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- W4310971892 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." @default.
- W4310971892 created "2022-12-21" @default.
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- W4310971892 date "2022-10-17" @default.
- W4310971892 modified "2023-09-29" @default.
- W4310971892 title "Improved Detection of Broken Rotor Bars by 1-D Self-ONNs" @default.
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- W4310971892 doi "https://doi.org/10.1109/iecon49645.2022.9968348" @default.
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