Transfer Across Different Machines by Transfer Function Estimation

Abstract

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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.

Keywords

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