NON-INVASIVE TECHNIQUE FOR IDENTIFYING BEARING FAULTS IN INDUCTION MOTOR

This report presents the analysis of results from laboratory work carried out for identifying bearing faults of an induction motor which plays a crucial role in many processes inside any sector or premises. The mechanical part of the induction motor does require some extra care especially the bearing of the induction motor and the bearing fault are the major contributor for overall faults occurred in induction motor. Thus, a non-invasive technique for identifying the bearing faults inside the induction motor is important. The objective of this project is to study and analyze the differences of the stator current waveform between a healthy bearing and non-healthy bearing of an induction motor. The Motor Current Signature Analysis (MCSA) is the most widely method used in detecting various faults in induction motor. By analyzing the stator current signature from the induction motor, the fault characteristic for bearing fault can be identified. The experiment is done by comparing healthy and faulty bearing of the induction motor. The fault characteristic for the bearing fault can be further extracted by using some digital signal processing technique such as the Fast Fourier Transform (FFT) and Wavelet Transform (WT). The differences of both healthy and faulty signature current waveform area able to be identified and are further analyzed. In conclusion, the MCSA is a powerful tool for early detection of bearing fault in induction motor.