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Communication Dans Un Congrès Année : 2022

Generalized likelihood ratio-based condition indicator maximization via Rayleigh quotient iteration

Kayacan Kestel
  • Fonction : Auteur
Cédric Peeters
  • Fonction : Auteur
  • PersonId : 1017735
Jérôme Antoni
R Brijder
  • Fonction : Auteur
Jan Helsen
  • Fonction : Auteur
  • PersonId : 1050994

Résumé

This study attempts to improve the performance of Generalized Likelihood Ratio Test-based indicators via blind filtering the of vibration signals. The key point is the optimization of the filter coefficients to maximize the indicator of interest. The filter coefficients are optimized through Rayleigh quotient iteration. The proposed method's performance and applicability are demonstrated on both simulated and real vibration signals measured on an experimental test rig. The outcome of the study shows that the Rayleigh quotient iteration is a potent tool for maximizing such complex condition monitoring indicators. Inspections over the filtered signals reveal that the optimal filters promote particular signal patterns linked to a bearing fault in vibration signals. The indicator estimated over the filtered signals is able to detect the bearing fault more robustly when compared to the raw signals.
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Dates et versions

hal-04018655 , version 1 (19-04-2023)

Identifiants

  • HAL Id : hal-04018655 , version 1

Citer

Kayacan Kestel, Cédric Peeters, Jérôme Antoni, Quentin Leclere, Francois Girardin, et al.. Generalized likelihood ratio-based condition indicator maximization via Rayleigh quotient iteration. ISMA 2022, 2022, Leuven (BE), Belgium. ⟨hal-04018655⟩
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