Open AccessFault diagnosis of an induction motor using data fusion based on neural networks
Author(s) -
Jorkesh Saeid,
Poshtan Javad
Publication year - 2021
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/smt2.12068
Subject(s) - robustness (evolution) , artificial neural network , induction motor , stator , control theory (sociology) , computer science , sensor fusion , engineering , pattern recognition (psychology) , artificial intelligence , voltage , electrical engineering , biochemistry , chemistry , control (management) , gene
In this paper, neural network‐based data fusion is used to detect fault and isolate stator winding short circuit, outer bearing race, and broken rotor bar defects in an induction motor. In addition, the robustness of the proposed method against the disturbance introduced by the coupled pump's unbalanced power source and dry running is investigated. First, three‐phase current and voltage signals are separated by means of independent component analysis (ICA), then extracted features are combined by adopting neural networks, and finally, the system's health condition is evaluated. Experimental results indicate that data fusion based on neural networks can evaluate with high reliability the system's health condition and provide better robustness in the presence of disturbances.