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A nonlinear support vector machine‐based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process
Author(s) -
Onel Melis,
Kieslich Chris A.,
Pistikopoulos Efstratios N.
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16497
Subject(s) - support vector machine , feature selection , artificial intelligence , fault detection and isolation , pattern recognition (psychology) , benchmark (surveying) , fault (geology) , computer science , feature (linguistics) , feature extraction , nonlinear system , data mining , engineering , machine learning , linguistics , philosophy , physics , geodesy , quantum mechanics , seismology , geology , actuator , geography
In this article, we present (1) a feature selection algorithm based on nonlinear support vector machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for the Tennessee Eastman benchmark process. The presented feature selection algorithm is derived from the sensitivity analysis of the dual C ‐SVM objective function. This enables simultaneous modeling and feature selection paving the way for simultaneous fault detection and diagnosis, where feature ranking guides fault diagnosis. We train fault‐specific two‐class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy and perform the fault diagnosis. Our results show that the developed SVM models outperform the available ones in the literature both in terms of detection accuracy and latency. Moreover, it is shown that the loss of information is minimized with the use of feature selection techniques compared to feature extraction techniques such as principal component analysis (PCA). This further facilitates a more accurate interpretation of the results. © 2018 American Institute of Chemical Engineers AIChE J , 65: 992–1005, 2019