Statistical process monitoring: basics and beyond

Abstract

This paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Several fault detection indices in the literature are analyzed and unified. Fault reconstruction for both sensor and process faults is presented which extends the traditional missing value replacement method. Fault diagnosis methods that have appeared recently are reviewed. The reconstruction-based approach and the contribution-based approach are analyzed and compared with simulation and industrial examples. The complementary nature of the reconstruction-and contribution-based approaches is highlighted. An industrial example of polyester film process monitoring is given to demonstrate the power of the contributionand reconstruction-based approaches in a hierarchical monitoring framework. Finally we demonstrate that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring. Additional topics are summarized at the end of the paper for future investigation.

Batch Process Monitoring Based on Fuzzy Segmentation of Multivariate Time-Series

“…They further gave a rule that the statistic less than one is considered normal. It has been suggested that the use of a single index is preferable in practice (Joe Qin, 2003).…”

Multimode Monitoring of Oxy-Gas Combustion Through Flame Imaging, Principal Component Analysis, and Kernel Support Vector Machine

“…The PCA is a well-established multivariate statistic model which projects standardized data into two orthogonal subspaces so as to reduce the dimensions of the data (Joe Qin, 2003). As it plays an important part in the proposed PCA-KSVM model, a brief introduction is given below for a reader convenience.…”

“…If the correlation matrix of X is R where R=X¢X/(M-1), the singular value decomposition of R can be represented as (Joe Qin, 2003),…”

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

“…Therefore, the scalar thresholds can qualify the process status. The approximated control limits of T 2 and Q statistics, with a confidence level α, can be determined from the normal operating data in several ways by applying the probability distribution assumptions (Kourti and MacGregor, 1995;Qin, 2003). The control limits can be calculated as follows:…”

“…Multivariate statistical methods, such as principal component analysis (PCA) and partial least squares (PLS), are widely used in industry for process monitoring (Nomikos and MacGregor, 1995;Qin, 2003;Ge and Song, 2008;Garcia-Alvarez et al, 2012). Other complementary multivariate statistical process monitoring methods, including canonical variate analysis, kernel PCA, dynamic PCA, and independent component analysis, have been proposed to address the limitations of PCA-or PLSbased monitoring strategies (Russell et al, 2000;Juricek et al, 2004;Lee et al, 2004a;2006).…”