Ensemble of metric learners for improving electrical submersible pump fault diagnosis

Abstract

Machine learning classification algorithms play a major role in diagnosing faults in industrial equipment. In this paper, we investigate the use of ensembles composed of deep neural networks for improving the electrical submersible pump fault diagnosis results. The proposed method relies on composing an ensemble of multiple convolutional neural networks trained with a metric function for extracting relevant features directly from the raw data. The final classification is given by a standard voting scheme after a Random Forest is trained for each feature set generated by each deep metric neural network and then the methods are compared with two previous methods already used for the electrical submersible pump fault diagnosis. The experiments were carried out using five different metric functions: Proxy-Anchor loss, CosFace loss, Triplet loss, Lifted Structured loss and Contrastive loss. Results show statistical evidence that the new approach using ensemble methods achieves better performance than the previous solutions. Moreover, results indicate that composing an ensemble of multiple distinct metric losses achieves a high macro F-measure with low variance when compared to an ensemble where all neural networks are trained with the same metric loss.