Angle⧹time cyclostationarity for the analysis of rolling element bearing vibrations

Abstract In speed-varying conditions, the assumption of cyclostationarity of rolling-element bearing vibrations is jeopardized. The emitted signal comprises an interaction between (i) time-dependent components related to the system dynamics (e.g. transfer function) and (ii) angle-dependent mechanisms related to the system kinematics (e.g. impact, load modulations…). This necessarily implies the inadequacy of classical cyclostationary tools no matter a temporal or angular vision is adopted. This consequently calls for an angle ⧹ time approach which preserves—via the angle variable—the cyclic evolution of the signal while maintaining—via the time variable—a temporal description of the system dynamics. The first object of this paper is to analytically characterize bearing fault vibrations and explore its angle ⧹ time cyclostationary property. The second object is to experimentally validate these results on real-world vibration signals and demonstrate the optimality of the angle–time approach over classical approaches for rolling element bearing diagnosis.

[1]  J. Antoni Cyclic spectral analysis of rolling-element bearing signals : Facts and fictions , 2007 .

[2]  J. Antoni Cyclic spectral analysis in practice , 2007 .

[3]  Robert B. Randall,et al.  A comparison of methods for separation of deterministic and random signals , 2011 .

[4]  Robert B. Randall,et al.  Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions , 2013 .

[5]  Antonio Napolitano,et al.  Generalized almost-cyclostationary processes and spectrally correlated processes: Two extensions of the class of the almost-cyclostationary processes , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[6]  Robert B. Randall,et al.  THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[7]  Antonio Napolitano,et al.  Generalizations of Cyclostationary Signal Processing: Spectral Analysis and Applications , 2012 .

[8]  Jérôme Antoni,et al.  On the extraction of rolling-element bearing fault signature in speed-varying conditions , 2014 .

[9]  Robert B. Randall,et al.  A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults , 2003 .

[10]  Jérôme Antoni,et al.  Application of averaged instantaneous power spectrum for diagnostics of machinery operating under non-stationary operational conditions , 2012 .

[11]  K. R. Fyfe,et al.  ANALYSIS OF COMPUTED ORDER TRACKING , 1997 .

[12]  R. Randall,et al.  OPTIMISATION OF BEARING DIAGNOSTIC TECHNIQUES USING SIMULATED AND ACTUAL BEARING FAULT SIGNALS , 2000 .

[13]  J. Antoni Cyclostationarity by examples , 2009 .

[14]  Fakher Chaari,et al.  Cyclostationarity: theory and methods , 2014 .

[15]  Paolo Pennacchi,et al.  A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions , 2013 .

[16]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[17]  Jérôme Antoni,et al.  Cyclostationary modelling of rotating machine vibration signals , 2004 .

[18]  Jérôme Antoni,et al.  Time-frequency approach to extraction of selected second-order cyclostationary vibration components for varying operational conditions , 2013 .