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• W2588392271 abstract "The vibration signal of rolling bearing is usually complex and the useful fault information is hidden in the background noise, therefore, it is a challenge to identify rolling bearing faults from the complex vibration environment. In this paper, a novel multilayer deep learning convolutional neural network (CNN) method to identify rollings bearing fault is proposed. Firstly, in order to avoid the influence of different characteristics of the input data on the identification accuracy, a normalization preprocessing method is applied to preprocess the vibration signals of rolling bearings. Secondly, a multilayer CNN based on deep learning is designed in this paper to improve the fault identification accuracy of rolling bearing. Simulation data and experimental data analysis results show that the proposed method has better performance than SVM method and ANN method without any manual feature extractor design." @default.
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• W2588392271 date "2017-02-15" @default.
• W2588392271 modified "2023-09-25" @default.
• W2588392271 title "Rolling bearing fault identification using multilayer deep learning convolutional neural network" @default.
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• W2588392271 doi "https://doi.org/10.21595/jve.2016.16939" @default.
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