COMPARISON BETWEEN TWO VERY EFFICIENT SIGNAL PROCESSING APPROACHES FOR VIBRATION-BASED CONDITION MONITORING OF ROLLING ELEMENT BEARINGS: THE MED-SK AND CS-BASED APPROACHES

摘要

This paper compares the most efficient signal processing approaches used forrnvibration-based condition monitoring of rolling element bearings. The first is based onrnpre-processing the vibration signal through the maximum entropy deconvolution methodrn(MED) followed by the spectral kurtosis (SK), before analyzing the spectrum of thernsignal envelope. The MED aims at maximizing the signal impulsivity by deconvolvingrnthe system transfer function through an optimization approach that maximizes thernkurtosis of the output. Then, the spectral kurtosis (SK) is applied to conceive the optimalrnfilter to be applied before computing the envelope spectrum. The second approach isrnbased on a cyclostationary modeling of the bearing signal. It applies the spectralrncoherence to the signal with a special attention on setting the estimation parameters. Thernspectral coherence is a bi-variable map of the cyclic frequency, α, and the spectralrnfrequency, f. The former variable describes the cyclic content of the modulations, whilernthe former describes the properties of the carrier. The improved envelope spectrum isrnthen computed by simply projecting its squared-magnitude with respect to the f-variable.rnThese methods are evaluated according to their potentiality to detect the fault in itsrnearliest stage. The comparison is be made on real bearing vibration signals in run-tofailurerntests.