IP500520 - Digital Twin Technology


Examination arrangement

Examination arrangement: Assignment
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Assignment 100/100

Course content

The course will introduce the students to Structural Health Monitoring (SHM) supported by Digital Twins (DTs). The basic idea with DTs is to minimize the use of physical sensors on a real structure (asset). The concept is to create a simulation model (analytical, FE or big data model) that replicates the physical asset. The DT is then instrumented with virtual sensors (robust and free of charge) that provide additional information used in predictive maintenance and decision support. The DT can either be implemented as a (fast) local edge, a (slow) IoT cloud solution, or as a combination of both for maximum flexibility. After finishing the course, the students shall be able to develop and implement a Digital Twin (DT) for SHM of a Hardrocx bicycle frame during hard riding (student competition).

Course topics are:

  1. INTRODUCTION to Structural Health Monitoring by Digital Twins
    • Why DT Modelling and simulation?
    • A review of various DT models (static versus dynamic)
    • The FMU/FMI concept (how to integrate solvers and DT models)
    • How to implement a SHM solution with DTs (Python and low-code tools.)
  • ASSETS (candidates for SHM):
    • Common structural failure modes (fatigue, buckling, yield etc)
    • How to monitor assets like cranes, bridges, windmills, machinery and vehicles
  • SENSORS used in SHM:
    • A review of physical sensors used in structural monitoring
    • How and why eliminate noise and drifting of sensor outputs
    • Low-pass, high pass filtering and FFT analysis
    • Python or Modellica programming
      • Why and how to turn data > information > knowledge>actions (decision support)
      • Machine learning versus analytics
    • Inverse methods for load identification based on physical sensor outputs
  • EDGE solutions:
    • Why and when implement an EDGE solution
    • Hardware solutions (Rasperry Pie, Arduino, Nordic, Intel Nuc etc)
  • CLOUD (IoT) solutions:
    • Why and when implement a CLOUD (IoT) solution
    • A presentation of various IoT systems (MindSphere, SAP, Tellu and Jotne IoT)
    • IoT communication (REST, MQTT)
  • Learning outcome

    Knowledge: The student will learn the main principles of Digital Twin modeling and simulation for Structural Health Modeling. This includes knowledge about sensors, data filtering, inverse methods for load identification and data processing for decision support. The student will get basic knowledge about edge and IoT solutions as well as DT modeling and simulation tools.

    The student shall learn about current pitfalls due to sensor noise and drifting as well as limitations in DT modeling using inappropriate boundary conditions, incorrect loads, and simulation settings. The student shall be able to identify the best candidates for SHM.

    Skills: The student shall be able to setup and run DTs and simple IoT solutions for SHM. Important skills are python programming, instrumentation, load identification, real time process modeling, simulation and visualization. The students will also be trained in low-code app (Mendix) and IoT dashboard development (MindSphere).

    General Competence: The student shall be able to master multidiscipline modeling, instrumentation, data filtering, and simulation of Digital Twins. The student will have a general competence on various IoT software solutions for structural health monitoring and visualization.

    Learning methods and activities

    Lectures, videos, exercises and examples from real applications will be used. There will be individual mandatory multiple-choice assignments and one project. The project will be related to structural health monitoring of a mountain bike from Hardrocx or the Palfinger offshore crane on Gunnerus.

    Assignments, project work and field exercises (Hardrocx bike competition).

    Tj @ YouTube is my digital twin (teaching assistant)

    Compulsory assignments

    • Mandatory assignments

    Further on evaluation

    Evaluation is done according to NTNU guidelines, with feedback from the class and meetings with the reference group. A course survey will be prepared for all students on Blackboard. A final evaluation report is done at the end of the course.

    Specific conditions

    Admission to a programme of study is required:
    Naval Architecture (850MD)
    Product and System Engineering (840MD)

    Required previous knowledge


    Course materials

    Suggested teaching material and project:

    • Hand-out slides
    • Papers on Structural Health Monitoring
    • Training videos on YouTube
    • Python software templates and Mendix tutorials (app and process design)
    • Hardrocx bikes used in Structural health Monitoring (project)

    Credit reductions

    Course code Reduction From To
    AIS4004 3.7 AUTUMN 2024

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


    Term no.: 1
    Teaching semester:  AUTUMN 2024

    Language of instruction: English

    Location: Ålesund

    Subject area(s)
    • Marine Technology
    Contact information
    Course coordinator: Lecturer(s):

    Department with academic responsibility
    Department of Ocean Operations and Civil Engineering


    Examination arrangement: Assignment

    Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
    Autumn ORD Assignment 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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