Thermocouple Signal Conditioning Using Augmented Device Tables and Table Look-Up Neural Networks, with Validation in J-Thermocouples

Abstract

The relatively high accuracy, large measurement range, and durability of thermocouple devices make these devices to probably be the most-widely used temperature measuring devices in industrial applications. The ability of thermocouples to sense temperature is derived from the generation of thermoelectric voltages arising due to temperature differences between the hot and cold junctions of the thermocouple. Thermocouple temperature measurement processes suffer from inaccuracies arising from both the unwanted or undetected variations in the cold junction temperature of the thermocouple, and nonlinearities in the generated thermoelectric voltage. This paper presents an enhancement of thermocouple temperature measurement using a combination of augmented thermocouple tables generated from thermocouple polynomial functions, look-up MLP neural networks trained to accept the thermocouple output voltage, and the cold or reference junction temperature measurements: to produce improved hot-junction temperature outputs. Experimental validation of the current approach for a J thermocouple, using data from augmented device tables, reproduced the measured temperature values with a worst-case error of 0.0094%.