Systems Engineering is a multifaceted discipline, involving human, organizational, and various technical variables that work together to create complex systems. This course is an introduction and overview of the methods and disciplines that systems engineers use to define, develop, and deploy systems. It includes specific integrated examples, projects, and team building exercises to aid in understanding and appreciating fundamental principles. Topics covered include; Introduction to Systems Engineering; Requirements Development; Functional Analysis and Requirements Allocation; System Architecture and System Design; Integration, Verification and Validation; Trade Studies; Systems Analysis, Modeling and Simulation; Specialty Engineering; Risk Management; and Technical Planning and Management.
This course introduces students to the business aspects of Systems Engineering (SE) and is designed to help SE professionals integrate Systems Engineering concepts into a professional business practice environment and to improve systems engineers understanding fundamental business practices and their relationship to systems engineering. This course will cover how to prepare and evaluate professional quality business plans, project budgets, financial proposals, timelines and technical outlines. This course will also cover topics such as working with stakeholders; understanding competitive advantage and perceived value of systems engineering; various roles of systems engineers from a business practices perspective; contracting for systems engineering services, how systems engineers impact and are impacted by the various corporate operating divisions, and how to ensure quality control. The course will consist of lectures, case studies, class projects and student presentations.
This course will study and contrast various important architectural frameworks, representations, tools, and methodologies in order to provide scalable and flexible approaches for enterprises operating in dynamic and complex environments. Enterprise-level system architecting tools will be discussed and demonstrated. At a minimum, the DoDAF, FEAF, Zachman, and TOGAF architectural frameworks will be discussed in depth. Other topics will include analysis of architectural alternatives to meet physical and logical objectives and providing information and systems assurance in an environment that takes people, processes, and technology into account. Modeling tools such as UML/SysML and the use of model-driven architectures will be presented. Validation of the architecture with stakeholders will be discussed. Methods of identifying risks and opportunities associated with the architectural choice will be explored. Practical examples will be included for illustration.
This course examines the use of Systems Engineering principles and best practices with respect to systems and systems-of-systems verification and validation (V&V). V&V processes, activities and methods as they apply across the product lifecycle will be examined. Case studies, papers and exercises will be used to examine the success and failure of verification, validation and test processes. Course topics include 1) How early systems engineering activities and solution sets affect integration, verification, validation and test; 2) V&V activities relative to product development phases; 3) Modeling quality, cost, time and risk; 4) Testing and non-testing methods; 5) V&V planning, execution and reporting; 6) Systems integration; and 7) V&V of critical and complex systems.
Requirements drive system definition and development. Properly managed requirements contribute to project success, while poorly defined and poorly managed requirements often lead to project failure. Modern systems are demanding even more attention to proper requirements definition and management. This course provides processes, techniques, and best practices necessary to develop and manage requirements in todays complex environments.
This course covers both the principles and practices of system optimization. The course includes both traditional mathematical treatments of optimization (including linear programming, non-linear programming, integer programming, stochastic methods such as Monte-Carlo methods, multi-objective system optimization, data envelope analysis) and practical, hands-on application with many real-world examples and student projects/exercises. Qualitative as well as quantitative approaches will be discussed. The course begins with an introduction and definitions of system, optimization, and system optimization. It then proceeds to explain the traditional mathematical tools and models used in system optimization including location, allocation, scheduling, and blending models as well as sensitivity analysis and network models. Optimized design is covered next. The course will conclude with several multi-objective optimization problems. Student projects and real-world examples will be heavily emphasized. A technical undergraduate degree (B.A. or B.S. or equivalent) is a prerequisite for this course.
Model-based systems engineering (MBSE) formalizes the practice of systems engineering through the use of models. This course is intended to answer the why, what and how of MBSE and provides background and motivation for transitioning from a document centric approach to a model-based approach to systems engineering. The course provides a foundation for MBSE by introducing SysML as a descriptive language for modeling systems and a method for applying SysML to support the specification, architecture design, and analysis of complex systems. The course also introduces other important aspects of implementing MBSE, including organizational and project planning considerations. The course includes a combination of slide presentations to introduce the fundamentals, coupled with class exercises and a class project to help the student grasp the fundamentals. A modeling tool is expected to be used for the class project.
Systems Thinking provides an arsenal of tools that enable program managers and systems engineers to better identify, understand, and control systems, and to improve their performance. In this course, we will study system identification and delineation, causal loops and feedback, system leverage points, delays and oscillations, mental models and unintended consequences, emergent properties, patterns, events, and self-organization, and use these tools to improve the performance of engineering, biological, business, and complex social systems. We will explore great system failures, how they might have been avoided, and how we can learn from them in developing and participating in current systems. Finally, we will learn how systems thinking explains the conflicting behavior of individuals, departments, businesses, and countries.
One of the biggest ways that you can influence the quality of your life is by improving the quality of your decisions. Complex Decision Making is intended for professionals in management positions and/ or those individuals, regardless of industry, who seek to enhance both their career potential and their overall quality of life. Based on logical principles, and informed by what we know about the limitations of human judgment and decision-making in complex situations, the course trains managers how to think about and structure decisions. These decisions incorporate both their everyday decisions as well as the tough, complex decisions that involve uncertainty, risk, several possible perspectives, and multiple competing objectives, thus improving the quality of the resulting decisions. In addition to teaching formal decision theory and application, we will explore cognitive biases that prevent us from being completely rational in our thinking and deciding. Exit this course able to define the right decision problem, clearly specify your objectives, create imaginative alternatives, understand consequences, grapple with trade-offs, clarify uncertainties, and think hard about your individual values and risk tolerance.
This course considers all facets of engineering dependable and secure systems, i.e., systems that are reliable, available, secure, and can be depended upon to deliver their intended capabilities despite hardware failures, software failures, network failures, external attack, and unexpected behavior. Topics include building dependable system architectures; resilience; security and quality of service of networks; dependability assessment; and software reliability. The class will consist of lectures, case studies, and a class project. (Prerequisite: SYS 501.)
This course will present reliability, maintainability, and related topics with the breadth of techniques and depth of detail that will benefit the systems engineer by allowing him/her to understand how they relate to the specification, development, testing, and fielding of reliable systems. The reliability of electronics, mechanical equipment, and software will be covered from the component level through their application at the system level. Other key topics will be: reliability prediction; failure modes, effects, and criticality analysis; stress testing; accelerated life testing; and reliability management. In addition, a series of relevant case studies will be studied and discussed.
An innovative approach to engineering complex systems of systems is developed. This approach relies heavily on case studies to drive the discovery of effective techniques. We will discuss complex systems of systems characteristics and behaviors, enterprises, the principled engineering of systems of systems, and distinctions between these forms and conventional approaches. A forward-looking, people-focused approach will be developed, with emphasis on systems thinking; posing a guiding architecture (not just architectural views) up-front that does not change much as the system evolves; balancing competing factors rather than subsystem optimization; pursuing opportunities as opposed to just mitigating risks; sharing information to build interpersonal trust; and communicating individual perspectives to collectively garner better views of the underlying reality. The overall goal is to revisit and broaden ones mindsight in order to build more effective, resilient, scalable, and durable systems. Prerequisites: SYS 501 and SYS 510.
One of the central priorities in WPIs educational philosophy is the application of academic skills and knowledge to real-world problems. The capstone project represents a substantive evaluation and application of coursework covered in the program. Students are encouraged to select projects with practical significance for the advancement of their companys competitive position as well as their own personal development. The project is administered, advised, and evaluated by WPI as part of the learning experience, but students are encouraged to seek mentorship from experienced colleagues in the Systems Engineering profession. The presence of or degree of participation from a mentor is made at the discretion of the student or the organization sponsoring the program.
Directed research students will work under the direct supervision of a WPI ECE, ME or CS faculty member on an experimental or theoretical problem which may involve an extensive literature search, experimental procedures and analysis. A comprehensive report in the style of a technical report or paper and an oral presentation are required.