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- W2034646853 abstract "This paper presents a novel method to enhance the detection and diagnosis of low-speed rolling-element bearing faults based on discrete wavelet packet analysis (DWPA). The method involves the automatic extraction of wavelet packets containing bearing fault-related features from the discrete wavelet packet analysis representation of machine vibrations. Automated selection of the wavelet packets of interest is achieved via an adaptive network-based fuzzy inference system (ANFIS), which can be implemented on-line. The resultant signal extracted by this technique is essentially an optimal multiple band-pass filter of the high-frequency bearing impact transients. Used in conjunction with the autoregressive (AR) spectrum of the envelope signal, a sensitive diagnosis of the bearing condition can be made. The discrete wavelet packet analysis multiple band-pass filtering of the signal results in a significantly improved signal-to-noise ratio compared to its high-pass counterpart, with an exceptional capacity to exclude contaminating sources of vibration. A more modest increase in the signal-to-noise ratio is achieved when compared to digital band-pass filtering, with the filter range adjusted to obtain the best possible isolation of the bearing transients." @default.
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- W2034646853 date "2001-09-01" @default.
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- W2034646853 title "MULTIPLE BAND-PASS AUTOREGRESSIVE DEMODULATION FOR ROLLING-ELEMENT BEARING FAULT DIAGNOSIS" @default.
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- W2034646853 doi "https://doi.org/10.1006/mssp.2001.1410" @default.
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