A Novel Multiscale Gated Structure Model for Soft Sensing of Nonstationary Process With Randomly Missing Data

Abstract

Due to operating condition drift, environmental changes, and system oscillations, industrial processes often exhibit nonstationary characteristics that involve both stable long-term trend and fluctuant short-term dynamics. In this article, a novel multiscale gated structure model (MGSM) is proposed for nonstationary process soft sensing, which includes long-term memory chain (stable and low frequency) and short-term dynamic chain (respond to fluctuations). The information decomposed from input data is introduced into the MGSM to learn long-term dependency relationships and dynamic behavior in the nonstationary process. In addition, a novel two-dimensional random missing function is designed to handle randomly missing data, which fully considers the data missing in variable-wise and time-wise dimensions. The proposed model is further constructed for the soft sensing of nonstationary processes with random missing data. Finally, application studies to the Tennessee Eastman process and a thermal power generating process show that the proposed method has significant advantages in the quality prediction of nonstationary process.