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 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 languageEnglish
Title of host publication2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042401
DOIs
Publication statusPublished - 09-02-2017
Event2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece
Duration: 06-12-201609-12-2016

Conference

Conference2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
CountryGreece
CityAthens
Period06-12-1609-12-16

Fingerprint

Hellinger Distance
Nonlinear Least Squares
Process Monitoring
Nonlinear Process
Partial Least Squares
Process monitoring
Probability distributions
Anomaly Detection
Projection
Monitoring
Correlated Data
Distance Metric
Multivariate Data
Dissimilarity
Reactor
Quantify
Probability Distribution
Modeling
Partial least squares
Nonlinear process

All Science Journal Classification (ASJC) codes

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

Cite this

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.. https://doi.org/10.1109/SSCI.2016.7849878
Harrou, Fouzi ; Madakyaru, Muddu ; Sun, Ying. / Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring. 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers Inc., 2017.
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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

Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring. / Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying.

2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7849878.

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

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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.

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