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Diagnosing Multiple Faults in Oil Rig Motor Pumps Using Support Vector Machine Classifier Ensembles

Wandekokem, Estefhan D., Mendel, Eduardo, Fabris, Fabio, Valentim, Marcelo, Batista, Rodrigo J., Varejao, Flavio M., Rauber, Thomas W. (2011) Diagnosing Multiple Faults in Oil Rig Motor Pumps Using Support Vector Machine Classifier Ensembles. In: Integrated Computer-Aided Engineering. 18 (1). pp. 61-74. IOS Press, Amsterdam, The Netherlands, The Netherlands (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dl.acm.org/citation.cfm?id=2019531.2019538

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.

Item Type: Conference or workshop item (Poster)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: F. Fabris
Date Deposited: 08 Dec 2013 14:03 UTC
Last Modified: 29 May 2019 11:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37375 (The current URI for this page, for reference purposes)
Fabris, Fabio: https://orcid.org/0000-0001-7159-4668
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