The envelope-based cyclic periodogram

Abstract Cyclostationary analysis has proven effective in identifying signal components for diagnostic purposes. A key descriptor in this framework is the cyclic power spectrum, traditionally estimated by the averaged cyclic periodogram and the smoothed cyclic periodogram. A lengthy debate about the best estimator finally found a solution in a cornerstone work by Antoni, who proposed a unified form for the two families, thus allowing a detailed statistical study of their properties. Since then, the focus of cyclostationary research has shifted towards algorithms, in terms of computational efficiency and simplicity of implementation. Traditional algorithms have proven computationally inefficient and the sophisticated “cyclostationary” definition of these estimators slowed their spread in the industry. The only attempt to increase the computational efficiency of cyclostationary estimators is represented by the cyclic modulation spectrum. This indicator exploits the relationship between cyclostationarity and envelope analysis. The link with envelope analysis allows a leap in computational efficiency and provides a “way in” for the understanding by industrial engineers. However, the new estimator lies outside the unified form described above and an unbiased version of the indicator has not been proposed. This paper will therefore extend the analysis of envelope-based estimators of the cyclic spectrum, proposing a new approach to include them in the unified form of cyclostationary estimators. This will enable the definition of a new envelope-based algorithm and the detailed analysis of the properties of the cyclic modulation spectrum. The computational efficiency of envelope-based algorithms will be also discussed quantitatively for the first time in comparison with the averaged cyclic periodogram. Finally, the algorithms will be validated with numerical and experimental examples.

[1]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

[2]  Herschel H. Loomis,et al.  Digital implementations of spectral correlation analyzers , 1993, IEEE Trans. Signal Process..

[3]  W. A. Brown,et al.  Computationally efficient algorithms for cyclic spectral analysis , 1991, IEEE Signal Processing Magazine.

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

[5]  Georgios B. Giannakis,et al.  Nonparametric polyspectral estimators for kth-order (almost) cyclostationary processes , 1994, IEEE Trans. Inf. Theory.

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

[7]  Kurt S. Riedel,et al.  Minimum bias multiple taper spectral estimation , 2018, IEEE Trans. Signal Process..

[8]  Paolo Pennacchi,et al.  Testing second order cyclostationarity in the squared envelope spectrum of non-white vibration signals , 2013 .

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

[10]  W. Gardner Exploitation of spectral redundancy in cyclostationary signals , 1991, IEEE Signal Processing Magazine.

[11]  Patrick Flandrin,et al.  Wigner-Ville spectral analysis of nonstationary processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[12]  Georgios B. Giannakis,et al.  Statistical tests for presence of cyclostationarity , 1994, IEEE Trans. Signal Process..

[13]  J. Antoni,et al.  Detection of Surface Ships From Interception of Cyclostationary Signature With the Cyclic Modulation Coherence , 2012, IEEE Journal of Oceanic Engineering.

[14]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

[15]  Paolo Pennacchi,et al.  The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings , 2014 .

[16]  M. Bartlett,et al.  ON THE EFFICIENCY OF PROCEDURES FOR SMOOTHING PERIODOGRAMS FROM TIME SERIES WITH CONTINUOUS SPECTRA , 1948 .

[17]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[18]  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 .

[19]  P. Welch The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[20]  William A. Gardner,et al.  Measurement of spectral correlation , 1986, IEEE Trans. Acoust. Speech Signal Process..