Application of Neural Networks and Axial Flux for the Detection of Stator and Rotor Faults of an Induction Motor-Reference-Cited by-同舟云学术

Application of Neural Networks and Axial Flux for the Detection of Stator and Rotor Faults of an Induction Motor

Published:2019-06-01 Issue:1 Volume:4 Page:203-215
ISSN:2543-4292
Container-title:Power Electronics and Drives
language:en
Short-container-title:

Author:

Ewert Paweł¹

Affiliation:

1. Department of Electrical Machines, Drives and Measurements , Wrocław University of Science and Technology , Wybrzeże Wyspiańskiego 27, 50-370 Wrocław , Poland .

Abstract

Abstract The paper presents the possibility of using neural networks in the detection of stator and rotor electrical faults of induction motors. Fault detection and identification are based on the analysis of symptoms obtained from the fast Fourier transform of the voltage induced by an axial flux in a measurement coil. Neural network teaching and testing were performed in a MATLAB–Simulink environment. The effectiveness of various neural network structures to detect damage, its type (rotor or stator damage) and damage levels (number of rotor bars cracked or stator winding shorted circuits) is presented.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/pead-2019-0001

Reference20 articles.

1. Bacha, K., Henao, H., Gossa, M. and Capolino, G.-A. (2008). Induction Machine Fault Detection Using Stray Flux EMF Measurement and Neural Network-Based Decision. Electric Power Systems Research, 78(7), pp. 1247–1255.10.1016/j.epsr.2007.10.006

2. Ceban, A., Pusca, R. and Romary, R. (2012). Study of Rotor Faults in Induction Motors Using External Magnetic Field Analysis. IEEE Transactions on Industrial Electronics, 59(5), pp. 2082–2093.10.1109/TIE.2011.2163285

3. Ewert, P. (2017). Use of axial flux in the detection of electrical faults in induction motors. In: 2017 International Symposium on Electrical Machines (SME), IEEE, Naleczow, Poland, 18–21 June 2017, pp. 1–6.10.1109/ISEM.2017.7993571

4. Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E. and Hedayati-Kia, S. (2014). Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques. IEEE Industrial Electronics Magazine, 8(2), pp. 31–42.10.1109/MIE.2013.2287651

5. Jung, J. H., Lee, J.-J. and Kwon, B.-H. (2006). Online Diagnosis of Induction Motors Using MCSA. IEEE Transactions on Industrial Electronics, 53(6), pp. 1842–1852.10.1109/TIE.2006.885131

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Robust fault recognition and correction scheme for induction motors using an effective IoT with deep learning approach;Measurement;2023-02

2. Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor;Energies;2021-12-17

3. Smart classifier based prognostics and health management of induction motor;Materials Today: Proceedings;2021

4. Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor;Power Electronics and Drives;2021-01-01

5. Efficiency of Cascaded Neural Networks in Detecting Initial Damage to Induction Motor Electric Windings;Electronics;2020-08-15