Statelogger for Predictive Maintenance

Acting Instead of Reacting: Decision Support for the Development of Predictive Maintenance Strategies

© Fraunhofer IFF

What good are advanced, efficient manufacturing and supply chain systems and equipment to anyone if they do not function dependably and reliably?

Implementing condition-based maintenance plans requires recording load profiles and current technical conditions and forecasting changes in conditions. These data have to be aggregated so that they can be used to support decisions about equipment operation and maintenance actions. We will help you organize predictive maintenance, by:

  • creating models based on experiential knowledge to quantify use,
  • ascertaining the wear allowance of components used variably,
  • forecasting changes in condition,
  • adjusting your maintenance strategy to be responsive, and
  • (re-)organizing maintenance operations and service partnerships,

… thus boosting the technical availability of your equipment and cutting the direct costs of downtime and maintenance.


Collaboration brings you the following benefits:  

  • current and readily available information on equipment condition and changes over time,
  • records on and analysis of use and maintenance history,
  • increased efficiency of maintenance by regularly reviewing the maintenance strategy, maintenance budgets and need for capital expenditures,
  • lower costs in your company by lowering energy use in the medium-term,
  • advantages when trading carbon emissions by purchasing unneeded certificates, e.g. through equipment upgrades and predictive maintenance,
  • cost transparency by analyzing equipment and product energy efficiency, and
  • an image boost through your company’s contribution to the conservation of valuable resources and the reduction of emissions harmful to the climate.


You additionally profit from our integrated expertise and years of experience in various industry and service sectors in:

  • initiating, implementing and facilitating change processes in maintenance,
  • analyzing operating and condition data,
  • forecasting condition changes and assessing risk,
  • selecting and applying sensors to extract condition data (temperature, oscillation, torque, position),
  • identifying and tracking maintained items,
  • developing software, and
  • assessing the effectiveness of maintenance actions by continuously monitoring overall equipment effectiveness (OEE).
© Fraunhofer IFF
The Statelogger software system