Measurement Resolution in Uncertainty Calculation with the GUM Method Approach: A LabVIEW Application

Yıl 2023, Cilt: 13 Sayı: 1, 32 - 39, 30.06.2023

Hakan Çıtak , Sabri Bıçakçı

https://doi.org/10.36222/ejt.1302461

Öz

GUM (Guide to the Expression of Uncertainty in Measurement) is a method used for calculating uncertainty in measurements. The method involves an uncertainty calculation approach which also constitutes a reference for the international ISO/IEC 17025 standard. In the GUM method, all uncertainties are expressed as standard uncertainty. An uncertainty may incorporate various components where impacts from multiple sources are taken into consideration. Resolution errors resulting from the sensitivity of the measurement equipment has a significant impact in the calculation of uncertainty. Sensitivity of an analog measurement device such as a multimeter depends on the resolution of the ADC it contains. Multimeters with 8-bit resolution ADCs are often used as measurement devices for sensor voltage values to be read once or several times. Factors such as high measurement resolution and reading errors by operators lead to an increase in uncertainty. Multiple data from a sensor or many sensors cause a significant increase in uncertainty, which results in a serious loss of time and labor. In order to mitigate said two factors which increase uncertainty in such cases, analog data needs to be converted to digital data at high resolution and transferred into computer medium. In this study, an D7714 analog/digital converter IC with 24-bit resolution has been used to transfer digital data into computer medium via myRIO 1950. A LabVIEW-based software has been developed to perform register settings for the AD7714 IC and to retrieve data at 24-bit resolution.

Anahtar Kelimeler

Measurement uncertainty, Resolution, AD7714, myRIO, LabVIEW

Kaynakça

• J. C. Damasceno, P. R. Couto, “Methods for evaluation of measurement uncertainty,” In Metrology, London, IntechOpen, pp. 9-28, 2018.
• M. S. B. Fernández, “Evaluation of uncertainty in the measurement of the stress-optic coefficient,” Journal of Non-Crystalline Solids, 607, 122249. 2023.
• T. Dorst, T. Schneider, S. Eichstädt, A. Schütze, “Influence of measurement uncertainty on machine learning results demonstrated for a smart gas sensor,” Journal of Sensors and Sensor Systems, 12(1), pp. 45-60, 2023.
• J. Huang, Y. Li, B. Jiang, L. Cao, “Analysis of measurement uncertainty of a surface acoustic wave micro-pressure sensor,” Measurement and Control, 52(1-2), pp. 116-121, 2019.
• H. Zangl, K. Hoermaier, “Educational aspects of uncertainty calculation with software tools,” Measurement, 101, pp. 257-264, 2017.
• Z. Tan, C. H. Chen, Y. Chae, G. C. Temes, “Incremental delta-sigma ADCs: A tutorial review,“ IEEE Transactions on Circuits and Systems I: Regular Papers, 67(12), pp. 4161-4173, 2020.
• E. Kabalcı, Y. Kabalcı, P. Siano, “Design and implementation of a smart metering infrastructure for low voltage microgrids,” International Journal of Electrical Power & Energy Systems, pp. 134, 107375, 2022.
• K. Nam, H. Kim, G. Choi, M. Yoo, H. Ko, “Low-noise delta-sigma analog front end with capacitor swapping technique for capacitive microsensors,“ Measurement and Control, 55(5-6), pp. 239-246, 2022.
• J. Jovanović, D. Denić, “Mixed-mode method used for Pt100 static transfer function linearization.” Measurement Science Review, 21(5), pp. 142-149, 2021.
• N. Jeelani. A. Hamid Bhat, “Constant switching frequency‐based delta‐sigma modulation of single‐phase AC–AC zeta converter.” International Journal of Circuit Theory and Applications, 51(1), pp. 97-114. 2023.
• J. M. Binder, A. Stark, N. Tomek, J. Scheuer, F. Frank, K.D. Jahnke, ... & F. Jelezko, “Qudi: A modular python suite for experiment control and data processing,” SoftwareX, 6, pp. 85-90, 2017.
• T. Drevinskas, M. Kaljurand, A. Maruška, “Capacitance‐to‐digital: A single chip detector for capillary electrophoresis,” Electrophoresis,” 35(16), pp. 2401-2407, 2014.
• S. Billa, A. Sukumaran, S. Pavan, “Analysis and design of continuous-time delta–sigma converters incorporating chopping,” IEEE Journal of Solid-State Circuits, 52(9), pp. 2350-2361, 2017.
• S. Pavan, R. Schreier, G. C. Temes, “Understanding delta-sigma data converters,” Wiley-IEEE Press, 2017, pp.584.
• R. Theertham, P. Koottala, S. Billa, S. Pavan, “Design techniques for high-resolution continuous-time delta–sigma converters with low in-band noise spectral density,” IEEE Journal of Solid-State Circuits, 55(9), pp. 2429-2442, 2020.