Open AccessFault Detection Analysis for Three Phase Induction Motor Drive System using Neural Network
Author(s) -
N. A. Mohar,
Ernie Che Mid,
S. M. Suboh,
N. H. Baharudin,
Nor Baizura Ahamad,
Nazaruddin Abdul Rahman,
Eliyana Ruslan,
D. A. Hadi
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 - 10.1088/1742-6596/1878/1/012039
Subject(s) - induction motor , fault (geology) , artificial neural network , reliability (semiconductor) , voltage , process (computing) , fault detection and isolation , engineering , computer science , fault indicator , reliability engineering , component (thermodynamics) , control engineering , control theory (sociology) , artificial intelligence , power (physics) , electrical engineering , physics , control (management) , quantum mechanics , seismology , geology , actuator , thermodynamics , operating system
One of the most important components of the industrial process is known to be the three-phase induction motor. This device, however, is prone to electrical and mechanical faults, which may cause a substantial component or financial losses. The fault analysis received growing attention due to a need to increase reliability and to decrease potential output loss due to machine breakdown. Thus, the purpose of this paper is to present a simple and reliable fault analysis based on the Neural Network (NN) is proposed. The NN method is a simpler approach without a diagnostic professional to review data and diagnose issues. Various fault disputes of induction motor are developed and analysed using the NN method. The main types of faults considered are over-voltage, under-voltage, and unbalanced voltage faults. The trained network is tested with simulated fault current and voltage data.