A machine learning approach to perform automatic detection and diagnosis of faults of electrical submersible pump systems is presented. Several thousand vibration patterns were acquired from vertically distributed accelerometers along the string of motors, pumps and protectors. Intermediate features are extracted from the raw vibration signals originating from the set of accelerometers. Each pattern was labelled by a human expert to provide ground truth with respect to the different operation classes (normal, sensor fault, rubbing, unbalance or misalignment). A software framework is used to compare several classifier architectures (K-Nearest-Neighbor, Random Forest, Support Vector Machine, Naïve Bayes and Decision Trees) in a bias aware performance evaluation. In order to boost the classification performance, an ensemble of different versions of a classifier architecture is constructed using the Decision Templates fusion function. The robustness of the system with respect to the emergence of new faults (i.e., untreated faults so far) is corroborated by a systematic analysis methodology.
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