MAKER1500 Digital Twin Technologies applied in Structural Health Monitoring Emneplan

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.

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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

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).

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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.

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

Pass/fail

One internal examiner.

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.

Arvind Keprate