Model Based Systems Engineering

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Please contact the Professional Education Short Course Team directly on: 02 5114 5573, or email: profedcourses@adfa.edu.au

Model Based Systems Engineering

Gain a deep understanding of MBSE, a game-changer in systems engineering. Learn processes & craft models for complex systems.

By UNSW Canberra Professional Education Courses

Date and time

Wed, 10 Jul 2024 4:00 PM - Thu, 11 Jul 2024 12:00 AM PDT

Location

Online

Refund Policy

Contact the organiser to request a refund.

About this event

Area of Interest: Capability Systems

Course Summary

This course is designed to provide you with a solid grasp of MBSE fundamentals and its advantages of over traditional systems engineering methods. Throughout the course, you will embark on a journey that demystifies the core concepts of MBSE. From understanding its foundational principles to diving into the processes involved, the course offers a holistic view of how MBSE is revolutionizing the engineering landscape.

You will engage in practical exercises that cover the various stages and tasks involved in contracting MBSE models, helping you to understand the nuances of building systems’ models. Furthermore, you will gain skills in interpreting and querying existing models and employing them to examine and manage systems effectively.

By the end of this course, participants will understand the MBSE lifecycle, model applications, and learn how to evaluate the quality of MBSE models, methods, and tools. They will also acquire insights into workforce requirements for MBSE, emphasizing the role of model artifacts throughout a system’s lifecycle.


Duration: 1 day from 9am - 5pm

Delivery Mode: Live simulcast online course


Course Content

This course covers the following topics:

- Introduction to MBSE and overview of its significance to systems’ development.

- Economic analysis of MBSE processes.

- Basic concepts and terminology of MBSE.

- Quality of MBSE elements.

- Understanding model artifacts and their usages.

- Workforce development in MBSE contexts.

- MBSE adoption – challenges and strategies.

- MBSE as part of the digital enterprise.


Who should attend

- Systems Engineers: looking to modernize their approach and stay current with the latest methodologies in their field.

- Project managers: who oversee engineering projects and want to better understand MBSE approach to ensure smoother project execution and transition from conventional SE.

- Technical leads: who aim to integrate modern systems engineering practices within their teams.

Organizational decision-makers: who consider the adoption of MBSE for their engineering processes and wanting to comprehend its benefits and implementation.


Learning Outcomes

By completing this course, learners will be able to:

· Apply core concepts and principles of MBSE to hypothetical engineering scerarios, showcasing the advantages of model-based approaches.

· Implement the stages of the MBSE process to a given system, illustrating the lifecycle from conceptualization to validation.

· Construct basic models using specific tools and software.

· Understand the main MBSE methodologies and approaches.

Collaborate in a team setting to coordinate and manage an MBSE project.

Prerequisite: Basic understanding of systems engineering: familiarity with traditional systems engineering concepts and practices.

What will you receive

• UNSW Canberra certificate of completion/attendance

• Masters credit. UNSW Canberra allows students who have successfully completed a minimum of 12 days of approved professional education courses to use those courses as credit in eligible postgraduate programs.

PRESENTER

This course is facilitated by Dr. Ebrahim Aly. Currently a research associate at UNSW Canberra’s Capability Systems Centre, Dr. Aly has an MSc in modeling of complex systems from Kyushu University, Japan, and a PhD in Systems Engineering from UNSW Canberra. His research interests include improving the efficiency of complex systems’ models, integrating MBSE processes with systems models, and exploring AI’s role in MBSE. He utilizes various methodologies in his research such as causal inference, Bayesian methods, system dynamics, statistical modeling, and MBSE.

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