DAIMP - Data Analytics in Maintenance Planning

This project aims to increase the use of big data analytic in maintenance planning. The current availability in Swedish industry is too low for implementing digital production concepts such as Industrie 4.0.

The purpose of the project is to increase productivity, robustness, and resource efficiency through reduction of failures and disturbances, especially in critical equipment.

The DAIMP project connects data structures on a machine level to analyses needed on a systems level. Expected results are for example: data and information structures for improved internal and external collaboration, algorithms for predictive and prescriptive analytics, and data–driven criticality analysis to support differentiated maintenance planning.

In addition to research-oriented work packages, the project will also work with evaluation and demonstration cases. One of them focuses on the role of data-driven maintenance planning when introducing new car models and production lines at Volvo Cars.

Project leader: Anders Skoogh, Chalmers University of Technology
anders.skoogh@chalmers.se

Partner organizations

  • VBG GroupTruck Equipment AB (Private, Sweden)
  • AXXOS Industrial Systems (Private, Sweden)
  • Mälardalens högskola (Academic, Sweden)
  • Royal Institute of Technology (KTH) (Academic, Sweden)
  • Scania CV AB (Private, Sweden)
  • Volvo Group (Private, Sweden)
  • Volvo Cars (Private, Sweden)
  • IFS world (Private, Sweden)
Start date 01/03/2016
End date The project is closed: 28/02/2019

Funded by

  • VINNOVA (Public, Sweden)

Published: Sat 30 Nov 2019.