Metric Learning for Electrical Submersible Pump Fault Diagnosis

Machine learning classification algorithms are highly dependent of a dataset composed of high-level features. In this paper, a deep learning approach is combined with traditional machine learning classifiers in order to circumvent the need of a specialist for extracting relevant features from one dimensional frequency-domain vibration signals. Our approach relies on a convolutional architecture trained with a triplet loss function for extracting relevant features directly from the raw data. A previously hand-crafted feature set, created by a specialist over the course of many years of research, is compared with the newly extracted feature set. Six conventional classifiers models (K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Quadratic Discriminant Analysis and Naive Bayes) are trained in both features set separately and compared in terms of macro F-measure. Results shows statistical evidence towards to the acceptance that the extracted feature set is as good as or better than the hand-crafted feature set, for classification purposes.