The spectral analysis of cyclo-non-stationary signals

Abboud, Dany; Baudin, Sophie; Antoni, Jérôme; Rémond, Didier; Eltabach, Mario; Sauvage, Olivier

doi:10.1016/j.ymssp.2015.09.034

Cited by 95 publications

(79 citation statements)

References 19 publications

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“…Hence, the extracted wavelet packet coefficients are subsequently order tracked with rotational speed information to transform it from the time to the angle domain. Hence, the order tracked wavelet coefficients preserve the angle-time cyclostationary properties of the bearing damage [22,23]. Rotational speed or phase information can be obtained from optical probes and shaft encoders [48] or in the absence of the rotational speed measurement equipment, tacholess order tracking approaches can be used [24][25][26][27].…”

Section: Feature Extractionmentioning

confidence: 99%

“…A wide variety of techniques have been developed and used for rolling element bearing diagnostics [5], such as envelope analysis which has been extended by [6,7] for variable speed applications, cyclostationary analysis [8,9] which can be seen as a generalisation of envelope analysis [8], regression analysis for variable loads [10], empirical mode decomposition and its extensions [11,12], wavelet analysis [13][14][15][16], the spectral kurtosis [17], the kurtogram and its variants [18,19], the sparsogram [20] and the infogram [21]. Varying rotational speeds complicate the condition monitoring process due to its influence on the properties of the vibration signal [7,22,23] and the rotational speed information of the shaft is also required. The rotational speed information can be difficult and impractical to measure for some machines; this makes tacholess order tracking methods very important for condition monitoring under varying speed conditions [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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A discrepancy analysis methodology for rolling element bearing diagnostics under variable speed conditions

Schmidt

Heyns

Gryllias

2019

Mechanical Systems and Signal Processing

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Performing condition monitoring on critical machines such as gearboxes is essential to ensure that the machines operate reliably. However, many gearboxes are exposed to variable operating conditions which impede the condition inference task. Rolling element bearing component failures are important causes of gearbox failures and therefore robust bearing diagnostic techniques are required. In this paper, a rolling element bearing diagnostic methodology based on novelty detection is proposed for machines operating under variable speed conditions. The methodology uses the wavelet packet transform, order tracking and a feature modelling approach to generate a diagnostic metric in the form of a discrepancy measure. The probability distribution of the diagnostic metric, statistically conditioned on the corresponding operating conditions is estimated, whereafter the condition of the rolling bearing element is inferred. The rolling element bearing diagnostic methodology is validated on data from a phenomenological gearbox model and two experimental datasets.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A discrepancy analysis methodology for rolling element bearing diagnostics under variable speed conditions

Schmidt

Heyns

Gryllias

2019

Mechanical Systems and Signal Processing

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“…Noting that the integration variable θ depends on time, θ ¼ θðtÞ, the OFSC is then expressed as [20,21]:…”

Section: Angle/time Cyclostationary Toolsmentioning

confidence: 99%

“…The occurrence and the level of rattle noise depending on operating conditions and in particular on the rotation speed, the interest in our work is focused on tests in run up conditions classically used to scan the NVH behaviour of gearboxes. The assumption of cyclostationarity has thus to be replaced by the so called assumption of "cyclo-non-stationarity" which is a recent field of research [18][19][20]. In this context, the order-frequency distribution proposed by D'Elia et al in reference [18] is particularly well suited to the analysis of rattle noise: the "cyclic frequency" expressed in events per revolution (epr) is directly linked to the periodicity of the impacts while the spectral content of these impacts are linked to the "spectral frequency" expressed in Hertz.…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive rattle noise detection in non-stationary conditions by an angle/time cyclostationary approach

Baudin

Rémond

Antoni

et al. 2016

Journal of Sound and Vibration

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a b s t r a c tThis work proposes an original non-intrusive approach to detect and quantify rattle noise in automotive gearboxes operating under non-stationary conditions by means of vibration or instantaneous angular speed measurements. Rattle noise is produced by vibro impacts between teeth of unloaded gears excited by the engine acyclism. It appears during acceleration or deceleration phases and its detection requires the analysis of nonstationary signals. In order to take advantage of the repetitive nature of the impacts, an angle/time cyclostationary approach is introduced. Rattle noise is thus characterized through the angle/time duality: the cyclic frequency expressed in events per revolution is directly linked to the periodicity of the impacts while their frequency content is expressed in Hertz. The proposed detection method uses an order/frequency spectral coherence and may be applied either on vibration signals or instantaneous angular speed signals. For validation purposes, a specific instrumentation of a gearbox is set up. The relative speed of the unloaded meshing gears is observed by means of optical encoders to directly detect the instants of impact which then serve as a basis for validation of the non-intrusive detection method proposed in this paper.

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“…Time-frequency transform analysis of vibration signal is one of the most widespread and effective method among the existing methods to incipient fault diagnosis [1,2]. Most of the vibration signals of rotating machinery are non-stationary and nonlinear signals [3,4]. The main methods of dealing with such signals are including Short-time Fourier Transform [5], Wavelet transform [6], quadratic time-frequency distribution, Empirical Mode Decomposition, etc.…”

Section: Introductionmentioning

confidence: 99%

An incipient fault diagnosis method for rotating machinery based on bilateral spectrum and precession energy difference density spectrum

Gu¹,

Zhu²,

Zeng³

et al. 2019

J. vibroeng.

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As an important characteristic information in incipient fault diagnosis of rotating machinery, the fault impulse signal is hard to be monitored due to the low signal amplitude and system disturbance/noise. Based on bilateral spectrum and precession energy difference density spectrum for the incipient fault diagnosis of rotating machinery, a novel diagnosis method is proposed in this paper to overcome this key problem. Compared with the existing methods to extract transient impulses from the vibrate signals, this paper designs a new fault feature parameter-precession energy difference density to characterize the feature of transient impulse. Furthermore, the complex signal and the negative frequency are introduced into the spectrum analysis and the forward and backward precession characteristics, which can be directly gained through the bilateral spectrum and relieves the problems not to be overlooked, such as high calculation, high error and time consuming. Finally, the feasibility and effectiveness of the proposed methods are demonstrated via a case study of a vertical mill reducer.

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The spectral analysis of cyclo-non-stationary signals

Cited by 95 publications

References 19 publications

A discrepancy analysis methodology for rolling element bearing diagnostics under variable speed conditions

A discrepancy analysis methodology for rolling element bearing diagnostics under variable speed conditions

Non-intrusive rattle noise detection in non-stationary conditions by an angle/time cyclostationary approach

An incipient fault diagnosis method for rotating machinery based on bilateral spectrum and precession energy difference density spectrum

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