Fault detection and diagnosis in process data using one-class support vector machines
S Mahadevan, SL Shah - Journal of process control, 2009 - Elsevier
In this paper, a new approach for fault detection and diagnosis based on One-Class Support
Vector Machines (1-class SVM) has been proposed. The approach is based on a non-linear
distance metric measured in a feature space. Just as in principal components analysis
(PCA) and dynamic principal components analysis (DPCA), appropriate distance metrics
and thresholds have been developed for fault detection. Fault diagnosis is then carried out
using the SVM-recursive feature elimination (SVM-RFE) feature selection method. The …
Vector Machines (1-class SVM) has been proposed. The approach is based on a non-linear
distance metric measured in a feature space. Just as in principal components analysis
(PCA) and dynamic principal components analysis (DPCA), appropriate distance metrics
and thresholds have been developed for fault detection. Fault diagnosis is then carried out
using the SVM-recursive feature elimination (SVM-RFE) feature selection method. The …
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