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• W2790195878 abstract "Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments." @default.
• W2790195878 created "2018-03-29" @default.
• W2790195878 creator A5080167383 @default.
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• W2790195878 date "2019-01-01" @default.
• W2790195878 modified "2023-10-09" @default.
• W2790195878 title "Rolling element bearing fault diagnosis using convolutional neural network and vibration image" @default.
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• W2790195878 doi "https://doi.org/10.1016/j.cogsys.2018.03.002" @default.
• W2790195878 hasPublicationYear "2019" @default.
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