Automatic diagnosis of submersible motor pump conditions in offshore oil exploration

We present a system for the detection and diagnosis of faults of a high performance electric submersible pump used in deep water oil exploration. During the installation phase 36 accelerometers acquire vibrational patterns under various load conditions. The machine condition is labeled with the help of human experts. The training set is submitted to an automatic model-free learning system based on Bayesian belief networks and compared to a reference Support Vector Machine classifier. Experiments are presented for three different condition classes, using sophisticated statistical evaluation methodologies to measure the classifier performance.