On-line detection of homogeneous operation ranges by dynamic principal component analysis based time-series segmentation

Abstract

Development of chemical process technologies shall be based on the analysis of process data. In the field of process monitoring the recursive Principal Component Analysis (PCA) is widely applied to detect any misbehavior of the technology. The investigation of transient states needs dynamic PCA to describe the dynamic behavior more accurately. By combining and integrating the recursive and dynamic PCA into time series segmentation techniques, efficient multivariate segmentation methods were resulted to detect homogenous operation ranges based on process data. The similarity of time-series segments is evaluated based on the Krzanowski-similarity factor, which compares the hyperplanes determined by the PCA models. With the help of developed time series segmentation framework, separation of operation regimes becomes possible for supporting process monitoring and control. The performance of the proposed methodology is presented throughout a linear process and the commonly applied Tennessee Eastman process.