The signal processing techniques developed for the diagnostics of me-chanical components operating in stationary conditions are often not applicable or are affected by a loss of effectiveness when applied to signals measured in transi-ent conditions. In this paper, an original signal processing tool is developed ex-ploiting some data-adaptive techniques such as Empirical Mode Decomposition, Minimum Entropy Deconvolution and the analytical approach of the Hilbert trans-form. The tool has been developed to detect localized faults on bearings of traction systems of high speed trains and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on envelope analysis or spectral kurtosis, which represent until now the landmark for bearings diagnostics.

Signal Processing Diagnostic Tool for Rolling Element Bearings Using EMD & MED

CHATTERTON, STEVEN;RICCI, ROBERTO;PENNACCHI, PAOLO EMILIO LINO MARIA;BORGHESANI, PIETRO
2013

Abstract

The signal processing techniques developed for the diagnostics of me-chanical components operating in stationary conditions are often not applicable or are affected by a loss of effectiveness when applied to signals measured in transi-ent conditions. In this paper, an original signal processing tool is developed ex-ploiting some data-adaptive techniques such as Empirical Mode Decomposition, Minimum Entropy Deconvolution and the analytical approach of the Hilbert trans-form. The tool has been developed to detect localized faults on bearings of traction systems of high speed trains and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on envelope analysis or spectral kurtosis, which represent until now the landmark for bearings diagnostics.
Proceedings of CMMNO2013
9783642393471
beearing diagnostics; empirical mode decomposition; minimum entropy deconvolution; rolling element bearings
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/746385
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