Paper Title
Fault Detection of Induction Motor through the Analysis of Stator Current Data

The fault detection of electrical or mechanical anomalies in induction motors has been a challenging problem for researchers over decades to ensure the safety and economic operations of industrial processes. To address this issue, this paper studies the stator current data obtained from three phase induction motor of the healthy and faulty motor with the aim of developing fault detection method for motor fault problems, such as bearing and broken-rotor-bar faults. Stator current data collected from induction motors were analyzed by the principal component analysis (PCA) to obtain principal components of statistical parameters of stator currents. The major principal components are fed as an input to feed forward neural network for classification of faults. This paper gives the application of PCA, ANN and simplicity of the proposed fault detection scheme for fault detection of induction motors. Index terms - Induction Motor, Principal Component Analysis (PCA), Artificial Neural Network