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

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

A Comparison of Two Feature-Based Ensemble Methods for Constructing Motor Pump Fault Diagnosis Classifiers

This paper presents the results achieved by fault classifier ensembles based on a model-free supervised learning approach for diagnosing faults on oil rigs motor pumps. The main goal is to compare two feature-based ensemble construction methods, and present a third variation from one of them. The use of ensembles instead of single classifier systems has been widely applied in classification problems lately. The diversification of classifiers performed by the methods presented in this work is obtained by varying the feature set each classifier uses, and also at one point, alternating the intrinsic parameters for the training algorithm. We show results obtained with the established genetic algorithm GEFS and our recently developed approach called BSFS, which has a lower computational cost. We rely on a database of real data, with 2000 acquisitions of vibration signals extracted from operational motor pumps. Our results compare the outcomes from the two methods mentioned, and present a modification in one of them that improved the accuracy, reinforcing the motivation for the usage of that method

Constructing feature-based ensemble classifiers for real-world machines fault diagnosis

This paper presents the results achieved by fault classifier ensembles based on supervised learning for diagnosing faults on oil rigs motor pumps. The main goal is to apply two feature-based ensemble construction methods to a real-world problem. Recent studies have shown that the use of ensembles of classifiers that are accurate and at the same time have diversifying results can improve the final classification accuracy, compared to a single accurate classifier. The diversification performed by the methods presented in this work is obtained by varying the feature set each classifier uses. We show results obtained with the established genetic algorithm GEFS and a recently developed approach called BSFS, which has lower computational cost. We rely on a database of real data, with 2000 acquisitions of vibration signals extracted from operational motor pumps. Our results show that the ensemble methods had a higher classification accuracy solving a real-world fault diagnosis problem than single classifiers. Also, we present promising results in our experiments with both algorithms, that successfully solves the problem