TOWARDS MODEL‐BASED SYSTEMS ENGINEERING (MBSE) PATTERNS TO EFFICIENTLY REUSE KNOW‐HOW

<title>ABSTRACT</title> <p>The International Council on Systems Engineering <ext-link ext-link-type="uri" xlink:href="https://www.incose.org/">https://www.incose.org/</ext-link> is a recognized standards body defining a system engineering knowledge-base, but this knowledge falls short of fully recognizing manufacturing in the Systems Engineering (SE) framework. To be inclusive, Manufacturing needs to join in the initiative of Model Based Systems Engineering to be relevant and succeed in the digital transformation in the field of systems engineering. This paper addresses this need in manufacturing by applying Model Based System Engineering (MBSE) to the identification and management of key characteristics so that a more relevant set of Manufacturing requirements can be introduced into the MBSE construct and help realize manufacturing resilience and become a full SE partner.</p> <p><bold>Citation:</bold> “Identification and Management of Key Characteristics in Product Development Using Model Based Systems Engineering,” In <italic>Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium</italic> (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023.</p>

Systems engineering is a transdisciplinary scientific and applied direction that combines the principles of a systems approach, methods, tools and standards that provide an integrated methodology and relevant technologies for the design and development of complex systems, their comprehensive analysis and verification, efficient and safe use. The modern approach to systems engineering is characterized by an accentuated modeling of systems throughout their life cycle with the ultimate goal of creating their digital twins. It is called model-based systems engineering (MBSE). Consistent integration of MBSE with software engineering leads to the formation of the MBSSE (Model-Based Systems and Software Engineering) direction. This textbook is devoted to this area, the main objective of which is to study the conceptual and scientific-methodological foundations of MBSE and MBSSE, models and methods of life cycle management of systems and software, basic standards of systems engineering, study and mastery of the UML and SysML systems modeling languages and the corresponding modeling tools, study of requirements engineering methods and development of a description of the system architecture, familiarization with the concept of digital twins and their role in MBSE, study of the MBSSE reference model and its relationship with the process standards of systems engineering, study of the methodological aspects of the system and software integration process. The final chapter of the textbook is devoted to the study of the mathematical foundations of systems engineering. The main aspects of the mathematical theory of systems by A. Wayne Wymore, which became an important stimulus for the development of the model-oriented approach in systems engineering, and modern research, including the apparatus of finite automata by David Harel, the formalism of modeling of discrete systems DEVS, research into category theory as a formal mathematical basis for model-based system design are considered. The authors recommend this course as a basic course for training IT professionals.

Nowadays, in software, electronics and automotive industries, it takes 1.5–2 years for a new product to appear, although it takes 3 or even more years to develop new space instruments. The update of higher education system standards is even more conservative. It takes 10 or more years. Because of this, graduates come into the industry with outdated knowledge. Thus, the problem is to synchronize the processes of updating technologies that ensure the creation of innovative products with educational programs. It can be solved by changing the paradigm of products creation based on the synthesis of deep and interdisciplinary educational, scientific, innovative and production activities with the model-based systems engineering software and methodological tools.The existing model-based systems engineering (MBSE) Software (SW) tools based on the Systems Modelling Language (SysML) are complex and expensive. This chapter raises the question, “Is it possible to make use of MBSE SW and methodological tools available to a wide audience of users?” To answer this question, the following MBSE approaches and tools were researched: Quality Function Deployment (QFD), House of Quality (HoQ), and SysML. Consequently, five methodological tools were developed for application during space instruments design and development life cycle stages. Tools are based on SysML, QFD, HoQ improvements and synthesis, and expressed in theoretical and practical algorithms.The theoretical algorithm determines “What” (input and output data) and “How” (by using which systems engineering (including MBSE) tools are methodically analysed at the certain space instruments life cycle stage. The practical algorithm automates the development of SysML diagrams by using data structured in HoQ models and widely available SW. It reduces the time to develop and update SysML requirements diagrams from several days to some minutes and enables implementation of SysML SW by many users. Altogether, the obtained results constitute the software and methodological toolkit (SMT) called “improved Quality Function Deployment for improved House of Quality”.To disseminate the obtained results, educational materials were created and validated during lectures on systems engineering. Lectures were supplemented with seminars and practicum. Practicum was focused on the design and development (D&D) of nanosatellites in the MBSE paradigm as the single project. The results showed that the developed SMT makes it possible to implement the life cycle of the cyber-physical system in the MBSE paradigm in a cost-effective way and a short time—in 6 months, students developed, conducted synthesis and flight tests of CubeSat-format satellite prototype and prepared the project documentation. In addition, students were motivated to study science, technology, engineering and math disciplines, MBSE tools, and become transdisciplinary specialists.In addition, developed SysML requirements models were applied to examine the “Preliminary Design” product life cycle (PLC) stage of different space projects. Such application allowed quick assessment of project results, identifying non-compliance with requirements, conducting beforehand all necessary corrections and generating correct new product documentation. Taking into account the mentioned advantages of the obtained MBSE SMT, it can be concluded that its usage improves the quality and accuracy of current projects and speeds up the planning process of future analogue projects by up to 60%. Consequently, projects (their life cycle stages) can be realized faster by 5–10%.These results allow to recommend for all interested in the innovative D&D of systems to initiate: formation of educational programs based on the project approach and MBSE paradigm; preparation of MBSE materials for online and offline teaching of students and teachers; development of domestic widely available software for the development of integrated SysML models with HoQ models that assures synchronization with CAE/CAD/CAM systems; support these recommendations for the implementation of the above-mentioned research results at educational organizations of different levels. The future research is focused on the application of the developed MBSE methodological toolkit during design and development with numerical modelling and operation of several CubeSat satellites.KeywordsSystems engineeringMBSEParadigmMethodological toolkitSysMLQFDHoQ

Traditional document‐based practices in systems engineering are being transitioned to model‐based ones. Adoption of model‐based systems engineering (MBSE) continues to grow in industry and government, and MBSE continues to be a major research theme in the systems engineering community. In fact, MBSE remains a central element in the International Council on Systems Engineering (INCOSE)’s vision for 2025. Examining systems engineering literature, this paper presents an assessment of the extent to which benefits and value of MBSE are supported by empirical evidence. A systematic review of research and practice papers in major systems engineering archival journals and conference proceedings was conducted. Evidence was categorized in four types, two of which inductively emerged from the results: measured, observed (without a formal measurement process), perceived (claimed without evidence), and backed by other references. Results indicate that two thirds of claimed MBSE benefits are only supported by perceived evidence, while only two papers reported measured evidence. The aggregate assessment presented in this paper indicates that claims about the value and benefits of MBSE are mainly based on expectation. We argue that evidence supporting the value and benefits of MBSE remains inconclusive.

The complex system engineering in the aerospace domain is undergoing a digital transformation, with Model-Based Systems Engineering (MBSE) serving as the core driving force. By establishing a unified model framework, MBSE has revolutionized traditional development paradigms and significantly enhanced multidisciplinary collaboration and full lifecycle management capabilities. However, MBSE still faces bottlenecks in model standardization, cross-domain integration, and dynamic decision-making. This paper examines MBSE model mappings across key aerospace mission phases: planning, requirement analysis, design, and on-orbit operations. It identifies core AI-empowered scenarios such as intelligent requirement capture, multidisciplinary collaborative optimization, and virtual-physical fusion verification. The study explores the synergistic application of AI and MBSE in aerospace complex system engineering, providing methodological support for the digital transformation and intelligent upgrading of China’s aerospace equipment. The complex system engineering in the aerospace domain is undergoing a digital transformation, with Model-Based Systems Engineering (MBSE) serving as the core driving force. By establishing a unified model framework, MBSE has revolutionized traditional development paradigms and significantly enhanced multidisciplinary collaboration and full lifecycle management capabilities. However, MBSE still faces bottlenecks in model standardization, cross-domain integration, and dynamic decision-making. This paper examines MBSE model mappings across key aerospace mission phases: planning, requirement analysis, design, and on-orbit operations. It identifies core AI-empowered scenarios such as intelligent requirement capture, multidisciplinary collaborative optimization, and virtual-physical fusion verification. The study explores the synergistic application of AI and MBSE in aerospace complex system engineering, providing methodological support for the digital transformation and intelligent upgrading of China’s aerospace equipment.

Those looking to advocate for Model‐Based Systems Engineering (MBSE) in the Systems Engineering field often turn to more established fields that have made a similar transition to models to assure others it will be beneficial. One practice that is often compared to MBSE is Computer‐Aided Design (CAD) from the field of mechanical engineering. However, the adequacy of this comparison is challenged upon a side‐by‐side examination of what MBSE and CAD are. Based on the established definitions, it is evident that while CAD can be considered a method of mechanical drawing, MBSE cannot be described as only a method. MBSE is more than installing and utilizing software, it is a process in and of itself. Comparing MBSE to CAD runs the risk of oversimplifying MBSE and setting up expectations that may not be met. Therefore, while CAD may represent a similar paradigm shift as MBSE in terms of digitalization, it may not serve as an adequate paradigm in terms of adoption and benefits. In this paper, we characterize and compare CAD and MBSE and identify the similarities and differences between them. We use the resulting insights to level the expectations of adopting and using MBSE.

Model Based Systems Engineering (MBSE) is an interesting alternative to traditional systems engineering methods. Instead of using electronic documents to record system information, MBSE uses a unified and coherent system model. Trade-offs are a major element of a space systems engineer's role in early system design. This can be a particularly challenging process in the domain of spacecraft, as the system designs are often very complex and the constraints can be difficult to characterize. There has been little previous research on the use of MBSE as a design space exploration tool or in support of trade-offs. This paper investigates the potential to use MBSE for design exploration and to understand trade-offs, through the creation of a new toolset including a SysML profile. The tool draws on generative design (allowing automatic guided generation of a multitude of design alternatives) and system optimization to rapidly generate and assess new designs using interactive analysis and visualizations. Techniques such as surrogate modelling, genetic algorithms and robustness measurements will be available in the toolset. The toolset was applied to a design scenario aiming to improve the trade off and design selection process for LEO Earth observation satellites. The upcoming ESA TRUTHS space mission was used as a case study and the design process was recorded and compared to a manual design exploration approach. The toolset was found to reduce the design exploration time by 38% to 96%, allow exploration of more designs in an equivalent time and provide better quantification of the relationships present in the design space, all without drops in selected design quality. For now, the toolset can only perform parameter variation in the design exploration and future work is expected to extend this to higher levels of variability. The study also discusses how the MBSE toolset could be applied to other missions, offering the same advantages to all early phase spacecraft designers.

Small satellites are more highly resource-constrained by mass, power, volume, delivery timelines, and financial cost relative to their larger counterparts. Small satellites are operationally challenging because subsystem functions are coupled and constrained by the limited available commodities (e.g. data, energy, and access times to ground resources). Furthermore, additional operational complexities arise because small satellite components are physically integrated, which may yield thermal or radio frequency interference. In this paper, we extend our initial Model Based Systems Engineering (MBSE) framework developed for a small satellite mission by demonstrating the ability to model different behaviors and scenarios. We integrate several simulation tools to execute SysML-based behavior models, including subsystem functions and internal states of the spacecraft. We demonstrate utility of this approach to drive the system analysis and design process. We demonstrate applicability of the simulation environment to capture realistic satellite operational scenarios, which include energy collection, the data acquisition, and downloading to ground stations. The integrated modeling environment enables users to extract feasibility, performance, and robustness metrics. This enables visualization of both the physical states (e.g. position, attitude) and functional states (e.g. operating points of various subsystems) of the satellite for representative mission scenarios. The modeling approach presented in this paper offers satellite designers and operators the opportunity to assess the feasibility of vehicle and network parameters, as well as the feasibility of operational schedules. This will enable future missions to benefit from using these models throughout the full design, test, and fly cycle. In particular, vehicle and network parameters and schedules can be verified prior to being implemented, during mission operations, and can also be updated in near real-time with operational performance feedback.

Combine the key concepts of MBSE with the essentials of their practical application. This tutorial is designed help you leverage model‐based techniques to better meet the engineering challenges of analyzing stakeholder needs and defining system solutions. The tutorial uses a practical top‐down system analysis and design example and exercise to explore the application of layered model‐based systems engineering (MBSE) to system definition challenges. It examines the range of views available to the systems engineer for understanding and communicating design choices and provides a practical introduction to constructing the model and generating each representation. As the analysis and design advances, the engineer will use a wide variety of views drawn from traditional and SysML representations to portray aspects of the model and gain understanding of stakeholder concerns and the design choices to be made. This tutorial works for both new and experienced systems engineers, capability analysts and others working on an integrated project team, and program/project/capability managers. While a basic understanding of the systems engineering process is assumed, the MBSE framework and approach provide the foundation for those not experienced in systems engineering. The detailed discussion of MBSE methodology and model representations including SysML and traditional SE products delivers value for all but the most experienced model‐based systems analysts and engineers. This is an MBSE methods, models, and representations tutorial, not a tool tutorial. The focus is on the practical application of essential MBSE for project success. The concepts taught and methods shared are applicable regardless of the systems engineering tools the attendee may choose.

Given the scope of some of the large scale system architecture models created at The Boeing Company, it is currently necessary for Boeing to customize its own Model Based Systems Engineering (MBSE) tool suites from Commercial‐Off‐The‐Shelf (COTS) platforms to accommodate these models. Boeing successfully applies a modified set of Systems Engineering (SE) architectures to specify the required MBSE tool suites and also has developed a unique SE skill set for engineers creating these specifications and developing and deploying the subsequent tool suites. Furthermore, Boeing uses an MBSE tool suite to create these tool suite specifications as models, and also manages the configuration of the MBSE tool suite within the models.

This study compares the types and quantities of knowledge that are captured by a model‐based systems engineering (MBSE) approach and a traditional architecting approach to measure the benefits of the MBSE approach in managing the complexity of a robotic space system. The MBSE approach was implemented with Cameo Systems Modeler using Systems Modeling Language (SysML) and applied to architecting an orbiting sample Capture and Orient Module (COM) system concept for a Capture, Containment, and Return System payload concept for potential Mars Sample Return. An architecture framework was established, covering system, subsystem, and assembly levels, along with structure, behavior, data, and requirements perspectives. The COM system architecture was captured in parallel using both the MBSE and non‐MBSE approaches in order to provide a side‐by‐side comparison of the approaches. The approaches were evaluated based on how well each represented the information content of the COM system architecture. A total of 4389 knowledge elements were classified using the Revised Bloom's Taxonomy knowledge dimension and used to quantitatively compare the two approaches. The MBSE approach more completely captured architectural knowledge than the non‐MBSE approach. Limitations to the SysML‐based MBSE approach were also identified, including its ability to fully represent certain high‐level conceptual, procedural, and metacognitive knowledge such as design principles, design approaches and rationales, risks, development strategies and rationales, organizational core competencies, and requirement verification methods. The overall results demonstrate the benefits of MBSE in managing the complexity of robotic space systems and strengthen the case for adopting MBSE within the systems engineering community.

This paper describes research into the application of Model-Based Systems Engineering (MBSE) tools and processes to Risk-Informed Design (RID). RID enables system risk analyses early in the lifecycle of spaceflight projects allowing designers to use risk as a design commodity and part of the overall trade space. RID uses a “minimum functionality” approach, whereby a minimal, single-string system design is first envisioned that only meets basic performance requirements without any regard to overall reliability or safety. Risk analyses are then used to apply informed design enhancements based on their contribution to risk reduction. A recent application of RID was the Altair Lunar Lander Project that was intended for human lunar exploration under NASA’s Constellation Program. The Altair project’s approach and results are reviewed and analyzed in this paper as a specific application of RID. In traditional projects, several tools such as Relex, Windchill or SAPHIRE, are used in parallel to apply risk informed design techniques. These analyses also traditionally occur later in the design cycle when changes are more difficult to implement. Safety and reliability analyses typically have no direct connection with the system architecture model, which accurately depicts the physical and functional constructs of a system, including the “ilities.” The model is directly impacted by the results of the analyses. This creates a time-consuming iterative process of analyses and modification because of the need to integrate several tools and teams. To improve this process, the research described here investigated the use of a single, cloud-based MBSE CAD tool called Innoslate that integrates failure analysis into the system architecture model. The specific focus of the research was on the analysis of system failure events through the use of a system architecture-modeling tool and the establishment of an MBSE process that enables system engineers to make risk-informed system modifications during development. The conclusion of the research was that MBSE in general, and Innoslate specifically, is capable of providing an integrated, effective and quantitative means of developing a risk-informed system design using a minimum functionality baseline process. This can be applied to human and robotic spaceflight systems and other systems with similar complexity. The research demonstrated that random distributions could be added to failure probabilities in order to add “noise” to the results, a task that can be laborious, if not impossible, if performed using a calculator or spreadsheet. The research also demonstrated an end-to-end MBSE process that was applied to a basic system model and the Altair Project. Recommendations for future work conclude the paper.

Model-Based System Engineering (MBSE) provides a number of ways on how to create, validate, and verify the complex system design; unfortunately, the inherent security aspects are addressed neither by the SysML language that is the main MBSE enabler nor by popular MBSE methods. Although there are many common points between MBSE and security requirements engineering, the key advantages of MBSE (such as managed complexity, reduced risk and cost, and improved communication across a multidisciplinary team) have not been exploited enough. This paper reviews security requirements engineering processes and modeling methods and standards and provides the MBSE security profile as well, which is formalized with the UML 2.5 profiling capability. The new UML-based security profile conforms to the ISO/IEC 27001 information security standard. In addition to the MBSE security profile, this paper also presents the security profile application use case and the feasibility study of current status for security and systems engineering processes.

Engineering design researchers develop theories and methods aimed at enhancing the design process and creating systems that deliver maximum value. As engineered systems have become more complex, the systems engineering community has adopted digital environments like Model-Based Systems Engineering (MBSE) to handle documentation, knowledge transfer, and analyses. However, a gap between research and practice has been acknowledged by the lack of existing design theories and methods being transferred into MBSE. While vendors and practitioners create new methods for MBSE, there is a significant need to transfer design theory into operationalized capabilities in MBSE environments. In this paper, we explore and demonstrate how design margins, a design research concept that designers can use to navigate system changeability, can be transferred into an operational state in a digital engineering environment. We implement margins in an MBSE environment because existing MBSE change management processes are reactive. There is a need for end-to-end change management processes. Incorporating margins provides opportunities for proactive change management in that engineers can address changes before they occur through strategic design and redesign. We present a 6-step workflow that establishes the requirements, architecture, metrics, and analyses (RAM-A) packages in SysML so that system parameter margins and design variable flexibilities can be modelled, calculated, and communicated. We also discuss implications of misalignment between MBSE and engineering design nomenclature, features of the proposed approach that support changeability analysis, and aspects of MBSE modeling tools that support the implementation of engineering design methods. Future work should investigate the scalability of the approach, with a focus on scoping the margin and changeability analysis to maintain high return on investment.

ABSTRACTSystem complexity continues to grow, creating many new challenges for engineers and decision makers. To maximize value delivery, “both” systems engineering and decision analysis are essential. The systems engineering profession has had a significant focus on improving systems engineering processes. While process plays an important role, the focus on process was often at the expense of foundational engineering axioms and their contribution to system value. As a consequence, systems engineers were viewed as process developers and managers versus technical leaders with a deep understanding of how system interactions are linked to stakeholder value. With the recent shift toward model‐based systems engineering (MBSE), systems engineering is “getting back to basics,” focusing on value delivery via first principles, using established laws of engineering and science. This paper describes how pattern‐based systems engineering (PBSE), as outlined within INCOSE's model‐based systems engineering (MBSE) initiative, explicates system value through modeling of first principles, re‐uniting systems engineering and decision analysis capabilities.