Traditional techniques for monitoring of rotating equipment have relied on observations of periodically measured dynamic sampled response measurements such as vibration. Plenty of literature is available on rotating equipment monitoring using vibration analysis techniques, the most popular among them being frequency (Fourier, Hilbert, Cepstrum and Bispectrum) analysis and time-frequency (STFT, wavelet) analysis. Rotating equipments have also been monitored based on the performance of the electrical motor using current measurements. Similar to vibration analysis, motor current signature analysis (MCSA) also employ signal processing techniques like those mentioned above. But, there is not much literature on a combined electrical and mechanical fault detection technique for rotating equipments. For instance, it is well known that bearing faults induce eccentricities in both current and vibration signals but attempts to monitor the fault have relied on only either one. This paper describes a novel time-frequency signal processing technique along with a hybrid electro-mechanical approach towards monitoring of rotating equipment.
This method is evaluated on two distinct testbeds; one is an experimental setup in which the characteristic vibration and current signals were observed for a lab scale induction motor under normal and fault conditions. The other testbed is an electromechanical model of a typical pump and compressor, similar to those found on offshore oil and gas platforms. Electrical (broken rotor bar, stator winding) and mechanical (bearing) fault are introduced into this model and the characteristics observed. Using measurement data from these testbeds, the signal processing technique is used to monitor the signals and ascertain the condition of the equipment. The results of the combined electro-mechanical monitoring scheme is compared with monitoring either vibration or current signals alone. The proposed scheme is also benchmarked against other signal processing technique currently available in literature.
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