Abstract
Traditional data-driven soft sensor methods can be regarded as an optimization process to minimize the predicted error. When applying the mean squared error as the objective function, the model tends to be trained to minimize the global errors of overall data samples. However, there are deviations in data from practical operation, in which the model performance in the estimation of the local variations in the target parameter worsens. This work presents a solution to this challenge by considering higher-order moments of prediction residuals, which enables the evaluation of deviations of the residual distribution from the normal distribution. By embedding constraints on the distribution of residuals into the objective function, the model tends to converge to the state where both stationary and deviation data can be accurately predicted. Data from the Tennessee Eastman process and an industrial cracking furnace are considered to validate the performance of the proposed modeling method.