Two-dimensional LSTM soft sensor using noisy process data

Abstract

Establishing a reliable data-based soft sensor still faces a series of challenges, particularly the presence of outliers and different kinds of noise which are non-negligible in process data. To address these challenges, a correntropy-based two-dimensional long short-term memory (TLSTM) model is developed to handle noisy process data. First, the multidimensional time series samples are reconstructed into numerous two-dimensional input matrices in the feature and time directions. Then, the convolution and pooling operations are used to extract useful information in the process variables related to the quality variable. Meanwhile, a gating mechanism is employed to learn the internal representation of time series. Finally, a correntropy-based strategy is utilized to assign relatively small weights to outliers automatically, enabling reliable prediction. Two cases illustrate the reliability and advantages of TLSTM in effectively extracting quality-related features for prediction.