Automatic feature definition and selection in fault diagnosis of oil rig motor pumps

We present a collection of pattern recognition techniques applied to fault detection and diagnosis of motor pumps. Vibrational patterns are the basis for describing the condition of the process. We rely on the data-driven approach to the learning of the fault classes, i.e. supervised learning in pattern recognition. Our work is motivated by the diversity of the studied defects, the availability of real data from operational oil rigs, and the use of statistical pattern recognition techniques usually not explored sufficiently in similar works. We show the results of automatic methods to define, select and combine features that describe the process and to classify the faults on the provided examples. The support vector machine is chosen as the classification architecture.