Search results

    Search results

    Show all results for ""
    Can not find any results or suggestions for "."

    Search tips

    • Make sure there are no spelling errors
    • Try different search terms or synonyms
    • Narrow your search for more hits

    How can we help?

    Contact Us

    Find Employees

    University of Skövde, link to startpage

    Search results

      Search results

      Show all results for ""
      Can not find any results or suggestions for "."

      Search tips

      • Make sure there are no spelling errors
      • Try different search terms or synonyms
      • Narrow your search for more hits

      How can we help?

      Contact Us

      Find Employees

      University of Skövde, link to startpage

      Predictive Maintenance with Internet-of-Things and Digital Twins

      Research Group Production and Automation Engineering
      Resarch Environment Virtual Engineering

      Predictive Maintenance with Internet-of-Things and Digital Twins

      Research Group Production and Automation Engineering
      Resarch Environment Virtual Engineering

      Quick Facts

      Full project name

      Integrated Manufacturing Analytics Platform for IoT-Enabled Predictive Maintenance (IMAP)

      Duration

      December 2021 – November 2024

      Funding and collaboration

      VINNOVA, Chalmers University of Technology, Jernbro, Automotive Components Floby, Scania CV AB, Volvo Car Corporation, Volvo Group Trucks Operations (VGTO)

      Predictive maintenance is one of the major thrust areas for many global manufacturing companies. Artificial intelligence, big data analytics and industrial internet of things (IoT) have already shown great potential in the area of maintenance. However, as more companies adopt these technologies, several key challenges have emerged hindering the progress towards complete digitalization of maintenance operations.

      The aim of this project is to develop an Integrated Manufacturing Analytics Platform (IMAP) that combines core Industry 4.0 technologies of industrial IoT, digital twins and analytics to realize the full potential of predictive maintenance and pave the way towards prescriptive maintenance.

      The core idea of IMAP is to supplement and validate data from existing IoT infrastructure with simulated data from lean digital twins, preprocess and integrate these multiple sources of data into the CMMS, and use machine learning, analytics and optimization techniques to monitor the health of equipment, thereby predicting the need for maintenance in advance, generating automated maintenance actions and corresponding maintenance work orders.

      Project expectations

      The project is expected to bring the following targeted improvements to production and maintenance KPIs:

      • increase in equipment uptime, availability and OEE
      • reduction in unplanned downtime of equipment,
      • reduction in time required to plan maintenance,
      • reduction in spare parts inventory, and
      • reduction in overall maintenance cost.

      Industry-wide studies have shown that predictive maintenance can increase equipment uptime and availability by 10-20%, reduce the time required to plan maintenance by 20-50%, and reduce overall maintenance costs by up to 25%.

      Project Leader

      Associate Professor of Production Engineering

      Participating Researchers

      Anders Skoogh
      Professor at Production Systems, Chalmers
      Published: 12/1/2021
      Edited: 12/1/2021
      Responsible: webmaster@his.se