A machine learning approach to automation and uncertainty evaluation for self-validating thermocouples

The introduction of a machine learning approach to self-validating thermocouples marks a significant advancement in industrial temperature measurement. These thermocouples, essential for monitoring process temperatures, often suffer from calibration drift in harsh environments, leading to inaccuracies. The innovative method employs a miniature phase-change cell positioned near the thermocouple's measurement junction, allowing for real-time recalibration based on the known melting temperature of an ingot. This process not only automates the detection of the melting plateau but also achieves remarkable accuracy, with test results indicating a 100% success rate in identifying the melting point. Furthermore, the model demonstrates a cross-validated R2 of 0.99 for predicting calibration drift, underscoring its reliability. By removing the need for manual intervention, this approach streamlines operations and enhances the precision of temperature measurements, which is critical in various in…