Open AccessFaults Diagnosis of BLDC Motors Using Artificial Neural Networks
Author(s) -
Hager Ali Hussain,
Ali Nasser Hussain,
Wathiq Rafia Abed
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1105/1/012003
Subject(s) - artificial neural network , stator , fault (geology) , computer science , dc motor , control engineering , engineering , automotive engineering , artificial intelligence , electrical engineering , seismology , geology
Recently, brushless DC motor (BLDC) has been implemented in many applications, especially critical applications. Due to many reasons, BLDC motor subjects to many types of faults including electrical and mechanical faults, therefore detect and diagnosis faults is very important in order to keep safety to the motor also reduce cost and maintenance. In this paper, an approach has been presented to diagnose the stator winding faults, control circuit switches fault and bearing faults. Artificial Neural Network (ANN) has been applied to diagnose faults at different operation conditions. The simulation result shows the ability of the proposed technique to diagnose faults with high accuracy.