Journal
IEEE SENSORS JOURNAL
Volume 19, Issue 23, Pages 11697-11705Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2019.2936520
Keywords
Anomaly detection; data mining algorithm; unsupervised monitoring; distillation column systems
Funding
- King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) [OSR-2019-CRG7-3800]
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Fault detection in industrial systems plays a core role in improving their safety, productivity and avoiding expensive maintenance. This paper proposed and verified 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.
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