Accounting for Vibration Noise in Stochastic Measurement Errors

by   Lionel Voirol, et al.

The measurement of data over time and/or space is of utmost importance in a wide range of domains from engineering to physics. Devices that perform these measurements therefore need to be extremely precise to obtain correct system diagnostics and accurate predictions, consequently requiring a rigorous calibration procedure which models their errors before being employed. While the deterministic components of these errors do not represent a major modelling challenge, most of the research over the past years has focused on delivering methods that can explain and estimate the complex stochastic components of these errors. This effort has allowed to greatly improve the precision and uncertainty quantification of measurement devices but has this far not accounted for a significant stochastic noise that arises for many of these devices: vibration noise. Indeed, having filtered out physical explanations for this noise, a residual stochastic component often carries over which can drastically affect measurement precision. This component can originate from different sources, including the internal mechanics of the measurement devices as well as the movement of these devices when placed on moving objects or vehicles. To remove this disturbance from signals, this work puts forward a modelling framework for this specific type of noise and adapts the Generalized Method of Wavelet Moments to estimate these models. We deliver the asymptotic properties of this method when applied to processes that include vibration noise and show the considerable practical advantages of this approach in simulation and applied case studies.


page 1

page 2

page 3

page 4


Scale-wise Variance Minimization for Optimal Virtual Signals: An Approach for Redundant Gyroscopes

The increased use of low-cost gyroscopes within inertial sensors for nav...

DBCal: Density Based Calibration of classifier predictions for uncertainty quantification

Measurement of uncertainty of predictions from machine learning methods ...

Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration

The task of inertial sensor calibration has required the development of ...

Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis

In a fissile material, the inherent multiplicity of neutrons born throug...

Calibration of computer models with heteroscedastic measurement errors

Computer models are commonly used to represent a wide range of real syst...

Calibration of computer models with heteroscedastic errors and application to plant relative growth rates

Computer models are commonly used to represent a wide range of real syst...

Bayesian NVH metamodels to assess interior cabin noise using measurement databases

In recent years, a great emphasis has been put on engineering the acoust...

Please sign up or login with your details

Forgot password? Click here to reset