Digital Twins for Validation of Dynamic Performance & Reliability of Motion Systems


Digital Twins for Validation of Dynamic Performance & Reliability of Motion Systems


The validation of dynamic performance & reliability of motion systems is costly and tedious. We will have to deal with:

  • Design variability (e.g. geometry size, power, hardware) due to customization; 
  • Inherent component variability (e.g. mechanical tolerances);
  • Variable parameter settings/operation in smart products;
  • Mission profiles like load conditions and usage conditions;

Barriers in order to more effectively validate new products/prototypes in this context:

  1. Some dynamic component interactions are still unknown or not modelled, although they are key for accurate HIL testing to find & search harmful component combinations;
  2. Lack of access to and usage of real operation conditions for relevant HIL testing;
  3. Lack of methods to effectively validate motion systems in this large ‘motion system-environmental’ variants space.

Project goals

With this project we will develop a framework to minimize the validation cost and time of new motion product variants and/or products used in different mission profiles. In view of the total unit cost of performance, the framework should allow to validate performance and assess lifetime of a (variant) motion product for a variable mission profile, based on capturing data from a fleet of drivetrains. The four enabling innovation goals are to:

  1. Capture & extract the required information of existing products and mission profiles: 
    1. Into the form of a digital twin of the product and the mission profile (load, environment,…); 
    2. Built up based on state of the art models and (historic) data from the lab (fleet of drivetrains) or from the field.
  2. Extrapolate this information in a multitude of dimensions:
    1. By transforming the information into parametrized system level digital twins joining insights from:
      1. Physics based system models assembled based on scalable models of components and their interactions;
      2. Data driven system models enhanced with physical insights (models).
  3. Design and validate product validation strategies:
    1. Improved HIL testing with dynamic component interaction knowledge;
    2. Improved full system testing of the EM behaviour (torques, speeds, voltages, currents, … ) leading to an assessment of the expected lifetime.
  4. Define a method for assessing the trustworthiness of the validation strategy.


DT4V_SBO is a Strategic Basic Research (SBO) project. We are looking for companies to join the User Group and work with us on the valorisation of the project.

Interested? Complete the form below and we will contact you as soon as possible.