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This paper proposes a regrouping particle swarm optimization-based neural network (RegPSONN) for rolling bearing fault diagnosis. The proposed method applied neural network for rolling bearing conditions classification, and regrouping particle swarm optimization (RegPSO) is utilized for network training, and ten time-domain feature parameters are selected to establish the input vector. To evaluate...
As a valid method of time-frequency analysis, Wavelet transform (WT) can offer great help for gearbox fault diagnosis. However, it requires much human expertise and prior knowledge to diagnose the faulty conditions of gearbox according to the time-frequency distribution. In addition, the coupling of different failures and noise makes it hard to accurately diagnose the running conditions of the gearbox...
This study presents a new method based on convolutional neural network (CNN) for the gearbox fault identification and classification, which does not need the complex feature extraction process as those traditional recognition algorithms do, and it also depress the uncertainty of arbitrary feature selection. The vibration signals of the gearbox under normal and hybrid fault conditions were collected,...
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