A Software-based Linearization Technique for Thermocouples using Recurrent Neural Network

Thermocouple is one of the widely used temperature sensor used in process industries. The biggest challenge of thermocouple measurements is its inherent nonlinearity. Software-based linearization techniques are more flexible and offer easy implementation using a microcontroller. The published works on software-based linearization have considered the current measurement sample, while the nonlinearity varies across the segments. The present work uses a simple 3-layer recurrent neural network (RNN) based model to consider a short history of immediately preceding predictions (temperatures) along with the current measurement (millivolt) to predict the current output (temperature). The RNN performance was evaluated with Type J, K and T-type thermocouple data from NIST database. The blind test accuracy was evaluated by computing the mean square error (MSE) and mean absolute error (MAE). The MSE (MAE) of 0.15 (0.33), 0.18 (0.34) and 0.11 (0.31) respectively was found from J, K and T-type thermocouples, using a total of 1791 samples from the three datasets. The proposed software-based linearizer offered improved performance over a piecewise linear interpolation and a simple 2-layer multilayer perceptron-based neural network model.