Efficient Frequency Demodulation in Detecting Various Bearing Defects using Stator Current with Wavelet De-noising

Induction motor is one of the widely used motors in industrial applications. This has become more prominent because of its safety, reliability, easy speed control and its performance. Though being highly efficient, it may be affected with various types of faults which can lead to complete breakdown of the motor. Since early fault detection makes it possible to take appropriate action to avoid an induction motor from breaking down, it is crucial to monitor the status of these motors. Among all the types of faults occurring in an induction motor, bearing faults takes the highest place. Therefore, in this work, detection of bearing faults in an induction motor using stator current signature analysis is proposed. Several frequency demodulation techniques, such as Hilbert transform, synchronous demodulation, and adaptive demodulation techniques, along with an efficient de-noising is included in order to improve the results, will be used to address the main drawbacks of conventional current signature analysis. Various categories of bearing faults will be tested for accurate fault detection along with different fault magnitudes. The proposed work will be examined on a 2 HP induction motor using MATLAB programming.