Abstract
We present a generic procedure for diagnosing faults using features extracted from noninvasive machine signals, based on supervised learning techniques to build the fault classifiers. An important novelty of our research is the use of 2000 examples of vibration signals obtained from operating faulty motor pumps, acquired from 25 oil platforms off the Brazilian coast during five years. Several faults can simultaneously occur in a motor pump. Each fault is individually detected in an input pattern by using a distinct ensemble of support vector machine (SVM) classifiers. We propose a novel method for building a SVM ensemble, based on using hill-climbing feature selection to create a set of accurate, diverse feature subsets, and further using a grid-search parameter tuning technique to vary the parameters of SVMs aiming to increase their individual accuracy. Thus our ensemble composing method is based on the hybridization of two distinct, simple techniques originally designed for producing accurate single SVMs. The experiments show that this proposed method achieved a higher estimated prediction accuracy in comparison to using a single SVM classifier or using the well-established genetic ensemble feature selection (GEFS) method for building SVM ensembles.