2017
DOI: 10.1016/j.ymssp.2016.06.033
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Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment

Abstract: Nowadays, the vibration analysis of rotating machine signals is a well-established methodology, rooted on powerful tools offered, in particular, by the theory of cyclostationary (CS) processes. Among them, the squared envelope spectrum (SES) is probably the most popular to detect random CS components which are typical symptoms, for instance, of rolling element bearing faults. Recent researches are shifted towards the extension of existing CS toolsoriginally devised in constant speed conditionsto the case of va… Show more

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Cited by 130 publications
(90 citation statements)
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“…Envelope analysis has been applied to overcome the constraint of constant operating speed of the rolling element bearing. The squared envelope spectrum has been extended Shock and Vibration to cases in which small speed fluctuations occur [40,41]. The spectral kurtosis technique adopts the concept of kurtosis to capture the impulsiveness of a signal.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…Envelope analysis has been applied to overcome the constraint of constant operating speed of the rolling element bearing. The squared envelope spectrum has been extended Shock and Vibration to cases in which small speed fluctuations occur [40,41]. The spectral kurtosis technique adopts the concept of kurtosis to capture the impulsiveness of a signal.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…The excitation of structural resonances by the impacts of damage components manifest in time-invariant frequency bands [15], with frequency band identification techniques such as the kurtogram [16] and the IFBI α gram [12] making it possible to automatically determine the frequency bands that are rich with impulsive information [17,18]. Schmidt et al [13] combined frequency band identification methods with healthy historical data to identify frequency bands with novel information, i.e., due to damage, whereafter the SA and SASE of the filtered signal are calculated for detecting and visualising the gear damage.…”
Section: Introductionmentioning
confidence: 99%
“…Reliable condition monitoring techniques are essential when performing condition-based maintenance on expensive rotating machine assets [1,2]. Advanced signal processing [3][4][5][6][7][8][9][10][11][12][13] and sophisticated supervised machine learning techniques [14][15][16][17][18][19][20][21][22][23] are actively investigated to improve the condition monitoring task. Deep learning techniques have also recently been used to not only infer the condition of the machine, but also to extract features from the raw dataset i.e.…”
Section: Introductionmentioning
confidence: 99%