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Novel Fault Diagnosis of a Conveyor Belt Mis-Tracking via Motor Current Signature Analysis.
Farhat, Mohamed Habib; Gelman, Len; Abdullahi, Abdulmumeen Onimisi; Ball, Andrew; Conaghan, Gerard; Kluis, Winston.
Afiliação
  • Farhat MH; School of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
  • Gelman L; School of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
  • Abdullahi AO; School of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
  • Ball A; School of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
  • Conaghan G; Daifuku Airport Technologies, Sutton Road, Hull HU7 0DR, UK.
  • Kluis W; Babcock International Group, Schiphol Boulevard 363, 1118 BJ Schiphol, The Netherlands.
Sensors (Basel) ; 23(7)2023 Mar 31.
Article em En | MEDLINE | ID: mdl-37050710
For the first time ever worldwide, this paper proposes, investigates, and validates, by multiple experiments, a new online automatic diagnostic technology for the belt mis-tracking of belt conveyor systems based on motor current signature analysis (MCSA). Three diagnostic technologies were investigated, experimentally evaluated, and compared for conveyor belt mis-tracking diagnosis. The proposed technologies are based on three higher-order spectral diagnostic features: bicoherence, tricoherence, and the cross-correlation of spectral moduli of order 3 (CCSM3). The investigation of the proposed technologies via comprehensive experiments has shown that technology based on the CCSM3 is highly effective for diagnosing a conveyor belt mis-tracking via MCSA.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article