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• W1986506585 abstract "The detection of local damage in rotating machinery (gears, bearings) via vibration signal analysis is one of the most powerful techniques in condition monitoring. However, in some cases, especially in heavy industrial machinery, it is difficult to detect damage because of the poor signal-to-noise ratio of the measured vibration. Therefore it is necessary to use unconventional advanced techniques to enhance the signal. In this paper, a novel approach based on parametric time-frequency analysis and further processing for: i) time-varying spectral content modelling, ii) the identification of informative frequency bands by statistical analysis, iii) local damage detection and iv) cycle identification via cepstral analysis, is presented. The proposed procedure is validated using real vibration data from bearings and gearboxes. It is worth noting that this methodology can be also successfully used in time-varying speed conditions (with limited fluctuation)." @default.
• W1986506585 created "2016-06-24" @default.
• W1986506585 creator A5030616645 @default.
• W1986506585 creator A5038658469 @default.
• W1986506585 date "2013-10-01" @default.
• W1986506585 modified "2023-09-25" @default.
• W1986506585 title "Parametric Time-Frequency Map and its Processing for Local Damage Detection in Rotating Machinery" @default.
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• W1986506585 doi "https://doi.org/10.4028/www.scientific.net/kem.588.214" @default.
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