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

Participants

Anders Skoogh (contact)

Docent vid Chalmers, Industrial and Materials Science, Production Systems

Collaborations

AXXOS Industrial Systems

Jönköping, Sweden

Mälardalens högskola

Västerås, Sweden

Royal Institute of Technology (KTH)

Stockholm, Sweden

Scania

Södertälje, Sweden

VBG GroupTruck Equipment AB

Vänersborg, Sweden

Volvo Cars

Göteborg, Sweden

Volvo Construction Equipment

Eskilstuna, Sweden

Volvo Group Sweden

Gothenburg, Sweden

Funding

VINNOVA

Funding years 2016–2019

Related Areas of Advance and Infrastructure

Sustainable development

Driving Forces

Production

Areas of Advance

More information

Project Web Page at Chalmers

https://www.chalmers.se/sv/projekt/Sidor/D...

Latest update

2016-08-24