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• W2968698829 startingPage "3334" @default.
• W2968698829 abstract "This article proposes a fault diagnosis (FD) method that is based on bispectrum analysis and a convolutional neural network (CNN) to identify bearing faults under inconsistent working conditions, such as high shaft speed variations with cracks of multiple scales and compound faults. First, the bispectra of the vibration signals are extracted, which exhibit consistent patterns under inconsistent working conditions. Second, CNN based on a stochastic optimization function (AdaMax) is proposed to extract interclass representations from the bispectra, hence reducing the overhead of feature extraction and selection. The AdaMax optimization is computationally efficient and is well suited to problems with big data sets. The obtained results of this article are compared with the current state-of-the-art bearing FD techniques, which demonstrates the superiority of the proposed model under inconsistent working conditions." @default.
• W2968698829 created "2019-08-22" @default.
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• W2968698829 date "2020-06-01" @default.
• W2968698829 modified "2023-10-18" @default.
• W2968698829 title "Fault Diagnosis of Rotary Machine Bearings Under Inconsistent Working Conditions" @default.
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• W2968698829 doi "https://doi.org/10.1109/tim.2019.2933342" @default.
• W2968698829 hasPublicationYear "2020" @default.
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