Title: Diagnosis of fusion defects an electromechanical training system (SEEM) through analysis of current: experimental approaches

Authors: Samir Kerfali; Slimane Bouras; Abdelkarim Bouras

Addresses: Electromechanical Engineering Laboratory, Department of Electromechanical, Badji Mokhtar-Annaba University, P.O. Box 12, 23000 Annaba, Algeria ' Electromechanical Engineering Laboratory, Department of Electromechanical, Badji Mokhtar-Annaba University, P.O. Box 12, 23000 Annaba, Algeria ' Department of Electromechanical, Electromechanical Systems Laboratory, BadjiMokhtar-Annaba University, P.O. Box 12, 23000 Annaba, Algeria

Abstract: This experimental work aims to diagnose multiple mechanical failures such as the mass imbalance, misalignment and wear of gear teeth. For large industrial electromechanical drives systems, these mechanical abnormalities are more virulent after those bearings which are generally the main cause. Traditionally, to monitor and diagnose these failures in transmissions, the analysis of the most discussed vibration and acoustic signals were mostly in use. However, the innovative approach of this publication offers a combination of MCSA currents of the stator and neutral phase with the visual interpretation of patterns extracted from the 3D representation of the currents phasic square. The experimental validation was carried out on an induction motor of 3 kW a reducer coupled to three levels.

Keywords: induction motors; unbalance faults; MCSA; motor current signature analysis; 3D pattern recognition; fault diagnosis; fusion defects; electromechanical training systems; current analysis; mechanical failures; mass imbalance; tooth misalignment; tooth wear; gear teeth; electromechanical drives.

DOI: 10.1504/IJAAC.2016.076456

International Journal of Automation and Control, 2016 Vol.10 No.2, pp.142 - 154

Received: 25 May 2015
Accepted: 25 Jan 2016
Published online: 09 May 2016 *

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