Using Artificial Neural Network to Monitor and Predict Induction Motor Bearing (imb) Failure

The purpose of this paper is to develop an appropriate artificial neural network (ANN) model of induction motor bearing (IMB) failure prediction. Acoustic emission (AE) represented the technique of collecting the data that was collected from the IMB and this data were measured in term of decibel (dB) and Distress level. The data was then used to develop the model using ANN for IMB failure prediction model. An experimental rig was setup to collect data on IMB by using Machine Health Checker (MHC) Memo assist with MHC Analysis software. In the development of ANN modeling, two networks were tested; Feedforward Neural Network (FFNN) and Elman Network for the performance of training, validation and testing with training algorithm, Levenberg-Marquardt Back-propagation and the suitable transfer function for hidden node and output node was logsig/purelin combination. The results show the performance of Elman network was good compared to FFNN to predict the IMB failure.