Combining Conceptual Clustering and Principal Component Analysis for State Space Based Process Monitoring

Abstract

Multivariate statistics and unsupervised machine learning have recently been studied by many researchers for process monitoring and fault diagnosis. These approaches often depend on calculating a similarity or distance measure to group data sets into clusters. Apart from giving predictions, they are not able to give causal explanations on why a specific set of data is assigned to a particular cluster. In this work, a conceptual clustering approach is presented for designing state space based monitoring systems, which is able to generate conceptual knowledge on the major variables which are responsible for clustering, as well as projecting the operation to a specific operational state. A critical issue in this approach is how to conceptually represent dynamic trend signals. For this purpose, principal component analysis is used for concept extraction from real-time dynamic trend signals. The method is introduced using a continuous stirred tank reactor as a case study. Application of the approach to a refinery methyl tert-butyl ether process is also presented.

Qualitative/quantitative simulation of process temporal behavior using clustered fuzzy digraphs

“…The approach used in this work mimics this process and Ž . was initially developed in our early study Wang and Li, 1999 . Ž .…”

Clustering multivariate time‐series data

“…Wang and Li [8] described another clustering methodology called 'conceptual clustering' for designing state-spacebased monitoring systems. This approach generates 'conceptual knowledge' about the major variables, and projects the data to a specific operational state.…”

“…where SF i;q is the similarity factor between the qth dataset and the ith cluster described by Equations (7) or (8). Let the aggregate dataset i in Equation (10) be the reference dataset.…”

“…It would be beneficial if these data could be categorized into groups of operating conditions so that the characteristics of these groups can be used for decision support in fault detection and diagnosis, gross error detection, etc. [3] There have been numerous textbooks [1,4] and publications on clustering of scientific data for a variety of areas such as taxonomy [5], agriculture [6], remote sensing [7], as well as process control [3,8].…”

An improved independent component regression modeling and quantitative calibration procedure

“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] They are an attractive option for handling various problems in many fields of chemical engineering that are ''data rich but information poor''. As one important area of statistical analysis, multivariate calibration, [9][10][11][12][13][14][15][16] has been widely used to establish quantitative relationships between process measurements (X), and quality properties (Y).…”