Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

Fouzi Harrou, Muddu Madakyaru, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions. The performances of the developed anomaly detection using NLPLS-based HD technique is illustrated using simulated plug flow reactor data.
Original languageEnglish (US)
Title of host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781509042401
DOIs
StatePublished - Feb 16 2017

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