CN-121980437-A - Model-based aerospace system fault identification and modeling method

CN121980437ACN 121980437 ACN121980437 ACN 121980437ACN-121980437-A

Abstract

The invention discloses a model-based space system fault identification and modeling method, which utilizes Sysml language modeling tools to analyze system requirements, determines input and output parameters according to system performance indexes to establish a system-level parameter model, determines a system structure composition model according to performance index requirements and the parameter model, identifies activities required by a system meeting functional performance requirements, establishes a system functional activity model, establishes a subsystem-level parameter model according to the system functional activity model, and accordingly analyzes downwards, establishes a fault element meta-model through a Sysml expansion mechanism, identifies fault information, develops fault analysis, establishes a fault and fault propagation model, and realizes forward design modeling containing the fault information. The invention realizes the identification of fault information and the establishment of a fault analysis model while developing the forward design of the system, solves the problem of collaborative modeling of the forward design and the reverse fault information, and helps the system to identify the risk optimization design in the design stage.

Inventors

LI RUIFENG
LONG DONGTENG
ZHANG RUI
HAN TIANLONG
CHENG HAILONG
ZHENG HENG

Assignees

中国航天标准化研究所

Dates

Publication Date: 20260505
Application Date: 20251210

Claims (9)

1. A model-based aerospace system fault identification and modeling method is characterized by comprising the following steps: S0, decomposing the system into different levels from top to bottom aiming at the top-level functional requirements and descriptions of the system or the task; S1, aiming at a current level, identifying activities to be carried out, defining an activity block diagram, analyzing and obtaining input and output performance parameters meeting corresponding behavior activities of a system, and establishing a SysML parameter model by using bdd module definition diagrams; s2, determining the structure composition of the current level and the flow relation of resources according to the input and output performance parameters of the current level, and establishing a SysML structure model by using an ibd structure diagram; S3, creating an act activity diagram on the activity block diagram, building a SysML behavior model, decomposing the activity of the current level to the lower layer, and defining the specific behavior to be executed in the next level; S4, returning to the iteration of S1 until reaching the lowest level, obtaining a SysML parameter model established by a bdd module definition diagram corresponding to each level, a SysML structure model established by an ibd structure diagram and a SysML behavior model established by an act activity diagram; S5, defining a fault element meta-model by using a SysML expansion mechanism through a profile block diagram configuration type stereotype in a bdd module definition diagram of a SysML parameter model; S6, aiming at the SysML behavior model, identifying an abnormal state which violates the specified states of all the active functions as a functional failure mode, and linking the abnormal state with an active block diagram of the SysML behavior model by Violate relations; S7, aiming at the SysML parameter model, identifying abnormal phenomena deviating from the specified range of each parameter as fault phenomena, and linking the abnormal phenomena with a parameter block diagram in the SysML parameter model by Deviate relation; S8, aiming at the SysML structure model, identifying structural integrity abnormality of each part as a structural failure mode, and linking the structural failure mode with a structural block diagram in the SysML structure model by Damage relation; S9, establishing fault and fault propagation models of all levels according to the fault phenomenon, the functional fault mode and the structural fault mode, and analyzing and formulating preventive and compensating measures according to the fault and the fault propagation models.
2. The method for identifying and modeling faults of a model-based aerospace system according to claim 1, wherein in S2, the resources comprise signals, data, energy and substances circulated among systems, subsystems or stand-alone internal structures.
3. The model-based aerospace system fault identification and modeling method according to claim 1, wherein in S5, fault element meta-models can be divided into fault information classes, logic relation classes, improvement measures classes and risk degrees classes; The fault information class comprises a fault mode, a fault influence and a fault reason; Logical relationship classes include violations Violate, deviations Deviate, corruption Damage, leading to Lead to, correlation Relate to, prevention Prevent, compensation Compensate; The improvement measures comprise preventive measures and compensating measures; The risk level class includes severity, occurrence probability, detection degree, and risk priority.
4. The method for identifying and modeling faults in a model-based aerospace system of claim 1, wherein Violate relationships include loss of function, partial loss of function, curtailment of function, overflow of function, hysteresis of function, interruption of function, unexpected function.
5. The method for identifying and modeling faults of a model-based aerospace system according to claim 1, wherein Deviate relations comprise no parameter value, too low parameter, too high parameter and unstable parameter.
6. The method for identifying and modeling faults of a model-based aerospace system according to claim 1, wherein Damage relationships comprise explosion, damage, buckling, deformation, scratching, injury, cracking, breaking, loosening, falling, rusting, abrasion, generation of excessive material, leakage, ignition and burning.
7. The method for identifying and modeling faults of a model-based aerospace system according to claim 1, wherein the method is characterized in that when faults are propagated: The fault mode of the level is the fault Cause of the fault mode of the upper level, the fault mode of the level is also the fault influence of the next level by the Cause relation link, the phenomenon identified by the parameter deviation of the level is the fault phenomenon of the fault mode of the previous level by the Effect relation link, the phenomenon identified by the parameter deviation of the level is the fault phenomenon of the fault mode of the previous level by the Perform relation link, and the final fault influence is the FINAL EFFECT relation link.
8. The method for identifying and modeling faults of a space system based on a model of claim 1, wherein the fault and fault propagation model is endowed with fault pattern block diagram Value attributes, and the severity, occurrence probability, detection degree and risk priority of a fault pattern of the hierarchy are defined.
9. The method for identifying and modeling faults in a model-based aerospace system according to claim 1, wherein risk analysis is carried out, and related preventive and compensating measures are formulated for each fault mode and are respectively linked with the fault modes in Prevent, compensate relations.

Description

Model-based aerospace system fault identification and modeling method Technical Field The invention relates to a model-based aerospace system fault identification and modeling method, and belongs to the technical field of model-based system engineering and risk management. Background The model-based system engineering (MBSE) is used as an emerging complex system research and development method, is widely applied and rapidly developed in the aerospace field in recent years, and MBSE is used for constructing a structured and traceable system model from a demand analysis stage through a formal modeling language and a unified digital model, so that the problems of information island and demand transfer deviation in the traditional method are effectively solved through the whole processes of design, simulation, verification and the like. At present, the main stream of designers for developing digital forward design is to only consider the function and performance design modeling of model products, but not consider the relations of faults, functional performance and the like, so that fault identification, modeling and risk analysis are often lagged behind the forward design and are separated from the forward design, or more depend on expert experience of a reliability designer, so that the cooperativity of the forward design and the risk analysis is lacking, and the risk assessment and weak link identification in the development stage are not facilitated. Disclosure of Invention The invention solves the problems of overcoming the defects of the prior art, providing a model-based space system fault identification and modeling method, comprehensively identifying fault information and fault propagation paths through fault logics such as functional violation, performance deviation, structural failure and the like in the process of developing the SysML forward design modeling according to the thought of 'forward design modeling + fault logics- & gt fault modeling', and establishing a fault analysis model. The problem of uncoordinated forward design and fault identification is solved, and basis is provided for risk analysis and evaluation in the design stage so as to reduce the design risk. The technical scheme of the invention is that the model-based aerospace system fault identification and modeling method comprises the following steps: S0, decomposing the system into different levels from top to bottom aiming at the top-level functional requirements and descriptions of the system or the task; S1, aiming at a current level, identifying activities to be carried out, defining an activity block diagram, analyzing and obtaining input and output performance parameters meeting corresponding behavior activities of a system, and establishing a SysML parameter model by using bdd module definition diagrams; s2, determining the structure composition of the current level and the flow relation of resources according to the input and output performance parameters of the current level, and establishing a SysML structure model by using an ibd structure diagram; S3, creating an act activity diagram on the activity block diagram, building a SysML behavior model, decomposing the activity of the current level to the lower layer, and defining the specific behavior to be executed in the next level; S4, returning to the iteration of S1 until reaching the lowest level, obtaining a SysML parameter model established by a bdd module definition diagram corresponding to each level, a SysML structure model established by an ibd structure diagram and a SysML behavior model established by an act activity diagram; S5, defining a fault element meta-model by using a SysML expansion mechanism through a profile block diagram configuration type stereotype in a bdd module definition diagram of a SysML parameter model; S6, aiming at the SysML behavior model, identifying an abnormal state which violates the specified states of all the active functions as a functional failure mode, and linking the abnormal state with an active block diagram of the SysML behavior model by Violate relations; S7, aiming at the SysML parameter model, identifying abnormal phenomena deviating from the specified range of each parameter as fault phenomena, and linking the abnormal phenomena with a parameter block diagram in the SysML parameter model by Deviate relation; S8, aiming at the SysML structure model, identifying structural integrity abnormality of each part as a structural failure mode, and linking the structural failure mode with a structural block diagram in the SysML structure model by Damage relation; S9, establishing fault and fault propagation models of all levels according to the fault phenomenon, the functional fault mode and the structural fault mode, and analyzing and formulating preventive and compensating measures according to the fault and the fault propagation models. Preferably, in S2, the resource includes signals, data, energy, and substances circulated be