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• W2471636057 abstract "The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions." @default.
• W2471636057 created "2016-07-22" @default.
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• W2471636057 date "2016-09-01" @default.
• W2471636057 modified "2023-10-02" @default.
• W2471636057 title "Detection of broken rotor bar faults in induction motor at low load using neural network" @default.
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• W2471636057 doi "https://doi.org/10.1016/j.isatra.2016.06.004" @default.
• W2471636057 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27329853" @default.
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