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- W3134348449 abstract "Abstract Due to the noise interference in the original vibration signals measured from vibration sensors, it is necessary to noise reduce the signals before extracting fault features from them. This paper proposes a fault feature extraction method combining the adaptive noise-complete ensemble empirical modal decomposition (CEEMDAN) and the time-reallocated multisynchronous compression transform (TMSST), which first decomposes the original signal, then combines the relevant index values of the decomposed signal to filter the optimal signal components, and finally uses the TMSST to extract the fault features from the reconstructed signal. In this paper, a set of simulated signal data and two sets of experimental data are used to evaluate the performance of the method, and the results show that the method works well for rolling bearing fault signal identification." @default.
- W3134348449 created "2021-03-15" @default.
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- W3134348449 date "2021-03-01" @default.
- W3134348449 modified "2023-09-25" @default.
- W3134348449 title "Fault diagnosis of rotating mechanical bearing based on adaptive noise-complete ensemble empirical modal decomposition and time-reallocated multisynchronous compression transform" @default.
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- W3134348449 doi "https://doi.org/10.1088/1742-6596/1820/1/012114" @default.
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