Single and Ensemble Fault Classifiers Based on Features Selected by Multi-Objective Genetic Algorithms

Abstract

The problem of identifying faults in systems and processes can be formulated as a problem of partitioning objects (i.e., the measured data patterns representing the symptoms) into classes (i.e., the types of faults causing the symptoms). In this view, two main steps need to be carried out in order to effectively perform the fault identification: i) the selection of the features carrying information relevant for the identification; ii) the classification of the measured data patterns of features into the different fault types. In this work, the two tasks are tackled by combining a multi-objective genetic algorithm search with a Fuzzy K-Nearest Neighbors classification. Two different approaches to the development of the fault classification model are considered: a single classifier based on a feature subset chosen a posteriori on the Pareto-front identified by the multi-objective genetic search and an ensemble of classifiers, each one built on a different feature subset taken from the genetic algorithm population at convergence. Examples of application of the proposed approaches are given with reference to two different industrial processes: the classification of simulated nuclear transients in the feedwater system of a Boiling Water Reactor and of multiple faults in rotating machinery.