Semisupervised dynamic soft sensor based on complementary ensemble empirical mode decomposition and deep learning

Noise, redundancy, and dynamic characteristics in industrial process data have been regarded as the key factors that affect the measurement accuracy of data-driven soft sensors. In this paper, a semi-supervised dynamic soft sensor is proposed to capture the dynamic characteristics of data while removing noise and redundancy within the data, thus ensuring improved accuracy. Complementary ensemble empirical mode decomposition and isometric feature mapping are combined to reduce noise and redundancy. A semi-supervised deep learning model is designed to capture the dynamic characteristics. Compared with traditional soft sensors, the effectiveness and superiority of this method are verified via an experiment using an air preheater of a power boiler. The proposed method achieves the lowest MAE of 0.1745 and the highest correlation coefficient of 0.9969. Compared to methods without data preprocessing, the MAE of the preprocessed soft sensor decreases by 22.28% on average, while the correlation coefficient increases by 0.24% on average.