Active learning for new-fault class sample recovery in electrical submersible pump fault diagnosis

Abstract

Applying effective supervised learning methods is difficult when dealing with complex industrial equipment such as Electrical Submersible Pump as the labeling work is costly and requires the dedication of an expert in the field. Moreover, the appearance of a new, unknown fault may require more time from the expert, not only for labeling the data but also for an arduous search for samples of this new type of fault. The solution developed in this paper uses uncertainty-based active learning for aiding experts through intelligent fault diagnosis and the search for potential samples of a new type of fault. The proposed approach was tested with a classical classification algorithm (random forest) applied to features extracted from the vibration signals. Two different feature extraction methods were investigated and contrasted: one using features defined by the expert and another using automatic feature extraction by a deep learning procedure. The algorithms were trained and tested on a dataset composed of 5616 vibration signals labeled in the frequency domain. The results show enough evidence to claim, at a significance value of 5%, that the active learning procedure proposed in this paper outperforms active learning based on random search. A new acquisition strategy for active learning is proposed to aid experts to find potential samples of a new fault. The recovered samples of the new fault aid improving performance of feature extractors based on deep learning since this type of method is highly affected by imbalanced data.