Digital Process Monitoring of Stationary Processing Equipment—A Step Toward an Optimized Digital Processing Plant: Presentation of a PhD-Project

Philip Krukenfellner, Helmut Flachberger

Research output: Contribution to journalArticleResearch


In response to the current trends of digital transformation in the raw materials extraction and processing industry, IFE Aufbereitungstechnik GmbH has set itself the challenging goal of developing robust and versatile sensor systems for monitoring the condition of their predominantly vibrating stationary processing units. It shall be used as a basis for establishing a predictive maintenance scheme and ultimately facilitate the reduction of equipment downtime. In the course of the research project, research activities will be directed in particular toward a better understanding of the operating state of vibrating machines, especially based on their vibration patterns. Namely, a range of experiments with various vibrating screens and vibratory conveyors as well as an already ongoing extended case study on a linear vibrating screen currently operating in a waste treatment plant will be conducted. Furthermore, DEM simulations of vibrating screens as well as a specifically engineered “Laboratory Vibrating Machine” will be part of the research process. The sensors in use to identify and measure the required vibration patterns will be newly developed vibration sensors called “Sapient Edge Sensors”, which, later on, will be combined with other sensors for measuring different parameters.
Translated title of the contributionDigitale Prozessüberwachung von stationären Aufbereitungsaggregaten als Schritt hin zur optimierten digitalen Aufbereitungsanlage—Vorstellung eines Dissertationsprojekts: Vorstellung eines Dissertationsprojektes
Original languageEnglish
Pages (from-to)184-187
Number of pages4
JournalBerg- und hüttenmännische Monatshefte : BHM
Issue number4
Publication statusPublished - 22 Mar 2023


  • Vibrating Screens
  • Vibration Analysis
  • Digital Transformation
  • Condition Monitoring
  • Predictive Maintenance
  • Sensors
  • Machine learning

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