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    Fault detection in the Tennessee Eastman benchmark process with nonlinear singular spectrum analysis

    Access Status
    Fulltext not available
    Authors
    Krishnannair, S.
    Aldrich, Chris
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Krishnannair, S. and Aldrich, C. 2017. Fault detection in the Tennessee Eastman benchmark process with nonlinear singular spectrum analysis. IFAC-PapersOnLine. 50 (1): pp. 8005-8010.
    Source Title
    IFAC-PapersOnLine
    DOI
    10.1016/j.ifacol.2017.08.1223
    ISSN
    2405-8963
    School
    Dept of Mining Eng & Metallurgical Eng
    URI
    http://hdl.handle.net/20.500.11937/63360
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 Multivariate statistical process monitoring methods aim at detecting and identifying faults in the performance of processes over time in order to keep the process under control. Singular spectrum analysis (SSA) is a potential tool for multivariate process monitoring. It allows the decomposition of dynamic process variables or time series into additive components that can be monitored separately to identify hidden faults that may otherwise not be detectable. However, SSA is a linear method and can give misleading information when it is applied to dynamic processes with strong nonlinearity. Therefore, in this paper, nonlinear versions of SSA based on the use of auto-associative neural networks or auto-encoders and dissimilarity matrices are considered. This is done based on the benchmark Tennessee Eastman process that is widely used in the evaluation of statistical process monitoring methods.

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