What is it about?
The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs) that use as inputs the results of the spectral analysis using the fast Fourier transform (FFT) of the reduced Park’s vector modulus (RPVM), along with the load values in which the motor operates.
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Why is it important?
The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).
Perspectives
The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).
Ameur Aissa
Universite Amar Telidji Laghouat
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This page is a summary of: Induction motors broken rotor bars detection using RPVM and neural network, COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, March 2019, Emerald,
DOI: 10.1108/compel-06-2018-0256.
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