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.
Anders Skoogh, Chalmers University of Technology
Chalmers, KTH, Mälardalen, Volvo GTO, Volvo Cars, Volvo CE, IFS, Axxos, Scania, VBG Group
increase productivity, robustness, resource efficiency in production systems, data-driven maintenance planning, industrie 4.0