Rolling element bearing fault diagnosis in rotating machines of oil extraction rigs

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. Also, pattern recognition techniques are applied to the obtained features to improve the classification precision. In a previous work, a graphic simulation was used to produce signals to illustrate the idea of the method. In this work we examine the performance of this method for monitoring bearing condition when applied to rotating machines of oil rigs, that is, when applied to real problems.