A Matrix Completion Method for Imputing Missing Values of Process Data

Abstract

Real-time process data are the foundation for the successful implementation of intelligent manufacturing in the chemical industry. However, in the actual production process, process data may randomly be missing due to various reasons, thus affecting the practical application of intelligent manufacturing technology. Therefore, this paper proposes the application of appropriate matrix completion algorithms to impute the missing values of real-time process data. Considering the characteristics of online missing value imputation problems, this paper proposes an improved method for a matrix completion algorithm that is suitable for real-time missing data imputation. By utilizing real device data, this paper studies the impact of algorithm parameters on the effect of missing value imputing and compares it with several classical missing value imputing methods. The results show that the introduced method achieves higher accuracy in data imputation compared to the baseline method. Furthermore, the proposed enhancement significantly improves the speed performance of algorithms.