Monitoring distillation column systems using improved nonlinear partial least squares-based strategies

Muddu Madakyaru, Fouzi Harrou, Ying Sun

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Fault detection in industrial systems plays a core role in improving their safety, productivity and avoiding expensive maintenance. This paper proposed and veried data-driven anomaly detection schemes based on a nonlinear latent variable model and statistical monitoring algorithms. Integrating both the suitable characteristics of partial least squares (PLS) and adaptive neural network fuzzy inference systems (ANFIS) procedure, PLS-ANFIS model is employed to allow for flexible modeling of multivariable nonlinear processes. Furthermore, PLS-ANFIS modeling was connected with k-nearest neighbors (kNN)-based data mining schemes and employed for nonlinear process monitoring. Specifically, residuals generated from the PLS-ANFIS model are used as the input to the kNN-based mechanism to uncover anomalies in the data. Moreover, kNN-based exponentially smoothing with parametric and nonparametric thresholds is adopted to better anomaly detection. The effectiveness of the proposed approach is evaluated using real measurements from an actual bubble cap distillation column.
Original languageEnglish (US)
Pages (from-to)11697-11705
Number of pages9
JournalIEEE Sensors Journal
Issue number23
StatePublished - Aug 22 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2019-CRG7-3800
Acknowledgements: The work presented in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800


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