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Multiscale statistical process control with multiresolution data
Author(s) -
Reis Marco S.,
Saraiva Pedro M.
Publication year - 2006
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.10805
Subject(s) - process (computing) , covariance , statistical process control , computer science , multiresolution analysis , resolution (logic) , implementation , process control , algorithm , data mining , artificial intelligence , mathematics , wavelet , statistics , discrete wavelet transform , wavelet transform , programming language , operating system
An approach is presented for conducting multiscale statistical process control that adequately integrates data at different resolutions (multiresolution data), called MR‐MSSPC. Its general structure is based on Bakshi's MSSPC framework designed to handle data at a single resolution. Significant modifications were introduced in order to process multiresolution information. The main MR‐MSSPC features are presented and illustrated through three examples. Issues related to real world implementations and with the interpretation of the multiscale covariance structure are addressed in a fourth example, where a CSTR system under feedback control is simulated. Our approach proved to be able to provide a clearer definition of the regions where significant events occur and a more sensitive response when the process is brought back to normal operation, when it is compared with previous approaches based on single resolution data. © 2006 American Institute of Chemical Engineers AIChE J, 2006