title: Detection of broken rotor bar faults in induction motor at low load using neural network.
abstract: The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions.
authors.affiliation: LMSE Laboratory, Department of Electrical Engineering, University of Biskra, Algeria. Electronic address: bessambesma@yahoo.fr.
authors.forename: B
authors.initials: B
authors.lastname: Bessam
authors.affiliation: LGEB Laboratory, Department of Electrical Engineering, University of Biskra, Algeria. Electronic address: menacer_arezki@hotmail.com.
authors.forename: A
authors.initials: A
authors.lastname: Menacer
authors.affiliation: LMSE Laboratory, Department of Electrical Engineering, University of Biskra, Algeria. Electronic address: m.boumehraz@univ-biskra.dz.
authors.forename: M
authors.initials: M
authors.lastname: Boumehraz
authors.affiliation: LGEB Laboratory, Department of Electrical Engineering, University El-Oued, Algeria. Electronic address: hakimahakima5@gmail.com.
authors.forename: H
authors.initials: H
authors.lastname: Cherif
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016208:Databases, Factual; D019544:Equipment Failure Analysis / Q000379:methods*; D005583:Fourier Analysis; D000069550:Machine Learning; D016571:Neural Networks, Computer
keywords: Broken bars; Diagnosis; Hilbert transform; Induction motor; Neural network