Thermometry Machine Learning Model for Digitized Metrological Calibration of Platinum Resistance Thermometer |
|---|
| Oqab N. Alotaibi, Rakan O. Alnefaie, Arwa K. Alrushud, Fahad A. AlMuhlaki, Rayan A. AlYousefi, Saad A. Bin Qoud, I. AlFaleh, N. Qahtani, A. El-Matarawey |
- Abstract:
- Temperature measurements rely on various types of thermometers, including but not limited to Platinum Resistance Thermometers (PRTs), thermocouples, and radiation thermometers. Among these, resistance thermometers are considered highly reliable for sensitive temperature measurements. To ensure the accuracy and precision of measurement results, it is essential to consider factors that affect either the temperature value itself (after conversion from ohms) or the uncertainty estimation when using resistance thermometers. One critical factor is the interpolation error that arises when converting resistance values to temperature using the ITS-90 equations. Discrepancies in the temperature values obtained through these methods can impact measurement reliability. Therefore, this study aims to develop a robust Python-based algorithm for calibrating PRTs with minimal errors, thereby reducing the impact on measurement uncertainty. The study will provide an open-source, step-by-step algorithm as part of the global digital transformation trend. This algorithm will serve as a valuable resource for researchers and practitioners seeking to enhance the reliability and accuracy of temperature measurements.
- Download:
- IMEKO-TC6-2025-023.pdf
- DOI:
- 10.21014/tc6-2025.023
- Event details
- IMEKO TC:
- TC6
- Event name:
- TC6 M4Dconf2025
- Title:
2025 IMEKO TC-6 International Conference on Metrology and Digital Transformation
- Place:
- Benevento, ITALY
- Time:
- 03 September 2025 - 05 September 2025