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  4. Wear Detection of Extruder Elements Based on Current Signature by Means of a Continuous Wavelet Transform
 
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Wear Detection of Extruder Elements Based on Current Signature by Means of a Continuous Wavelet Transform

Type
Journal article
Language
English
Date issued
2023
Author
Danielak, Marek
Witaszek, Kamil 
Ekielski, Adam
Żelaziński, Tomasz
Dudnyk, Alla
Durczak, Karol 
Faculty
Wydział Inżynierii Środowiska i Inżynierii Mechanicznej
Journal
Processes
ISSN
2227-9717
DOI
10.3390/pr11113240
Web address
https://www.mdpi.com/2227-9717/11/11/3240
Volume
11
Number
11
Pages from-to
art. 3240
Abstract (EN)
Assessing the wear of components in a single-screw extruder and its condition during the process is difficult. In this context, wavelet analysis was used to investigate the wear condition of extruder elements, which yielded data on current waveforms obtained from 1 kHz frequency converters. To date, no tests of this type have been conducted on single-screw food extruders, which further emphasizes the relevance of the research undertaken by the authors. Experimental tests have been conducted to verify the hypothesis that it is possible to assess the level of wear of the working elements of an extruder by monitoring the variations in the frequencies on the current spectrum using wavelet analysis tools. The root mean square (RMS) values of the current were compared for two configurations of the working elements of the device, i.e., new and used. Observation of the frequency variations of the current spectrum values using wavelet analysis tools can provide valuable information on the technical condition of the working elements of an industrial extruder. Therefore, they can indicate the need for prompt replacement of friction elements in order to improve the efficiency and performance of the machine.
Keywords (EN)
  • wavelet analysis

  • extruder

  • detection

  • fault diagnosis

  • continuous wavelet transform

  • time-frequency analysis

  • motor current signal analysis

License
cc-bycc-by CC-BY - Attribution
Open access date
November 17, 2023
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