An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

Abstract

Induction motors constitute the largest proportion of motors in industry. This type of
motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure.
Stator winding faults account for approximately 36% of these failures. As such, condition monitoring
is used to protect motors from sudden breakdowns. This paper proposes the use of neural networks as an
efficient diagnostic tool for estimating the percentage of stator winding shorted turns in three-phase
induction motors. A MATLAB-based model was developed and simulated under different fault-load
combination cases for different sizes of motors. The motor’s developed electromechanical torque was
selected as a fault indicator. For the design and training of the neural network, the mean, variance, max,
min, and F120 time based on statistical and frequency-related features were found to be very distinct for
correlating the captured electromechanical torque with its corresponding percentage of shorted turns. In
the training phase of the neural network, five different motors were used and are referred to as seen
motors. On the other hand, for testing the efficiency of the developed diagnostic tool, the
electromechanical torque under different fault-load combination cases, previously never seen from the
first five motors and those of two new motors (referred to as unseen), was used. Testing results revealed
accuracy in the range of 88–99%.