Abstract
Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs.
Highlights
In recent years, researches on the condition monitoring and health prognosis of electrical equipment are drawing more and more concerns
Rotor Faults Diagnosis Based on motor current signature analysis (MCSA). It can be observed in Equation (1) that the fifth and seventh harmonics have larger magnitudes, and the rotor fault monitoring based on the detection of fault components produced by the fundamental, the fifth- and seventh-time and space harmonics with the frequencies of (1 ± 2s)f 1, (5 ± 2s)f 1, (5 − 4s)f 1, (5 − 6s)f 1, (7 ± 2s)f 1, (7 − 6s)f 1, and (7 − 8s)f 1 is significantly advantageous
To evaluate the performance of the proposed stator fault diagnosis method, the flux should be observed for further torque calculation
Summary
Researches on the condition monitoring and health prognosis of electrical equipment are drawing more and more concerns. Many signal spectrum analysis techniques [16,20,21,22] based on time domain, frequency domain, and time–frequency domain of motor current signature analysis (MCSA). The fault diagnosis method based on the support vector machine (SVM) is employed in [37,38] for motor faults. When the pulse width modulation (PWM) voltage is supplied to a motor, current harmonics can be used to characterize faults in IMs under stator and rotor faults.
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