IEEE Transactions on Industry Applications · 2019 · 58 citations · 22 references
EngineeringElectrical EngineeringElectric MachinesInstrumentationPower ElectronicsDiagnosticsFault DetectionFault DiagnosisInduction MachinesMagnetic SensorsSynchronous MachinesExperimental DiagnosticsHarmonicsIncipient Fault DetectionMachine Fault DiagnosisMotor Fault DiagnosisPearson Correlation CoefficientShort-circuit Fault DetectionStator Winding Faults
This paper presents a statistical methodology for detection of inter-turn short-circuit fault in asynchronous and synchronous machines. This methodology uses a correlation coefficient obtained from external magnetic field measured in the machine vicinity. It is a noninvasive method which follows up the signals of two external flux sensors located symmetrically around the machine axis (180° spatially shifted). The principle is based on the calculation of the Pearson correlation coefficient between two signals delivered by two sensors S1 and S2 when the machine operates at different load conditions, which allows us to detect incipient faults in electrical induction and synchronous machines with a high probability of detection. Experimental tests are realized using two specific rewound machines to create inter-turn short-circuit faults with different severity levels.
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