Data driven optimal filtering for phase and frequency of noisy oscillations: application to vortex flowmetering

摘要

A new method for extracting the phase of oscillations from noisy time seriesis proposed. To obtain the phase, the signal is filtered in such a way that thefilter output has minimal relative variation in the amplitude (MIRVA) over allfilters with complex-valued impulse response. The argument of the filter outputyields the phase. Implementation of the algorithm and interpretation of theresult are discussed. We argue that the phase obtained by the proposed methodhas a low susceptibility to measurement noise and a low rate of artificialphase slips. The method is applied for the detection and classification of modelocking in vortex flowmeters. A novel measure for the strength of mode lockingis proposed.