Monitoring process transitions by Kalman filtering and time-series segmentation

The analysis of historical process data of technological systems plays important role in process monitoring, modelling and control. Time-series segmentation algorithms are often used to detect homogenous periods of operation based on input-output process data. However, historical process data alone may not be sufficient for the monitoring of complex processes. This paper incorporates the first-principle model of the process into the segmentation algorithm. The key idea is to use a modelbased nonlinear state-estimation algorithm to detect the changes in the correlation among the state-variables. The homogeneity of the time-series segments is measured using a PCA similarity factor calculated from the covariance matrices given by the state-estimation algorithm. The whole approach is applied to the monitoring of an industrial high-density polyethylene plant. Key words: Process monitoring, Time-series segmentation, Nonlinear state-estimation, Polyethylene production 

• Type: Journal
• Year: 2005
• Where: CCE
• Authors: Balazs Feil, János Abonyi, Sandor Z. Németh, Peter Arva
• Keywords: CCE 2005, CCE 2007, Historical Process Data, Process Data, Time-series Segmentation