Orthogonal projection based statistical feature extraction for
continuous process monitoring
Abstract
Multivariate statistical techniques have been widely applied in
industrial processes to detect abnormal behaviors, while their
performance could be unsatisfactory due to insufficient extraction of
complex data characteristics. A method named Orthogonal transformed
statistics Mahalanobis distance (OTSMD) is developed to handle this
issue. As a feature-based method, OTSMD simultaneously considers various
data characteristics through monitoring statistical features of process
variables. Orthogonal transformed components (OTCs) are first calculated
to capture variable correlation, and a set of statistical features is
determined to extract other crucial characteristics, especially for the
process nonstationarity. Statistical features of OTCs, which reveals
implied process information, are continuously obtained using a sliding
window, and a Mahalanobis distance index is utilized for fault
detection. Compared with existing methods, OTSMD extracts data
characteristics more comprehensively with a lower dimension, making it
more effective in monitoring various faults. The results are illustrated
through a numerical example, and two chemical industrial processes.