Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/3559
Title: | Improved Detection of Broken Rotor Bars by 1-D Self-ONNs | Authors: | Eren L. Devecioglu O.C. İnce, Türker Askar M. |
Keywords: | Broken rotor bar detection induction motors operational neural networks Computer aided diagnosis Convolutional neural networks Cost effectiveness Fault detection Learning systems Bar detection Broken rotor bar Broken rotor bar detection Convolutional neural network Inductions motors Machine learning techniques Mechanical faults Neural-networks Operational neural network Self-organised Induction motors |
Publisher: | IEEE Computer Society | Abstract: | Recently, machine learning techniques have been increasingly applied to the detection of both mechanical and electrical faults in induction motors. Broken rotor bars are one of the most common fault types that seriously affect the efficiency and lifetime of induction motors. In this study, compact 1-D self-organized operational neural networks (Self-ONNs) are applied to improve the detection and classification of broken rotor bars in induction motors. 1-D convolutional neural networks (CNNs) are a special case of Self-ONNs and they are usually preferred to traditional fault diagnosis systems with separately designed feature extraction and classification blocks as they provide cost-effective and practical hardware implementation. The proposed system improves the detection and classification performance of 1-D CNNs while still providing similar advantages and preserving real-time computational ability. © 2022 IEEE. | Description: | 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 -- 17 October 2022 through 20 October 2022 -- 184962 | URI: | https://doi.org/10.1109/IECON49645.2022.9968348 https://hdl.handle.net/20.500.14365/3559 |
ISBN: | 9.78167E+12 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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