Calibration and Characterization of a Reduced Form-Factor High Accuracy Three-Axis Teslameter

Cassar, Johann; Sammut, Andrew; Sammut, Nicholas; Calvi, M.; Mitrovic, Zarko; Popović, R.S.

doi:10.3390/electronics9010151

Cited by 4 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using LI, it is possible to solve nonlinearity calibration when order of polynomial is higher than three. It is discussed that higher order regression represents more precise fit for calibration data since it will get very close to original function, meaning higher convergency [25,31,33,64,65]. However, higher orders also have tendency to oscillate which may give erratic value between the points.…”

Section: Theoretical Basics Of Non-linearity Calibrationmentioning

confidence: 99%

“…Thus, the sensor input-output's relationship can be solved using interpolation or least square approximation to define polynomial function for such a set of data in order to reach an efficient and accurate outcome (e.g. [23,[30][31][32][33][34][35]). Lagrange interpolation (LI) method is a proper choice for low-cost calibration scheme where the order of polynomial is higher degree.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Calibration approach to quantify nonlinearity of MEMS pore pressure sensors using optimal interpolation

Barzegar¹,

Tadich²,

Sainsbury³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

MEMS-based instruments have become more attractive in recent years for many industries, particularly geotechnical monitoring owing to their small size and low capital cost. However, overcoming nonlinearity errors is a major concern to ensure accuracy, precision, and repeatability of measurement. Nonlinearity error in measuring instruments can be solved using polynomial function of different degree based on severity of error. In this study, Lagrange polynomial fitting method is applied for nonlinearity calibration of a newly developed MEMS pore pressure sensor by means of optimum calibration points. A procedure for optimum selection of the calibration points to get the best calibration characteristics of a pore pressure sensor is investigated. For this work, the calibration characteristics are evaluated by Lagrange interpolation using special set of Chebyshev nodes, D, A and R-optimum points. The D-A-R optimum points are constructed by Imperialist Competitive Algorithm (ICA). The value of the optimal approach is also compared with a uniform approach using equidistant points through actual readings. The results show the increased accuracy and precision of measurement using optimum approach. This increased accuracy allows the application of MEMs to sense smaller changes in pore pressure readings providing unique opportunity for passive estimation of subsurface properties.

show abstract

Section: Theoretical Basics Of Non-linearity Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibration approach to quantify nonlinearity of MEMS pore pressure sensors using optimal interpolation

Barzegar¹,

Tadich²,

Sainsbury³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Analysis of the first magnetic results of the PSI APPLE X undulators in elliptical polarisation

Liang

Calvi

Couprie

et al. 2021

Nuclear Instruments and Methods in Physics Research Section A:

View full text Add to dashboard Cite

A calibration approach for compensating hysteresis and nonlinearity error in an MEMS based instrument for porewater pressure monitoring

Barzegar,

Gharehdash,

Timms

2024

IET Science Measure & Tech

View full text Add to dashboard Cite

Micro‐electro‐mechanical system (MEMS) technology is becoming increasingly popular in geotechnical and hydrological monitoring due to their distinct features, including its small size, low cost, low energy consumption, and resistance to vibration shock. Due to the nature of design, they exhibit a series of errors, and most importantly, hysteresis and nonlinearity, which should be compensated before any practical application in order to achieve the highest degree of accuracy and precision. This article proposes a practical approach to enhance the accuracy of a newly developed MEMS instrument for groundwater monitoring. The hysteresis and nonlinearity errors were compensated using an integrated two‐step approach with an optimized Preisach model and an optimized spline approximation, respectively. A hybrid differential evolution‐hill climbing algorithm was utilized to achieve optimum models. This optimization algorithm integrates global and local search, offering higher accuracy and computational stability with a lower calibration point requirement. Analysing the results indicates that the nonlinearity error shows an improvement of more than 94% and hysteresis decreased up to 67%. The results illustrate that the compensation can be performed very well using the proposed methodology with lower uncertainty, mean square error, and standard deviation compared to non‐optimized models.

show abstract

Calibration and Characterization of a Reduced Form-Factor High Accuracy Three-Axis Teslameter

Cited by 4 publications

References 8 publications

Calibration approach to quantify nonlinearity of MEMS pore pressure sensors using optimal interpolation

Calibration approach to quantify nonlinearity of MEMS pore pressure sensors using optimal interpolation

Analysis of the first magnetic results of the PSI APPLE X undulators in elliptical polarisation

A calibration approach for compensating hysteresis and nonlinearity error in an MEMS based instrument for porewater pressure monitoring

Contact Info

Product

Resources

About