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.