Data-driven optimal filtering for phase and frequency of noisy oscillations: Application to vortex flow metering

Area of Science:

• Signal Processing
• Fluid Dynamics
• Measurement Science

Background:

• Accurate phase measurement from noisy time series is crucial for analyzing oscillatory systems.
• Existing methods can be susceptible to noise and artificial phase slips, limiting their reliability.
• Vortex flow meters exhibit complex dynamics, including mode locking, which requires precise phase analysis.

Purpose of the Study:

• To propose a new method for robust phase measurement from noisy time series.
• To minimize susceptibility to measurement noise and reduce artificial phase slips.
• To apply the method for detecting and classifying mode locking in vortex flow meters.

Main Methods:

• A signal is filtered using a complex-valued impulse response filter designed to minimize relative amplitude variation.
• The phase is extracted as the argument of the filter output.
• The method's implementation and interpretation are discussed, along with a new measure for mode locking strength.

Main Results:

• The proposed phase measurement method demonstrates low susceptibility to noise.
• The technique significantly reduces the rate of artificial phase slips compared to conventional methods.
• The method is successfully applied to detect and classify mode locking phenomena in vortex flow meter data.

Conclusions:

• The developed method offers a reliable approach for phase extraction from noisy oscillatory signals.
• This technique provides a valuable tool for analyzing complex dynamics, such as mode locking in fluid systems.
• The proposed measure for mode locking strength aids in the characterization of vortex flow meter behavior.