Kumar, Rahul R. and Cirrincione, Giansalvo and Cirrincione, Maurizio and Tortella, A. and Andriollo, M. (2021) A Topological Neural-Based Scheme for Classification of Faults in Induction Machines. IEEE Transactions on Industry Applications, 57 (1). pp. 272-283. ISSN 0093-9994
Full text not available from this repository.Abstract
This article presents a data-driven approach for the classification of faults in induction machines. The designed scheme involves newly engineered features extracted from the line current signals, which provides an improved fault discrimination. For this purpose, a topological-based fast projection technique (curvilinear component analysis) is used as a tool to reduce the dimensionality of the data and interpret the feature behavior. Consequently, a shallow convolutional neural network has been designed to classify the three-phase stator current signals. Experimental tests at different operating conditions have assessed the procedure, confirming its effectiveness and suitability for online and real-time diagnostics.
| Item Type: | Journal Article |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Fulori Nainoca - Waqairagata |
| Date Deposited: | 26 Jan 2021 23:10 |
| Last Modified: | 29 Sep 2022 02:18 |
| URI: | http://repository.usp.ac.fj/id/eprint/12575 |
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