Systems engineering management in the FRG

Contributors

The Rockwell International-Collins Avionics Divisions are developing an avionics systems engineering graduate program with Iowa State University. The author discusses the proposed curriculum and the four avionics systems engineering courses that will be offered. The four courses are: Systems Engineering and Analysis, Avionics Systems Engineering, System Project Management, and Software Systems Engineering. The Systems Engineering and Analysis course introduces system engineering concepts and principles. The second course applies the system engineering techniques presented in the first course to avionics. Current avionics functions are presented and then used to discuss avionics systems. The third course discusses avionics systems engineering from the project management viewpoint. The Software Systems Engineering course examines the systems aspect of software. >

Systems methodology comprises approaches to systems analysis on the one hand, and systems engineering on the other. Systems analysis develops an understanding of a system, its elements, and its environment that describes their functional, structural, and behavioral aspects. Systems engineering transforms operational user needs into system architectures, performance and functional requirements for system elements, and internal and external interface definitions. The common element of both systems analysis and systems engineering is design. Design in systems methodology is the combination of two interactive loops, one addressing the relationship of the design object to its environment, the other addressing the relationship of the design object to its parts. For systems analysis, e.g., the medical science of physiology, these loops consider structure, function, and process in the context of environment to develop information (what), knowledge (how), and understanding (why) of the system and elements being studied. This chapter presents the interactive loops of the design process in systems engineering, and explains the use of analogous interactive loops in systems analysis, considering Harvey’s analysis of the function of the human heart and Cold War analysis of Soviet national missile defenses. The core systems analysis insights of Singer, Churchman, Ackoff, and Gharajedaghi are adapted into an exposition that accurately describes both the pioneering scientific work of Harvey and the modern pragmatic work of Cold War military intelligence analysts.

Formalization of bond graph using higher-order-logic theorem proving

Integrating Core Systems Engineering Design Concepts into Traditional Engineering DisciplinesIn this study we describe the integration of core systems engineering design concepts intotraditional mechanical and electrical engineering curriculum at the University of Arkansas atLittle Rock (UALR) undergraduate Systems Engineering program. The program was launched in1999 in response to the industry demands for engineering graduates capable of managingcomplex engineering projects. The program has since graduated over 150 students who areworking in industry (aerospace, defense, manufacturing, design, and consulting), federal andstate government, or attending graduate school. The program curriculum has four broadcomponents: a 35 hour university core that covers basic math and science (11 hours) and generaleducation (24 hours); a 30 hour college core that enhances the math and science preparation witha course in engineering ethics; a 32 systems engineering component that introduces core systemsengineering concepts; and, a 32 hour option component that teaches the mechanical or electricalengineering disciplines. The program is accredited by ABET under general criteria, and plans areunderway to satisfy mechanical and electrical engineering program criteria. Students entering theprogram are introduced to systems engineering core concepts through courses in EngineeringEconomy, DES Modeling and Simulation, Optimization Methods, Decision and Risk Analysis,and Systems Engineering Design and Analysis. Among them, Engineering Economy coversengineering project evaluation and economic decision making. Optimization Methods in SystemsEngineering covers linear and nonlinear methods of engineering design optimization. DESModeling and Simulation covers theory and practice of discrete event simulation modeling.Decision and Risk Analysis covers uncertainty modeling and analytical techniques for rationaldecision making. Systems Engineering Design and Analysis covers the systems engineering life-cycle process, including identification of system requirements, engineering design andarchitecture, system modeling, testing and integration, and system operation and support. The128-hr program culminates in a two-semester Systems Engineering Capstone Design thatemphasizes the multi-disciplinary nature of the program. The students perform functional designin the first semester followed by detailed physical design and testing in the second semester. TheUALR program graduates skilled systems engineers who combine holistic thinking with deepproduct knowledge, strong technical background, effective communication skills, and goodinterpersonal skills.

Mechatronic systems become ever more complex because of their increasing number of interconnected safety critical components and sophistication. MBSE (Model‐based Systems Engineering) and MBSA (Model‐Based Safety Assessment) are the most commonly adopted approaches to deal with the design and safety analysis of mechatronic systems. Unfortunately, both approaches are normally adopted separately, especially in the earlier phases of system design, thus leading to a lack of communication between system engineers and the safety team. This work aims to fill that gap at a high level, that is, through process interaction. This paper proposes an enhanced V‐model for the design of safety‐critical mechatronic systems. It relates a system development process with specific safety assessment methods. Specifically, the proposed workflow details exchange flows between the RFLP (Requirements, Functional, Logical, Physical) method, the FHA (Functional Hazard Analysis), the FMEA (Failure Mode and Effects Analysis), the MBSA and simulation, and the FTA (Fault Tree Analysis). These analyses are complemented with multiphysics modeling and simulation to observe system behavior in functional and failure scenarios, with the aim of requirements verification. The design workflow has been applied to a winged Unmanned Aerial Vehicle to apply the parallel process and the necessary interaction of MBSE and MBSA approaches. The information flows between the individual activities proved effective for designing a safe system before the verification phase. The main benefit of the proposed workflow is providing both the design and safety team with some interaction points, thus avoiding a lack of safety‐critical analysis in the early phases of system design.

The Aircraft Data Hierarchy (ADH) represents a transformative Model-based Systems Analysis & Engineering (MBSA&E) data storage format for standardized aerospace data exchange, specifically designed to address critical challenges in the aerospace industry's digital transformation of its multidisciplinary design processes. This technical document outlines the development, structure, and prototype Python implementation of an open-source ADH as a solution that integrates systems analysis with systems engineering using a recursive Model-Based Systems-of-Systems Architecture (MSoSA) structure aligned with established standards. The ADH facilitates efficient exchange of critical information—geometry definitions, disciplinary tool inputs/outputs, and engineering requirements—through a centralized, validated data structure implemented using Pydantic v2, with support for serialization in JSON, YAML, and XML formats. The initial ADH architecture was designed by Boeing with support from NASA, GE Aerospace, Collins Aerospace, and the University of Michigan to gather a wide and varied set of requirements. This paper details the ADH's high-level architecture, including an example integration with NASA’s MBSA&E framework, its hierarchical organization of aerospace data, and its adaptability to accommodate diverse aircraft configurations. The use of a centralized authoritative data source like the ADH reduces data duplication, enhances cross-disciplinary and cross-organizational collaboration, improves traceability, and supports digital thread continuity. This ultimately enables more efficient and innovative aircraft design processes. The ADH is poised to become a fundamental component of future aircraft design systems. However, continued investment and industry involvement through AIAA and other avenues are needed to realize the full transformative potential of the ADH.

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.

The Air Traffic Control industry is being increasingly exposed to rising levels of risk, as criminals and cyber‐attackers look to exploit system vulnerabilities. Air Navigation Service Providers become more demanding regarding cybersecurity concerns in the products they acquire. Consequently, systems engineers need to consider cyber security concerns early in their system's development life cycle. Model‐Based Systems Engineering methodologies are widely used to manage complex engineering projects in terms of system requirements, design, analysis, verification, and validation activities, leaving cyber security aspects aside. This paper presents a conceptual solution of a model‐based security method that aims to enable systems engineers to perform threat modeling analysis of cyber‐physical systems early and incorporate mitigation strategies into the system design, thereby reducing the cyber‐physical system's overall security‐related risks. Based on a real‐life case study the method will be validated later during execution period from Jan. – May 2022.

Systems engineering integrating interdisciplinary engineering team to design and manage complex engineering design projects. Systems engineering knowledge could best be delivered by incorporating the activities in the systems engineering V development lifecycle in the learning process. However, systems engineering concepts are hard to form a solid identifiable personal experience even for professional engineers. This article discusses the project design and assessment structure in a systems engineering course to facilitate the learning process. The 3D printing process is organised as a learning resource for students to verify their system design in a demonstrable format. Students are required to go through stages of the V development lifecycle while designing and developing a hurdle robot as the engineering design outcome. Students are allocated into groups tried to design and produce different hurdle robots with the support of extra learning resources in the learning management system. The student groups that follow closely the V model lifecycle seem to settle down with the final designs quickly. This proves that the environment has facilitated learning of the V development lifecycle process. With the support of 3D printing technology, students are able to test and verify what they have designed so that they can experience the systems engineering core activities.

Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical concerns and explores the latest methodologies, tools, and real-world examples relevant to today’s dynamic ISE landscape. Readers will gain a practical toolkit for effective integration and utilization of AI in system design and operation. The book also presents the current state of AI across big data analytics, machine learning, artificial intelligence tools, cloud-based AI applications, neural-based technologies, modeling and simulation in the metaverse, intelligent systems engineering, and more, and discusses future trends. Written by renowned international contributors for an international audience, Advances in Artificial Intelligence Applications in Industrial and Systems Engineering includes information on: Reinforcement learning, computer vision and perception, and safety considerations for autonomous systems (AS)(NLP) topics including language understanding and generation, sentiment analysis and text classification, and machine translationAI in healthcare, covering medical imaging and diagnostics, drug discovery and personalized medicine, and patient monitoring and predictive analysisCybersecurity, covering threat detection and intrusion prevention, fraud detection and risk management, and network securitySocial good applications including poverty alleviation and education, environmental sustainability, and disaster response and humanitarian aid. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is a timely, essential reference for engineering, computer science, and business professionals worldwide.

In modern systems, the operational capability of the system to the user needs is expanding rapidly to accommodate the size of the system, functionality, and interfaces are becoming increasingly complex. Accordingly, the systematic practice of project management and systems engineering in the system development process, as an important element in successful systems development is recognized. EIA/ANSI 632, ISO/IEC15288, the leading international standard for systems engineering and is the leading international standard on project management PMBOK. CMMI is also contains information about the activities of project management and systems engineering and worldwide basis to assess the maturity of an organization's ability to develop system being used. But CMMI model is too complex of structure and there are many overlap parts of contents. So there are many problems for members of organization understanding all of CMMI model, applying organization and, achieving improvement activity. In this study, through the analysis and integration between the model and the related standard coverage activities essential for successful systems development in organizations that require systems engineering and project management capabilities(SEPMC) for self-assessment and continuous improvement activities to provide useful reference guideline.

The management of software development for single and dual processor system designs is making progress towards becoming a mature discipline. A good part of the progress can be attributed to the development and use of standard system and software engineering methods and design principles.However, new computer system designs (networking, distributed systems, embedded systems, multi- and coprocessors, fault tolerant systems, etc.) will create new challenges for managers of software development. The reason for this effect on management is that some of the system and software engineering methods and design principles developed for single and dual processor system designs are not valid for these newer designs.Some of the issues that software development project managers will need to cope with are:1. Life cycle model adjustments2. Rapid prototyping activities3. Different hardware and software phasing4. Increased tool development5. New trade-offs and hybrid developments of off-the-shelf software and newly developed software6. Development of concurrent design principles7. New software design principles to support fault tolerance and the use of new memory technologiesThe above items are just now being recognized as problems, and solutions for them either do not exist, or are not widely known.These problems create a series of new challenges that managers must deal with for software development based on the new architectures and requirements. The purposes of this paper are to discuss these issues and to identify some solutions that can serve in the interim as the technology changes to meet these new challenges.

Under the complex operational environment of the antimissile operation, the application of Systems Modeling Language (SysML) parametric model in weapon system effectiveness evaluation is explored. The function of parametric graph model, the construction process of the model and the optimization method of the model are researched. Using feedback mechanism to collect, collate and calculate the parameters of the model, and realize the leap from the index relationship description to the calculation of the index. Finally, the feasibility and effectiveness of the proposed method is verified by using the system of antimissile weapon system as an example.

In FY 2004, JPL launched an initiative to improve the way it practices systems engineering. The Lab's senior management formed the systems engineering advancement (SEA) project in order to significantly advance the practice and organizational capabilities of systems engineering at JPL on flight projects and ground support tasks. The scope of the SEA project includes the systems engineering work performed in all three dimensions of a program, project, or task: 1) the full life-cycle, i.e., concept through end of operations; 2) the full depth, i.e., program, project, system, subsystem, element (SE Levels 1 to 5); 3) the full technical scope, e.g., the flight, ground and launch systems, avionics, power, propulsion, telecommunications, thermal, etc. The initial focus of their efforts defined the following basic systems engineering functions at JPL: systems architecture, requirements management, interface definition, technical resource management, system design and analysis, system verification and validation, risk management, technical peer reviews, design process management and systems engineering task management. They also developed a list of highly valued personal behaviors of systems engineers, and are working to inculcate those behaviors into members of their systems engineering community. The SEA project is developing products, services, and training to support managers and practitioners throughout the entire system life-cycle. As these are developed, each one needs to be systematically deployed. Hence, the SEA project developed a deployment process that includes four aspects: infrastructure and operations, communication and outreach, education and training, and consulting support. In addition, the SEA project has taken a proactive approach to organizational change management and customer relationship management - both concepts and approaches not usually invoked in an engineering environment. This paper describes JPL's approach to advancing the practice of systems engineering at the Lab. It describes the general approach used and how they addressed the three key aspects of change: people, process and technology. It highlights a list of highly valued personal behaviors of systems engineers, discusses the various products, services and training that were developed, describes the deployment approach used, and concludes with several lessons learned.