Transfer Across Different Machines by Transfer Function Estimation

Omri Matania, Renata Klein, Jacob Bortman

Research output: Contribution to journalArticlepeer-review

Abstract

A digital twin is a promising evolving tool for prognostic health monitoring. However, in rotating machinery, the transfer function between the rotating components and the sensor distorts the vibration signal, hence, complicating the ability to apply a digital twin to new systems. This paper demonstrates the importance of estimating the transfer function for a successful transfer across different machines (TDM). Furthermore, there are few algorithms in the literature for transfer function estimation. The current algorithms can estimate the magnitude of the transfer function without its original phase. In this study, a new approach is presented that enables the estimation of the transfer function with its phase for a gear signal. The performance of the new algorithm is demonstrated by measured signals and by a simulated transfer function.

Original languageEnglish
Article number811073
Pages (from-to)811073
JournalFrontiers in Artificial Intelligence
Volume5
DOIs
StatePublished - 2 Mar 2022

Keywords

  • adaptive clutter separation (ACS)
  • autoregressive moving-average (ARMA) model
  • minimum phase
  • transfer across different machines (TDM)
  • transfer function estimation
  • zeros and poles

ASJC Scopus subject areas

  • Artificial Intelligence

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