Fault Diagnosis on Induction Motor using Machine Learning and Signal
  Processing
Muhammad Samiullah Hasan Ali Shehryar Zahoor Anas Ali
The detection and identification of induction motor faults using machine
learning and signal processing is a valuable approach to avoiding plant
disturbances and shutdowns in the context of Industry 4.0. In this work, we
present a study on the detection and identification of induction motor faults
using machine learning and signal processing with MATLAB Simulink. We developed
a model of a three-phase induction motor in MATLAB Simulink to generate healthy
and faulty motor data. The data collected included stator currents, rotor
currents, input power, slip, rotor speed, and efficiency. We generated four
faults in the induction motor: open circuit fault, short circuit fault,
overload, and broken rotor bars. We collected a total of 150,000 data points
with a 60-40% ratio of healthy to faulty motor data. We applied Fast Fourier
Transform (FFT) to detect and identify healthy and unhealthy conditions and
added a distinctive feature in our data. The generated dataset was trained
different machine learning models. On comparing the accuracy of the models on
the test set, we concluded that the Decision Tree algorithm performed the best
with an accuracy of about 92%. Our study contributes to the literature by
providing a valuable approach to fault detection and classification with
machine learning models for industrial applications.