-time working condition data comprises temperature parameters, vibration parameters, voltage parameters and current parameters.

[0136] The specific preprocessing process is as follows: first, the extreme abnormal values in the temperature parameters, vibration parameters, voltage parameters and current parameters are uniformly removed by the box plot method to eliminate the influence of sensor failure or sudden interference, then low-pass filtering is used for the vibration parameters to remove high-frequency noise, moving average method is used for temperature, voltage and current parameters to smooth small fluctuations, finally the voltage and current parameters are converted into relative deviation values, the mean value within the effective range of the temperature parameters is retained, and the effective signal after filtering of the vibration parameters is retained.

[0137] In the embodiment of the present application, the temperature parameters, vibration parameters, voltage parameters and current parameters are pre-processed to obtain a plurality of target working condition parameters. Since the working condition parameters have undergone a preprocessing step, abnormal values, noise and redundant information can be filtered, and the calculation accuracy is effectively improved.

[0138] S302, calculating a plurality of target correction factors using a plurality of target working condition parameters; The target correction factor refers to a factor for dynamically correcting the component failure rate, which is calculated based on a plurality of target working condition parameters according to a respective preset rule.

[0139] According to the preset rules of different target working condition parameters, each target working condition parameter is calculated respectively to obtain the corresponding temperature correction factor, vibration correction factor, voltage correction factor and current correction factor.

[0140] It is worth mentioning that the threshold value and correction range of the temperature correction factor can be adapted and adjusted according to the model, service life and application scenario of the component; the frequency threshold value and correction amount for each increase in frequency in the vibration correction factor can be regularly updated according to the vibration tolerance test data of the equipment; the deviation range and corresponding correction range of the voltage and current correction factors can also be dynamically optimized in combination with the stability of the power supply system and the tolerance of the component to adapt to the actual needs under different working conditions, which is not limited here.

[0141] Take the temperature correction factor as an example: the preset rule is that the correction factor increases by 0.1 for every 10℃ increase when the temperature exceeds 80℃. Assuming that the current temperature is 92℃, the temperature exceeds 12℃, and thus the temperature correction factor K1 is calculated as K1 = 1 + (12 / 10) * 0.1 = 1.12.

[0142] It can be understood that the vibration parameter, the voltage parameter, and the current parameter are calculated according to the preset rule to obtain the target correction factor, and the calculation of the temperature correction factor is the same, which will not be repeated here.

[0143] In the embodiments of the present application, the target correction factors of different parameters are calculated according to the preset rule to obtain multiple target correction factors, which dynamically compensates for the error of a single static model, and at the same time, the threshold and amplitude of the correction factor can be dynamically adjusted according to the component model, application scenario, etc., which is more flexible and further improves the reliability prediction analysis accuracy.

[0144] S303, calculate a comprehensive correction factor using the multiple target correction factors.

[0145] The comprehensive correction factor refers to the total correction coefficient obtained by integrating the temperature correction factor, the vibration correction factor, the voltage correction factor, and the current correction factor by the weighted average method. The weights of the temperature correction factor, the vibration correction factor, the voltage correction factor, and the current correction factor are set according to the influence degree on the failure rate.

[0146] Preferably, the weight of the temperature correction factor is 0.4, the weight of the vibration correction factor is 0.3, the weight of the voltage correction factor is 0.2, and the weight of the current correction factor is 0.1.

[0147] For example, the calculation method of the comprehensive correction factor is as follows: Kintegrated = K1 * 0.4 + K2 * 0.3 + K3 * 0.2 + K4 * 0.1 In the formula, Kintegrated is the comprehensive correction factor, K1 is the temperature correction factor, K2 is the vibration correction factor, K3 is the voltage correction factor, and K4 is the current correction factor.

[0148] In the embodiments of the present application, the temperature correction factor, the vibration correction factor, the voltage correction factor, and the current correction factor are distributed according to the preset weight to determine the comprehensive correction factor, which effectively avoids the limitation of a single correction factor considering only a certain working condition, and the weight can be set according to the influence degree of each factor on the failure rate, thereby improving the pertinence and accuracy of the correction.

[0149] S304, calculate a corrected node failure rate using the lower node failure rate and the comprehensive correction factor.

[0150] In the embodiment of the present application, the modified node failure rate is obtained according to the product of the lower node failure rate and the comprehensive correction factor, the comprehensive calibration of the failure rate under multiple working conditions is realized, the modified failure rate is more suitable for the failure characteristics of the components in the actual complex environment. At the same time, when the actual working condition changes, only the comprehensive correction factor needs to be updated, and the new modified failure rate can be quickly obtained without rederiving the basic calculation model of the lower node failure rate, which is convenient for flexible application in different application scenarios.

[0151] S4, calculating the upper node failure rate by using the modified lower node failure rate.

[0152] The upper node failure rate refers to the probability of failure of the upper node obtained by calculating the modified failure rates of all lower nodes contained in the upper node.

[0153] In the embodiment of the present application, the upper node failure rate is calculated according to the modified lower node failure rate.

[0154] Preferably, S4 includes the following sub-steps: S401, obtaining the modified lower node failure rate of all lower nodes contained in the upper node and the corresponding number. The type of the lower node is a capacitor, the modified lower node failure rate (x10 -6 / h) is 0.012, and the number is 12; The type of the lower node is a resistor, the modified lower node failure rate (x10 -6 / h) is 0.005, and the number is 20; The type of the lower node is a connector, the modified lower node failure rate (x10 -6 / h) is 0.03, and the number is 3; Therefore, the failure rates of the above three types of lower nodes and the total number are obtained.

[0155] In the embodiment of the present application, the modified failure rates of all lower part nodes contained in the component parent node and the corresponding number are obtained, and the combination of the failure rate and the number ensures that the upper node failure rate calculation can accurately integrate the failure rates of all lower nodes, so as to improve the calculation accuracy and improve the reliability prediction accuracy.

[0156] S402, weighted sum of the modified lower node failure rate of all lower nodes and the corresponding number is obtained to obtain the upper node failure rate.

[0157] The modified lower node failure rate and the corresponding number of all lower nodes are input into a preset formula to calculate the upper node failure rate. Upper node failure rate = ∑ i Modified lower node failure rate x number.

[0158] According to the data of S401, it can be understood that the failure rate of the upper node = (0.012 x 10 -6 / h x 12) + (0.005 x 10 -6 / h x 20) + (0.03 x 10 -6 / h x 3) = 0.3334 x 10 -6 / h.

[0159] In the embodiment of the application, the failure rate of the upper node is obtained by multiplying the modified failure rate of each lower node by the number and then summing, which reflects the difference in failure contribution of different types of components, and the number weight reflects the physical proportion in the upper node, ensuring that the failure rate of the upper node can accurately integrate the failure information of the lower nodes.

[0160] S5, input the upper node failure rate and the lower node failure rate into a preset analysis function to obtain a reliability index value.

[0161] The preset analysis function refers to a function for calculating a reliability index value based on the upper node failure rate or the lower node failure rate, and the core is to integrate the failure rate data of different levels of nodes to output a quantitative result reflecting the overall or local reliability level of the product.

[0162] The reliability index value refers to a parameter for quantifying the reliability level of the target component calculated by the preset analysis function.

[0163] In the application, the upper node failure rate and the lower node failure rate are input into different preset analysis functions to obtain a parameter for quantifying the reliability level of the target component.

[0164] Preferably, S5 includes the following sub-steps: S501, the reliability index value includes a first index value, a second index value and a third index value.

[0165] The first index value refers to the mean time between failures (MTBF), wherein the MTBF directly reflects the uninterrupted failure duration of the node in the normal working state, and the larger the value, the higher the product reliability.

[0166] The second index value refers to the reliability (R), wherein the reliability quantifies the probability of the node completing the specified function within the specified time.

[0167] The third index value refers to mean time to repair (MTTR), wherein the MTTR reflects an average time consumption for a node to return to normal work after a failure, is a key index for measuring product maintainability, and the smaller the value is, the higher the maintenance efficiency is.

[0168] In the implementation of the present application, three reliability index values are included, which are respectively a first index value, a second index value and a third index value, and the reliability level of the target component is comprehensively evaluated from three dimensions of failure-free work capability, task completion probability and failure repair efficiency through the three types of reliability indexes, thereby improving the reliability analysis and prediction precision of the target component.

[0169] S502, input the upper node failure rate to a first preset analysis function for calculation to obtain a first evaluation value.

[0170] The first preset analysis function is used for calculating the mean time between failures (MTBF), wherein the first preset analysis function is: In the formula, the failure rate multiplier is a conversion coefficient for converting the unit of the failure rate to a conversion coefficient matched with the unit of the MTBF.

[0171] It can be understood that when the unit of the failure rate is "1 / h", in order to make the unit of the MTBF generally "h", the failure rate multiplier needs to be 1 in the formula to ensure that the calculation result of the MTBF is correct in unit; If the unit of the failure rate is "10 -6 / h", in order to make the unit of the MTBF "h", the failure rate multiplier needs to be 1x10 -6 .

[0172] The first evaluation value is calculated based on the failure rate multiplier and the upper node failure rate determined in S4.

[0173] In the embodiment of the present application, the unit conversion is realized through the failure rate multiplier, and the calculation of the first evaluation value is combined with the upper node failure rate, thereby effectively ensuring the accuracy of the first evaluation value and providing a quantitative basis for evaluating the failure-free work capability of the upper node.

[0174] S503, obtain a task duration parameter, input the task duration parameter and the upper node failure rate to a second preset analysis function for calculation to obtain a second evaluation value.

[0175] The task duration parameter refers to a task duration set for evaluating the reliability of the node in a specific task scenario in reliability prediction. The second preset analysis function is specifically: R=e -λt In the formula, R is reliability, e is a natural constant as the base number of an exponential function, and is the failure rate of the upper node, and t is the task duration parameter.

[0176] It can be understood that the single continuous working duration of the node is 1000 hours, the failure rate of the upper node is calculated as 0.3334*10 -6 / h according to S401, and then the parameters are substituted into the second preset analysis function to obtain a second evaluation value e -3.34×10-4 .

[0177] In the embodiment of the present application, the time dimension of the actual working scene is directly associated with the task duration parameter, and then the reliability is calculated in combination with the failure rate of the upper node, so that the reliability performance of the node in a specific task cycle can be reflected, and the generalization evaluation defect of the prior art using the traditional static model is effectively solved.

[0178] S504, obtain a maintenance characteristic parameter, input the failure rate of the lower node into a third preset analysis function based on the maintenance characteristic parameter to obtain a third evaluation value.

[0179] The maintenance characteristic parameter refers to a parameter used to determine the average repair time calculation method, so as to distinguish whether the "MTTR type" is "specified" or "calculated".

[0180] When the maintenance characteristic parameter is specified, that is, the "MTTR type" is "specified", the third evaluation value is a specified value.

[0181] It can be understood that the upper node is in a repairable state, and the maintenance characteristic parameter is "specified", that is, it is not necessary to rely on the parameter calculation of the lower node, and a preset specified value is directly used, so that the evaluation process is simplified while the matching of the result with the actual maintenance experience is ensured.

[0182] When the maintenance characteristic parameter is calculated, the third evaluation value is obtained by inputting the failure rate of the lower node into the third preset analysis function.

[0183] It can be understood that the upper node is in a repairable state, and it is assumed that the upper node includes two lower nodes (resistor modules and capacitor modules), wherein the MTTR1 of the resistor is 45 minutes, and the expected failure rate is 0.003 / h; the MTTR2 of the capacitor is 60 minutes, and the expected failure rate is 0.001 / h, then the unit (minute) of the maintenance characteristic parameter is converted into hour, that is, MTTR1=45min÷60=0.75 / h, MTTR2=90min÷60=1.5 / h, and then input into the third preset analysis function for calculation.

[0184] The third preset analysis function is: MTTR i MTTR of the lower-level node, λ i Failure rate of the lower-level node.

[0185] In the embodiments of the present application, the setting of the MTTR type determines the acquisition method of the node MTTR, adapts the calculation requirements of the third evaluation value in different scenarios, supports both the direct use of empirical values or preset values, and supports dynamic calculation based on lower-level node data, thereby improving the flexibility of reliability evaluation.

[0186] S6, obtaining a reliability evaluation report based on the comparison result of the preset target threshold standard and the reliability index value.

[0187] The preset target threshold standard refers to a benchmark value for measuring whether the first index value (MTBF), the second index value (reliability), and the third index value (MTTR) meet the requirements, which is set in advance according to the design requirements of the product, industry specifications, application scenarios, or user needs, etc. before reliability evaluation.

[0188] In the embodiments of the present application, the preset target threshold standard and the reliability index value are compared one by one to obtain the reliability evaluation report.

[0189] Preferably, S6 includes the following sub-steps: S601, comparing the first index value, the second index value, and the third index value with the corresponding preset target threshold standard to obtain multiple reliability evaluation results.

[0190] For the mean time between failures, the preset target threshold standard is a certain minimum time requirement. For example, “MTBF ≥ 10000 hours”, which is not limited here.

[0191] For reliability, the preset target threshold standard is a certain minimum probability requirement. For example, “reliability ≥ 0.999”, which is not limited here.

[0192] For the mean time to repair, the preset target threshold standard is a certain maximum time limit. For example, “MTTR ≤ 2 hours”, which is not limited here.

[0193] In the embodiments of the present application, the mean time between failures, reliability, and mean time to repair are compared one by one with their corresponding preset target threshold standards, and the first index value, the second index value, and the third index value are compared one by one with their corresponding preset target threshold standards, to obtain multiple reliability evaluation results, thereby clearly determining the compliance of product reliability from the three dimensions of fault-free capability, task completion probability, and repair efficiency.

[0194] S602, obtaining a reliability evaluation report when the multiple reliability comparison results all meet the preset target threshold standard.

[0195] It can be understood that if the average failure interval time is greater than or equal to the preset MTBF threshold value, if the reliability is greater than or equal to the preset reliability threshold value, and the average repair time is less than or equal to the preset MTTR threshold value, the three index values all meet the case, and a qualified reliability evaluation report is generated.

[0196] In the embodiment of the present application, it is judged whether each reliability comparison result meets the preset target threshold value standard, and if it meets, a reliability evaluation report is generated.

[0197] S603, when any reliability evaluation result does not meet the preset target threshold value standard, the weak link data of the lower level node is located based on the part data of the product structure tree, and a reliability evaluation report containing the weak link is obtained.

[0198] Suppose that the average failure interval time of the upper level node is greater than or equal to 10,000 hours, the reliability is 0.998, and the average repair time is less than or equal to 2 hours. After comparison by S601, it is found that the reliability does not meet the preset threshold value, and the weak link checking process is triggered.

[0199] It can be understood that the reliability of the upper level node is determined by the reliability of multiple lower level nodes. When the reliability of the upper level node does not meet the preset target threshold value, the index data of its child nodes is checked layer by layer to locate the key lower level node whose reliability is significantly lower than that of other nodes, and then it is analyzed whether there is a subordinate part node. If there is, the failure rate of the subordinate part node is analyzed to determine the root cause of the low reliability of the node, so as to determine the weak link. Finally, these information is integrated into the reliability evaluation report, and the specific information (such as parameter value, influencing factor) of the non-compliant upper level node, the key lower level node and its subordinate weak link is clearly marked, which provides a clear basis for subsequent design optimization.

[0200] If there is no subordinate part node, the key lower level node itself is the weak link. At this time, the non-compliant upper level node, the key lower level node as the weak link, and the specific parameter information (such as failure rate value, reliability value, etc.) of the key lower level node need to be clearly marked in the reliability evaluation report, which directly serves as the root cause of the non-compliance of the reliability of the upper level node, and provides a clear basis for subsequent design optimization (such as replacing the model, improving the process, etc.) of the key lower level node.

[0201] Embodiment two: Please refer to Figure 1 The embodiment of the present application provides another electronic component reliability prediction method, which comprises: S1, a product structure tree of a target component is constructed, the product structure tree comprises upper level nodes and lower level nodes, and each node stores a feature data set, wherein the upper level node comprises at least one lower level node.

[0202] In the embodiment of the present application, a product structure tree of the target component is constructed, the product structure tree comprises component parent nodes and part child nodes, each parent node comprises at least one child node, and each node stores a feature data set.

[0203] S2, calculating the failure rate of the lower-level node by using the feature data set based on the failure rate type of the lower-level node.

[0204] In the embodiment of the present application, in the reliability prediction calculation, the failure rate of the child node is calculated by judging different failure rate types and combining the feature data set of the child node.

[0205] Preferably, S2 comprises the following sub-steps: S201, obtaining non-working state environment parameters, classification parameters and duty cycles, wherein the duty cycle is the working time ratio of the target electronic component in a task cycle.

[0206] In the embodiment of the present application, the non-working state environment parameters, the classification parameters and the duty cycles in the node are obtained.

[0207] S202, when the failure rate type is a preset type, using a preset failure rate value as the failure rate of the lower-level node.

[0208] In the embodiment of the present application, when the failure rate type is a preset type, a preset failure rate value is used as the failure rate of the lower-level node.

[0209] S203, when the failure rate type is a calculation type, calculating an initial failure rate by using the feature data set based on the classification parameters.

[0210] In the embodiment of the present application, when the failure rate type is a calculation type, different classification parameters are judged, and the feature data set is used for calculation according to the calculation mode corresponding to different classification parameters to obtain the initial failure rate.

[0211] Preferably, S203 comprises the following sub-steps: S2031, the initial failure rate comprises a first failure rate and a second failure rate.

[0212] In the embodiment of the present application, the initial failure rate comprises two kinds of data, which are the first failure rate and the second failure rate.

[0213] S2032, the feature data set comprises environment category parameters, part category parameters, part attribute parameters, failure parameters and failure method parameters.

[0214] In the embodiment of the present application, the environment category parameters, the part category parameters, the part attribute parameters, the failure parameters and the failure method parameters are obtained.

[0215] S2033, when the classification parameter is regular, the part classification parameter, the environment classification parameter and the working temperature are input into the coupling analysis model to obtain a first failure rate.

[0216] The coupling analysis model includes GJB299C stress analysis method, GJB299C counting method, MIL-HDBK-217FN2 stress analysis method and MIL-HDBK-217FN2 counting method.

[0217] Taking the GJB299C stress analysis method as an example of the coupling analysis model: When the part classification is a resistor of the "regular" type, referring to the GJB299C standard, the coupling analysis model of the first failure rate is λp=λb×πE×πT, the general failure rate λb is determined by the part classification parameter, the environment classification is "GF1-general ground fixed" (corresponding to the active environment classification "ground") according to the GJB299C environment classification table, the shell temperature is 50 DEG C, the temperature rise is 10 DEG C, and the temperature parameter is T=50+10+273.15=333.15K, and the environment factor πE corresponding to different environment classifications is determined by querying the table in the appendix of the GJB299C standard, so that the first failure rate is calculated.

[0218] In the embodiment of the application, the regular electronic components are selected, the corresponding coupling analysis model is selected according to different part classification parameters, and the first failure rate is calculated in combination with the environment classification parameter, the shell temperature and the temperature rise, without manually deriving complex physical formulas, so that the time consumption of single failure rate calculation is effectively reduced.

[0219] S2034, the second failure rate is calculated by using the correction coefficient and the first failure rate, wherein the correction parameter K is valued based on the environment classification parameter in the profile and the non-working state environment corresponding relationship. For details, see Table 1-3 of Embodiment 1, and the correction parameter K is the same as the value mode of the above-mentioned embodiment 1.

[0220] It can be understood that, assuming that the second failure rate of the capacitor needs to be calculated, the environment classification parameter in the capacitor task profile is "aircraft (AIC-transport cabin)" (corresponding to the aircraft environment in the GJB299C environment classification), and the non-working state environment is "ground" marked as a non-working state in the task profile, then according to the non-working state correction factor table, it can be determined that the capacitor, when the environment classification is "aircraft" and the non-working state environment is "ground", the correction parameter K=0.03.

[0221] Therefore, the first failure rate is 0.02×10 -6 / h, and the second failure rate is 0.02×10 -6 / h×0.03=0.0006×10 -6 / h.

[0222] The task profile is a basic input for task reliability prediction, and the prediction calculation is carried out based on the environmental conditions (such as environmental categories, temperature, etc.) set based on the task profile, so as to reflect the reliability performance of the product in different task scenarios.

[0223] In the embodiment of the present application, the correction parameter K is determined based on the environmental category parameter and the non-working state environment in the task profile, and the second failure rate is calculated in combination with the first failure rate, which more accurately reflects the failure rate of the electronic components in different task scenarios, thereby improving the accuracy and scene adaptability of the reliability prediction based on the task profile.

[0224] S2035, when the classification parameter is the preset database, the corresponding failure rate value is matched from the preset database as the first failure rate according to the part attribute parameter, and the second failure rate is a preset value.

[0225] S2036, when the classification parameter is self-defined, the first failure rate is determined by inputting the failure parameter into the preset failure function based on the failure method parameter, and the second failure rate is a preset value.

[0226] S204, based on the non-working state environment parameter, the initial failure rate and the duty cycle are used to calculate the failure rate of the lower node.

[0227] Preferably, step S204 includes the following sub-steps: S2041, when the non-working state environment parameter has a value, it is judged whether the current stage is a working state, if yes, the first failure rate, the second failure rate and the duty cycle are used to calculate the failure rate of the lower node.

[0228] The working state is the state of a specific stage in the task profile, which specifically refers to the working state information of a stage of the task profile.

[0229] The formula for calculating the failure rate of the lower node based on the first failure rate, the second failure rate and the duty cycle is as follows: Lower node failure rate = first failure rate x duty cycle / 100 + second failure rate x (100-duty cycle) / 100.

[0230] It can be understood that, taking a transistor with a classification parameter of "regular" as an example, the known conditions are: the first failure rate is calculated by GJB299C stress analysis method 0.05x10 -6 / h, the second failure rate is determined based on the non-working state combination of "air-ground" in the task profile, the correction parameter K = 0.02 is found, and therefore the second failure rate = 0.05x10 -6 / hx0.02 = 0.001x10 -6 / h, the transistor in the current working state stage running time ratio is 60%, so the duty cycle = 60, inputting the parameters into the above formula, the lower node failure rate = 0.0304x10 -6 / h.

[0231] It is worth mentioning that when the task profile has a value of the non-working state environmental parameter, if the current stage is the working state, the failure rate of the lower node needs to consider the failure rate of the working state and the non-working state, and the time proportion of the stage in the task, and the failure rate of the lower node in the current stage is obtained by multiplying the first failure rate by the percentage of the duty cycle, and adding the second failure rate multiplied by (100-duty cycle) percentage, so as to dynamically reflect the comprehensive influence of the working and non-working state alternation on the node failure rate.

[0232] If the current stage is the non-working state, the second failure rate is used to calculate the failure rate of the lower node.

[0233] The formula for calculating the failure rate of the lower node according to the second failure rate is as follows: Lower node failure rate = second failure rate = first failure rate x correction parameter K.

[0234] It can be understood that when the task profile has a value of the non-working state environmental parameter, if the current stage is the non-working state, the value of the second failure rate is taken as the failure rate of the lower node. The specific calculation method is as above, which will not be repeated here.

[0235] In the embodiment of the application, the working and non-working states in the task profile are distinguished, the duty cycle and the correction parameter are combined to dynamically calculate the failure rate of the lower node, and the influence of the failure rate in different states is accurately fused, so that the adaptability and accuracy of the electronic component reliability prediction based on the task profile in the complex task scene are improved.

[0236] S2042, when the non-working state environmental parameter has no value, it is judged whether the current stage is the working state, if yes, the first failure rate and the duty cycle are used to calculate the failure rate of the lower node.

[0237] The formula for calculating the failure rate of the lower node based on the first failure rate and the duty cycle is as follows: Lower node failure rate = first failure rate x duty cycle / 100.

[0238] It can be understood that taking the integrated circuit with the classification parameter as "regular" as an example, when the non-working state environmental parameter has no value and the current stage is the working state, the first failure rate is calculated to be 0.08x10 -6The integrated circuit runs for 75% of the time in the current working state phase, so the duty cycle = 75, and the above data is substituted into the formula to calculate the failure rate of the lower node = 0.06x10 -6 / h.

[0239] If the current phase is a non-working state, the failure rate of the lower node is determined based on the second failure rate.

[0240] The failure rate of the lower node = the second failure rate = the first failure rate x the correction parameter K.

[0241] The determination of the correction parameter K is determined by S2034, and it can be understood that when the non-working state environment parameter does not have a value and the current phase is a non-working state, the failure rate of the lower node is determined by the second failure rate.

[0242] It is worth mentioning that after the failure rate of the lower node is determined in S2041 or S2041, the total failure rate of the lower node can be determined, that is, the total failure rate of the lower node is calculated based on the task profile as follows: The total failure rate of the lower node = SUM (the failure rate of the lower node (i) x the percentage of the phase (i)).

[0243] It can be understood that the node includes 2 phases in a certain task profile, and the parameters of each phase are as follows: Phase 1: working state, non-working state environment parameter has a value, and the failure rate of the lower node (i = 1) = 0.005x10 -6 / h, and the percentage of the phase (i = 1) = 30%; Phase 2: non-working state, non-working state environment parameter has a value, and the failure rate of the lower node (i = 2) = 0.001x10 -6 / h, and the percentage of the phase (i = 2) = 50%; The above data is substituted into the total failure rate calculation formula of the lower node, and the total failure rate of the lower node = 0.005x10 -6 / hx30% + 0.001x10 -6 / hx50%.

[0244] In the embodiments of the present application, the failure rate of the lower node is calculated by corresponding formulas according to whether the non-working state environment parameter exists and the current phase state, and the product of the total failure rate and the percentage of the phase is accumulated to obtain the total failure rate, which accurately adapts to different task scenarios, thereby effectively improving the flexibility and accuracy of reliability analysis.

[0245] S3, real-time acquisition of working condition data of the target component, correction of the failure rate of the lower node based on the working condition data, and obtaining of the corrected failure rate of the lower node.

[0246] In the embodiment of the present application, the failure rate of the lower node is corrected according to the real-time collected working condition data to obtain a corrected failure rate of the lower node.

[0247] S4, calculating the failure rate of the upper node by using the corrected failure rate of the lower node.

[0248] It can be understood that if the task profile only has the first stage, then the failure rate of the upper node is calculated according to the failure rate of the lower node, and if the task profile has multiple stages, the failure rate of the upper node is calculated based on the total failure rate of the lower node.

[0249] In the embodiment of the present application, the failure rate of the upper node is calculated according to the corrected failure rate of the lower node.

[0250] S5, inputting the failure rate of the upper node and the failure rate of the lower node into a preset analysis function to obtain a reliability index value.

[0251] Preferably, S5 includes the following sub-steps: S501, the reliability index value includes a first index value, a second index value and a third index value.

[0252] S502, inputting the failure rate of the upper node into a first preset analysis function to obtain a first evaluation value.

[0253] S503, obtaining a task duration parameter, inputting the task duration parameter and the failure rate of the upper node into a second preset analysis function to obtain a second evaluation value.

[0254] The task duration parameter refers to a task duration set for evaluating the reliability of the node in a specific task scenario in reliability prediction. The second preset analysis function is specifically: R = e -λt In the formula, R is the reliability, e is a natural constant as the base number of the exponential function, λ is the failure rate of the upper node, and t is the task duration parameter, which is the specified task time in the task profile.

[0255] The specified task time in the task profile refers to the duration for which the electronic component or node needs to work continuously, which is set in advance for the task scenario when the task profile is constructed. The specific calculation method is consistent with that in the first embodiment, and will not be described here.

[0256] In the embodiment of the present application, three reliability index values are included, which are a first index value, a second index value and a third index value. The reliability level of the target component is comprehensively evaluated from three dimensions of fault-free working ability, task completion probability and fault repair efficiency through the three types of reliability index, thereby improving the reliability analysis and prediction accuracy of the target component.

[0257] S504, acquire a maintenance characteristic parameter, input the failure rate of the lower-level node to the third preset analysis function based on the maintenance characteristic parameter, calculate to obtain a third evaluation value.

[0258] S6, based on the comparison result of the preset target threshold standard and the reliability index value, obtain a reliability evaluation report.

[0259] In the embodiment of the application, the preset target threshold standard and the reliability index value are compared one by one to obtain the reliability evaluation report.

[0260] Embodiment three: Please refer to Figure 2 The embodiment of the application provides an electronic component reliability prediction system, which comprises: The initialization module 101 is used for constructing a product structure tree of the target component, and the product structure tree comprises upper-level nodes and lower-level nodes, and each node stores a feature data set, wherein the upper-level node comprises at least one lower-level node; The first calculation module 102 is used for calculating the failure rate of the lower-level node by using the feature data set based on the failure rate type of the lower-level node; the correction module 103 is used for collecting working condition data of the target component in real time, correcting the failure rate of the lower-level node based on the working condition data, and obtaining the corrected failure rate of the lower-level node; The second calculation module 104 is used for calculating the failure rate of the upper-level node by using the corrected failure rate of the lower-level node; The analysis module 105 is used for inputting the failure rate of the upper-level node to a preset analysis function to obtain a reliability index value. The output module 106 is used for obtaining a reliability evaluation report based on the comparison result of the preset target threshold standard and the reliability index value.

[0261] Since the above is a system corresponding to an electronic component reliability prediction method, the implementation principle is similar to that of an electronic component reliability prediction system, which will not be described here.

[0262] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0263] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-described functions.

[0264] The embodiments of the specific implementation are the preferred embodiments of the present application, and are not limited to the protection scope of the present application, wherein the same parts are denoted by the same reference numerals. Therefore: any equivalent changes made according to the structure, shape, principle of the present application should be covered within the protection scope of the present application.

Claims

1. A method for predicting the reliability of electronic components, characterized in that: include: Constructing a product structure tree of the target component, the product structure tree including upper-level nodes and lower-level nodes, each node storing a feature data set, wherein the upper-level node includes at least one lower-level node; Based on the failure rate type of the lower-level nodes, the failure rate of the lower-level nodes is calculated using the feature data set; Collect the working condition data of the target components in real time, correct the failure rate of the lower-level nodes based on the working condition data, and obtain the corrected failure rate of the lower-level nodes; The failure rate of the upper-level node is calculated using the corrected failure rate of the lower-level node; Input the failure rate of the upper node and the failure rate of the lower node into the preset analysis function to obtain the reliability index value; Based on the comparison results of the preset target threshold standard and the reliability index value, a reliability assessment report is obtained.

2. The electronic component reliability prediction method according to claim 1, characterized in that: Based on the failure rate type of the lower-level node, use The characteristic data set calculates the failure rate of the lower-level nodes, including: Obtaining non-working state environmental parameters, classification parameters, and duty cycle, where the duty cycle is the proportion of working time of the target electronic component within the task cycle; When the failure rate type is the preset type, the preset failure rate value is used as the failure rate of the lower-level node; When the failure rate type is calculated, the initial failure rate is calculated using the feature data set based on the classification parameters; Based on the non-working environment parameters, the initial failure rate and duty cycle are used to calculate the failure rate of the lower-level nodes.

3. The electronic component reliability prediction method according to claim 2, characterized in that: When the failure rate type is calculated, the initial failure rate is calculated using the feature dataset based on the classification parameters, including: The initial failure rate includes the first failure rate and the second failure rate; The feature data set includes environment category parameters, part category parameters, part attribute parameters, failure parameters, and failure method parameters; When the classification parameter is normal, the part category parameter is input into the coupling analysis model to obtain the first failure rate; Calculating a second failure rate using a correction coefficient and the first failure rate, wherein the correction coefficient is determined based on a correspondence between an environment category parameter and a non-working state environment parameter; When the classification parameter is a preset database, the corresponding failure rate value is matched from the preset database according to the part attribute parameter as the first failure rate, and the second failure rate is a preset value; When the classification parameter is customized, based on the failure method parameter, the failure parameter is input into the preset failure function to determine the first failure rate, and the second failure rate is a preset value.

4. The electronic component reliability prediction method according to claim 3, characterized in that: When the classification parameter is customized, based on the failure method parameter, the failure parameter is input into the preset failure function to determine the first failure rate, including: Failure method parameters include failure rate method, test data extrapolation method, risk probability method and Arrhenius method; When the failure method parameter is the failure rate method, the preset failure rate is used as the first failure rate; When the failure method parameter is the test data extrapolation method, the failure parameter is input into the first preset failure function to obtain a first failure rate; When the failure method parameter is the risk probability method, the failure parameter is input into the second preset failure function to obtain a first failure rate; When the failure method parameter is the Arrhenius method, the failure parameter is input into the third preset failure function to obtain a first failure rate.

5. The electronic component reliability prediction method according to claim 2, characterized in that: Based on the non-working environment parameters, the initial failure rate and duty cycle are used to calculate the failure rate of the lower-level nodes, including: When the non-working state environmental parameter has a value, the failure rate of the lower-level node is calculated using the first failure rate, the second failure rate and the duty cycle; When the non-working state environmental parameter does not have a value, the first failure rate and the duty cycle are used to calculate the failure rate of the lower-level node.

6. The electronic component reliability prediction method according to claim 1, wherein: Collect the operating condition data of the target components in real time, and correct the failure rate of the lower-level nodes based on the operating condition data to obtain the corrected failure rate of the lower-level nodes, including: Preprocessing the real-time operating condition parameters to obtain multiple target operating condition parameters, wherein the real-time operating condition data includes temperature parameters, vibration parameters, voltage parameters, and current parameters; and calculating multiple target correction factors using the multiple target operating condition parameters; Calculate the comprehensive correction factor using multiple target correction factors; The corrected node failure rate is calculated using the lower-level node failure rate and the comprehensive correction factor.

7. The electronic component reliability prediction method according to claim 1, wherein: The failure rate of the upper-level node is calculated using the corrected failure rate of the lower-level node, including: Obtain the corrected failure rate and corresponding number of all subordinate nodes contained in the superior node; The upper-level node failure rate is obtained by taking a weighted sum of the corrected lower-level node failure rates and the corresponding quantities of all lower-level nodes.

8. The electronic component reliability prediction method according to any one of claims 1 to 7, characterized in that: Input the upper node failure rate and the lower node failure rate into the preset analysis function to obtain the reliability index value, including: The reliability index value includes a first index value, a second index value and a third index value; Inputting the upper node failure rate into a first preset analysis function for calculation to obtain a first evaluation value; Obtaining a task duration parameter, inputting the task duration parameter and the upper-level node failure rate into a second preset analysis function for calculation to obtain a second evaluation value; A maintenance characteristic parameter is obtained, and based on the maintenance characteristic parameter, the failure rate of the lower-level node is input into a third preset analysis function for calculation to obtain a third evaluation value.

9. The electronic component reliability prediction method according to claim 8, characterized in that: Based on the comparison results of the preset target threshold standard and the reliability index value, a reliability assessment report is obtained, including: Comparing the first indicator value, the second indicator value, and the third indicator value with corresponding preset target threshold values ​​to obtain multiple reliability evaluation results; When multiple reliability comparison results meet the preset target threshold standard, a reliability evaluation report is obtained; When any reliability assessment result does not meet the preset target threshold standard, the weak link data of the lower node is located based on the part data of the product structure tree to obtain a reliability assessment report containing the weak link.

10. An electronic component reliability prediction system, characterized in that: include: An initialization module is used to construct a product structure tree of the target component, the product structure tree including upper-level nodes and lower-level nodes, each node storing a feature data set, wherein an upper-level node includes at least one lower-level node; A first calculation module is used to calculate the failure rate of the lower-level node using a characteristic data set based on the failure rate type of the lower-level node; a correction module is used to collect operating condition data of the target component in real time, correct the failure rate of the lower-level node based on the operating condition data, and obtain the corrected failure rate of the lower-level node; A second calculation module is used to calculate the upper-level node failure rate using the corrected lower-level node failure rate; An analysis module is used to input the failure rate of the upper node into a preset analysis function to obtain a reliability index value; The output module is used to obtain a reliability evaluation report based on the comparison results of the preset target threshold standard and the reliability index value.

Images