Time series analysis of the response of measurement instruments

TL;DR

This paper explores the use of Time Series Analysis techniques to understand measurement system responses, revealing the presence of flicker noise and refining uncertainty estimates in metrology.

Contribution

It demonstrates how TSA methods can identify colored noise in measurement data, improving the accuracy of uncertainty assessments beyond classical variance.

Findings

Detection of flicker noise in measurement systems

Identification of regimes where white noise assumptions fail

Enhanced measurement uncertainty characterization

Abstract

In this work the significance of treating a set of measurements as a time series is being explored. Time Series Analysis (TSA) techniques, part of the Exploratory Data Analysis (EDA) approach, can provide much insight regarding the stochastic correlations that are induced on the outcome of an experiment by the measurement system and can provide criteria for the limited use of the classical variance in metrology. Specifically, techniques such as the Lag Plots, Autocorrelation Function, Power Spectral Density and Allan Variance are used to analyze series of sequential measurements, collected at equal time intervals from an electromechanical transducer. These techniques are used in conjunction with power law models of stochastic noise in order to characterize time or frequency regimes for which the usually assumed white noise model is adequate for the description of the measurement system response. However, through the detection of colored noise, usually referred to as flicker noise, which is expected to appear in almost all electronic devices, a lower threshold of measurement uncertainty for this particular system is obtained and the white noise model is no longer accurate.