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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,...
This paper presents a novel bearing fault classification method based on firefly algorithm. This method is originated by nature-inspired swarm intelligence, which contributes to a supervised classification task by labeling a few samples. The main advantages of this method are: 1) first, it does not require a large amount of samples in the population; 2) second, the coding of firefly is very simple;...
This research presents a rough set and back propagation neural network based scheme for rolling bearings fault diagnosis. The scheme is designed to classify the fault type. Experiments results indicate that rough set is helpful to reduce dimensionality, discard deceptive features and extract an optimal subset from the raw feature set, and the proposed rough sets combined with BP neural network (RNN)...
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