STATOR FAILURES AND DIAGNOSTIC NEURAL-BASED METHODS IN 3-PHASE SQUIRREL-CAGE INDUCTION MOTORS | ||||
| International Conference on Aerospace Sciences and Aviation Technology | ||||
| Article 75, Volume 10, 10th International Conference On Aerospace Sciences & Aviation Technology, May 2003, Page 1079-1089 PDF (2.06 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asat.2019.24731 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| I. F. Elarabawy1; Ragy R.2; A. A. Nasser3; Gamal M.4 | ||||
| 1Professor, Dpt. of Electrical Engineering, Alexandria University, Alexandria, Egypt. | ||||
| 2PhD., Dpt. of Electrical Engineering, Alexandria University, Alexandria, Egypt. | ||||
| 3PhD., Air Defense College, Alexandria, Egypt. | ||||
| 4Graduate Student, Air Defense College, Alexandria, Egypt. | ||||
| Abstract | ||||
| This paper presents the stator winding fault detection. Stator failures are classified. Effect's of various stresses on the stator lifetime and how do causes contribute to stator failure are studied. A technique known as model-based diagnostic technique is used to study the behavior of the stator under abnormal conditions. The squirrel-cage induction motor is taken as a case study for practical verification. A neural-based diagnosis for the stator failures has been carried out. The algorithm has proven adequacy in predicting stator faults. | ||||
| Keywords | ||||
| Induction motor; squirrel-cage; Signature analysis; Fault Detection; Stator faults and Neural Network Application | ||||
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