MBSE model conversion-based aviation equipment system reliability analysis method and system

Abstract

The invention 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, and the method comprises the steps: S1, building the function model reliability of an aviation equipment system through an MBSE model mapping conversion technology; s2, establishing a fault logic model of the aeronautical equipment system, and completing key fault identification and analysis evaluation of the aeronautical equipment system; s3, determining reliability index parameters of the aeronautical equipment system according to key fault identification and analysis results of the aeronautical equipment system; and S4, determining an aviation equipment system reliability analysis result according to the aviation equipment system reliability index parameters, and completing reliability analysis. According to the method, the model conversion method is adopted to analyze the reliability related indexes of the aeronautical equipment system, the situation that reliability analysis results of the same system are greatly differentiated due to manual intervention and do not conform to the actual system state can be avoided, and comprehensiveness and accuracy of fault logic relation analysis of the aeronautical equipment system are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of aviation equipment system modeling and analysis, and in particular relates to an aviation equipment system reliability analysis method and system based on MBSE model conversion. Background Art

[0002] "Reliability" is one of the general quality characteristics of aviation equipment, which is defined as the ability of aviation equipment to complete specified functions under specified conditions and within specified time. 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 at home and abroad as a highly implementable complex engineering system design method. Compared with traditional system engineering methods, MBSE emphasizes the use of standardized models in the equipment development process. This not only solves the problem that traditional system engineering is difficult to implement, but also fills the shortcomings of existing digital development technology in demand analysis and architecture design. Therefore, it has been widely used in the aviation and aerospace fields at home and abroad. Carrying out system reliability design and analysis based on MBSE is not only to solve the problems of insufficient reliability design and analysis methods for complex systems, but also to integrate reliability design into the MBSE forward R&D project of the new generation of aviation equipment to achieve the integration of reliability design and functional performance design. The establishment of a full-process MBSE method that converts the functional design of aviation equipment system architecture based on SysML model to Altarica, Modelica, etc., which can generate reliability index parameters and reliability analysis reports that meet aviation standards, is the core of the technical problem focused on by this invention.

[0003] At present, reliability analysis of complex aviation equipment systems mostly adopts two technical routes: 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, it is necessary to describe the normal functional behavior that the system should complete when the input and its own state are normal. The modeling methods used for different types of systems will also be different. For discrete systems, a model similar to a state machine is usually used to describe the dynamic behavior of the system; while 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, it is necessary to set the fault behavior to describe the fault excitation and state transition behavior of the system. At the same time, it is also necessary to clearly set the requirements to be verified, and specify the qualitative and quantitative requirements for reliability that need to be analyzed and verified. Second, in the system requirements analysis stage, the system function list and usage requirements recorded in the MBSE model are used to conduct preliminary functional failure analysis to refine the system's reliability requirements. In the system function analysis and logical architecture design stage, with the help of the failure mode database, a comprehensive analysis model is established, focusing on the optimization of the system architecture. When the physical architecture design of the system is completed, a dynamic simulation evaluation of the system under normal and fault conditions is carried out to further improve the system's fault control logic and algorithm.

[0004] There are many problems with the above two technical routes: Traditional system reliability analysis mainly relies on technical experts, which is highly subjective, and the accuracy of the analysis results is highly dependent on the personal level of engineers. For the same system, the reliability analysis results conducted by different engineers may vary greatly due to differences in knowledge background and thinking mode. For complex systems with dynamic reconstruction characteristics, fault timing correlation and logic correlation, it has become impractical to analyze their fault logic relationship through manual reasoning. Even if the analysis can be performed, its comprehensiveness and accuracy are difficult to guarantee. In addition, the collaborative design of system reliability and functional performance is becoming more and more difficult. In the document-based design mode, the input of system reliability analysis usually comes from information such as system design schemes. However, due to the inherent fuzziness and ambiguity of text descriptions, it is difficult for analysts to accurately understand the system design principles, resulting in inconsistency between the input of reliability analysis and the actual status of the product. Most reliability analyses based on MBSE perform fault injection in the original SysML model, build a fault propagation model, and then calculate reliability indicators. However, although the SysML model is currently a widely recognized system engineering modeling language and is suitable for the early functional architecture and physical architecture design of the system, it does not support performance simulation and its support for reliability is insufficient. When parsing and reading the elements of the system design model according to requirements, the model information extraction process is completely customized and cannot adapt to the differences in model construction caused by different modeling habits and modeling specifications. Therefore, it is necessary to unify the multi-domain joint simulation solution based on model conversion for reliability analysis and demonstration. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an aviation equipment system reliability analysis method based on MBSE model conversion. The present invention adopts the model conversion method to analyze the reliability-related indicators of the aviation equipment system, which can avoid excessive differences in the reliability analysis results of the same system due to human intervention, which is inconsistent with the actual system status, and ensures the comprehensiveness and accuracy of the analysis of the logical relationship of aviation equipment system faults.

[0006] To achieve the above object, the present invention discloses the following technical solution:

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

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

[0009] 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 to achieve 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:

[0010] S11: Input the extensible markup file exported by 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;

[0011] 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 rule and assign it to the root structure;

[0012] 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;

[0013] S14: Processing the connection status of the class component port: traversing all class component ports; obtaining each component connected to the class component port; recording the class component call flow direction, and establishing output and input ports according to the reverse direction of input and output;

[0014] S2: Establish a fault logic model for aviation equipment systems and complete key fault identification, analysis and evaluation of aviation equipment systems;

[0015] According to the AltaRica functional model and the Modelica functional model in step S1, the fault setting and expansion of the aviation equipment system are carried out, and the fault logic model of the aviation equipment system is established to realize the automated key fault identification FMEA and key fault analysis and evaluation FTA; the key fault modeling and simulation of the aviation equipment system are carried out using the fault logic model of the aviation equipment system, and the reliability of the continuous state and fault interference of the aviation equipment system is analyzed to determine the mean flight failure time MFHBF of the aviation equipment system, the mean failure time MTBF of the aviation equipment system, the mission success probability MCSP of the aviation equipment system, the mean serious failure time MTBCF of the aviation equipment system and the mission reliability MR of the aviation equipment system;

[0016] 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;

[0017] According to 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, the failure time distribution of the failure mode is fitted, and the mission success rate is evaluated using the aviation equipment system reliability index parameters; the aviation equipment system reliability index parameters include: a basic reliability measurement parameter FR and a mission reliability measurement parameter TR;

[0018] The basic reliability parameter FR of the metric is:

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

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

[0021] The measurement task reliability parameter TR is:

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

[0023] Among them, TR is the parameter for measuring mission reliability; MCSP is the mission success probability of the aviation equipment system; MTBCF is the mean time between serious failures of the aviation equipment system; MR is the mission reliability of the aviation equipment system; b1 is the weight coefficient of the mission success probability of the aviation equipment system; b2 is the weight coefficient of the mean time between serious failures of the aviation equipment system; b3 is the weight coefficient of the mission reliability of the aviation equipment system;

[0024] S4: Determine the reliability analysis results of the aviation equipment system according to the reliability index parameters of the aviation equipment system and complete 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 for constructing the reliability analysis result AES of the aviation equipment system is:

[0026] AES=a·FR+b·TR

[0027] Among them, AES is the reliability analysis result of aviation equipment system; a is the weight coefficient for measuring basic reliability parameters; b is the weight coefficient for measuring mission reliability parameters;

[0028] According to the working environment and mission requirements of the aviation equipment system, the threshold of the mission reliability index is set to determine whether the aviation equipment system reliability analysis result AES meets expectations, and then determine the reliability of the aviation equipment system.

[0029] Preferably, 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.

[0030] Preferably, the mapping relationship between the AltaRica functional model and the aviation equipment system model SysML in step S1 needs to be formally set and analyzed for each model element to clarify the mapping relationship between the aviation equipment system model SysML and the AltaRica functional model;

[0031] 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.

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

[0033] Create an aviation equipment system model based on the language modeling tool of the aviation equipment system model SysML, clarify the components or subsystems of the aviation equipment system model SysML, apply the standard protocol of converting sysml into modelica's extended file to create a corresponding analytical description of the components or subsystems of the aviation equipment system model SysML, and use the language modeling tool of the Modelica functional model for analysis;

[0034] The mapping between the SysML and Modelica functional model elements of the aviation equipment system model includes the mapping relationship between class settings Cs, preset variables Pv, component category declarations Cd and equation algorithms Eg.

[0035] The mapping relationship of the class setting Cs is: according to the object characteristics described by the class set in the aviation equipment system model SysML, they are respectively mapped to the classes in the Modelica functional model;

[0036] The mapping relationship of the preset variable Pv is: the Variable type of the Modelica functional model is mapped to the variable type of the aviation equipment system model SysML;

[0037] The mapping relationship of the component class declaration Cd is: the component of the Modelica functional model is mapped to the class of the Modelica functional model;

[0038] The mapping relationship of the equation algorithm Eg is: the equation of the Modelica functional model represents the dynamic behavior of the aviation equipment system model and describes the procedural physical behavior. The mapping relationship is used to describe the physical mapping relationship of the internal sub-models of the aviation equipment system model.

[0039] Preferably, the critical fault identification FMEA in step S2 is specifically used to identify the critical failure modes of each level of the system for completing the mission of the aviation equipment system, so as to further formulate fault control measures and realize the design process control of mission reliability;

[0040] The aviation equipment system fault logic model can analyze the impact of all bottom-level unit failures on the top-level functional failures of the aviation equipment system through the setting of fault modes and local fault logical relationships. Therefore, it is automatically converted into a key fault identification FMEA through algorithm compilation based on sub-model syntax elements to support the analysis of aviation equipment system mission reliability. The key fault identification FMEA analysis process based on the AltaRica functional model is as follows: each fault mode can be backward transmitted through the port fault, and the fault impact on the top-level function of the system is realized through the port connection between each level. The local impact is the impact of the fault mode on the port fault of the functional module. The upper layer impact is the fault impact of the final output port of the model level 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 levels, the analysis results of the upper layer impact and the final impact are the same.

[0041] Preferably, the critical fault analysis assessment FTA in step S2 is based on the analysis process of the AltaRica functional model:

[0042] Combined with the top-level setting of the AltaRica functional model, the top-level functional port of the aviation equipment system is taken as the object, and the fault status extension setting content of each port is corresponded to the top event of each fault tree. Based on the local fault setting of each module, the fault output of the forward module is used as the fault input of the backward module in a preset order, and the fault logic architecture of each fault tree is determined by data flow reverse deduction. Finally, according to the modeling content of the local fault impact relationship of each module, the fault state set by each module is directly used as the bottom event of the fault tree to carry out subsequent analysis and evaluation.

[0043] Preferably, the measured basic reliability parameter FR in step S3 specifically includes: the mean flight failure interval hours MFHBF of the aviation equipment system and the mean failure interval time MTBF of the aviation equipment system;

[0044] The mean flight failure time between hours (MFHBF) of the aviation equipment system is used as the main field parameter for measuring the reliability of the aviation equipment system. The flight time of the aircraft needs to be recorded. Within the set 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:

[0045]

[0046] Among them, MFHBF is the mean flight failure interval hours of the aviation equipment system; AEH is the flight time of the aviation equipment system; FF is the total number of aviation equipment system failures;

[0047] The mean time between failures (MTBF) of the aviation equipment system is the ratio of the working time of the aviation equipment system to the total number of failures under set conditions and within a set time:

[0048]

[0049] Among them, MTBF is the mean time between failures of the aviation equipment system; AEH1 is the working time of the aviation equipment system.

[0050] Preferably, the measurement mission reliability parameter TR in step S3 specifically includes: the mission success probability MCSP of the aviation equipment system, the mean time between serious failures 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 that the aviation equipment system successfully performs the specified mission within the specified mission profile:

[0052]

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

[0054] The mean time between serious failures (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] Among them, MTBCF is the mean time between serious failures of the aviation equipment system; TH is the total mission time of the aviation equipment system; 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 mission within the specified mission profile; when the serious failure obeys the exponential distribution, the calculation formula is:

[0058]

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

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

[0061] The functional model reliability module is based on the aviation equipment system model SysML, and through the model mapping conversion technology, automatically generates the mapping relationship 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, so as to realize the system design data sharing and model integration between the functional models;

[0062] The mission reliability module sets and expands faults based on the AltaRica functional model, establishes a system fault logic model, and realizes the calculation of automated key fault identification and key fault analysis and evaluation based on the fault logic model, which can be compatible with the existing mission reliability modeling method; for complex multi-dynamic systems and fault-related 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;

[0063] The reliability index parameter analysis and calculation module fits the failure time distribution of each failure mode according to 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.

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

[0065] (1) The present invention realizes the automatic generation of the aviation equipment system model SysML to the AltaRica functional model, thereby improving work efficiency.

[0066] (2) The present invention provides an effective model conversion algorithm to realize the component module conversion method of the mapping relationship between functional models of different aviation equipment systems, thereby ensuring the uniformity of the same aviation equipment system under different models.

[0067] (3) The present invention converts the aviation equipment system model SysML into the AltaRica functional model and the Modelica functional model at the same time, ensuring that the aviation equipment system can obtain reliability-related indicators for subsequent reliability analysis.

[0068] (4) The model conversion method adopted by the present invention can automatically or semi-automatically analyze the reliability-related indicators of the aviation equipment system, thereby avoiding excessive differences in the reliability analysis results of the same aviation equipment system due to human intervention, which is inconsistent with the actual status of the aviation equipment system.

[0069] (5) The present invention can also be used for complex aviation equipment systems with dynamic reconstruction characteristics, fault timing correlation and logic correlation, to ensure the comprehensiveness and accuracy of the fault logic relationship analysis, and thus complete the judgment of the reliability of the aviation equipment system. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 The flowchart of the aviation equipment system reliability analysis method based on MBSE model conversion of the present invention;

[0071] Figure 2 A flowchart of converting the aviation equipment system model SysML to the AltaRica functional model of the present invention;

[0072] Figure 3 It is a specific implementation procedure diagram of the conversion of the aviation equipment system model SysML to the AltaRica functional model of the present invention;

[0073] Figure 4 It is a schematic diagram of a specific implementation module for converting the aviation equipment system model SysML to the AltaRica functional model of the present invention;

[0074] Figure 5 Generate a process diagram for the Altarica-based FMEA of the present invention;

[0075] Figure 6 This is a diagram of the FTA generation process based on Altarica of the present invention. DETAILED DESCRIPTION

[0076] The exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0077] The present invention provides an aviation equipment system reliability analysis method based on MBSE model conversion, such as Figure 1As shown, the reliability of the functional model of the aviation equipment system is established through the MBSE model mapping conversion technology; the fault logic model of the aviation equipment system is established to complete the key fault identification and analysis and evaluation of the aviation equipment system; 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; according to the reliability index parameters of the aviation equipment system, the reliability analysis results of the aviation equipment system are determined to complete the reliability analysis; which includes:

[0078] Step S1: Establish the functional model mapping relationship of the aviation equipment system through MBSE model mapping conversion technology.

[0079] 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 to achieve data interaction and model integration between the functional model and the aviation equipment system model SysML. Figure 2 The flowchart of the conversion of the aviation equipment system model SysML to AltaRica functional model based on the MBSE model mapping conversion technology of the present invention is shown; the extensible markup file exported by the SysML model is used to obtain the top-level module selected by the user, define the top-level root structure of AltaRica, judge the top-level module, and finally establish the mapping relationship between models. The specific program implementation is as follows Figure 3 The specific process of automatically generating the mapping relationship between the aviation equipment system model SysML and the AltaRica functional model is as follows:

[0080] Step S11: Input the extensible markup file exported by the aviation equipment system model SysML; set the top-level root structure of the AltaRica functional model according to the top-level module selected by the user.

[0081] Step 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.

[0082] Step 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.

[0083] Step S14: Process the connection status of the class component port: 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 according to the reverse direction of input and output.

[0084] The aviation equipment system model SysML 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, and provides system input for reliability analysis. In the embodiment of the present invention, SysML is an object-oriented system modeling language that accurately describes the fault sequence correlation, polymorphism, fault-tolerant behavior, and reconstruction behavior of the system to construct the complex working process and functional behavior of the system, and can provide comprehensive system input for reliability work.

[0085] like Figure 4 The figure shows a schematic diagram of a specific implementation module for the conversion of the aviation equipment system model SysML to the AltaRica functional model of the present invention; the mapping relationship between the AltaRica functional model and the aviation equipment system model SysML requires formalized setting analysis for each model element to clarify the corresponding mapping relationship between the aviation equipment system model SysML and the AltaRica functional model; the 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 to achieve the analysis of aviation equipment system design information. For the SysML-AltaRica model conversion, it is first necessary to carry out formalized setting analysis for each model element to clarify the corresponding mapping relationship between SysML and AltaRica, as shown in Table 1. Regarding the extraction of key model elements in the SysML model, the key elements are extracted by exporting XMI from commercial software to achieve the analysis of system design information.

[0086] Table 1 SysML-Altarica mapping relationship

[0087] SysML Model AltaRica Model uml:Class Module(class) uml:BasicProperty Module state variables (state) uml: Custom Property Submodule (class) uml:Port implementation type event Module input port (inflow) uml:Port request type event Module output port (outflow) uml:Connector Wiring between modules (assert)

[0088] The AltaRica functional model in the embodiment of the present invention can automatically carry out reliability analysis work such as FMEA and FTA by expanding the normal functional behavior model of the system, avoiding the problems of difficult modeling and inaccurate model construction caused by manual sorting out of the complex functional logic of the system, thereby improving engineering application efficiency.

[0089] The mapping relationship between Modelica functional model and aviation equipment system model SysML is as follows: create aviation equipment system model according to the language modeling tool based on aviation equipment system model SysML, clarify the components or subsystems of aviation equipment system model SysML, apply the standard protocol of sysml to convert into modelica extension file to create a corresponding analytical description of components or subsystems of aviation equipment system model SysML, and use the language modeling tool of Modelica functional model to analyze; the mapping of aviation equipment system model SysML and Modelica functional model elements includes: the mapping relationship between class setting Cs, preset variable Pv, component category declaration Cd and equation algorithm Eg.

[0090] The mapping relationship of the class setting Cs 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. According to the characteristics of the objects described by the class set in SysML, they are mapped to different classes in Modelica. For example, the block used to describe the causal relationship of the signal is mapped to the "Block" class in Modelica, and the block used to describe the port is mapped to the "Connector" class, etc. The mapping relationship between SysMLstereotype and the corresponding Modelica general and special classes is shown in Table 2.

[0091] Table 2 SysML and Modelica class mapping relationship

[0092]

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

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

[0095] Modelica variable types SysML variable types SysML4Modelica variable types 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 the component class declaration Cd is: the component of the Modelica functional model is mapped to the class of the Modelica functional model; component declaration mapping relationship. Modelica component (Component) is an instance of ModelicaClass. The mapping relationship between SysML and Modelica instantiation objects is shown in Table 4.

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

[0098] ModelicaComponent SysMLBlockProperties SysML4Modelica component settings Restriction class record or type UML4SysML::Property 《modelicaValueProperty》 Restriction class class, model or block UML4SysML::Part 《modelicaPart》 Restricted connector class UML4SysML::Port 《modelicaPort》 Restricted functions UML4SysML::Parameter "modelicaFunctionParameter"

[0099] The mapping relationship between equation and algorithm Eg is as follows: The equation of the Modelica functional model represents the dynamic behavior of the aviation equipment system model, describes the procedural physical behavior, and the mapping relationship is used to describe the physical mapping relationship of the internal sub-models of the aviation equipment system model. Equation and algorithm mapping relationship: Modelica equations represent the dynamic behavior of the model, algorithms are used to describe the procedural physical behavior, and connection relationships are used to describe the physical connection relationships of the meta-models within the model. The mapping relationship between SysML and Modelica for equations, algorithms, and connections is shown in Table 5.

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

[0101] Modelica Settings SysML Settings SysML4Modelica Settings Equation UML4SysML::Classifier 《modelicaEquation》 Algorithm UML4SysML::Behavior 《modelicaAlgorithm》 Connection UML4SysML::Connector 《modelicaConnection》

[0102] The Modelica functional model in the embodiment of the present invention can construct a system architecture model based on performance parameters, and use the system performance constraint relationship to accurately characterize the actual system conditions, load conditions, real-time performance changes and other information. Through simulation means, it can realize the analysis of failure modes such as degradation, downgrade, and failure-related failures of system performance.

[0103] Step S2: Establish a fault logic model for the aviation equipment system and complete the key fault identification, analysis and evaluation of the aviation equipment system.

[0104] 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 fault logic model of the aviation equipment system is established to realize the automated key fault identification FMEA and key fault analysis and evaluation FTA; the key fault model of the aviation equipment system is used to model and simulate the aviation equipment system, analyze the continuous state of the aviation equipment system, the reliability of fault interference, and determine the mean flight failure time between hours MFHBF of the aviation equipment system, the mean failure time between MTBF of the aviation equipment system, the mission success probability MCSP of the aviation equipment system, the mean serious failure time between MTBCF of the aviation equipment system and the mission reliability MR of the aviation equipment system.

[0105] like Figure 5 The figure shows the FMEA generation process diagram based on Altarica of the present invention; the critical fault identification FMEA is specifically used to identify the critical fault modes of each level of the system for completing the mission of the aviation equipment system, so as to further formulate fault control measures and realize the design process control of mission reliability; the aviation equipment system fault logic model can analyze the influence relationship of all bottom-level unit failures on the top-level function failure of the aviation equipment system through the setting of fault mode and local fault logic relationship, so it is automatically converted into the critical fault identification FMEA through algorithm compilation based on sub-model syntax elements to support the relevant analysis work of aviation equipment system mission reliability; the critical fault identification FMEA analysis process based on the AltaRica functional model is as follows: each fault mode can be backward transmitted through the port fault, and the fault impact on the top-level function of the system is realized through the port connection between each level; the local impact is the port fault impact of the fault mode on the functional module; the upper layer impact is the fault impact of the final output port of the model level 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 levels, the analysis result of the upper layer impact is the same as that of the final impact.

[0106] like Figure 6 The diagram of the FTA generation process based on Altarica of the present invention is shown. The critical fault analysis and evaluation FTA is based on the analysis process of the Altarica functional model: combined with the top-level setting of the Altarica functional model, taking the top-level functional port of the aviation equipment system as the object, the fault state expansion setting content of each port corresponds to the top event of each fault tree, based on the local fault setting of each module, according to the preset order, the fault output of the forward module is used as the fault input of the backward module, and the fault logic architecture of each fault tree is determined by reverse deduction of the data flow; finally, according to the modeling content of the local fault impact relationship of each module, the fault state set by each module is directly used as the bottom event of the fault tree to carry out subsequent analysis and evaluation work.

[0107] The present invention adopts the AltaRica functional model and the Modelica functional model, and is used for obtaining the same reliability index parameters at the same time. This is because due to the different characteristics of aviation equipment systems, a single functional model may not be able to guarantee the acquisition of all reliability index parameters. Therefore, when a reliability index parameter can be obtained by both the AltaRica functional model and the Modelica functional model, the theoretical values ​​of the two should be the same. Therefore, the value of one of the functional models can be directly taken, or the average value of the two can be taken. When a reliability index parameter can only be obtained by one of the functional models, the value of this reliability index parameter is directly obtained.

[0108] Step 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.

[0109] The simulation results of the aviation equipment system fault logic model established according to the AltaRica functional model and the Modelica functional model in step S2 are used to fit the failure time distribution of the failure mode, and the aviation equipment system reliability index parameters are used to evaluate the mission success rate; the aviation equipment system reliability index parameters include: measuring basic reliability parameters FR and measuring mission reliability parameters TR.

[0110] The basic reliability parameter FR is:

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

[0112] Among them, FR is a basic reliability parameter; MFHBF is the mean flight failure interval in hours for the aviation equipment system; MTBF is the mean time between failures for the aviation equipment system; a1 is the weight coefficient of the mean flight failure interval in hours for the aviation equipment system; a2 is the weight coefficient of the mean time between failures for the aviation equipment system.

[0113] Measuring basic reliability parameters FR, specifically including: mean flight failure time between hours MFHBF and mean failure time between hours MTBF of aviation equipment systems.

[0114] The mean flight failure time between hours (MFHBF) of aviation equipment systems is 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:

[0115]

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

[0117] The mean time between failures (MTBF) of an aviation equipment system is the ratio of the working time of the aviation equipment system to the total number of failures under set conditions and within a set time:

[0118]

[0119] Among them, MTBF is the mean time between failures of the aviation equipment system; AEH1 is the working time of the aviation equipment system.

[0120] The measurement task reliability parameter TR is:

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

[0122] Among them, TR is the parameter for measuring mission reliability; MCSP is the mission success probability of the aviation equipment system; MTBCF is the mean time between serious failures of the aviation equipment system; MR is the mission reliability of the aviation equipment system; b1 is the weight coefficient of the mission success probability of the aviation equipment system; b2 is the weight coefficient of the mean time between serious failures of the aviation equipment system; b3 is the weight coefficient of the mission reliability of the aviation equipment system.

[0123] The mission reliability parameters TR are measured, including: the mission success probability MCSP of the aviation equipment system, the mean time between serious failures MTBCF of the aviation equipment system and the mission reliability MR of the aviation equipment system.

[0124] The mission success probability MCSP of the aviation equipment system is the probability that the aviation equipment system successfully performs the specified mission within the specified mission profile:

[0125]

[0126] Among them, MCSP is the mission success probability of the aviation equipment system; TST is the number of mission successes of the aviation equipment system; TT is the total number of missions performed by the aviation equipment system.

[0127] The mean time between serious failures (MTBCF) of an aviation equipment system is the ratio of the total mission time of an aviation equipment system to the total number of serious failures in a specified series of mission profiles:

[0128]

[0129] Among them, MTBCF is the mean time between serious failures of the aviation equipment system; TH is the total mission 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 an 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 follows an exponential distribution, the calculation formula is:

[0131]

[0132] Among them, 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 basic reliability parameter FR and the task reliability parameter TR in step S3 are obtained, and the calculation formula for constructing the reliability analysis result AES of the aviation equipment system is:

[0135] AES=a·FR+b·TR

[0136] Among them, AES is the reliability analysis result of aviation equipment system; a is the weight coefficient for measuring basic reliability parameters; b is the weight coefficient for measuring mission reliability parameters.

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

[0138] The embodiment of the present invention takes a certain high lift subsystem as an example to analyze and calculate the mean time between serious failures MTBCF value of the aviation equipment system and the mission reliability MR of the aviation equipment system. The failure mode failure time distribution obtained by the upstream is shown in Table 6, and the minimum cut set of the fault model is: (CD), (HG), (LWZ, RWZ), (LQX, RQX), (F1, F2), (M1, M2). The mission flight time is set to 5h. Since the flaps need to be controlled by the handle command during the take-off and landing processes, if the flaps retraction function fails within the 5h mission cycle, the high lift system will fail to increase the lift mission. According to the time distribution of the failure mode of each component, the prepared python script is called to simulate and calculate the mean time between serious failures MTBCF value of the aviation equipment system and the mission reliability MR of the aviation equipment system. The result is: MTBCF = 5012h, and the mission reliability MR of the aviation equipment system is 99.952%; the reliability analysis result AES of the aviation equipment system is judged to be in line with expectations through the working environment and mission requirements of the aviation equipment system, and the result is reliable.

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

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

[0141] The functional model reliability module in the present invention is mainly based on the aviation equipment system model SysML. Through the model mapping conversion technology, 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, 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 expands faults based on the AltaRica functional model, establishes a system fault logic model, and realizes the calculation of automated critical fault identification FMEA and critical fault analysis and evaluation FTA based on the fault logic model, which can maintain compatibility with existing mission reliability modeling methods; for complex multi-dynamic systems and fault-related 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.

[0143] The reliability index parameter analysis and calculation module fits the failure time distribution of each failure mode according to the modeling and simulation results of the above-mentioned Modelica functional model and AltaRica functional model, and then calculates the reliability index to evaluate the mission success rate.

[0144] The present invention has the following beneficial effects: The model conversion method adopted in the embodiment of the present invention can automatically or semi-automatically analyze the reliability-related indicators of the aviation equipment system, thereby avoiding the excessive differences in the reliability analysis results of the same aviation equipment system due to human intervention, which is inconsistent with the actual state of the aviation equipment system. The present invention can also be used for complex aviation equipment systems with dynamic reconstruction characteristics, fault sequence correlation and logic correlation, to ensure the comprehensiveness and accuracy of the fault logic relationship analysis, and then complete the judgment of the reliability of the aviation equipment system. The analysis of the high-lift subsystem in the embodiment proves that the task reliability judgment result of the aviation equipment system reaches 99.952%, which proves that the present invention can better realize the fault analysis of the aviation equipment system.

[0145] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A reliability analysis method for aviation equipment system based on MBSE model conversion, characterized in that: 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 to achieve 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 by 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 rule 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 the class component port: traversing all class component ports; obtaining each component connected to the class component port; recording the class component call flow direction, and establishing output and input ports according to the reverse direction of 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 the Modelica functional model in step S1, the fault setting and expansion of the aviation equipment system are carried out, and the fault logic model of the aviation equipment system is established to realize the automated key fault identification FMEA and key fault analysis and evaluation FTA; the key fault modeling and simulation of the aviation equipment system are carried out using the fault logic model of the aviation equipment system, and the reliability of the continuous state and fault interference of the aviation equipment system is analyzed to determine the mean flight failure time MFHBF of the aviation equipment system, the mean failure time MTBF of the aviation equipment system, the mission success probability MCSP of the aviation equipment system, the mean serious failure time MTBCF of the aviation equipment system and the mission reliability MR of the aviation equipment system; 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; According to 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, the failure time distribution of the failure mode is fitted, and the reliability index parameters of the aviation equipment system are used to evaluate the mission success rate; The reliability index parameters of the aviation equipment system include: a basic reliability parameter FR for measuring basic reliability and a mission reliability parameter TR for measuring mission reliability; The basic reliability parameter FR of the metric is: FR=a1*MFHBF+a2*MTBF Among them, FR is the basic reliability parameter; MFHBF is the mean flight failure interval hours of the aviation equipment system; MTBF is the mean failure interval time of the aviation equipment system; a1 is the weight coefficient of the mean flight failure interval hours of the aviation equipment system; a2 is the weight coefficient of the mean failure interval time of the aviation equipment system; The measurement task reliability parameter TR is: TR=b1*MCSP+b2*MTBF+b3*MR Among them, TR is the parameter for measuring mission reliability; MCSP is the mission success probability of the aviation equipment system; MTBCF is the mean time between serious failures of the aviation equipment system; MR is the mission reliability of the aviation equipment system; b1 is the weight coefficient of the mission success probability of the aviation equipment system; b2 is the weight coefficient of the mean time between serious failures of the aviation equipment system; b3 is the weight coefficient of the mission reliability of the aviation equipment system; S4: Determine the reliability analysis results of the aviation equipment system according to the reliability index parameters of the aviation equipment system and complete the reliability analysis; The calculation results of the basic reliability parameter FR and the task reliability parameter TR in step S3 are obtained, and the calculation formula for constructing the reliability analysis result AES of the aviation equipment system is: AES=a·FR+b·TR Among them, AES is the reliability analysis result of aviation equipment system; a is the weight coefficient for measuring basic reliability parameters; b is the weight coefficient for measuring mission 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 determine whether the aviation equipment system reliability analysis result AES meets expectations, and then determine 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 based on the language modeling tool of the aviation equipment system model SysML, clarify the components or subsystems of the aviation equipment system model SysML, apply the standard protocol of converting sysml into modelica's extended file to create a corresponding analytical description of the components or subsystems of the aviation equipment system model SysML, and use the language modeling tool of the Modelica functional model for analysis; The mapping between the SysML and Modelica functional model elements of the aviation equipment system model includes the mapping relationship between class settings Cs, preset variables Pv, component category declarations Cd and equation algorithms Eg. The mapping relationship of the class setting Cs is: according to the object characteristics described by the class set in the aviation equipment system model SysML, they are respectively mapped to the classes in the Modelica functional model; The mapping relationship of the preset variable Pv is: the Variable type of the Modelica functional model is mapped to the variable type of the aviation equipment system model SysML; The mapping relationship of the component class declaration Cd is: the component of the Modelica functional model is mapped to the class of the Modelica functional model; The mapping relationship of the equation algorithm Eg is: the equation of the Modelica functional model represents the dynamic behavior of the aviation equipment system model and describes the procedural physical behavior. The mapping relationship is used to describe the physical mapping relationship of the internal sub-models of 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: The critical fault identification FMEA in step S2 is specifically used to identify the critical failure modes of each level of the system for completing the mission of the aviation equipment system, so as to further formulate fault control measures and realize the design process control of mission reliability; The aviation equipment system fault logic model can analyze the impact of all bottom-level unit failures on the top-level functional failures of the aviation equipment system through the setting of fault modes and local fault logical relationships. Therefore, it is automatically converted into a key fault identification FMEA through algorithm compilation based on sub-model syntax elements to support the analysis of aviation equipment system mission reliability. The key fault identification FMEA analysis process based on the AltaRica functional model is as follows: each fault mode can be backward transmitted through the port fault, and the fault impact on the top-level function of the system is realized through the port connection between each level. The local impact is the impact of the fault mode on the port fault of the functional module. The upper layer impact is the fault impact of the final output port of the model level 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 levels, the analysis results of the upper layer 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: The critical fault analysis assessment FTA in step S2 is based on the analysis process of the AltaRica functional model: Combined with the top-level setting of the AltaRica functional model, the top-level functional port of the aviation equipment system is taken as the object, and the fault status extension setting content of each port is corresponded to the top event of each fault tree. Based on the local fault setting of each module, the fault output of the forward module is used as the fault input of the backward module in a preset order, and the fault logic architecture of each fault tree is determined by data flow reverse deduction. Finally, according to the modeling content of the local fault impact relationship of each module, the fault state set by each module is directly used as the bottom event of the fault tree to carry out subsequent analysis and evaluation.

7. The aviation equipment system reliability analysis method based on MBSE model conversion according to claim 1 is characterized by: The measured basic reliability parameters FR in step S3 specifically include: the mean flight failure interval hours MFHBF of the aviation equipment system and the mean failure interval time MTBF of the aviation equipment system; The mean flight failure time between hours (MFHBF) of the aviation equipment system is used as the main field parameter for measuring the reliability of the aviation equipment system. The flight time of the aircraft needs to be recorded. Within the set 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: Among them, MFHBF is the mean flight failure interval hours of the aviation equipment system; AEH is the flight time of the aviation equipment system; FF is the total number of aviation equipment system failures; The mean time between failures (MTBF) of the aviation equipment system is the ratio of the working time of the aviation equipment system to the total number of failures under set conditions and within a set time: Among them, MTBF is the mean time between failures of the aviation equipment system; AEH1 is the working time of the aviation equipment system.

8. The aviation equipment system reliability analysis method based on MBSE model conversion according to claim 1 is characterized by: The measurement mission reliability parameter TR in step S3 specifically includes: the mission success probability MCSP of the aviation equipment system, the mean time between serious failures MTBCF of the aviation equipment system, and the mission reliability MR of the aviation equipment system; The mission success probability MCSP of the aviation equipment system is the probability that the aviation equipment system successfully performs the specified mission within the specified mission profile: Among them, MCSP is the mission success probability of the aviation equipment system; TST is the number of successful missions of the aviation equipment system; TT is the total number of missions performed by the aviation equipment system; The mean time between serious failures (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: Among them, MTBCF is the mean time between serious failures of the aviation equipment system; TH is the total mission time of the aviation equipment system; AFF is the total number of serious failures of the aviation equipment system; 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: Among them, MR is the mission reliability of the aviation equipment system; T is the specified mission time of the aviation equipment system.

9. The second aspect of the present invention proposes an analysis system for an aviation equipment system reliability analysis method based on MBSE model conversion, characterized in that: It includes: function 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, and through the model mapping conversion technology, automatically generates the mapping relationship 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, so as to realize the system design data sharing and model integration between the functional models; The mission reliability module sets and expands faults based on the AltaRica functional model, establishes a system fault logic model, and realizes the calculation of automated key fault identification and key fault analysis and evaluation based on the fault logic model, which can be compatible with the existing mission reliability modeling method; for complex multi-dynamic systems and fault-related 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 according to 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.

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