Abstract
ABSTRACTMultivariate data collected from batches is usually monitored via control charts (CCs) based on MPCA and MPLS for batch to batch comparison. In addition, distribution free approaches include other dimensionality reduction methods for batch and time-wise analysis. However, techniques for multivariate data focused on variable-wise analysis haven’t been widely developed. Here, we propose a nonparametric quality control strategy for off-line monitoring of batches and variables, besides visual clustering of observations within batches. In our approach, CCs based on Dual STATIS are created using robust bagplots to enhance signal detection in batch and variable-wise analysis, while parallel coordinate plots are used in identification of unusual observations’ behavior per variable, regardless distributional assumptions. This proposed strategy poses the main advantage of detecting different type of changes through meaningful visualization tools, allowing easier interpretation of results in industrial settings.
Highlights
Nowadays, several industries rely on batch processing, including for example, chemicals, pharmaceuticals, food products, fabrics, metals, bakeries, pulp, and paper manufacturers
We propose a nonparametric quality control strategy based on Interstructure Space (IS) and CO control charts (CCs) which enables off-line monitoring of batch processes using Dual STATIS and bagplots for establishing control regions
The first is related to preprocessing techniques applied to the raw data, the second is the DS-PC strategy to define the nonparametric control region, the third is the advantage of detecting different types of changes with the same CCs, and the fourth is the use of a well-known visual clustering method in multivariate process control as a diagnosis method for the out-of-control points signalized in the proposed CCs
Summary
Several industries rely on batch processing, including for example, chemicals, pharmaceuticals, food products, fabrics, metals, bakeries, pulp, and paper manufacturers. Statistical process control (SPC) for monitoring industrial processes always involves simultaneous monitoring or control of two or more correlated quality-process characteristics. In this sense, multivariate control charts (CCs) replace the univariate ones, PRODUCTION & MANUFACTURING RESEARCH usually applied in monitoring, because analyzing these characteristics under the univariate case can be misleading (Montgomery, 2009). Results obtained from multivariate CCs are strongly recommended when quality characteristics are correlated (Bersimis, Psarakis, & Panaretos, 2007). Other multivariate CCs are presented and reviewed by Jackson (1991); Wierda (1994); Lowry and Montgomery (1995) and Harris, Seppala, and Desborough (1999)
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