RECURRENT NEURAL NETWORK (RNN) BASED BEARING FAULT CLASSIFICATION OF INDUCTION MOTOR EMPLOYED IN HOME WATER PUMP SYSTEM

Abstract

In home appliances, the water pump is used to supply the water from a room to the other rooms. Defects of the water pump are not distributing the water, inner stator winding short circuit, and bearing failure. In this paper, bearing fault detection of induction motor (IM) used in home water pump system is developed by using recurrent neural network (RNN) method. It is difficult to detect fault bearing of IM using a mathematical model. So that, a recurrent neural network (RNN) method is applied to solves this problem. These bearing faults classifications are based on IM stator current waveform. Bearing fault types are all normal (AN), front fault (FF), rear fault (RF), and all fault (AF). While, the detection process consist of three step. They are taking bearing fault data, features extraction, and RNN fault detection. The bearing fault data is taken from the stator currents of IM by using soundcard oscilloscope software. Second step is features extraction process to obtain more bearing fault signs. In this step, stator currents of IM is converted from time domain into frequency domain by using Fast Fourier Transform (FFT). Last stage is RNN model to clasify the bearing fault of IM. The effectiveness of proposed RNN method is clarified by using four bearing fault types.