AIS4004 - Digital Twins for Predictive Maintenance


New from the academic year 2024/2025

Examination arrangement

Examination arrangement: Portfolio
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio 100/100

Course content

The course will introduce students with the principles of predictive maintenance facilitated by digital twins. Central to the concept of digital twins is the optimization of physical sensor usage on real assets. The fundamental idea involves constructing a simulation model, utilizing analytical, finite element, or big data models, to replicate the behavior of the physical asset. This digital twin is then equipped with virtual sensors that furnish supplementary information crucial for predictive maintenance and decision support. Implementation of the digital twin can take the form of a swift local edge, a gradual IoT cloud solution, or a combination of both for optimal flexibility. This approach not only minimizes reliance on physical sensors but also enhances adaptability in deploying digital twins tailored to specific operational needs. The course contains a selection of the following topics:

  • Modelling and simulation
  • State and parameter estimations/filters
  • Actuator and sensor systems
  • Condition monitoring and predictive maintenance
  • Inverse methods
  • Edge and cloud solutions

More details about the curriculum will provided during the start of semester.

Learning outcome


Students will gain a comprehensive understanding of the fundamental principles underlying digital twin modeling and simulation for predictive maintenance. This encompasses acquiring knowledge about sensors, data filtering techniques, inverse methods for load identification, and data processing strategies crucial for decision support. Additionally, students will receive foundational knowledge concerning edge and IoT solutions, as well as familiarity with digital modeling and simulation tools. In particular, upon completion of the course:

  • The student knows the basic principal of digital twins and predictive maintenance
  • The student knows modeling methods for digital twins
  • The student knows various method for filtering
  • The student knows about edge and IoT solutions
  • The student understands the limits of digital twins
  • The student understands how simple and complex systems may be modelled, simulated and visualized


Upon completion of the course:

  • The student can model and simulate a physical system
  • The student is able to develop a digital twin of a simple system
  • The student is able to implement various data filtering methods
  • The student is able to implement digital twins for predictive maintenance
  • The student can analyze the system in the time-domain
  • The student can visualize simulations of the system

General competence

Upon completion of the course:

  • The student is able to use digital tools for simulation of physical systems
  • The student can reflect on the usefulness of simulations for solving real world problems
  • The student can formulate scientific problems, propose solutions, and present results both orally and in writing to a technical audience
  • The student can make evaluations about societal and ethical aspects of technological developments

Learning methods and activities

Learning activities generally include a mix of lectures, tutorials and practical lab/project work. A constructivist approach for learning is endorsed, with focus on problem solving and practical application of theory.

Compulsory assignments

  • Compulsory learning activities

Further on evaluation

The final grade is based on an overall evaluation of the portfolio, which consists of work that is carried out, documented and digitally submitted during the term. The assignments may consist of the following:

  • video demonstration of the work
  • class presentation
  • technical report
  • possibly other kinds of assignments.

Both individual and team assignments may be given. Assignments are designed to help students achieve specific course learning outcomes, and formative feedback is given during the period of the portfolio.

The re-sit exam is an oral exam in August.

Note that the course also has some compulsory activities that must be approved in order for the portfolio to be assessed.

More information will be provided at the start of the course.

Specific conditions

Admission to a programme of study is required:
Mechatronics and Automation (MSMECAUT)

Required previous knowledge

The course has no prerequisites. It is a requirement that students are enrolled in the study programme to which the course belongs.

Course materials

An updated course overview, including curriculum is presented at the start of the semester.

Credit reductions

Course code Reduction From To
IP500520 3.7 AUTUMN 2024
More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  SPRING 2025

Language of instruction: English, Norwegian

Location: Ålesund

Subject area(s)
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of ICT and Natural Sciences


Examination arrangement: Portfolio

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD Portfolio 100/100 INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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