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Transfer Across Different Machines by Transfer Function Estimation



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.


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Series Title
-
Call Number
-
Publisher Frontiers in Artificial Intelligence : Switzerland.,
Collation
006
Language
English
ISBN/ISSN
2624-8212
Classification
NONE
Content Type
-

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Accreditation
Scopus Q3

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