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Monitoring process transitions by Kalman filtering and time-series segmentation by Balazs Feil; Janos Abonyi; Sandor Nemeth; Peter Arva is a Engineering article available to read on EtoBox.

What is Monitoring process transitions by Kalman filtering and time-series segmentation about?

The analysis of historical process data of technological systems plays important role in process monitoring, modelling and control. Timeseries 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 model-based non-linear 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.

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Author

Balazs Feil; Janos Abonyi; Sandor Nemeth; Peter Arva

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0098-1354)

Published

2005

Language

EN

Field

Engineering (Physical Sciences)