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The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis


HighlightsSpectral Kurtosis performance declines as the SNR decreases or in presence of impulsive noise.New method (Autogram) is proposed to cope with these drawbacks.Autocorrelation (AC) of the squared envelope of the demodulated signal is used for this purpose.Autogram takes advantage of periodicity of cyclostationarity of bearing defect signals.AbstractEnvelope analysis is one of the most advantageous methods for rolling element bearing diagnostics but finding a suitable frequency band for demodulation has been a substantial challenge for a long time. Introduction of the Spectral Kurtosis (SK) and Kurtogram mostly solved this problem but in situations where signal to noise ratio is very low or in presence of non-Gaussian noise these methods will fail. This major drawback may noticeably decrease their effectiveness and goal of this paper is to overcome this problem. Vibration signals from rolling element bearings exhibit high levels of second-order cyclostationarity, especially in the presence of localized faults. The autocovariance function of a 2nd order cyclostationary signal is periodic and the proposed method, named Autogram, takes advantage of this property to enhance the conventional Kurtogram. The method computes the kurtosis of the unbiased Autocorrelation (AC) of the squared envelope of the demodulated signal, rather than the kurtosis of the filtered time signal. Moreover, to take advantage of unique features of the lower and upper portions of the AC, two modified forms of kurtosis are introduced and the resulting colormaps are called Upper and Lower Autogram. In addition, a thresholding method is also proposed to enhance the quality of the frequency spectrum analysis. A new indicator, Combined Squared Envelope Spectrum, is employed to consider all the frequency bands with valuable diagnostic information and to improve the fault detectability of the Autogram. The proposed method is tested on experimental data and compared with literature results so to assess its performances in rolling element bearing diagnostics.......

【作者名称】: Ali Moshrefzadeh, Alessandro Fasana
【作者单位】: DIMEAS, Politecnico di Torino, DIMEAS, Politecnico di Torino
【关 键 词】: Autogram, Rolling element bearing, Cyclostationary, Autocorrelation, Spectral Kurtosis, Kurtogram
【期刊名称】: Mechanical systems and signal processing
【期刊论文数据库】: [DBS_Articles_01]
【期刊论文编号】: 108,383,661
【摘要长度】: 3,176
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