Learn More
The partitioning of a time-series into internally homogeneous segments is an important data mining problem. The changes of the variables of a multivariate time-series are usually vague and do not focus on any particular time point. Therefore it is not practical to define crisp bounds of the segments. Although fuzzy clustering algorithms are widely used to(More)
The segmentation of time-series is a constrained clustering problem: the data points should be grouped by their similarity, but with the constraint that all points in a cluster must come from successive time points. The changes of the variables of a time-series are usually vague and do not focused on any particular time point. Therefore it is not practical(More)
In modern production systems huge amounts of process operational data are recorded. These data definitely have the potential to provide information for product and process design, monitoring and control. This paper presents a brief survey of simple Exploratory Data Analysis procedures have been found to be useful in the qualitative analysis of historical(More)
Segmentation is the most frequently used subroutine in clustering, indexing, sum-marization, anomaly detection, and classification of time series. Although in many real-life applications a lot of variables must be simultaneously monitored, most of the segmentation algorithms are used for the analysis of only one time-variant variable. Hence, this paper(More)
Time-series segmentation algorithms, such as methods based on Principal Component Analysis (PCA) and fuzzy clustering, are based on input-output process data. However, historical process data alone may not be sufficient for the monitoring of process transitions. Hence, the key idea of this paper is to incorporate the first-principle model based state(More)
Nonlinear state estimation is a useful approach to the monitoring of industrial (polymerization) processes. This paper investigates how this approach can be followed to the development of a soft sensor of the product quality (melt index). The bottleneck of the successful application of advanced state estimation algorithms is the identification of models(More)
The huge amount of data recorded by modern production systems definitely have the potential to provide information for product and process design, monitoring and control. This paper presents a soft-computing based approach for the extraction of knowledge from the historical data of production. Since Self-Organizing Maps (SOM) provide compact representation(More)
The aim of this paper is to analyze how different catalyst activity profiles influence the operating strategies of industrial reactors. Based on this analysis a method that can be used to transform information given by laboratory reactor experiments into a form which can be used to estimate the productivity of the catalyst and the quality properties of(More)
Process manufacturing is increasingly being driven by market forces and customer needs and perceptions, resulting in necessity of flexible multi-product manufacturing. The increasing automation and tighter quality constraints related to these processes make the operator's job more and more difficult. This makes decision support systems for the operator more(More)