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  4. Using vibration data to classify conditions in disk stack separators
 
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2022
Journal Article
Title

Using vibration data to classify conditions in disk stack separators

Abstract
Mounting sensors in disk stack separators is often a major challenge due to the operating conditions. However, a process cannot be optimally monitored without sensors. Virtual sensors can be a solution to calculate the sought parameters from measurable values. We measured the vibrations of disk stack separators and applied machine learning (ML) to detect whether the separator contains only water or whether particles are also present. We combined seven ML classification algorithms with three feature engineering strategies and evaluated our model successfully on vibration data of an experimental disk stack separator. Our experimental results demonstrate that random forest in combination with manual feature engineering using domain specific knowledge about suitable features outperforms all other models with an accuracy of 91.27 %.
Author(s)
Merkelbach, Silke
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Afroze, Lameya
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Janssen, Nils
Enzberg, Nikolaus Sebastian von
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Kühn, Arno  
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Dumitrescu, Roman  
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Journal
Vibroengineering PROCEDIA  
Conference
International Conference on VIBROENGINEERING 2022  
Open Access
DOI
10.21595/vp.2022.23000
Language
English
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Keyword(s)
  • condition monitoring

  • disk stack separator

  • machine learning

  • vibration analysis

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