Open AccessDiagnosing of Bearing Faults in Induction Motor by Adopting DWT-Based Artificial Neural Network (ANN)
Author(s) -
Haider Lateef Thamer,
Baqer Turki Al-Lamey,
Diyah Al-Thammer
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1773/1/012005
Subject(s) - stator , artificial neural network , induction motor , bearing (navigation) , signal (programming language) , computer science , artificial intelligence , control theory (sociology) , pattern recognition (psychology) , engineering , voltage , electrical engineering , control (management) , programming language
This paper introduces technology for detecting bearing damage to the 3-phase induction machine that is divided into two stages. In the first stage, the stator current signal (iq) is decomposed by adopting (DWT) and extract RMS values of the current signal (iq). The second stage is to enter the external RMS values of the stator current signal (iq) into the trained artificial neural network to detect these defects. With this mechanism, One can protect the induction motor from damage. This is done by using a simulation program simulink/MATLAB2019. The computed results show that better performance can be achieved using such a technique.