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We present a generic procedure for diagnosing faults using features extracted from noninvasive machine signals, based on supervised learning techniques to build the fault classifiers. An important novelty of our research is the use of 2000 examples of vibration signals obtained from operating faulty motor pumps, acquired from 25 oil platforms off the(More)
—This paper presents vibration analysis techniques for fault detection in rotating machines. Rolling-element bearing defects inside a motor pump are the object of study. A dynamic model of the faults usually found in this context is presented. Initially a graphic simulation is used to produce the signals. Signal processing techniques, like frequency(More)
—We extend the visualization technique of high-dimensional patterns conceived by Sammon to the case when the patterns have been previously mapped to an implicitly defined Hilbert feature space in which distances can be measured by kernels. The principal benefit of our technique is the possibility to gain insight into the distribution of the patterns, even(More)
—This paper presents the results achieved by fault classifier ensembles based on supervised learning for diagnosing faults on oil rigs motor pumps. The main goal is to apply two feature-based ensemble construction methods to a real-world problem. Recent studies have shown that the use of ensembles of classifiers that are accurate and at the same time have(More)