The study programme consists of short and skills-based micro courses that give you a basic understanding of some of the techniques and equipment often associated with Makerspaces.
To apply for this programme you must have Higher Education Entrance Qualification (samordnaopptak.no).
How to apply
If you are an OsloMet student, you can enrol to the individual Micro Courses directly in StudentWeb.
If you are not an OsloMet student, you must apply to the Micro Courses program via Søknadsweb (fsweb.no) with study code 3551.
- 1 March: early admission
- 15 April: ordinary admission
Time and duration
Digital Twin Technologies applied in Structural Health Monitoring will have teaching autumn 2023.
The courses at Makerspace Micro Courses are designed to be complementary to any professional education and are developed by a network of departments and sections, most of them located at Faculty of Technology, Art and Design (TKD).
They provide opportunities for interdisciplinary collaboration across departments and faculties.
Moreover, these courses facilitate cross-collaborations between students from life-long learning, bachelor, master and PhD students at OsloMet.
The courses can be taken by students from all study programmes, and by individuals that want to continue their education for personal and/or professional development.
The programme is designed to fit an audience of all levels of tech experience and backgrounds.
A hands-on learning experience is provided through practical exercises and workshops both online and on campus at the OsloMet Makerspace and AI Makerspace facilities.
Since the Makerspace Micro Courses is not bound directly to any specific academic degree, it is used as a launching platform for educational activities in emerging technologies. More courses are certain to emerge in the future.
Digital Twin Technologies applied in Structural Health Monitoring (2.5 ECTS)
This is a micro course offered to students who want to learn the basics of Digital Twin Technology applied in Condition and Structural Health Monitoring. The course will give an introduction to predictive maintenance of structures and rotor systems supported by digital twin models. The students will get hands on experience with both the hardware and simulation models controlled by an IoT system.
This course will teach how to collect and apply the right data from both physical and virtual models (the digital twin) to lower the maintenance costs while extending the service life of products. The students will also learn the most important concepts and terms in structural dynamics like eigenfrequencies and eigenmodes. These terms will be explained by two simple but very intuitive demo rigs adding "live action" to the classroom teaching!
These two rigs will provide an experimental-based learning approach. One rig will demonstrate rotor dynamics and what happens when running an unbalanced motor axle at critical speeds. The other rig will demonstrate the interaction between applied loads and inherent eigenfrequencies and mode shapes (resonance problems). The physical rigs will be complemented by simulation models providing additional information about the physical rigs when critical loaded.
The programme uses Canvas as a learning platform, whereas Zoom or Teams is used for digital teaching. Digital teaching will alternate between live lectures and digital learning resources.
Digital Twin Technologies applied in Structural Health Monitoring
The teaching will comprise of physical lectures, lab work and finally hands on exercises over a period of 3-4 weeks.
In the first lecture, theory related to Digital Twins will be covered. In the second lecture instructors will make a demonstration of how to build a digital twin and students will be encouraged to ask questions.
In the third lecture students will be assigned a problem (similar to the demonstration in lecture 2) and they will be required to build a digital twin for the engineering component. The last lecture will consist of a hands-on exercise of building a digital twin for a system (more complex than in lecture 3).
Course descriptionOptional course Spans multiple semesters
Exam and assessment
Exams are submitted at the end of the semester when all module requirements in the respective courses are met.
Digital Twin Technologies applied in Structural Health Monitoring: The exam is a final project where groups of 2 students will be required to submit 1000 words report and a digital twin for the mechanical component. Students will be given 14 days to complete the project.
Questions about this course?
You can contact us by e-mail if you have questions about this course.