Author/Authors
D. Wang and J.A. Romagnoli، نويسنده ,
DocumentNumber
1384700
Title Of Article
Robust multi-scale principal components analysis with applications to process monitoring
شماره ركورد
11340
Latin Abstract
Robust multi-scale principal component analysis (RMSPCA) improves multi-scale principal components analysis (MSPCA)
techniques by incorporating the uncertainty of signal noise distributions and eliminating/down-weighting the effects of abnormal
data in the training set. The novelty of the approach is to integrate MSPCA with the robustness to the typical normality assumption
of noisy data. By using an M-estimator based on the generalized T distribution, RMSPCA adaptively transforms the data in the
score space at each scale in order to eliminate/down-weight the effects of the outliers in the original data. The robust estimation
of the covariance or correlation matrix at each scale is obtained by the proposed approach so that accurate MSPCA models can
be obtained for process monitoring purposes. The performance of the proposed approach in process fault detection is illustrated
and compared with that of the conventional MSPCA approach through a pilot-scale setting.
From Page
869
NaturalLanguageKeyword
wavelets , PCA , Robust estimation , Fault detection , Process monitoring
JournalTitle
Studia Iranica
To Page
882
To Page
882
Link To Document