Precise sinusoidal signal extraction from noisy waveform in vibration calibration

TL;DR

This paper presents optimized signal processing techniques for extracting small sinusoidal vibration signals from noisy data, significantly improving calibration accuracy for vibration sensors like accelerometers.

Contribution

The work introduces and validates three novel methods that reduce noise impact in vibration calibration, enhancing precision and lowering uncertainty.

Findings

Uncertainty reduced by two orders of magnitude.

Spectral leakage impacts calibration accuracy.

Proposed methods outperform standard techniques.

Abstract

Precise extraction of sinusoidal vibration parameters is essential for the dynamic calibration of vibration sensors, such as accelerometers. However, several standard methods have not yet been optimized for large background noise. In this work, signal processing methods to extract small vibration signals from noisy data in the case of accelerometer calibration is discussed. The results show that spectral leakage degrades calibration accuracy. Three methods based on the use of a filter, window function, and numerical differentiation are investigated with theoretical calculations, simulations, and experiments. These methods can effectively reduce the contribution of the calibration system noise. The uncertainty of micro vibration calibration in the National Metrology Institute of Japan is reduced by two orders of magnitudes using the proposed methods. The theoretical analyses in this work can lay the foundation for the optimization of signal processing in vibration calibration, and can be applied to other dynamic calibration fields.