AUTHOR=Matania Omri , Klein Renata , Bortman Jacob 

TITLE=Transfer Across Different Machines by Transfer Function Estimation

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=5

YEAR=2022

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.811073

DOI=10.3389/frai.2022.811073

ISSN=2624-8212

ABSTRACT=<p>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.</p>