DSV project leade
Tony Lindgren, Senior Lecturer

Participants / project team at DSV
Ram Gurung, PhD student

Scania Vehicle Service Information
Linköping University, Department of Computer Science and Engineering (ISY) department vehicle

Project period 2017-11 -01 - 2020-10-31
Funding Vinnova and Scania


Data-driven methods based on machine learning have shown their strength in many applications. In a previous research project, IRIS funded by FFI and Scania, it has been demonstrated how data-driven methods could be used to plan optimal maintenance. The project has highlighted two characteristics that are important and need further research, the ability to handle concept drift in data and the possibilities for interpretability. Concept drift arises when the fleet of products that the models have been created to predict the health of changes over time. This occurs for example naturally in the automotive industry when new modules are available for sale or the launch of updated features. Strongly linked to concept drift is interpretability. In order for engineers to understand if their design changes affect the models, it would be a strength if they could understand how different variables and properties affect the models’ outcomes. The project will strengthen the field of research in machine learning and prognostics, and especially in the field of concept drift and interpretability of models.

The main work packages of the project are:

  1. Data drift and dynamic data  
    Project manager Erik Frisk, Linköping University.
  2. Interpretability of models
    Project Manager: Tony Lindgren, DSV.
  3. Deployment of data driven models 
  4. Project Manager: Mona Elofsson, Scania.
  5. Effective data collection
    Project leader: Karin Avatare, Scania.
  6. Data warehouse
    Project leader: Henrik Brandin, Scania.

The main applicant for the project is Scania CV AB (project leader: Jonas Biteus) and the other parties are Linköping University, Department of Systems Engineering, Stockholm University, Department of Computer Science, and Royal Institute of Technology, School of Information and Communication Technology.

The project is scheduled to last 3 years between November 2017 and October 2020 with a total budget of approx. 18.2 MSEK.