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Deterministic-random separation in nonstationary regime

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TLDR
In this article, a generalized synchronous average (GSA) was proposed to extract the deterministic part of a cyclo-non-stationary vibration signal, i.e. the analog of the periodic part of cyclostationary signals.
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This article is published in Journal of Sound and Vibration.The article was published on 2016-02-03 and is currently open access. It has received 50 citations till now. The article focuses on the topics: Cyclostationary process & Vibration.

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Citations
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Journal ArticleDOI

Sparse time series modeling of the baseline vibration from a gearbox under time-varying speed condition

TL;DR: A sparse FP-AR model is proposed that uses sparse AR terms and non-identical functional spaces to improve modeling accuracy and benefits the fixed-axis gearbox in early detection of faults and better assessment of fault progressions.
Journal Article

Data-driven vibration signal filtering procedure based on the α-stable distribution

TL;DR: A novel procedure for data-driven enhancement of informative signal by model each sub-signal in time-frequency representation by α-stable distribution, which is a generalization of standard Gaussian one and allows for modeling sub-Signals related to both informative and non-informative frequencies.
Journal ArticleDOI

A methodology for identifying information rich frequency bands for diagnostics of mechanical components-of-interest under time-varying operating conditions

TL;DR: A new method, referred to as the IFBI α gram, is proposed that is capable of identifying frequency bands that are rich with diagnostic information related to specific cyclic components, which allows the optimal frequency band to be determined for diagnosing the component-of-interest.
Journal ArticleDOI

Order spectrum analysis enhanced by surrogate test and Vold-Kalman filtering for rotating machinery fault diagnosis under time-varying speed conditions

TL;DR: In this paper, a scheme is proposed to identify and remove speed non-synchronous components of either constant or time-varying frequency, and a surrogate test is generalized through new candidate construction method and non-stationarity based indicator for test criterion.
References
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Journal ArticleDOI

On Estimation of a Probability Density Function and Mode

TL;DR: In this paper, the problem of the estimation of a probability density function and of determining the mode of the probability function is discussed. Only estimates which are consistent and asymptotically normal are constructed.
Journal ArticleDOI

Remarks on Some Nonparametric Estimates of a Density Function

TL;DR: In this article, some aspects of the estimation of the density function of a univariate probability distribution are discussed, and the asymptotic mean square error of a particular class of estimates is evaluated.
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Rolling element bearing diagnostics—A tutorial

TL;DR: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears.
Journal ArticleDOI

Cyclostationarity by examples

TL;DR: In this paper, a tutorial on cyclostationarity oriented towards mechanical applications is presented, with 20 examples devoted to illustrating key concepts on actual mechanical signals and demonstrating how cyclostatarity can be taken advantage of in machine diagnostics, identification of mechanical systems and separation of mechanical sources.
Journal ArticleDOI

Analysis of computed order tracking

TL;DR: In this article, the authors examined which factors and assumptions, inherent in this computed order tracking method, have the greatest effect on its accuracy and found that the method is extremely sensitive to the timing accuracy of the keyphasor pulses and that great improvements in the spectral accuracy were observed when making use of higher-order interpolation functions.
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Frequently Asked Questions (12)
Q1. What are the contributions in "Deterministic-random separation in nonstationary regime" ?

A first object of this paper is to investigate the nature of the nonstationarity induced by the response of a linear time-invariant system subjected to speed varying excitation. For this purpose, the concept of a cyclo-non-stationary signal is introduced, which extends the class of cyclostationary signals to speed-varying regimes. A brief statistical study of it is performed, aiming to provide the user with confidence intervals that reflect the `` quality '' of the estimator according to the SNR and the estimated speed. 

a white Gaussian noise is added on the response to obtain a SNR¼2 and the signal duration is enlarged 10 times (i.e. one million samples) while keeping the same speed variation. 

In vibration analysis of rotating machine, the squared envelope spectrum (SES) is one of the most efficient indicators for the assessment of CS2 sources which are typical symptoms of damage in rolling element bearing faults [35]. 

According to Eq. (20), the high error in the former is due to the low number of cycles associated with the regime, while the high error in the second is due to the high mean instantaneous power. 

The flow-chart of this method is provided in Fig. 4.Since all signals are finite-length in practice, the asymptotic conditions in the GSA definition (see Eq. (15) cannot be met, resulting in a bias and variance of the raw estimator. 

In this case, the Fourier coefficients of the response at a given instant are principally dependent on the operating speed at that instant, as well as past and future instances of the speed profile. 

In this context, the kernel density estimation (KDE) method—also termed the Parzen–Rosenblatt window method—provides a non-parametric solution for an efficient smoothing operation [29,30]. 

there are two parameters that govern the bias, namely the speed resolution, δω, and the second derivative of the actual GSA with respect to speed. 

A natural way to model the effect of the transmission path in rotating machines is by exciting a single degree-of-freedom (dof) system by a constant-amplitude chirp. 

In details, the speed profile is divided into a predefined set of speed intervals called regimes defined by their central frequency ωr and the speed resolution δω. 

In particular, it has been assumed that the speed variations are slower than the signal cycle; although this condition seems reasonable in several applications, it can easily be relaxed in the general case by averaging portions of signal smaller than cycles. 

with A¼ 10; ξ¼ 0:05; ωn ¼ 2π 200, ωd ¼ωnffiffiffiffiffiffiffiffiffiffiffiffi1 ξ2q, and ~U tð Þ the Heaviside function used to enforce the system causality.