Deep Neural Network-Based Linearization and Cold Junction Compensation of Thermocouple

In this proposed work, temperature sensors, namely, a thermocouple and thermistor were linearized using deep neural networks. The deep feedforward neural network (DFNN) technique was proposed to linearize the K-type thermocouple’s output, in the given temperature range −100 °C to 1372 °C, while nonlinearity was reduced from 2.03% to 0.002% full scale span (FSS). Deep layer recurrent neural network (DLR-NN) was used to reduce the nonlinearity of a negative temperature coefficient (NTC) thermistor from 84.63% to 0.13% FSS. The linearized thermistor was used for cold junction compensation (CJC) of the thermocouple. In both the thermocouple and thermistor, linearization was achieved in a single stage for a wide range digitally using deep neural networks alone. There were no analog pre-signal conditioning circuits, unlike the existing neural network-based linearization techniques in literature. A hardware setup of a stand-alone module for linearization was designed using the Raspberry pi microcontroller consisting of two soft modules, one for thermocouple linearization and the other for thermistor linearization. The proposed system was experimentally tested using a K-type thermocouple on a thermal calibrator in a 0 °C–300 °C range. The cold junction compensated output of the thermocouple had a maximum absolute error of 0.34 °C when ambient temperature varied from 0 °C to 40 °C. The results were satisfactory and better than the existing National Institute of Standards and Technology (NIST) standard. This linearization technique can be extended to other thermocouple types as well as other nonlinear sensors.