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Multiscale statistical process control with multiresolution data

Authors

  • Marco S. Reis,

    Corresponding author
    1. GEPSI-PSE Group, Dept. of Chemical Engineering, University of Coimbra, Pólo II –Pinhal de Marrocos, 3030–290 Coimbra, Portugal
    • GEPSI-PSE Group, Dept. of Chemical Engineering, University of Coimbra, Pólo II –Pinhal de Marrocos, 3030–290 Coimbra, Portugal
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  • Pedro M. Saraiva

    1. GEPSI-PSE Group, Dept. of Chemical Engineering, University of Coimbra, Pólo II –Pinhal de Marrocos, 3030–290 Coimbra, Portugal
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Abstract

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

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