Binary feature selection classifier ensemble for fault diagnosis of submersible motor pump

The main motivation to develop this work is to create a diagnosis system able to facilitate the work of human experts responsible for detecting faults before acquisition of submersible petroleum motor pump systems. A new approach for multiclass learning by reduction to multiple, binary classifiers, in a one-versus-one scheme, is presented as an alternative artificial intelligence solution to diagnose faults. Such an idea is based on the hypothesis that each pair of process conditions has different optimal feature sets to improve the classification performance. Thus, features are selected from datasets containing only two classes. Then, classifiers are trained with the selected features. The combination uses the average confidence of each classifier pair prediction to calculate the ensemble answer. Experimental results show that the proposed approach improves classification performance in a statistically significant way, when compared with correlated work. A secondary contribution is the analysis of the most difficult fault to be identified, namely rubbing.