Broken rotor bar fault detection in inverter-fed squirrel cage induction motors using stator current analysis and fuzzy logic

This paper presents the implementation of broken rotor bar fault detection in an inverter-fed induction motor using motor current signal analysis (MCSA) and prognosis with fuzzy logic. Recently, inverter-fed induction motors have become very popular because of their adjustable speed drive. They have been used in many vital control applications such as rolling mills, variable speed compressors, pumps, and fans. The condition monitoring of these motors can significantly reduce the cost of maintenance in the early detection of faults. In this study, MCSA is applied to an inverter-fed induction motor to detect broken rotor bar faults. The diagnosis of a broken rotor bar fault in the squirrel cage induction motor, driven by an inverter, has been studied for stable, full load condition and has been carried out experimentally by analyzing the power spectrum density of the motor stator current. Motor stator currents are uploaded to a PC with the software of the inverter used and the current harmonics are obtained using LabVIEW for every fault condition. After extracting the characteristic frequencies of the broken rotor bar failure, a fuzzy logic algorithm is implemented for classifying the fault. Although there is much research on rotor bar faults for line-connected induction motors, there are no studies on the inverter-fed induction motor and fault diagnosis with fuzzy logic. The implementation results showed that the method is very efficient and useful for prognosis of the rotor faults.