Feature extraction in an ensemble of multiple local classifiers for fault diagnosis in industrial processes

Abstract

Abstract This work proposes a classification model for fault diagnosis. In a first stage, an unsupervised clustering algorithm discovers groups of potential fault classes. Subsequently, a specialized classifier is trained for each of the clusters, thus diminishing complexity and augmenting performance. The quality of the diagnosis is further improved by combining the classifiers in an ensemble. As a benchmark, data provided by the Tennessee Eastman chemical plant simulator was used, with promising results.