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• W2038331350 abstract "This paper proposes a systematic procedure based on a pattern recognition technique for fault diagnosis of induction motors bearings through the artificial neural networks (ANNs). In this method, the use of time domain features as a proper alternative to frequency features is proposed to improve diagnosis ability. The features are obtained from direct processing of the signal segments using very simple calculation. Three different cases including, healthy, inner race defect and outer race defect are investigated using the proposed algorithm. The ANNs are trained with a subset of the experimental data for known machine conditions. Once the network is trained, efficiency of the proposed method is evaluated using the remaining set of data. The obtained results indicate that using time domain features can be effective in accurate diagnosis of various motor bearing faults with high precision and low computational burden." @default.
• W2038331350 created "2016-06-24" @default.
• W2038331350 creator A5085290220 @default.
• W2038331350 date "2012-01-01" @default.
• W2038331350 modified "2023-10-02" @default.
• W2038331350 title "Induction motors bearing fault detection using pattern recognition techniques" @default.
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• W2038331350 doi "https://doi.org/10.1016/j.eswa.2011.06.042" @default.
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