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ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling

Auteur(s): ORCID

ORCID
ORCID
ORCID
ORCID
Médium: article de revue
Langue(s): anglais
Publié dans: Advances in Civil Engineering, , v. 2021
Page(s): 1-7
DOI: 10.1155/2021/6677639
Abstrait:

The process of mining filling, when the slurry is transported to the goaf by the filling pipeline, is very important to find the location and size of the caking in the filling pipeline in time for the safe and stable operation of the mine filling pipeline. It is an important research work to detect different flow patterns after two-dimensional section reconstruction in closed filling pipeline based on ECT (electrical capacitance tomography) visualization method. Slurry flow in pipeline is regarded as a two-phase flow, and the multishape distribution was reconstructed into images by ECT and intelligently recognized by broad learning system (BLS) algorithm. BLS is a feedforward neural network with few optimization parameters and fast training speed. In this paper, three features of two-phase sample images, the number of regional blocks, the roundness of regional blocks, and barycenter of regional blocks, are combined with network structure of BLS to recognize different flow patterns. Through the simulation, the recognition accuracy of two-phase fillback image is more than 99%. This conclusion indicates the effectiveness of BLS to predict different two-phase flow patterns; it also provides a new solution for the pattern recognition of the flow pattern in the mining filling pipeline.

Copyright: © 2021 Xuebin Qin et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10602029
  • Publié(e) le:
    17.04.2021
  • Modifié(e) le:
    02.06.2021
 
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