Abstract
This paper presents the detection of rotor bar and bearing failures in an induction motor using stator current signal analysis (MCSA). Recently, electric motors have become very popular thanks to their reasonable price and reliability. They have been used in many critical control applications such as rolling mills, inverter compressors, pumps, and blowers. Monitoring engine performance can significantly reduce maintenance costs in early detection of faults. In this study, MCSA is applied to electric motors to detect defective rotor bar and bearing failures. Diagnosis of a defective rotor bar and bearing failure in squirrel cage induction motors was studied under full load condition and was tested by power spectral density analysis of stator current using Arduino data acquisition card and signal processing tool in LabView software. The results show that this method is very effective and useful for diagnosing rotor and bearing failures.
This paper presents the detection of rotor bar and bearing failures in an induction motor using stator current signal analysis (MCSA). Recently
electric motors have become very popular thanks to their reasonable price and reliability. They have been used in many critical control applications such as rolling mills
inverter compressors
pumps
and blowers. Monitoring engine performance can significantly reduce maintenance costs in early detection of faults. In this study
MCSA is applied to electric motors to detect defective rotor bar and bearing failures. Diagnosis of a defective rotor bar and bearing failure in squirrel cage induction motors was studied under full load condition and was tested by power spectral density analysis of stator current using Arduino data acquisition card and signal processing tool in LabView software. The results show that this method is very effective and useful for diagnosing rotor and bearing failures.