EPN-V2

MAKER1500 Digital Twin Technologies applied in Structural Health Monitoring Course description

Course name in Norwegian
Digital Twin Technologies applied in Structural Health Monitoring
Weight
2.5 ECTS
Year of study
2023/2024
Course history
  • Introduction

    Gradert skala A-F.

    Ved ikke bestått bacheloroppgave har studenten anledning til å levere omarbeidet versjon to (2) ganger. Dersom studenten ønsker å forbedre en bestått karakter på bacheloroppgaven, kan oppgaven omarbeides én (1) gang. Siden besvarelsen er knyttet opp til praksisgjennomføring stilles det ikke krav om å utarbeide en ny, egenformulert problemstilling ved 2. og 3. forsøk. Det gis inntil 2 timer veiledning ved karakteren F. Det gis ikke veiledning ved forbedring av karakter.

  • Recommended preliminary courses

    None

  • Required preliminary courses

    To sensorer.

  • Learning outcomes

    After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

     

    Knowledge

    On successful completion of this course the student has knowledge of:

    • How a digital twin model can provide additional information of a physical product for predictive maintenance
    • How a large digital finite element model can provide strain and stress time histories for fatigue prediction in real time (virtual strain gages)
    • How resonance problems occur when dynamic loads are interacting with eigenfrequencies and corresponding mode shapes of a structure
    • How data can be transformed to information, knowledge and action (decision support)
    • How well a digital model can represent a real product in terms of structural dynamics
    • Inverse methods applied in load prediction (for response driven twins)
    • Industrial applications of digital twins

     

    Skills

    On successful completion of this course the student has the ability to:

    • Read and display data from physical sensors (Python programming)
    • How to program (Python) filters to reduce noise and drifting from sensor data
    • Customize the IoT dashboard for data visualization using Streamlit
    • Redesign products to eliminate resonance problems

     

    General competences

    On successful completion of this course the student:

    • Has the basic skills in digital twin supported structural health monitoring
  • Teaching and learning methods

    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 requirements

    None

  • Assessment

    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.

  • Permitted exam materials and equipment

    All aids are permitted as long as the rules for source referencing are followed

  • Grading scale

    Pass/fail

  • Examiners

    One internal examiner.

  • Target group and admission

    The micro course is open to all students at OsloMet with Higher Education Entrance Qualification. Applicants from outside OsloMet must apply to the Makerspace Micro Courses program through Søknadsweb.

  • Course contact person

    Arvind Keprate