Integrated simulation reliability redundancy margin optimization method based on SysML and FMU

Abstract

The invention relates to a reliability redundancy margin optimization method based on SysML and FMU integrated simulation. According to the method, firstly, SysML is used for establishing a reliability system model, then dynamic behavior simulation is achieved through a redundancy margin optimization algorithm integrated with FMU packaging, and further deep reliability analysis is carried out in a simulation environment. Based on the analysis, the redundancy design of the system is optimized, and the reliability margin of the system is improved. Through the iterative simulation and optimization process, the method can efficiently find the optimal redundancy configuration meeting the reliability requirement of the system. According to the method, the accuracy and efficiency of system design are improved, and a novel and practical solution is provided for reliability optimization of a complex system.

Description

Technical Field

[0001] This invention relates to the technical field, specifically to a method for optimizing system reliability redundancy margin through simulation using integrated SysML and FMU. This method is primarily applied in the design and development of complex systems, aiming to improve system reliability and performance.

[0002] By combining SysML's system modeling capabilities with FMU's dynamic simulation functions, this invention provides a novel and efficient solution in the technical field for analyzing and optimizing system redundancy design, thereby ensuring that the system maintains sufficient reliability margins even in the face of potential failures. This technology is not only applicable to traditional engineering systems but can also be extended to high-tech fields such as intelligent manufacturing, autonomous driving, and aerospace, providing strong support for the reliability design of complex systems. Background Technology

[0003] With the increasing complexity and sophistication of modern engineering systems, ensuring system reliability and stability has become a primary task in engineering design. Traditional system design and analysis methods often fall short when dealing with highly integrated and interdependent complex systems. Particularly in the optimization of redundancy design, traditional methods, typically based on empirical judgments or simplified mathematical models, struggle to fully consider the system's dynamic behavior and various potential failure modes.

[0004] SysML, a modeling language designed specifically for systems engineering, provides a standardized method for describing, analyzing, designing, and verifying complex systems. Through SysML, engineers can express the structure, behavior, and requirements of systems more clearly and accurately, providing a solid foundation for subsequent system analysis and optimization.

[0005] However, SysML models themselves cannot directly reflect the dynamic behavior of a system, which greatly limits their application in system redundancy design and reliability analysis. To overcome this deficiency, FMU technology has received widespread attention and application in recent years. An FMU is a standardized model for describing the behavior of dynamic systems, and it can be combined with SysML models to provide powerful support for the dynamic simulation of complex systems.

[0006] Under the current technological context, although SysML and FMU each have their unique advantages, effectively combining the two to achieve comprehensive optimization of redundancy design and reliability analysis of complex systems remains a challenging problem. This invention is proposed against this technological backdrop, aiming to develop a novel reliability redundancy margin optimization method by integrating SysML and FMU to meet the urgent reliability requirements of modern complex engineering systems.

[0007] The method of this invention not only considers the static structure of the system but also fully takes into account its dynamic behavior characteristics, making the optimization of redundancy design more scientific and accurate. By combining the system modeling capabilities of SysML and the dynamic simulation functions of FMU, this invention provides a novel solution for the reliability design and optimization of complex systems. Summary of the Invention

[0008] To address the problems of existing technologies, this invention provides an integrated simulation-based reliability redundancy margin optimization method based on SysML and FMU. This method combines the system modeling advantages of SysML with the dynamic behavior simulation capabilities of FMU, aiming to scientifically optimize the redundancy design of complex systems through accurate system simulation and comprehensive reliability analysis, thereby improving the system's reliability margin.

[0009] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0010] System Modeling: SysML is used to comprehensively describe the system's structure, behavior, requirements, and constraints. Through SysML's rich graphical representations, the various components, interfaces, behaviors, and logical relationships between them of the system can be clearly displayed, providing an accurate model foundation for subsequent dynamic simulation and reliability analysis.

[0011] Dynamic Behavior Simulation: Deep integration of FMU and SysML model enables accurate simulation of system dynamic behavior. The introduction of FMU gives SysML model the ability to simulate dynamic changes in the system in real-world operating environments, which is crucial for evaluating the system's performance and reliability under complex operating conditions.

[0012] Reliability Analysis: A comprehensive reliability analysis of the system is performed in an integrated simulation environment. By collecting data generated during the simulation, reliability metrics such as failure rate and mean time between failures (MTBF) are evaluated under different operating conditions, thereby accurately identifying critical components and potential failure modes of the system.

[0013] Redundancy design optimization: Based on the results of reliability analysis, the redundancy design of the system is optimized in a targeted manner. By identifying the components that need to be redundant and the appropriate level of redundancy, the reliability margin of the system is effectively improved, ensuring that the system can maintain stable performance when facing potential failures.

[0014] Iterative simulation and optimization: By continuously adjusting the redundancy design parameters and running the simulation model, the reliability indicators of the optimized system are evaluated. This iterative process will continue until the system meets the preset reliability requirements, thereby ensuring the optimal configuration of the redundancy design.

[0015] The beneficial effects of this invention are as follows:

[0016] 1. Encapsulate reliability redundancy margin optimization algorithms designed based on different tools into FMU models, use SysML to construct reliability system models of different granularities, map the reliability system models with the corresponding granularity FMU models and integrate them for simulation, obtain an integrated simulation model that is adapted to the system level of the model, balance the computational simulation boundary and simulation computing power requirements of different system granularity levels, and optimize the design, cost, and model granularity partitioning;

[0017] 2. The reliability redundancy margin optimization algorithm is encapsulated as an FMU and delivered as a black-box model. During project execution, it is integrated into the SysML reliability system model, realizing the internal algorithm isolation of the FMU and avoiding the risk of intellectual property disputes caused by the submission of algorithm models by various research units. At the same time, version management and control of the FMU are carried out based on SysML tools, and the model delivery is standardized by using systems engineering methods to improve the efficiency of FMU model delivery. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the implementation process of the integrated simulation reliability redundancy margin optimization method based on SysML and FMU of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The reliability redundancy margin optimization method based on integrated simulation of SysML and FMU proposed in this invention, such as... Figure 1 The schematic diagram shown illustrates the implementation process of the integrated simulation reliability redundancy margin optimization method based on SysML and FMU. The specific implementation method includes the following steps:

[0021] Step 1: Establish a SysML system model

[0022] Model initialization: First, use a SysML tool (such as Enterprise Architect or MagicDraw) to create a new system model project.

[0023] Define the system structure: Use SysML's Block Definition Graph (BDD) and Internal Structure Graph (IBD) to clarify the various components, interfaces and their interrelationships of the system.

[0024] Describing system behavior: The dynamic behavior and state transitions of the system are described using activity diagrams (AD) and state diagrams (SD).

[0025] Define requirements and constraints: Use requirement diagrams (RD) and parameter diagrams (PARAM) to express the functional requirements and non-functional constraints of the system.

[0026] Step 2: Integrate FMU for dynamic simulation

[0027] Selecting an FMU: Based on the needs of the system model, select or develop an appropriate FMU to describe the dynamic behavior of a specific component or subsystem.

[0028] FMU to SysML Model Mapping: This step determines the interfaces between the FMU and corresponding components in the SysML model, enabling data interaction and synchronization. In the SysML model, the imported FMU is connected to other components or subsystems, and relevant simulation parameters and interface mappings are configured. The SysML model provides the system context for FMU operation, while the FMU provides multidisciplinary model functions and algorithms.

[0029] Simulation environment setup: Integrate SysML models and FMUs into simulation platforms (such as MATLAB / Simulink, Modelica) to build a complete simulation environment.

[0030] Run the simulation: Set the simulation parameters, run the simulation model, and observe and record the dynamic behavior data of the system.

[0031] Step 3: Conduct reliability analysis

[0032] Data collection: Collect status data of key components, failure time, and failure impact range information from the simulation process.

[0033] Reliability metrics calculation: Based on the collected data, calculate the system's reliability metrics, such as reliability, failure rate, and mean time between failures (MTBF).

[0034] Failure Mode Identification: By analyzing data, key components and potential failure modes of the system are identified, providing a basis for redundant design.

[0035] Step 4: Optimize Redundancy Design

[0036] Determine redundancy strategy: Based on the reliability analysis results, determine the components and redundancy levels that need to be added (such as hot backup and cold backup).

[0037] Update the SysML model: Add redundant components and corresponding control logic to the SysML model.

[0038] Verify the redundancy design: By running the simulation model again, verify the effectiveness of the redundancy design and its improvement on system reliability.

[0039] Step 5: Iterative Simulation and Optimization

[0040] Adjust redundancy parameters: Based on the verification results, adjust the parameters of the redundancy design (such as redundancy level, number of backups, and switchover threshold).

[0041] Repeated simulation: After each parameter adjustment, the simulation model is run repeatedly to evaluate the optimization effect.

[0042] Achieving optimal configuration: Through multiple iterations and adjustments, the optimal redundancy configuration that meets the system reliability requirements is found.

[0043] Through the above specific implementation methods, the present invention can achieve scientific optimization of redundancy design in complex systems and significantly improve the reliability margin of the system.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A reliability redundancy margin optimization method based on integrated simulation of SysML and FMU, characterized in that: The following steps are involved: Establish a SysML reliability system model to define the structure, behavior, requirements and constraints of the reliability system; Encapsulating the reliability redundancy margin optimization algorithm into an FMU model and integrating it with the SysML reliability system model, so as to perform integrated simulation operation on the reliability redundancy margin optimization algorithm in combination with the SysML reliability system; Conduct reliability analysis of the system in an integrated simulation environment to identify key components and potential failure modes of the system; Optimize the system's redundancy design based on reliability analysis results; Through iterative simulation and optimization, the redundancy margin configuration that meets the system reliability requirements is found.

2. The reliability redundancy margin optimization method based on SysML and FMU integrated simulation according to claim 1 is characterized in that: The step of establishing the SysML reliability system model includes: using the graphical representation method defined in the SysML language to describe the system components, interfaces, behaviors and the relationships between them.

3. The reliability redundancy margin optimization method based on SysML and FMU integrated simulation according to claim 1 or 2, characterized in that: The step of integrating the FMU model with the SysML reliability system model includes: mapping the FMU model with the SysML reliability system model, and performing data interaction and synchronization between the FMU model and the SysML reliability system model when performing the integrated simulation operation.

4. The reliability redundancy margin optimization method based on integrated simulation of SysML and FMU according to any one of the preceding claims, characterized in that: The step of performing reliability analysis on the system includes: using simulation data to evaluate reliability indicators of the system under different working conditions, including reliability, failure rate and mean time between failures.

5. The reliability redundancy margin optimization method based on integrated simulation of SysML and FMU according to any one of the preceding claims, characterized in that: The step of optimizing the redundant design of the system includes: determining the components and redundancy levels that need to be added with redundancy according to the reliability analysis results, so as to improve the reliability margin of the system.

6. The reliability redundancy margin optimization method based on integrated simulation of SysML and FMU according to any one of the preceding claims, characterized in that: The steps of iterative simulation and optimization include: continuously adjusting redundant design parameters, running simulation models, and evaluating optimized system reliability indicators until preset reliability requirements are met.

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