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Permanent magnet synchronous machines (PMSMs) drives are attractive and have been used in several applications, because of their noteworthy advantages. In some applications, the continuous operation is necessary, and then the PMSM drive outage is unacceptable or may cause great losses. Thus, several studies have been accomplished in order to detect incipient faults in PMSMs. In this context, the electrical signature analysis (ESA) technique is highlighted, because of the feasibility and non-invasive features. ESA allows the fault detection by only analyzing the electrical machine quantities. This paper proposes a study of ESA for PMSM condition monitoring. The review of some fault patterns is presented as well as the development of a scale model laboratory to simulate faults in a real PMSM in operation. The PMSM is used to drive a fluid pumping system, and different types of fluids are tested, being different load conditions from the PMSM point of view. The presented results are promising, encouraging ESA based methodologies for PMSM fault detection. [ABSTRACT FROM AUTHOR]

Electrical signature analysis (ESA) has been successfully applied to predictive maintenance of synchronous machines. The fault diagnosis is performed by analyzing failure patterns in the current or voltage spectra, which allow the discrimination of a healthy and a faulty condition on the monitored machine. Generally, the failure patterns are a function of the line frequency, rotor rotation frequency, and some structure features of the machine. The rotor rotation frequency pattern, for instance, is indicative of rotor mechanical problems and rotor winding interturn short-circuit. An increase of this frequency component magnitude may indicate the incipience of a rotor fault but may not discriminate the nature of the fault (electrical or mechanical). Thus, it is necessary to distinguish the effects of electrical and mechanical faults in these components in order to get a reliable diagnosis of the machine. No works have been found in the literature approaching this specific issue. This paper proposes a simple and innovative methodology to distinguish the effect of electrical and mechanical faults in the mentioned ESA failure patterns based on the method of symmetrical components. Its effectiveness is validated by experiments performed on a synchronous generator test rig. The proposed condition monitoring system is simple, low cost, and low intrusive, as it only relies on stator electrical quantities. Moreover, it is in operation in a Brazilian 404-MW thermal power station. [ABSTRACT FROM PUBLISHER]

Early detection of faults in electrical machines, particularly in induction motors, has become necessary and critical in reducing costs by avoiding unexpected and unnecessary maintenance and outages in industrial applications. Additionally, most of these faults are due to problems in bearings. Thus, in this paper, experimental bearing fault detection of a three-phase induction motor is performed by analyzing the squared envelope spectrum of the stator current. Spectral kurtosis-based algorithms, namely, the fast kurtogram and the wavelet kurtogram, are also applied to improve the envelope analysis. Experimental tests are performed, considering outer bearing faults at different stages, and the results are promising. [ABSTRACT FROM PUBLISHER]