Dynamic Process Monitoring Based on Integrated Statistic of Principal Component Analysis and Slow Feature Analysis

Abstract

Process monitoring has attracted extensive interest for real-time operating evaluation due to the expectation of a safe and higher-quality production in chemical industry. Principal component analysis (PCA), an effective method for data dimensionality reduction, has been widely utilized in static process monitoring. However, industrial process data generally show dynamic characteristics because certain sequences of process variables are autocorrelated due to internal mechanisms. Recently, slow feature analysis (SFA) has been proved to have a good performance in extracting the slowly changing features in dynamic processes. To monitor both static and dynamic relations, a novel dynamic process monitoring method based on integrated statistic of PCA and SFA is proposed by extracting data feature of static and dynamic variables respectively. Variables are first grouped into static and dynamic categories according to their autocorrelation. For static part, PCA is applied to extract static variable features and calculate T2 and SPE statistics, while SFA is adopted in dynamic part for the extraction of dynamic variable features, and T2, Te2, S2 and Se2 statistics can be calculated accordingly. At last, these statistics can be integrated by a support vector data description (SVDD) to give a final process monitoring result. The performance of the proposed method is compared with other dynamic process monitoring methods on the benchmark Tennessee Eastman process (TEP).