Thomas W. Rauber

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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 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)
We present a supervised learning classification method for model-free fault detection and diagnosis, aiming to improve the maintenance quality of motor pumps installed on oil rigs. We investigate our generic fault diagnosis method on 2000 examples of real-world vibra-tional signals obtained from operational faulty industrial machines. The diagnostic system(More)