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<p>This contribution presents the current results of a&#xa0;PhD research project aimed at enabling an energy- and product-optimised operation of mobile impact crushers through advanced data collection and processing. The study focuses on systematic sensor data acquisition, data cleaning, and correlation analysis to support predictive modelling of the particle size distribution (PSD) of crusher products. A&#xa0;structured methodology involving parameter selection, statistical filtering, and time-series smoothing was applied to real-world data collected from SBM’s REMAX 600 mobile impact crusher. This work serves as a&#xa0;foundation for further development of machine learning models and a&#xa0;future autonomous control agent for real-time optimisation of crushing operations.</p>

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Contributions to Data Collection and Data Processing for Energy- and Product-optimised Operation in Mobile Crushing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月6日

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  • Viktoriia Skosareva,
  • Eric Fimbinger,
  • Matthias Egger,
  • Helmut Flachberger

摘要

This contribution presents the current results of a PhD research project aimed at enabling an energy- and product-optimised operation of mobile impact crushers through advanced data collection and processing. The study focuses on systematic sensor data acquisition, data cleaning, and correlation analysis to support predictive modelling of the particle size distribution (PSD) of crusher products. A structured methodology involving parameter selection, statistical filtering, and time-series smoothing was applied to real-world data collected from SBM’s REMAX 600 mobile impact crusher. This work serves as a foundation for further development of machine learning models and a future autonomous control agent for real-time optimisation of crushing operations.