Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring
dc.contributor.author | Harrou, Fouzi | |
dc.contributor.author | Madakyaru, Muddu | |
dc.contributor.author | Sun, Ying | |
dc.date.accessioned | 2017-05-31T11:23:11Z | |
dc.date.available | 2017-05-31T11:23:11Z | |
dc.date.issued | 2017-02-16 | |
dc.identifier.citation | Harrou F, Madakyaru M, Sun Y (2016) Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2016.7849878. | |
dc.identifier.doi | 10.1109/SSCI.2016.7849878 | |
dc.identifier.uri | http://hdl.handle.net/10754/623873 | |
dc.description.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. | |
dc.description.sponsorship | This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.url | http://ieeexplore.ieee.org/document/7849878/ | |
dc.rights | (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. | |
dc.subject | Computational modeling | |
dc.subject | Data models | |
dc.subject | High definition video | |
dc.subject | Measurement | |
dc.subject | Monitoring | |
dc.subject | Predictive models | |
dc.subject | Probability distribution | |
dc.title | Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring | |
dc.type | Conference Paper | |
dc.contributor.department | Applied Mathematics and Computational Science Program | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Statistics Program | |
dc.identifier.journal | 2016 IEEE Symposium Series on Computational Intelligence (SSCI) | |
dc.conference.date | 2016-12-06 to 2016-12-09 | |
dc.conference.name | 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 | |
dc.conference.location | Athens, GRC | |
dc.eprint.version | Post-print | |
dc.contributor.institution | Department of Chemical Engineering, Manipal Institute of Technology, Manipal University, India | |
kaust.person | Harrou, Fouzi | |
kaust.person | Sun, Ying | |
kaust.grant.number | OSR-2015-CRG4-2582 | |
refterms.dateFOA | 2018-06-14T05:27:29Z | |
dc.date.published-online | 2017-02-16 | |
dc.date.published-print | 2016-12 |
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