Application of statistical neuronal networks for diagnostics of induction machine rotor faults

• Published in: STA : proceedings : 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering : December 19-21, 2016, Sousse, Tunisia pp. 199 - 204
• Main Authors: Marmouch, Sameh, Aroui, Tarek, Koubaa, Yassine
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2016
• Subjects: analysis motor current signature analysis; Bars; Biological neural networks; induction machines; Induction motors; Neurons; probabilistic neuronal network; Radial basis function networks; radial basis functions neuronal network; Rotors; Training
• DOI: 10.1109/STA.2016.7952063

Induction machines are extensively used in industries and are subject to unexpected breakdowns. It is necessary, therefore, to prevent them from such breakdown through the maintenance that works according to a well-trained planning. A considerable number of diagnosis techniques have been used such as Motor Current Signature Analysis (MCSA), Axial Flux Monitoring and Vibration Monitoring. This paper shows the effectiveness of the artificial neuronal network (radial basis function neuronal network and the probabilistic neuronal network) basis on MCSA for rotor faults diagnosis.