Robust inferential control using kernel density methods

P. R. Goulding, B. Lennox, Q. Chen, D. J. Sandoz

    Research output: Contribution to journalArticlepeer-review

    Abstract

    The use of kernel density estimation (KDE) methods to address the issue of control under process uncertainty and unreliability is investigated. It is shown how the KDE-derived joint probability density function of plant operational data can be used to assist in this task. It is also shown how the estimated density function can be used to support robust inference of important plant variables in addition to the detection and isolation of faults. (C) 2000 Elsevier Science Ltd.
    Original languageEnglish
    Pages (from-to)835-840
    Number of pages5
    JournalComputers and Chemical Engineering
    Volume24
    Issue number2-7
    DOIs
    Publication statusPublished - 15 Jul 2000

    Keywords

    • Chemical process control
    • Control systems
    • Fault detection and isolation
    • Kernel density methods
    • Multivariate statistical process control

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