Reliability analysis method and system for aviation equipment system based on mbse model conversion

Abstract

The application belongs to the technical field of aviation equipment system modeling and analysis, and particularly relates to an aviation equipment system reliability analysis method and system based on MBSE model conversion, which comprises the following steps: S1, establishing the functional model reliability of the aviation equipment system through MBSE model mapping conversion technology; S2, establishing the fault logic model of the aviation equipment system, and completing the key fault identification and analysis evaluation of the aviation equipment system; S3, determining the reliability index parameters of the aviation equipment system according to the key fault identification and analysis results of the aviation equipment system; S4, determining the reliability analysis results of the aviation equipment system according to the reliability index parameters of the aviation equipment system, and completing the reliability analysis. The model conversion method is used to analyze the reliability related indexes of the aviation equipment system, so that the reliability analysis results of the same system caused by manual intervention can be avoided to be too different and inconsistent with the actual system state, and the comprehensiveness and accuracy of the fault logic relationship analysis of the aviation equipment system are ensured.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of aviation equipment system modeling and analysis, and particularly relates to an aviation equipment system reliability analysis method and system based on MBSE model conversion. BACKGROUND

[0002] "Reliability" is one of the general quality characteristics of aviation equipment, which is set as the ability of aviation equipment to complete the specified function within the specified time and under the specified conditions. For aviation equipment, reliability directly affects its mission execution capability and the reliability of the entire life cycle. Model Based System Engineering (MBSE) is widely recognized as a highly implementable complex engineering system design method at home and abroad. Compared with the traditional system engineering method, MBSE emphasizes the use of standardized models in the equipment development process. This not only solves the problem of difficult implementation of traditional system engineering, but also fills the gap in the existing digital development technology in the aspects of requirement analysis and architecture design. Therefore, it has been widely applied in the fields of aviation and aerospace at home and abroad. Carrying out system reliability design and analysis based on MBSE is not only to solve the problem of insufficient means of complex system reliability design and analysis, but also to integrate reliability design into the MBSE forward research and development engineering of the new generation of aviation equipment, so as to realize the integration of reliability design and functional performance design. Establishing a full-process MBSE method from the conversion of aviation equipment system architecture function design taking SysML model as the core to Altarica, Modelica and other systems capable of generating aviation standard reliability index parameters and reliability analysis reports is the core of the technical problem focused on by the present application.

[0003] Currently, there are two technical routes for reliability analysis of complex aviation equipment systems: one is reliability analysis based on expert intervention of system behavior model. The steps are as follows: in order to establish the normal behavior model of the system, the normal function behavior of the system under the condition of normal input and normal state should be described. For different types of systems, the modeling methods used will also be different. For discrete systems, state machine-like models are usually used to describe the dynamic behavior of the system; for continuous systems, performance modeling methods are often used to describe the continuous behavior of the system. On the basis of the normal system behavior model, the fault behavior needs to be set to describe the system fault excitation and state transition behavior. At the same time, the requirements to be verified need to be clearly set, and the reliability qualitative and quantitative requirements that need to be analyzed and verified are specified. The second is to use the system function list and use requirements recorded in the MBSE model to conduct preliminary functional failure analysis to refine the system's requirements for reliability during the system requirement analysis stage. In the system function analysis and logical architecture design stage, with the help of the fault mode database, a comprehensive analysis model is established, and the focus is on the optimization of the system architecture. When the physical architecture design of the system is completed, the dynamic simulation evaluation of the system under normal and fault conditions is carried out to further improve the fault control logic and algorithm of the system.

[0004] There are many problems in the above two technical routes: the traditional system reliability analysis mainly depends on technical experts, and has strong subjectivity, and the accuracy of the analysis result is highly dependent on the personal level of the engineer. For the same system, different engineers may have very different reliability analysis results due to differences in knowledge background and thinking mode. For complex systems with dynamic reconfiguration characteristics, fault timing correlation and logical correlation, it has become impractical to analyze their fault logic relationship through artificial reasoning. Even if analysis can be performed, its comprehensiveness and accuracy are difficult to guarantee. In addition, the collaborative design of system reliability and functional performance becomes more and more difficult. Under the design mode based on documents, the input of system reliability analysis usually comes from the information of system design scheme and the like. However, due to the inherent ambiguity and ambiguity of the text description, the analysis personnel are difficult to accurately understand the system design principle, thereby leading to the inconsistency between the input of reliability analysis and the actual state of the product. Most of the reliability analysis based on MBSE is to inject faults in the original SysML model, and then calculate the reliability indicators after constructing the fault propagation model. However, although the SysML model is a widely recognized system engineering modeling language, which is suitable for the functional architecture and physical architecture design of the system in the early stage, it does not support performance simulation, and the support for reliability is insufficient. When the elements of the system design model are parsed and read according to the requirements, the model information extraction process is completely customized, and cannot adapt to the differences in model construction caused by different modeling habits and different modeling specifications. Therefore, it is necessary to carry out reliability analysis and demonstration based on the unified multi-field joint simulation scheme of model conversion. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides an aviation equipment system reliability analysis method based on MBSE model conversion. The present application uses a model conversion method to analyze the reliability-related indicators of the aviation equipment system, which can avoid the overlarge difference between the reliability analysis results of the same system caused by manual intervention, and ensure the comprehensiveness and accuracy of the aviation equipment system fault logic relationship analysis.

[0006] To achieve the above object, the technical scheme of the present application is as follows:

[0007] An aviation equipment system reliability analysis method based on MBSE model conversion, comprising:

[0008] S1: establishing a functional model mapping relationship of the aviation equipment system through an MBSE model mapping conversion technology;

[0009] The mapping relationship between the SysML model of the aviation equipment system and the AltaRica function model and the Modelica function model is automatically generated through the MBSE model mapping conversion technology, and the data interaction and model integration between the function model and the SysML model of the aviation equipment system are realized. The process of automatically generating the mapping relationship between the SysML model of the aviation equipment system and the AltaRica function model is as follows:

[0010] S11: An extensible markup file derived from the SysML model of the aviation equipment system is input; and a top-level module selected by a user and a top-level root structure of the AltaRica function model are set;

[0011] S12: All classes in the extensible markup file in step S11 are traversed, and the class in which the selected top-level module is located is converted into a class of the AltaRica function model according to a mapping rule and is given to the root structure;

[0012] S13: The class in which the selected top-level module is located is traversed, and all custom attributes of the class are recursively converted into submodules of the AltaRica function model; a class component port connection state is generated corresponding to a basic type in a custom attribute structure hierarchy;

[0013] S14: The class component port connection state is processed: all class component ports are traversed; a component connected to each class component port is obtained; a class component call flow direction is recorded, and output and input ports are established according to the reverse direction;

[0014] S2: An aviation equipment system fault logic model is established, and key fault identification and analysis evaluation of the aviation equipment system are completed;

[0015] According to the AltaRica function model and the Modelica function model in step S1, fault setting and expansion of the aviation equipment system are performed, the aviation equipment system fault logic model is established, and automatic key fault identification FMEA and key fault analysis evaluation FTA are realized. The aviation equipment system key fault modeling and simulation are performed using the aviation equipment system fault logic model, the reliability of the continuous state and fault interference of the aviation equipment system is analyzed, the mean flight hours between failures MFHBF of the aviation equipment system, the mean time between failures MTBF of the aviation equipment system, the mission success probability MCSP of the aviation equipment system, the mean time between critical failures MTBCF of the aviation equipment system, and the mission reliability MR of the aviation equipment system are determined;

[0016] S3: According to the key fault identification and analysis results of the aviation equipment system, the reliability index parameters of the aviation equipment system are determined;

[0017] The simulation result of the aviation equipment system fault logic model established according to the AltaRica function model and the Modelica function model in step S2 fits the fault time distribution of the fault mode, and the task success rate is evaluated using aviation equipment system reliability index parameters; the aviation equipment system reliability index parameters include: a basic reliability parameter FR and a task reliability parameter TR;

[0018] The basic reliability parameter FR is:

[0019] FR=a1*MFHBF+a2*MTBF

[0020] Wherein, FR is the basic reliability parameter; MFHBF is the average flight failure interval hours of the aviation equipment system; MTBF is the average failure interval time of the aviation equipment system; a1 is the weight coefficient of the average flight failure interval hours of the aviation equipment system; a2 is the weight coefficient of the average failure interval time of the aviation equipment system;

[0021] The task reliability parameter TR is:

[0022] TR=b1*MCSP+b2*MTBCF+b3*MR

[0023] Wherein, TR is the task reliability parameter; MCSP is the task success probability of the aviation equipment system; MTBCF is the average serious failure interval time of the aviation equipment system; MR is the task reliability of the aviation equipment system; b1 is the weight coefficient of the task success probability of the aviation equipment system; b2 is the weight coefficient of the average serious failure interval time of the aviation equipment system; b3 is the weight coefficient of the task reliability of the aviation equipment system;

[0024] S4: determining the aviation equipment system reliability analysis result according to the aviation equipment system reliability index parameters, and completing the reliability analysis;

[0025] The calculation results of the basic reliability parameter FR and the task reliability parameter TR in step S3 are obtained, and the calculation formula of the aviation equipment system reliability analysis result AES is constructed as:

[0026] AES=a*FR+b*TR

[0027] Wherein, AES is the aviation equipment system reliability analysis result; a is the weight coefficient of the basic reliability parameter; b is the weight coefficient of the task reliability parameter;

[0028] According to the working environment and task requirements of the aviation equipment system, the threshold value of the task reliability index is set, whether the aviation equipment system reliability analysis result AES meets the expectation is judged, and then the aviation equipment system reliability is judged.

[0029] Preferably, the aviation equipment system model SysML in step S1 is constructed by the object-oriented system modeling language, accurately describes the aviation equipment system fault sequence correlation, polymorphism, fault tolerance behavior, and reconstruction behavior, constructs the complex working process and functional behavior of the aviation equipment system, and provides system input for reliability analysis.

[0030] Preferably, the mapping relationship between the AltaRica functional model and the aviation equipment system model SysML in step S1 needs to carry out formal setting analysis for each model element, and the mapping relationship corresponding to the aviation equipment system model SysML and the AltaRica functional model is determined.

[0031] The extraction of key model elements in the aviation equipment system model SysML is achieved by extracting key elements from the existing database to extract the key elements and realize the analysis of the aviation equipment system design information.

[0032] Preferably, the mapping relationship between the Modelica functional model and the aviation equipment system model SysML in step S1 is as follows:

[0033] According to the language modeling tool based on the aviation equipment system model SysML, the aviation equipment system model is created, the components or subsystems of the aviation equipment system model SysML are determined, an extended file standard protocol for converting sysml into modelica is applied to create a corresponding analysis description of the components or subsystems of the aviation equipment system model SysML, and the language modeling tool of the Modelica functional model is used for analysis.

[0034] The mapping of the aviation equipment system model SysML and the Modelica functional model elements includes the mapping relationship of class setting Cs, preset variable Pv, component category declaration Cd, and equation algorithm Eg.

[0035] The mapping relationship of the class setting Cs is that the object features described by the class setting in the aviation equipment system model SysML are respectively mapped into the class in the Modelica functional model.

[0036] The mapping relationship of the preset variable Pv is that the Variable type of the Modelica functional model is mapped into the variable type of the aviation equipment system model SysML.

[0037] The mapping relationship of the component category declaration Cd is that the component of the Modelica functional model is mapped into the class of the Modelica functional model.

[0038] The mapping relationship of the equation algorithm Eg is that the equation representation of the Modelica functional model maps the dynamic behavior of the aerospace equipment system model, describes the procedural physical behavior, and is used for describing the physical mapping relationship of the internal sub-models of the aerospace equipment system model.

[0039] Preferably, the critical failure identification FMEA in step S2 is specifically used for identifying the critical failure modes of each level of the system for completing the task of the aerospace equipment system, so as to further develop failure control measures and realize the design process control of task reliability.

[0040] The fault logic model of the aerospace equipment system can analyze the influence relationship of all bottom unit failures on the top function failure of the aerospace equipment system through the fault mode and local fault logic relationship setting, and therefore is automatically converted into the critical failure identification FMEA through the algorithm compilation based on the syntax elements of the sub-models to support the task reliability related analysis work of the aerospace equipment system. The critical failure identification FMEA analysis process based on the AltaRica functional model is that each fault mode can be backward transferred through the port failure, and the fault influence on the top function of the system is realized through the port connection between each level. The local influence is the port failure influence of the fault mode on the functional module; the last level influence is the fault influence of the final output port of the model level where the fault mode is located; the final influence is set as the fault influence of the output port of the highest level of the current model; if the model has only two levels, the analysis results of the last level influence and the final influence are the same.

[0041] Preferably, the critical failure analysis and evaluation FTA in step S2 is the analysis process based on the AltaRica functional model, which is:

[0042] In combination with the top layer setting of the AltaRica functional model, the fault states of each port are extended and set as the top events of each fault tree with the top function port of the aerospace equipment system as the object. Based on the local fault setting of each module, the fault output of the forward module is taken as the fault input of the backward module according to the pre-set order, the fault logic architecture of each fault tree is determined by using the data flow reverse deduction mode, and finally the fault states set by each module are directly taken as the bottom events of the fault tree according to the local fault influence relationship modeling content of each module to carry out the subsequent analysis and evaluation work.

[0043] Preferably, the basic reliability parameter FR in step S3 specifically includes: the mean flight hours between failures MFHBF of the aerospace equipment system and the mean time between failures MTBF of the aerospace equipment system.

[0044] The average flight failure interval hours MFHBF of the aviation equipment system is a main field use parameter for measuring the reliability of the aviation equipment system, and the flight time of the aircraft needs to be recorded; the ratio of the total flight time accumulated by the aviation equipment system in a set time to the total number of failures in the same period is:

[0045]

[0046] Wherein, the MFHBF is the average flight failure interval hours of the aviation equipment system; the AEH is the flight time of the aviation equipment system; and the FF is the total number of failures of the aviation equipment system.

[0047] The average failure interval time MTBF of the aviation equipment system is the ratio of the working time of the aviation equipment system to the total number of failures in a set condition and a set time:

[0048]

[0049] Wherein, the MTBF is the average failure interval time of the aviation equipment system; and the AEH1 is the working time of the aviation equipment system.

[0050] Preferably, the task reliability parameter TR in the step S3 specifically includes: the mission success probability MCSP of the aviation equipment system, the average serious failure interval time MTBCF of the aviation equipment system, and the mission reliability MR of the aviation equipment system.

[0051] The mission success probability MCSP of the aviation equipment system is the probability of successfully performing the specified task of the aviation equipment system in the specified mission profile:

[0052]

[0053] Wherein, the MCSP is the mission success probability of the aviation equipment system; the TST is the number of successful missions of the aviation equipment system; and the TT is the total number of missions performed by the aviation equipment system.

[0054] The average serious failure interval time MTBCF of the aviation equipment system is the ratio of the total mission time of the aviation equipment system to the total number of serious failures in a specified series of mission profiles:

[0055]

[0056] Wherein, the MTBCF is the average serious failure interval time of the aviation equipment system; the TH is the total mission time of the aviation equipment system; and the AFF is the total number of serious failures of the aviation equipment system.

[0057] The mission reliability MR of the aviation equipment system is the probability that the aviation equipment system successfully completes the specified task within the specified mission profile; when the serious failure obeys the exponential distribution, the calculation formula is:

[0058]

[0059] Wherein, MR is the mission reliability of the aviation equipment system; T is the specified task time of the aviation equipment system.

[0060] The second aspect of the present application provides an analysis system of the aviation equipment system reliability analysis method based on the MBSE model conversion, characterized in that it comprises: a function model reliability module, a mission reliability module and a reliability index parameter analysis and calculation module.

[0061] The function model reliability module is based on the aviation equipment system model SysML, and automatically generates the mapping relationship between the aviation equipment system model SysML and the AltaRica function model and the aviation equipment system model SysML and the Modelica function model through model mapping conversion technology, realizes the system design data sharing and model integration between the function models.

[0062] The mission reliability module sets and extends the fault based on the AltaRica function model, establishes a system fault logic model, realizes the automatic key fault identification and key fault analysis and evaluation calculation based on the fault logic model, and can keep compatible with the existing mission reliability modeling method; for complex multi-dynamic systems and fault related systems, the Modelica function model is used to analyze the reliability of continuous state systems and fault independent systems through fault modeling and simulation.

[0063] The reliability index parameter analysis and calculation module fits the fault time distribution of each fault mode according to the modeling and simulation results of the above-mentioned Modelica function model and AltaRica function model, and performs reliability index calculation to evaluate the task success rate.

[0064] Compared with the prior art, the present application has the following beneficial effects:

[0065] (1) The present application realizes the automatic generation of the aviation equipment system model SysML to the AltaRica function model, and improves the work efficiency.

[0066] (2) The present application provides an effective model conversion algorithm to realize the component module conversion mode of the mapping relationship between different aviation equipment system function models, and ensures the normalization of the same aviation equipment system under different models.

[0067] (3) The present application converts the aviation equipment system model SysML to the AltaRica function model and the Modelica function model at the same time, ensures that the aviation equipment system can obtain reliability-related indexes, and is used for subsequent reliability analysis.

[0068] (4) The present application adopts the model conversion method to automatically or semi-automatically analyze the reliability-related indexes of the aviation equipment system, avoids the difference of the reliability analysis results of the same aviation equipment system caused by manual intervention, and is inconsistent with the actual state of the aviation equipment system.

[0069] (5) The present application can also be used for complex aviation equipment systems with dynamic reconfiguration characteristics, fault timing correlation and logic correlation, ensures the comprehensiveness and accuracy of the fault logic relationship analysis, and further completes the reliability judgment of the aviation equipment system. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 The present application is based on the MBSE model conversion aviation equipment system reliability analysis method flow chart;

[0071] Figure 2 The present application is based on the MBSE model conversion aviation equipment system reliability analysis method flow chart;

[0072] Figure 3 The present application is based on the MBSE model conversion aviation equipment system reliability analysis method flow chart;

[0073] Figure 4 The present application is based on the MBSE model conversion aviation equipment system reliability analysis method flow chart;

[0074] Figure 5 The present application is based on the MBSE model conversion aviation equipment system reliability analysis method flow chart;

[0075] Figure 6 The present application is based on the MBSE model conversion aviation equipment system reliability analysis method flow chart. DETAILED DESCRIPTION

[0076] The exemplary embodiments, features and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference signs in the drawings represent functionally the same or similar elements. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0077] The present application provides a kind of aviation equipment system reliability analysis method based on MBSE model conversion, as Figure 1As shown, the function model reliability of the aviation equipment system is established by the MBSE model mapping conversion technology; the fault logic model of the aviation equipment system is established, the key fault identification and analysis evaluation of the aviation equipment system are completed; the aviation equipment system reliability index parameters are determined according to the key fault identification and analysis results of the aviation equipment system; the aviation equipment system reliability analysis results are determined according to the aviation equipment system reliability index parameters, and the reliability analysis is completed; which includes:

[0078] Step S1: The mapping relationship of the function model of the aviation equipment system is established by the MBSE model mapping conversion technology.

[0079] By the MBSE model mapping conversion technology, the mapping relationship of the aviation equipment system model SysML and the AltaRica function model and the Modelica function model is automatically generated, and the data interaction and model integration between the function model and the aviation equipment system model SysML are realized. As shown in Figure 2 As shown in the flow chart of the aviation equipment system model SysML to the AltaRica function model conversion based on the MBSE model mapping conversion technology; the extensible markup file derived from the SysML model is obtained, the top module selected by the user is defined, the top root structure of the AltaRica is defined, the top module is judged, and finally the mapping relationship between the models is established, and the specific program implementation is as shown in Figure 3 The mapping relationship between the aviation equipment system model SysML and the AltaRica function model is automatically generated, and the specific process is as follows:

[0080] Step S11: The extensible markup file derived from the aviation equipment system model SysML is input; the top module selected by the user is set, and the top root structure of the AltaRica function model is set.

[0081] Step S12: All classes in the extensible markup file in step S11 are traversed, the class in which the selected top module is located is converted into the class of the AltaRica function model according to the mapping rule and is given to the root structure.

[0082] Step S13: The class in which the selected top module is located is filtered out, and all custom attributes under the subordinate are recursively converted into the submodules of the AltaRica function model; the basic type in the custom attribute structure hierarchy corresponds to generate a class component port connection state.

[0083] Step S14: Processing the class component port connection state: traversing all class component ports; obtaining each component connected with the class component port; recording the class component call flow direction, and establishing the output and input ports according to the reverse of the input and output.

[0084] The aviation equipment system model SysML is a kind of object-oriented system modeling language, and the complex working process and functional behavior of the aviation equipment system are constructed by accurately describing the system fault sequence correlation, polymorphism, fault tolerance behavior and reconstruction behavior, and the system input is provided for reliability analysis.

[0085] As shown in Figure 4 Fig. 1 is a specific implementation module schematic diagram of the aviation equipment system model SysML to AltaRica function model conversion of the present application; the mapping relationship between the AltaRica function model and the aviation equipment system model SysML needs to carry out formal setting analysis for each model element, and the corresponding mapping relationship between the aviation equipment system model SysML and the AltaRica function model is determined; the extraction of key model elements in the aviation equipment system model SysML is realized by extracting the key elements from the existing database to extract the key elements to realize the analysis of the aviation equipment system design information. For the SysML-AltaRica model conversion, firstly, formal setting analysis needs to be carried out for each model element, and the corresponding mapping relationship between SysML and AltaRica is determined, as shown in Table 1. For the extraction of key model elements in the SysML model, the XMI is extracted from the commercial software to extract the key elements to realize the analysis of the system design information.

[0086] Table 1 SysML-Altarica mapping relationship

[0087] SysML model AltaRica model uml:Class Module (class) uml: basic Property Module state variable (state) uml: self-setting Property Submodule (class) uml: Port implementation type event Module input port (inflow) uml: Port request type event Module output port (outflow) uml: Connector Connection between modules (assert)

[0088] The AltaRica function model in the embodiment of the present application can automatically carry out FMEA, FTA and other reliability analysis work by extending the system normal function behavior model, avoid the problems of large modeling difficulty and inaccurate model construction caused by manually combing the complex function logic of the system, and provide engineering application efficiency.

[0089] The mapping relationship between the Modelica function model and the aviation equipment system model SysML is as follows: the aviation equipment system model is created according to the language modeling tool based on the aviation equipment system model SysML, the components or subsystems of the aviation equipment system model SysML are determined, an extended file standard protocol for converting SysML into Modelica is applied to create an analysis description of a corresponding component or subsystem of the aviation equipment system model SysML, and the language modeling tool of the Modelica function model is used for analysis.

[0090] The mapping relationship of the class setting Cs is that the object features described by the class setting in the aviation equipment system model SysML are respectively mapped into the class in the Modelica function model. According to the features of the object described by the class setting in SysML, different classes in Modelica are respectively mapped, for example, the block used for describing the signal causality relationship is mapped into the “Block” class of Modelica, the block used for describing the port is mapped into the “Connector” class, and the mapping relationship between the SysML stereotype and the corresponding Modelica general and special class is shown in Table 2.

[0091] Table 2 Mapping relationship between SysML and Modelica classes

[0092]

[0093] The mapping relationship of the preset variable Pv is that the Variable type of the Modelica function model is mapped into the variable type of the aviation equipment system model SysML; the mapping relationship of the variable preset type is that the Variable type of Modelica is mapped into the Value Type of SysML, and the mapping relationship is shown in Table 3, which is instantiated in the form of “SysML4Modelica::Types::Modelica-Type” in SysML4Modelica.

[0094] Table 3 Mapping relationship between SysML and Modelica preset variables

[0095] Modelica variable type SysML variable type SysML4Modelica variable type Real SysML::Blocks::Real ModelicaReal Integer SysML::Blocks::Integer ModelicaInteger Boolean SysML::Blocks::Boolean ModelicaBoolean String SysML::Blocks::String ModelicaString

[0096] The mapping relationship of component class declaration Cd is: the component of the Modelica function model is mapped to the class of the Modelica function model; the component declaration mapping relationship. The Modelica component (Component) is an instance of ModelicaClass. The mapping relationship of SysML and Modelica instantiation objects is shown in Table 4.

[0097] Table 4 Mapping relationship of SysML and Modelica instantiation objects

[0098] ModelicaComponent SysML Block Properties SysML4Modelica component setting Restricting class record or type UML4SysML::Property Modelica Value Property Restricting class class, model or block UML4SysML::Part Modelica Part Restricting class connector UML4SysML::Port Modelica Port Restricting class function UML4SysML::Parameter Modelica Function Parameter

[0099] The mapping relationship of equation algorithm Eg is: the equation of the Modelica function model represents the dynamic behavior of the aviation equipment system model, describes the process physical behavior, and the mapping relationship is used to describe the physical mapping relationship of the internal sub-model of the aviation equipment system model. Equation and algorithm mapping relationship: the Modelica equation represents the dynamic behavior of the model, the algorithm is used to describe the process physical behavior, and the connection relationship is used to describe the physical connection relationship of the internal meta-model of the model. The mapping relationship of SysML and Modelica for equations, algorithms and connections is shown in Table 5.

[0100] Table 5 Mapping relationship of SysML and Modelica equations, algorithms and connections

[0101] Modelica setting SysML setting SysML4Modelica setting Equation UML4SysML::Classifier Modelica Equation Algorithm UML4SysML::Behavior Modelica Algorithm Connection UML4SysML::Connector Modelica Connection

[0102] The Modelica function model in the embodiment of the application can construct a system architecture model based on performance parameters, accurately represent information such as actual situation, load situation and real-time performance change of the system by using system performance constraint relationship, and realize analysis of fault modes related to system performance degradation, degradation and failure through simulation means.

[0103] Step S2: Establishing an aviation equipment system fault logic model, completing key fault identification and analysis evaluation of the aviation equipment system.

[0104] According to the fault setting and expansion of the aviation equipment system in step S1, the aviation equipment system fault logic model is established, the automatic key fault identification FMEA and key fault analysis and evaluation FTA are realized, the key fault modeling and simulation of the aviation equipment system are carried out using the aviation equipment system fault logic model, the reliability of the continuous state and fault interference of the aviation equipment system is analyzed, and the mean flight fault interval hours MFHBF, the mean time between failures MTBF, the mission success probability MCSP, the mean time between critical failures MTBCF and the mission reliability MR of the aviation equipment system are determined.

[0105] As shown in Figure 5 The FMEA generation process diagram based on Altarica of the application is shown; the key fault identification FMEA is specifically used for identifying the key fault modes of each level of the aviation equipment system for completing the task, so as to further formulate fault control measures and realize the design process control of the task reliability; the aviation equipment system fault logic model can analyze the influence relationship of all bottom unit failures on the top function failure of the aviation equipment system through the fault mode and local fault logic relationship setting, and therefore is automatically converted into the key fault identification FMEA to support the aviation equipment system task reliability related analysis work through the algorithm compilation based on the sub-model syntax element; the key fault identification FMEA analysis process based on the AltaRica function model is as follows: each fault mode can be backward transferred through the port fault, and the fault influence on the top function of the system is realized through the port connection between each level; the local influence is the fault influence of the fault mode on the port of the function module; the last layer influence is the fault influence of the final output port of the model level where the fault mode is located; the final influence is set as the fault influence of the output port of the highest level of the current model; if the model has only two layers, the analysis results of the last layer influence and the final influence are the same.

[0106] As shown in Figure 6 The FTA generation process diagram based on Altarica of the application is shown; the key fault analysis and evaluation FTA based on the AltaRica function model is as follows: the top layer setting of the AltaRica function model is combined, the fault state expansion setting content of each port is set as the top event of each fault tree with the top function port of the aviation equipment system as the object, the fault logic architecture of each fault tree is determined by adopting the data flow reverse deduction mode according to the pre-set order, taking the fault output of the forward module as the fault input of the backward module based on the local fault setting of each module; finally, the fault state set by each module is directly taken as the bottom event of the fault tree according to the local fault influence relationship modeling content of each module, so as to carry out the subsequent analysis and evaluation work.

[0107] In the present application, the AltaRica function model and the Modelica function model are adopted and used for obtaining the same reliability index parameters, because different characteristics of the aviation equipment system may cause a single function model to be unable to guarantee obtaining all reliability index parameters, therefore, when a reliability index parameter can be obtained by both the AltaRica function model and the Modelica function model, the values of the two models should be the same in theory, and thus the value of one of the function models can be directly taken, or the average value of the two can be taken, and when a reliability index parameter can only be obtained by one of the function models, the value of the reliability index parameter is directly obtained.

[0108] Step S3: determining the reliability index parameters of the aviation equipment system according to the identification and analysis results of the key failures of the aviation equipment system.

[0109] According to the simulation results of the fault logic model of the aviation equipment system established by the AltaRica function model and the Modelica function model in step S2, the fault time distribution of the fault mode is fitted, and the task success rate is evaluated using the reliability index parameters of the aviation equipment system; the reliability index parameters of the aviation equipment system include the basic reliability parameter FR and the task reliability parameter TR.

[0110] The basic reliability parameter FR is:

[0111] FR=a1*MFHBF+a2*MTBF

[0112] Wherein, FR is the basic reliability parameter; MFHBF is the average flight failure interval hours of the aviation equipment system; MTBF is the average failure interval time of the aviation equipment system; a1 is the weight coefficient of the average flight failure interval hours of the aviation equipment system; a2 is the weight coefficient of the average failure interval time of the aviation equipment system.

[0113] The basic reliability parameter FR specifically includes the average flight failure interval hours MFHBF of the aviation equipment system and the average failure interval time MTBF of the aviation equipment system.

[0114] The average flight failure interval hours MFHBF of the aviation equipment system is the main field use parameter for measuring the reliability of the aviation equipment system, and the flight time of the aircraft needs to be recorded; the ratio of the total flight time accumulated by the aviation equipment system in a set time to the total number of failures in the same period is:

[0115]

[0116] Wherein, the MFHBF is the average flight failure interval hours of the aviation equipment system; the AEH is the flight time of the aviation equipment system; and the FF is the total number of failures of the aviation equipment system.

[0117] The average failure interval time MTBF of the aviation equipment system is the ratio between the working time and the total number of failures of the aviation equipment system under the set conditions and within the set time, and is:

[0118]

[0119] Wherein, the MTBF is the average failure interval time of the aviation equipment system; and the AEH1 is the working time of the aviation equipment system.

[0120] The metric task reliability parameter TR is:

[0121] TR = b1*MCSP + b2*MTBCF + b3*MR

[0122] Wherein, the TR is the metric task reliability parameter; the MCSP is the task success probability of the aviation equipment system; the MTBCF is the average serious failure interval time of the aviation equipment system; the MR is the task reliability of the aviation equipment system; the b1 is the weight coefficient of the task success probability of the aviation equipment system; the b2 is the weight coefficient of the average serious failure interval time of the aviation equipment system; and the b3 is the weight coefficient of the task reliability of the aviation equipment system.

[0123] The metric task reliability parameter TR specifically includes the task success probability MCSP of the aviation equipment system, the average serious failure interval time MTBCF of the aviation equipment system, and the task reliability MR of the aviation equipment system.

[0124] The task success probability MCSP of the aviation equipment system is the probability of successfully performing the specified task within the specified task profile of the aviation equipment system, and is:

[0125]

[0126] Wherein, the MCSP is the task success probability of the aviation equipment system; the TST is the number of times of task success of the aviation equipment system; and the TT is the total number of times of performing the task of the aviation equipment system.

[0127] The average serious failure interval time MTBCF of the aviation equipment system is the ratio between the total task time and the total number of serious failures of the aviation equipment system within the specified series of task profiles, and is:

[0128]

[0129] Wherein, MTBCF is the average serious failure interval time of the aviation equipment system; TH is the total time of the aviation equipment system; AFF is the total number of serious failures of the aviation equipment system.

[0130] The mission reliability MR of the aviation equipment system is the probability that the aviation equipment system successfully completes the specified mission within the specified mission profile; when the serious failure obeys the exponential distribution, the calculation formula is:

[0131]

[0132] Wherein, MR is the mission reliability of the aviation equipment system; T is the specified mission time of the aviation equipment system.

[0133] Step S4: Determine the reliability analysis result of the aviation equipment system according to the reliability index parameters of the aviation equipment system, and complete the reliability analysis.

[0134] The calculation results of the measured basic reliability parameters FR and the measured mission reliability parameters TR in step S3 are obtained, and the calculation formula of the reliability analysis result AES of the aviation equipment system is constructed as:

[0135] AES = a·FR + b·TR

[0136] Wherein, AES is the reliability analysis result of the aviation equipment system; a is the weight coefficient of the measured basic reliability parameter; b is the weight coefficient of the measured mission reliability parameter.

[0137] According to the working environment and task requirements of the aviation equipment system, the threshold of the mission reliability index is set, whether the reliability analysis result AES of the aviation equipment system meets the expectation is judged, and then the reliability of the aviation equipment system is judged.

[0138] The embodiment of the application takes a certain high-lift sub-system as an example to analyze and calculate the mean time between critical failures (MTBCF) of an aviation equipment system and the mission reliability (MR) of the aviation equipment system. The failure mode and failure time distribution obtained upstream is shown in Table 6, and the minimal cut sets of the failure model are: (CD), (HG), (LWZ, RWZ), (LQX, RQX), (F1, F2), (M1, M2). The flight time for performing a mission is set to 5h, and since the flap retraction and extension need to be controlled by a handle command during the take-off and landing processes, if the flap retraction and extension function fails during the 5h mission cycle, the high-lift system will fail to increase the lift, and according to the time distribution of the occurrence of the failure modes of the components, the python script prepared is called to simulate and calculate the mean time between critical failures (MTBCF) of the aviation equipment system and the mission reliability (MR) of the aviation equipment system, and the results are: MTBCF = 5012h, and the mission reliability (MR) of the aviation equipment system is 99.952%; the reliability analysis result of the aviation equipment system (AES) is determined to be reliable by judging the working environment and mission requirements of the aviation equipment system.

[0139] Table 6: Calculation results of failure mode and failure time distribution

[0140] Serial number Failure mode code Failure time / h 1 CD Weibull distribution (10030, 3.04, 48360) 2 HG Weibull distribution (9401, 2.88, 49640) 3 LWZ Weibull distribution (10200, 2.84, 51090) 4 RWZ Weibull distribution (10200, 2.84, 51090) 5 LQX Weibull distribution (15020, 2.85, 51280) 6 RQX Weibull distribution (15020, 2.85, 51280) 7 F1 Weibull distribution (10270, 2.83, 51890) 8 F2 Weibull distribution (10270, 2.83, 51890) 9 M1 Weibull distribution (19550, 2.84, 98590) 10 M2 Weibull distribution (19550, 2.84, 98590)

[0141] In the application, the functional model reliability module is mainly based on the aviation equipment system model SysML, and the mapping relationship between the aviation equipment system model SysML and the AltaRica functional model and the aviation equipment system model SysML and the Modelica functional model is automatically generated through model mapping and conversion technology, so as to realize the system design data sharing and model integration between functional models and reduce the amount of manual intervention in modeling.

[0142] The mission reliability module sets and extends the failure based on the AltaRica functional model, establishes a system failure logic model, realizes the calculation of automatic key failure identification (FMEA) and key failure analysis and evaluation (FTA) based on the failure logic model, and is compatible with existing mission reliability modeling methods; for complex multi-dynamic systems and failure-related systems, the Modelica functional model is used to analyze the reliability of continuous state systems and failure-independent systems through failure modeling and simulation.

[0143] After the failure time distribution of each failure mode is fitted based on the modeling and simulation results of the above-mentioned Modelica functional model and AltaRica functional model, the reliability index parameter analysis and calculation module calculates the reliability index to evaluate the mission success rate.

[0144] The application has the following beneficial effects: the model conversion method in the embodiment of the application can automatically or semi-automatically analyze the reliability related indexes of the aviation equipment system, and avoid the overlarge difference of the reliability analysis results of the same aviation equipment system caused by manual intervention, which is inconsistent with the actual state of the aviation equipment system. The application can also be used for the complex aviation equipment system with dynamic reconfiguration characteristics, failure time sequence correlation and logic correlation, and can ensure the comprehensiveness and accuracy of the failure logic relationship analysis, and then complete the reliability judgment of the aviation equipment system. The analysis of the high-lift sub-system in the embodiment proves that the task reliability judgment result of the aviation equipment system reaches 99.952%, and proves that the application can better realize the failure analysis of the aviation equipment system.

[0145] The above-described embodiments are only used to describe the preferred embodiments of the application, and do not limit the scope of the application. Without departing from the design spirit of the application, various modifications and improvements of the technical solutions of the application made by those skilled in the art shall fall within the protection scope of the claims of the application.

Claims

1. A reliability analysis method for aviation equipment systems based on MBSE model conversion, characterized by: It includes: S1: Establish the functional model mapping relationship of aviation equipment system through MBSE model mapping conversion technology; Through MBSE model mapping and conversion technology, the mapping relationship between the aviation equipment system model SysML and the AltaRica functional model and Modelica functional model is automatically generated, realizing data interaction and model integration between the functional model and the aviation equipment system model SysML. The process of automatically generating the mapping relationship between the aviation equipment system model SysML and the AltaRica functional model is as follows: S11: Input the extensible markup file exported from the SysML of the aviation equipment system model; set the top-level root structure of the AltaRica functional model according to the top-level module selected by the user; S12: traverse all the classes in the extensible markup file in step S11, convert the class of the selected top-level module into the class of the AltaRica functional model according to the mapping rules and assign it to the root structure; S13: The class of the filtered top-level module traverses the custom attributes and recursively converts all subordinate custom attributes into sub-modules of the AltaRica functional model; a class component port connection state is generated corresponding to the basic type in the custom attribute structure hierarchy; S14: Processing the connection status of class component ports: traverse all class component ports; obtain each component connected to the class component port; record the class component call flow direction, and establish output and input ports in reverse order according to the input and output; S2: Establish a fault logic model for aviation equipment systems and complete key fault identification, analysis and evaluation of aviation equipment systems; According to the AltaRica functional model and Modelica functional model in step S1, the fault setting and expansion of the aviation equipment system are carried out, and the aviation equipment system fault logic model is established to realize the automatic key fault identification. and critical failure analysis assessments Use the aviation equipment system fault logic model to model and simulate the key faults of the aviation equipment system, analyze the reliability of the continuous state and fault interference of the aviation equipment system, and determine the mean flight failure interval hours of the aviation equipment system. , Mean time between failures of aviation equipment systems , the mission success probability of aviation equipment systems , Mean time between serious failures of aviation equipment systems and mission reliability of aviation equipment systems ; S3: Determine the reliability index parameters of the aviation equipment system based on the key fault identification and analysis results of the aviation equipment system; Fitting the failure time distribution of the failure mode based on the simulation results of the aviation equipment system fault logic model established by the AltaRica functional model and the Modelica functional model in step S2, and evaluating the mission success rate using the aviation equipment system reliability index parameters; The aviation equipment system reliability index parameters include: measuring basic reliability parameters and measure task reliability parameters ; The basic reliability parameters of the metric for: ； in, To measure basic reliability parameters; The mean flight failure interval hours for aviation equipment systems; The mean time between failures of aviation equipment systems; is the weight coefficient of the mean flight failure interval hours of the aviation equipment system; is the weight coefficient of the mean time between failures of aviation equipment systems; The measurement task reliability parameter for: ； in, To measure the reliability parameters of the task; mission success probability for aviation equipment systems; The mean time between serious failures of aviation equipment systems; Mission reliability of aviation equipment systems; is the weight coefficient of the mission success probability of the aviation equipment system; is the weight coefficient of the mean time between serious failures of aviation equipment systems; is the weight coefficient of the mission reliability of the aviation equipment system; S4: Determine the reliability analysis results of the aviation equipment system based on the aviation equipment system reliability index parameters and complete the reliability analysis; Obtain the basic reliability parameters measured in step S3 and measure task reliability parameters The calculation results of the aviation equipment system reliability analysis are constructed The calculation formula is: ； in, The reliability analysis results of aviation equipment systems; is the weight coefficient for measuring basic reliability parameters; is the weight coefficient for measuring task reliability parameters; According to the working environment and mission requirements of the aviation equipment system, the threshold of the mission reliability index is set to judge the reliability analysis results of the aviation equipment system. Whether it meets expectations, and then judge the reliability of the aviation equipment system.

2. The aviation equipment system reliability analysis method based on MBSE model conversion according to claim 1 is characterized by: The aviation equipment system model SysML in step S1 is an object-oriented system modeling language that accurately describes the fault sequence correlation, polymorphism, fault-tolerant behavior, and reconstruction behavior of the aviation equipment system to construct the complex working process and functional behavior of the aviation equipment system, providing system input for reliability analysis.

3. The aviation equipment system reliability analysis method based on MBSE model conversion according to claim 1 is characterized by: The mapping relationship between the AltaRica functional model and the aviation equipment system model SysML in step S1 requires formal setting analysis for each model element to clarify the mapping relationship between the aviation equipment system model SysML and the AltaRica functional model; Extraction of key model elements in the aviation equipment system model SysML is achieved by exporting the marked aviation equipment system model data from the existing database to extract key elements and realize the analysis of aviation equipment system design information.

4. The aviation equipment system reliability analysis method based on MBSE model conversion according to claim 1 is characterized by: The mapping relationship between the Modelica functional model in step S1 and the aviation equipment system model SysML is as follows: Create an aviation equipment system model using the SysML language modeling tool based on the aviation equipment system model, identify the components or subsystems of the aviation equipment system model SysML, apply the SysML to Modelica extension file standard protocol to create a corresponding analytical description of the components or subsystems of the aviation equipment system model SysML, and analyze it using the Modelica functional model language modeling tool; The mapping of SysML and Modelica functional model elements of aviation equipment system model includes: class setting , preset variables , component class declaration Sum equation algorithm The mapping relationship; The class setting The mapping relationship is as follows: according to the object characteristics described by the class set in the aviation equipment system model SysML, they are mapped to the classes in the Modelica functional model respectively; The preset variable The mapping relationship is: the Variable type of the Modelica functional model is mapped to the variable type of the aviation equipment system model SysML; The component class declaration The mapping relationship is: the components of the Modelica functional model are mapped to the classes of the Modelica functional model; The equation algorithm The mapping relationship is as follows: the equations of the Modelica functional model represent the dynamic behavior of the aviation equipment system model and describe the procedural physical behavior. The mapping relationship is used to describe the physical mapping relationship of the sub-models within the aviation equipment system model.

5. The aviation equipment system reliability analysis method based on MBSE model conversion according to claim 1 is characterized by: Identification of critical faults in step S2 Specifically used to identify key failure modes at all levels of the system to complete the mission of aviation equipment systems, so as to further formulate failure control measures and achieve design process control of mission reliability; The aviation equipment system fault logic model is set by the fault mode and local fault logic relationship, which can analyze the impact of all bottom-level unit failures on the top-level function failure of the aviation equipment system. Therefore, it is automatically converted into key fault identification through algorithm compilation based on sub-model syntax elements. To support the mission reliability analysis of aviation equipment systems; identify key faults based on AltaRica functional models The analysis process is as follows: each fault mode can be transmitted backward through port faults, and the fault impact on the top-level function of the system can be realized through the port connection between each layer; the local impact is the impact of the fault mode on the port fault of the functional module; the upper-level impact is the fault impact of the final output port of the model layer where the fault mode is located; the final impact is set to the fault impact of the output port of the highest level of the current model; if the model has only two layers, the analysis results of the upper-level impact and the final impact are the same.

6. The aviation equipment system reliability analysis method based on MBSE model conversion according to claim 1 is characterized by: Critical Failure Analysis Evaluation in Step S2 The analysis process based on the AltaRica functional model is: Combined with the top-level settings of the AltaRica functional model, taking the top-level functional ports of the aviation equipment system as the object, the fault status extension settings of each port are mapped to the top event of each fault tree. Based on the local fault settings of each module, the fault output of the forward module is used as the fault input of the backward module in a pre-set order, and the fault logic architecture of each fault tree is determined by data flow reverse deduction. Finally, according to the local fault impact relationship modeling content of each module, the fault status set by each module is directly used as the bottom event of the fault tree to carry out subsequent analysis and evaluation work.

7. The aviation equipment system reliability analysis method based on MBSE model conversion according to claim 1 is characterized by: Measuring basic reliability parameters in step S3 , specifically including: Mean flight failure interval hours for aviation equipment systems and the mean time between failures of aviation equipment systems ; The mean flight failure interval hours for the aviation equipment system As the main field parameter for measuring the reliability of aviation equipment systems, it is necessary to record the flight time of the aircraft. Within a set period of time, the ratio of the total accumulated flight time of the aviation equipment system to the total number of failures in the same period is: ； in, The mean flight failure interval hours for aviation equipment systems; flight time for aviation equipment systems; is the total number of aviation equipment system failures; The mean time between failures of the aviation equipment system The ratio of the working time of the aviation equipment system to the total number of failures under the set conditions and within the set time is: ； in, The mean time between failures of aviation equipment systems; Working hours for aviation equipment systems.

8. The aviation equipment system reliability analysis method based on MBSE model conversion according to claim 1 is characterized by: Measuring task reliability parameters in step S3 , specifically including: mission success probability of aviation equipment system , Mean time between serious failures of aviation equipment systems and mission reliability of aviation equipment systems ; The mission success probability of the aviation equipment system The probability that the aviation equipment system successfully performs the specified mission within the specified mission profile is: ； in, mission success probability for aviation equipment systems; The number of successful missions for aviation equipment systems; Total number of missions performed for aviation equipment systems; The mean time between serious failures of the aviation equipment system The ratio of the total mission time of the aviation equipment system to the total number of serious failures in a specified series of mission profiles is: ； in, The mean time between serious failures of aviation equipment systems; Total mission time for aviation equipment systems; The total number of serious failures of aviation equipment systems; Mission reliability of the aviation equipment system It is the probability that the aviation equipment system successfully completes the specified mission within the specified mission profile. When the serious failure obeys the exponential distribution, the calculation formula is: ； in, Mission reliability of aviation equipment systems; The prescribed mission time for aviation equipment systems.

9. An analysis system for the aviation equipment system reliability analysis method based on MBSE model conversion according to any one of claims 1 to 8, characterized in that: It includes: functional model reliability module, task reliability module and reliability index parameter analysis and calculation module; The functional model reliability module is based on the aviation equipment system model SysML. Through model mapping and conversion technology, it automatically generates mapping relationships between the aviation equipment system model SysML and the AltaRica functional model, and between the aviation equipment system model SysML and the Modelica functional model, thereby realizing system design data sharing and model integration between functional models. The mission reliability module performs fault definition and expansion based on the AltaRica functional model, establishes a system fault logic model, and implements automated critical fault identification and critical fault analysis and evaluation calculations based on the fault logic model, maintaining compatibility with existing mission reliability modeling methods. For complex multi-dynamic systems and fault-dependent systems, the Modelica functional model is used to analyze the reliability of continuous-state systems and systems with dependent faults through fault modeling and simulation. The reliability index parameter analysis and calculation module fits the failure time distribution of each failure mode based on the modeling and simulation results of the above-mentioned Modelica functional model and AltaRica functional model, and performs reliability index calculation to evaluate the mission success rate.

Images