On unifying multiblock analysis with application to decentralized process monitoring

Abstract

SUMMARYWesterhuis et al. (J. Chemometrics 1998; 12: 301-321) show that the scores of consensus PCA and multiblock PLS (Westerhuis and Coenegracht, J. Chemometrics 1997; 11: 379-392) can be calculated directly from the regular PCA and PLS scores respectively. In this paper we show that both the loadings and scores of consensus PCA can be calculated directly from those of regular PCA, and the multiblock PLS loadings, weights and scores can be calculated directly from those of regular PLS. The orthogonal properties of four multiblock PCA and PLS algorithms are explored. The use of multiblock PCA and PLS for decentralized monitoring and diagnosis is derived in terms of regular PCA and PLS scores and residuals. While the multiblock analysis algorithms are basically equivalent to regular PCA and PLS, blocking of process variables in a large-scale plant based on process knowledge helps to localize the root cause of the fault in a decentralized manner. New definitions of block and variable contributions to SPE and T 2 are proposed for decentralized monitoring. This decentralized monitoring method based on proper variable blocking is successfully applied to an industrial polyester film process.

Developments and Applications of Nonlinear Principal Component Analysis – a Review

“…Since the first applications of PCA [21], this technique has found its way into a wide range of different application areas, for example signal processing [75], factor analysis [29,44], system identification [77], chemometrics [20,66] and more recently, general data mining [11,70,58] including image processing [17,72] and pattern recognition [47,10], as well as process monitoring and quality control [1,82] including multiway [48], multiblock [52] and multiscale [3] extensions. This success is mainly related to the ability of PCA to describe significant information/variation within the recorded data typically by the first few score variables, which simplifies data analysis tasks accordingly.…”

Hierarchical multivariate analysis of cockle phenomena

“…However, online monitoring and detection applications are possible as discussed in Section 4.1. Additionally, if a satisfactory regression model can be developed, it is possible to create and employ statistical process control (SPC) charts for control applications [2,3,5]. Tools that can create such charts include SIMCA-4000 TM (Umetrics AB, Umeå, Sweden).…”

“…Multivariate statistical methods have been used in similar applications such as process monitoring [2,3] and spectroscopy [4]. A variant of this that employs a hierarchy of these methods to decompose the data is used in this work.…”

Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis

“…A better alternative is then to divide the batch data sets into several blocks and build local MPCA models for each data block. This approach has significant benefits because the latent variable structure is allowed to change at each phase (Qin et al, 2001;Ränner et al, 1998;Wold et al, 1996). Analyzing the data with a multiblock model also allows for detecting more specific locations of faults in a process (Smilde et al, 2000).…”

“…Therefore, adaptive multiblock MPCA monitoring simply groups the contributions to the Q i -statistic of adaptive MPCA in terms of blocks (Qin et al, 2001).…”