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• W4313377501 endingPage "112333" @default.
• W4313377501 startingPage "112333" @default.
• W4313377501 abstract "Extracting weak repetitive transients is crucial for rolling bearing fault diagnosis. Most existing periodic fault feature extraction techniques are prone to be affected by the interference of transmission paths, background noise, and incidental shocks. One of the primary reasons is that a constant spectrum segmentation strategy may result in a loss of significant fault information. Another reason is that many indicators do not simultaneously consider impulsiveness and cyclostationarity of fault transient impulses. Therefore, an autonomous weak transient fault enhancement strategy called Encogram is proposed in this paper, which can adaptively segment frequency bands based on spectrum distribution characteristics and immunize an influence of inevitable incidental shocks and strong background noise. Compared to Kurtogram, Autogram, and other state-of-the-art signal processing methods, the proposed Encogram can obtain a frequency band with rich fault information and less noise interference." @default.
• W4313377501 created "2023-01-06" @default.
• W4313377501 creator A5028710085 @default.
• W4313377501 creator A5043079814 @default.
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• W4313377501 date "2023-01-01" @default.
• W4313377501 modified "2023-10-14" @default.
• W4313377501 title "Encogram: An autonomous weak transient fault enhancement strategy and its application in bearing fault diagnosis" @default.
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• W4313377501 doi "https://doi.org/10.1016/j.measurement.2022.112333" @default.
• W4313377501 hasPublicationYear "2023" @default.
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