An essential research objective in artificial vision are shape &sctipto~ which are invariant for mslation, scale changes and rotations of a bidimensional pattem. k varj-ety of approaches has proved the capacity to characterize
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 derive the Bhattacharyya distance between two Dirichlet densities. As an application we use image segmentation by a Split-and-Merge algorithm.
—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)
This paper presents vibration analysis techniques for fault detection in rotating machines. Rolling element bearing defects inside a motor pump are the subject of study. Signal processing techniques, like frequency filters, Hilbert transform , and spectral analysis are used to extract features used later as a base to classify the condition of machines.… (More)