Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

Fouzi Harrou; Muddu Madakyaru; Ying Sun

doi:10.1109/SSCI.2016.7849878

Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

Fouzi Harrou, Muddu Madakyaru, Ying Sun

Department of Chemical Engineering, Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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 language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509042401
DOIs	https://doi.org/10.1109/SSCI.2016.7849878
Publication status	Published - 09-02-2017
Event	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece Duration: 06-12-2016 → 09-12-2016

Conference

Conference	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/Territory	Greece
City	Athens
Period	06-12-16 → 09-12-16

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems and Management
Control and Optimization
Artificial Intelligence

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10.1109/SSCI.2016.7849878

Cite this

@inproceedings{6365cf00d88544759038fa3b888baeee,

title = "Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring",

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.",

author = "Fouzi Harrou and Muddu Madakyaru and Ying Sun",

year = "2017",

month = feb,

day = "9",

doi = "10.1109/SSCI.2016.7849878",

language = "English",

booktitle = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

}

Harrou, F, Madakyaru, M & Sun, Y 2017, Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring. in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016., 7849878, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 06-12-16. https://doi.org/10.1109/SSCI.2016.7849878

TY - GEN

T1 - Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

AU - Harrou, Fouzi

AU - Madakyaru, Muddu

AU - Sun, Ying

PY - 2017/2/9

Y1 - 2017/2/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85016073168&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016073168&partnerID=8YFLogxK

U2 - 10.1109/SSCI.2016.7849878

DO - 10.1109/SSCI.2016.7849878

M3 - Conference contribution

AN - SCOPUS:85016073168

BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Y2 - 6 December 2016 through 9 December 2016

ER -

Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

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