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• W4310295635 endingPage "107754632211404" @default.
• W4310295635 startingPage "107754632211404" @default.
• W4310295635 abstract "Weak bearing fault feature extraction (FFE) research has previously focused on bearing fault signal of transient impulse extraction whereas higher harmonic feature extraction of fault feature frequency aspects are relatively less. While traditional envelope demodulation method has certain limitations to capture the rolling bearing fault characteristic frequency of higher harmonic. To this end, combining adaptive chirp mode decomposition (ACMD), improved maximum correlation kurtosis deconvolution (IMCKD), and 1.5-dimensional Teager energy cyclo-stationary spectrum (1.5-DTECS), in the current work we propose a three-stage defect detection system. The performance of the proposed method is evaluated by analyzing the three-stage FFE (STFFE) of multiple kinds of rolling bearing fault data. The findings reveal that the three-stage fault detection approach successfully suppresses noise, highlights the fault impact, and extracts the higher order harmonics of the bearing defect characteristic frequency more effectively. The research contributes to the field of bearing fault high harmonic component extraction and provides guidance on techniques related to the extraction of bearing impulse characteristic." @default.
• W4310295635 created "2022-11-30" @default.
• W4310295635 creator A5021453047 @default.
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• W4310295635 date "2022-11-25" @default.
• W4310295635 modified "2023-10-02" @default.
• W4310295635 title "A three-stage method for efficiently extracting the higher-order harmonics of bearing fault characteristic frequencies" @default.
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• W4310295635 doi "https://doi.org/10.1177/10775463221140440" @default.
• W4310295635 hasPublicationYear "2022" @default.
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