Course - Model Based System Engineering - Model Based Safety Assessment - PK8213
Model Based System Engineering - Model Based Safety Assessment
About
About the course
Course content
To face the increasing complexity of technical systems, the different engineering disciplines have virtualized their content to a large extent: each industrial system comes now with hundreds if not thousands of models. These models are used not only in the design phase of systems, but also for their operation and even for their decommissioning.
This course is about models, with a special focus on risk assessment models.
It is organised as a series of seminars on various subjects related to model-based system engineering and model-based safety assessment: mathematical frameworks, algorithms and heuristics, modeling languages and paradigms, modeling methodologies and best practices, safety standards...
Examples of topics: perspective on system architecture; mathematical models of degradation; finite state automata, high level modeling languages; advanced data-structures and algorithms for probabilistic risk analysis; optimization methods, safety integrity levels...
Learning outcome
The course gives a vision of some active research topics and a state of the art on model-based system engineering and model based safety assessment.
Learning methods and activities
Seminars will be given by professors of the RAMS group at IPK or invited professors.
Aside attending seminars, students will be assigned an individual homework. This homework will typically consist in studying a (reasonable size) use case, designing a model for that use case, performing some experiments on that model and reporting the results of these experiments.
The use case will be preferably related to their PhD subject.
Further on evaluation
Portfolio assessment is the basis for the grade in the course. The portfolio includes case exercises counting 50 % and an oral exam counting 50 %. The results for the entire portfolio are graded passed or not passed. Passed requires at least 70 % score in total for the Portfolio.
Recommended previous knowledge
A basic knowledge of methods for risk analysis is recommended.
Some knowledge of programming is also useful.
Required previous knowledge
None
Course materials
A list of scientific articles or book chapters will be given for each seminar, in addition to the slides that will aim at giving a self-content overview of the subjects.
Subject areas
- Production and Quality Engineering